Systems and methods for focused ultrasound enabled liquid biopsy - Patents.com

Abstract

Devices, systems, and methods are disclosed for controlling the operation of a Focused Ultrasound Blood-Brain Barrier Opening (FUS-BBBO) device using an individualized closed-loop feedback control method. Devices, systems, and methods are also disclosed for performing transcranial cavitation localization using time-of-arrival techniques combined with signal processing using differential cavitation. Methods are also disclosed for performing liquid biopsies to diagnose brain disorders using the FUS-BBBO method to enhance the release of biomarkers from the brain into the CSF and blood.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Patent Application Serial No. 63 / 247914, filed September 25, 2021, the entirety of which is incorporated herein by reference.

[0002] STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT This invention was made with government support under Grant No. N00014-19-1-2335 awarded by the Office of Naval Research and Grant EB030102 awarded by the National Institutes of Health. The Government has certain rights in this invention.

[0003] Supporting information Not applicable.

[0004] Technical Field The present disclosure relates generally to systems and methods for 3D passive transcranial cavitation detection, and further to systems, devices, and methods for focused ultrasound-enabled brain-blood barrier opening (FUS-BBBO) as well as focused ultrasound-enabled liquid biopsy. [Background technology]

[0005] 2. Background of the Invention

[0006] Cavitation is the fundamental physical mechanism of various focused ultrasound (FUS) mediated brain treatments. Precise knowledge of the 3D location of cavitation in real time can improve treatment targeting accuracy and avoid off-target tissue damage.

[0007] The technique of combining focused ultrasound (FUS) with microbubbles is increasingly being used for various neurological applications that use cavitation as the primary physical mechanism, such as FUS combined with microbubble-mediated blood-brain barrier (BBB) ​​disruption for targeting and gene delivery, cavitation-enhanced non-thermal ablation, FUS-enabled liquid biopsy, and transcranial histotripsy. Real-time, three-dimensional (3D) transcranial cavitation localization is crucial in these applications to ensure accurate targeting of FUS and to avoid off-target tissue damage. Transcranial cavitation localization faces a major challenge posed by the skull, which induces large phase anomalies and amplitude attenuation in the cavitation signal detected transcranially.

[0008] Existing passive cavitation detection techniques typically use single-element ultrasonic sensors to detect the presence of cavitation by analyzing the spectral characteristics of the detected acoustic emissions. Although single-element receivers can effectively detect the presence of cavitation activity, measurements obtained by single-element receivers are insufficient to derive the spatial localization of cavitation.

[0009] Recently, 3D passive cavitation imaging using a hemispherical phased array combined with passive beamforming and a skull-specific aberration correction algorithm based on computed tomography (CT) has been developed for 3D imaging of microbubbles associated with FUS-mediated BBB disruption and transcranial histotripsy therapy. Existing methods of 3D passive cavitation imaging generate spatial distributions of cavitation activity but require expensive customized phased arrays with 256 or more elements, and the phased array data must be processed using complex and time-consuming computational algorithms.

[0010] The blood-brain barrier (BBB) ​​is a natural barrier in the brain that prevents most therapeutic agents administered systemically from reaching the brain parenchyma. Focused ultrasound (FUS) combined with intravenously injected microbubbles for blood-brain barrier opening (FUS-BBBO) has been established as a promising technique to deliver therapeutic agents to targeted brain regions without invasive surgery. Its safety and efficacy have been demonstrated in small animals, large animals, and humans. A relatively narrow window of acoustic energy within which FUS-BBBO can be safely and effectively performed has been identified. Insufficient FUS energy results in limited BBB opening, while excessive FUS energy potentially leads to vascular destruction and permanent tissue damage. Cavitation is the fundamental physical mechanism of FUS-BBBO. Depending on the acoustic pressure, microbubble cavitation can range from stable cavitation (SC) to inertial cavitation (IC). Microbubbles experience sustained small amplitude dose oscillations (SC) at low acoustic pressures, which may increase BBB permeability without causing any vascular damage. Microbubbles typically expand to large sizes and collapse violently at high acoustic pressures (IC), which increases BBB permeability but may cause vascular destruction. To keep FUS delivery within a safe and effective window, a feedback control algorithm based on passive cavitation detection (PCD) has been proposed to monitor cavitation in real time and provide feedback control of the pressure of FUS sonication.

[0011] One existing feedback control algorithm to achieve a safe FUS-BBBO included increasing the sonication pressure until harmonic or subharmonic signals from microbubble release were detected (the boost phase), decreasing the acoustic pressure to 50% of the final boosted value, and maintaining the acoustic pressure at this level in an open-loop manner for subsequent treatments (i.e., the maintenance phase). This approach considered individual differences in the detected cavitation signal as a threshold defined based on a calibration performed on individual subjects during the boost phase. Individual differences in the detected cavitation signal may result from several factors, including variations in in situ acoustic pressure in the brain due to changes in skull thickness and the incidence angle of the FUS beam, variations in microbubble concentration and size distribution for each injection, and heterogeneous spatial distribution of microbubbles in the brain due to differences in vascular density, vascular size, and blood flow. However, there are two potential limitations to this existing feedback control algorithm. The pressure boost phase requires a pressure overshoot to reach a threshold and then decrease to a safe level, which poses the risk of causing tissue damage. Furthermore, the maintenance phase uses an open-loop approach that maintains the acoustic pressure at a fixed value. To minimize overexposure in the increase phase, other existing feedback control algorithms either modulated the acoustic power level until the average harmonic signal reached a target between 6-7.5 dB above the noise level detected before microbubble injection, and then fixed it at the average pressure level, resulting in this target range for the remainder of the sonication, or defined the sonication level using a relative spectrum defined as the ratio of the instantaneous signal power spectrum after microbubble injection to the corresponding baseline power spectrum before microbubble injection.

[0012] Several closed-loop feedback control algorithms have been proposed for FUS-BBBO. One existing closed-loop algorithm uses an adaptive proportional-integral controller for drug delivery across the BBB in a rat glioma model, which monitors cavitation emission throughout the experiment and adjusts the ultrasound pressure level based on the previous state of the controller and the targeted cavitation level (TCL). In these existing methods, the TCL was defined as the maximum harmonic emission level achieved without broadband detection based on previous experiments, and the same TCL was used for all subjects. Other existing closed-loop algorithms adjusted the sonication pressure such that each pulse maintained a cavitation level within a predefined range. An additional closed-loop algorithm implemented a closed-loop nonlinear state controller that controlled the acoustic exposure level based on passive cavitation imaging, which enabled spatially specific measurements of cavitation activity for spatially selective feedback control of FUS-BBBO. However, passive cavitation imaging requires the use of a customized ultrasound imaging system coupled to advanced beamforming techniques, which limits the wide application of this method in FUS-BBBO. Although these existing closed-loop feedback control algorithms control the cavitation activity in a closed-loop manner and in real time, they apply the same predefined TCL for all subjects without considering individual differences in the baseline cavitation signal. Summary of the Invention

[0013] In various aspects, systems, devices, and methods are disclosed for performing a liquid biopsy to diagnose a brain disorder in a subject. In various other aspects, the present application discloses devices, systems, and methods for controlling the operation of a Focused Ultrasound Blood-Brain Barrier Opening (FUS-BBBO) device. In various additional aspects, systems, devices, and methods are disclosed for transcranially locating cavitations within a subject's skull.

[0014] In one embodiment, a method of performing a liquid biopsy to diagnose a brain disorder in a subject is disclosed, the method includes injecting a predetermined amount of microbubbles into the subject, opening the blood-brain barrier of the subject using a focused ultrasound blood-brain barrier opening (FUS-BBBO) device to release at least one biomarker from the brain of the subject into the blood and CSF of the subject, obtaining a biological sample containing the at least one biomarker, and diagnosing the brain disorder based on the at least one biomarker separated from the biological sample. The biological sample may be a blood sample or a CSF sample from the subject. In some embodiments, opening the blood-brain barrier using the FUS-BBBO device includes sonicating the brain of the subject at a baseline sonication pressure and detecting a baseline stable cavitation level from the subject using the FUS-BBBO device. The baseline stable cavitation level is above signal noise and below a stable cavitation level sufficient to cause BBBO, and the subject is injected with a predetermined amount of microbubbles prior to sonication. In some embodiments, opening the blood-brain barrier with the FUS-BBBO device further comprises sonicating the subject at a series of incrementally increasing sonication pressures and detecting a corresponding series of cavitation levels until a target cavitation level (TCL) is detected. In some embodiments, opening the blood-brain barrier with the FUS-BBBO device further comprises continuously sonicating the subject to maintain the TCL and induce BBBO in the subject. The target cavitation level is a predetermined amount higher than the baseline stable cavitation level. In some embodiments, detecting the baseline cavitation level, the series of cavitation levels, and the target cavitation level further comprises detecting a microbubble cavitation signal generated by the microbubbles in response to sonication with the FUS-BBBO device. In some embodiments, the microbubble cavitation signal is processed using a fast Fourier transform (FFT) algorithm to generate the baseline cavitation level, the cavitation level, and the TCL.In some aspects, the target cavitation level may be one of 0.5 dB, 1 dB, 2 dB, 3 dB, and 4 dB higher than the baseline stable cavitation level.

[0015] In another aspect, a system is disclosed for controlling the operation of a Focused Ultrasound Blood-Brain Barrier Opening (FUS-BBBO) device configured to perform a Focused Ultrasound Blood-Brain Barrier Opening (FUS-BBBO) on a subject. The system includes a computing device operatively connected to the FUS-BBBO device and a computing device with at least one processor. The at least one processor is configured to sonicate the brain of the subject at a baseline sonication pressure and detect a baseline stable cavitation level from the subject using the FUS-BBBO device. The baseline stable cavitation level is above signal noise and below a stable cavitation level sufficient to cause BBBO. The subject is injected with microbubbles prior to sonication. The at least one processor is further configured to sonicate the subject at a series of incrementally increasing sonication pressures and detect a corresponding series of cavitation levels until a target cavitation level (TCL) is detected. The target cavitation level is a predetermined amount above the baseline stable cavitation level. The at least one processor is further configured to continuously sonicate the subject to maintain the TCL to cause BBBO in the subject. In some embodiments, the system further comprises at least one passive cavitation detection (PCD) transducer that detects the baseline cavitation level, the sequence of cavitation levels, and the target cavitation level. In some embodiments, detecting the baseline cavitation level, the sequence of cavitation levels, and the target cavitation level further comprises detecting a microbubble cavitation signal generated by the microbubbles in response to ultrasonic treatment by the FUS-BBBO device. In some embodiments, the microbubble cavitation signal is processed to generate the baseline cavitation level, the cavitation level, and the TCL using a fast Fourier transform (FFT) algorithm. In some embodiments, the microbubble cavitation signal comprises an acoustic signal having a frequency within a bandwidth of a center frequency of the PCD transducer.In some embodiments, the target cavitation level includes one of 0.5 dB, 1 dB, 2 dB, 3 dB, and 4 dB above the baseline stable cavitation level.

[0016] In an additional embodiment, a method of performing FUS-BBBO on a subject is disclosed. The method includes injecting microbubbles into the subject, sonicating the subject at a baseline sonication pressure, and detecting a baseline stable cavitation level from the subject after the injection of the microbubbles using a FUS-BBBO device. The baseline stable cavitation level is above signal noise and below a stable cavitation level sufficient to cause BBBO. The method further includes sonicating the subject at a series of incrementally increasing sonication pressures and detecting a corresponding series of cavitation levels until a target cavitation level (TCL) is detected. The target cavitation level is a predetermined amount above the baseline stable cavitation level. The method further includes continuously sonicating the subject to maintain the TCL and cause BBBO in the subject. In some embodiments, detecting the baseline cavitation level, the series of cavitation levels, and the target cavitation level is performed using at least one passive cavitation detection (PCD) transducer. In some embodiments, detecting the baseline cavitation level, the series of cavitation levels, and the target cavitation level further comprises detecting a microbubble cavitation signal generated by the microbubbles in response to ultrasonic treatment by the FUS-BBBO device. In some embodiments, the microbubble cavitation signal is processed using a Fast Fourier Transform (FFT) algorithm to generate the baseline cavitation level, the cavitation level, and the TCL. In some embodiments, the microbubble cavitation signal comprises an acoustic signal having a frequency within a bandwidth of a center frequency of the PCD transducer. In some embodiments, the target cavitation level comprises one of 0.5 dB, 1 dB, 2 dB, 3 dB, and 4 dB higher than the baseline stable cavitation level.

[0017] In another additional aspect, a device for transcranial cavitation localization in a subject is disclosed. The device includes four acoustic sensors for detecting cavitation signals inside the skull of the subject. The four acoustic sensors include S1, S2, S3, and S4. The four acoustic sensors are positioned in a fixed pattern configured to fit the skull of the subject. The device further includes a focused ultrasound (FUS) transducer for sonicating a volume of interest inside the skull of the subject, and a computing device with at least one processor. The at least one processor is configured to sonicate the volume of interest using the FUS transducer, receive a plurality of cavitation signals from inside the skull of the subject injected with microbubbles at the four acoustic sensors, identify at least three time delays based on the plurality of cavitation signals, and identify a location of a cavitation signal source based on the at least three time delays. The at least three time delays include a difference in the time of arrival of the cavitation signal at one of the acoustic sensors S1, S2, S3, and S4 relative to one of the remaining acoustic sensors. In some aspects, the four acoustic sensors are located in a hemispherical pattern. In some aspects, the four acoustic sensors are located such that three acoustic sensors are arranged along the circumference of a circle and one acoustic sensor is located within the circle and vertically offset from the plane of the circle. In some aspects, each time delay of the at least three time delays is identified based on a maximum value of a cross-correlation between a first sample of the cavitation signal detected at a first acoustic detector and a second sample of the cavitation signal detected at a second acoustic detector. In some aspects, the location of the cavitation signal source is identified using a time difference of arrival (TDOA) method.

[0018] Other objects and features will be in part apparent and in part pointed out hereinafter. [Brief description of the drawings]

[0019] [Figure 1] FIG. 1 is a block diagram that illustrates a schematic diagram of a system according to one embodiment of the present disclosure. [Diagram 2] FIG. 2 is a block diagram that generally illustrates a computing device according to one embodiment of the present disclosure. [Diagram 3] FIG. 3 is a block diagram that illustrates a schematic of a remote or user computing device according to one embodiment of the present disclosure. [Figure 4] FIG. 4 is a block diagram illustrating a server system according to one embodiment of the present disclosure. [Figure 5A] FIG. 5A is an image of the four-sensor network used in the experiments described in this application. [Figure 5B] FIG. 5B is an image showing the sensor network of FIG. 5A positioned around an ex-vitro human skull. [Figure 5C] Figure 5C is a schematic diagram of the four-sensor network experimental setup to localize the location of microbubbles formed in a tube of water sonicated by a single FUS transducer. [Figure 6] FIG. 6 is a schematic diagram of an experimental setup as used in the experiments described herein, including the sensor network of FIG. 5A positioned around an ex-vitro human skull as shown in FIG. 5B, along with additional equipment and elements. [Figure 7A] FIG. 7A is a graph showing a time domain comparison of signals acquired with and without the influence of the skull. [Figure 7B] FIG. 7B is a graph showing a frequency domain comparison of signals acquired with and without the influence of the skull. [Figure 8A] FIG. 8A is a graph showing the time history of signals acquired with and without microbubbles. [Figure 8B] FIG. 8B is a graph showing the time history of the subtracted difference between the signals with and without microbubbles shown in FIG. 8A. [Figure 9] FIG. 9 is a series of graphs showing the time history of signals measured by the four sensors of the network shown in FIG. 5A, illustrating the time delays between pairs of sensors. [Figure 10A]FIG. 10A is a B-mode ultrasound image showing the cavitation setup location. [Figure 10B] FIG. 10B is a graph showing cavitation locations obtained using the disclosed DCL method. [Figure 10C] FIG. 10C is a B-mode image of FIG. 10A superimposed with the DCL cavitation locations of FIG. 10B. [Figure 11A] FIG. 11A is an image showing the sensor network of FIG. 5A with sensor S2 located at the P1 position, which corresponds to the occipital crest of the skull. [Figure 11B] FIG. 11B is an image showing the sensor network of FIG. 5A with sensor S2 located at the P2 position, which corresponds to the frontal crest of the skull. [Figure 11C] FIG. 11C is an image showing the sensor network of FIG. 5A with sensor S2 located at the P3 position, which corresponds to a position away from the occipital and frontal ridges of the skull. [Figure 12A] FIG. 12A is a graph showing the sensor position and cavitation position controlled by the 3D positioner using the experimental setup of FIG. [Figure 12B] FIG. 12B is a bar graph summarizing the accuracy of the disclosed DCL method without and with skull (averaged across all 19 locations, with 10 replicates for each location), with the bar graph showing the mean value along with the standard deviation. [Figure 12C] FIG. 12C is a graph showing the location of the cavitation source in the xy plane with the skull and the location estimated by the disclosed DCL method. [Figure 12D] FIG. 12D is a graph showing the location of the cavitation source in the xz plane with the skull and the location estimated by the disclosed DCL method. [Figure 12E] FIG. 12E is a graph showing the location of the cavitation source in the xy plane without the skull and the location estimated by the disclosed DCL method. [Figure 12F]FIG. 12F is a graph showing the location of the cavitation source in the xz plane without the skull and the location estimated by the disclosed DCL method. [Figure 13A] FIG. 13A is a bar graph showing cavitation source localization accuracy with and without skull as a function of source location along the X-axis, including a total of 19 locations and 30 iterations for each location. [Figure 13B] FIG. 13B is a bar graph showing cavitation source localization accuracy with and without skull as a function of source location along the Y-axis, including a total of 19 locations and 30 iterations for each location. [Figure 13C] FIG. 13C is a bar graph showing the cavitation source localization accuracy with and without the skull as a function of source location along the Z axis, including a total of 19 locations and 30 iterations for each location. [Figure 14] FIG. 14 is a bar graph showing the accuracy of transcranial localization for different sensor network orientations as shown in FIG. 11A (P1), FIG. 11B (P2), and FIG. 11C (P3) using the disclosed DCL method, averaged over 30 iterations for each sensor location. [Figure 15] FIG. 15 is a bar graph showing the accuracy of transcranial localization as a function of FUS peak negative pressure for a cavitation source located at the geometric focus of the sensor network using the disclosed DCL method, averaged over 30 iterations in each case. [Figure 16] FIG. 16 is a bar graph showing the accuracy of transcranial localization as a function of source cycles for a cavitation source located at the geometric focus of the sensor network, averaged over 30 iterations in each case. [Figure 17A] FIG. 17A is an image of the four-sensor network used in the experiments described in this application. [Figure 17B] FIG. 17B is an image showing the sensor network of FIG. 17A positioned around an ex-vitro human skull. [Figure 17C] FIG. 17C is a schematic diagram of the four-sensor network experimental setup to localize microbubbles formed in a tube of water sonicated by a single FUS transducer. [Figure 18A] FIG. 18A is a graph showing the sensor position and cavitation position controlled by the 3D positioner using the experimental setup of FIG. 17C. [Figure 18B] FIG. 18B is a bar graph summarizing the position error of the microbubble positions determined using the disclosed DCL method for the skull-less and skull-present cases, the bar graph showing the mean with standard deviation. [Figure 18C] FIG. 18C is a graph summarizing the locations of cavitation sources in the xy plane within the skull (darker dots) and their estimated locations (lighter dots) using the disclosed DCL method. [Figure 18D] FIG. 18D is a graph summarizing the locations of cavitation sources in the xz plane within the skull (darker dots) and their estimated locations (lighter dots) using the disclosed DCL method. [Figure 19] Figure 19 is a schematic diagram of the feedback-controlled FUS system. The experimental setup consisted of three parts: (1) Transmit: FUS transducer, function generator, and power amplifier. (2) Receive: PCD, preamplifier, and computer-based oscilloscope and Picoscope. (3) Feedback control: A customized MATLAB-based graphic user interface (GUI) that implements the feedback control algorithm. [Figure 20]FIG. 20 is a schematic diagram of the feedback control algorithm. Microbubble injection was initiated 15 seconds prior to FUS sonication and continued until the end of FUS sonication. During FUS sonication with microbubble injection, cavitation was monitored in real time by PCD. The baseline cavitation level for each mouse was defined by 10 repeated PCD measurements taken during dummy FUS sonication. Once the TCL was defined (i.e., 0.5 dB, 1 dB, 2 dB, 3 dB, or 4 dB higher than baseline SC level), FUS sonication was performed using a feedback control algorithm with a two-phase process: a pressure increase phase to drive the SC level to the TCL and a maintenance phase to maintain the SC level within the target range (i.e., TCL ± tolerance). The tolerance was set at ±0.4 dB to allow for SC level fluctuations. [Figure 21] FIG. 21 includes a series of graphs illustrating how to determine the TCL of the disclosed individualized feedback control algorithm at each target level (i.e., 0.5 dB, 1 dB, 2 dB, 3 dB, or 4 dB). Each group contained 5 mice, and each circular dot represents the results obtained from each mouse. Box limits, 25th and 75th percentiles; whiskers, 5th and 95th percentiles, centerline, median). [Figure 22A] Figure 22A includes a series of graphs summarizing the measured SC levels as a function of time in different TCLs. Each greyscale shade represents the SC level obtained from each mouse. The solid lines on the right side of the second row represent the mean SC levels for each TCL group (i.e., 0.5 dB, 1 dB, 2 dB, 3 dB, or 4 dB higher than baseline SC levels). [Figure 22B] Figure 22B is a graph of the percentage of good burst rates for each TCL, showing the stability of the feedback control algorithm in each TCL. Box plots show the median and standard deviation. Each circular dot represents the results obtained from each mouse. [Figure 22C]FIG. 22C is a graph summarizing the average IC levels measured over time in each TCL. [Figure 22D] FIG. 22D is a graph summarizing IC probability at different TCLs. [Figure 23A] Figure 23A includes representative photographs and corresponding fluorescence images at five TCLs (i.e., 0.5 dB, 1 dB, 2 dB, 3 dB, and 4 dB). The grayscale intensity bar on the right indicates the fluorescence intensity. [Figure 23B] FIG. 23B is a graph summarizing the normalized fluorescence intensity in each TCL. [Figure 23C] Figure 23C is a graph summarizing the drug (i.e., Evans Blue) delivery area in each TCL. It was observed that FUS-BBBD delivery efficiency and drug delivery area increased when the TCL was increased from 0.5 dB to 3 dB, and slightly decreased at 4 dB. Each circular point represents the results obtained from each mouse. *P<0.05, **P<0.01, and ***P<0.001. [Figure 24A] FIG. 24A includes representative H&E stained images of the FUS-treated side of the brainstem in each TCL. [Figure 24B] Figure 24B is a graph comparing the hemorrhage area of ​​each TCL to the corresponding contralateral side. *P<0.05 [Figure 25A] FIG. 25A is a graph of representative SC levels of the feedback control algorithm used in the clinical trial. [Figure 25B] FIG. 25B is a graph showing the correlation between fluorescence intensity during the expansion phase and SC dose. [Figure 25C] FIG. 25C is a graph showing the correlation between fluorescence intensity and SC dose during the maintenance phase. [Figure 26A] Figure 26A is a schematic diagram of the hardware setup for MRI-guided sono-biopsy in mice. The FUS transducer was connected to the head of the mouse using ultrasound gel and a bladder filled with degassed water. [Figure 26B]Figure 26B is a pair of MRI images. A contrast-enhanced (CE) T1-weighted MRI scan was acquired before FUS to quantify tumor volume (brighter spot at bottom left). A post-FUS MRI scan confirmed the FUS-induced BBB disruption (bright area at bottom right) as an increase in CE volume. [Figure 26C] Figure 26C is a graph showing that FUS significantly increased CE volume (n=19, p=0.0000038; ****p<0.0001; Wilcoxon signed rank test for paired samples) from 24.59±13.21 mm3 to 46.09±20.44 mm3. Black bars indicate the mean. [Figure 27A] Figure 27A is a pair of two 1D amplitude plots for the blood LBx and sono-biopsy groups demonstrating detection of EGFRvIII in the plasma of each representative subject. The line indicates the threshold fluorescence for identifying droplets containing positive EGFRvIII expression. [Figure 27B] Figure 27B is a graph showing that levels in the sono-biopsy group (n=17; 19.06±24.74 copies / μL) were significantly greater than those in the blood LBx group (n=14; 0.02±0.08 copies / μL) (p=0.00089; ***p<0.001; Wilcoxon signed rank test for two unpaired samples). Black bars indicate the mean. [Figure 27C] Figure 27C is a pair of two 1D amplitude plots of the blood LBx and sono-biopsy groups for detection of TERT C228T in the plasma of each representative subject. The line indicates the threshold fluorescence for identifying droplets containing positive TERT C228T expression. [Figure 27D]Figure 27D is a graph showing that FUS significantly increased TERT C228T ctDNA levels in plasma from 0.06±0.18 copies / μL in the blood LBx group (n=21) to 0.64±1.19 copies / μL in the sono-biopsy group (n=24; p=0.015; *p<0.01; Wilcoxon signed rank test for two unpaired samples). Black bars indicate the mean. [Figure 27E] Figure 27E is a graph showing that using ddPCR, sono-biopsy is more sensitive than blood LBx, with detection rates of 64.71% for EGFRvIII and 45.83% for TERT C228T, compared to 7.14% and 14.29%, respectively, for blood LBx. ND: Not detected. [Figure 28A] Figure 28A is a representative H&E stained image of a subject treated with sono-biopsy. The arrow indicates microhemorrhage in the tumor ROI. [Figure 28B] FIG. 28B is a graph showing that microhemorrhage density in parenchyma after sono-biopsy (0.47±0.68 positive pixels / μm2, n=5) was not significantly different from that after blood LBx (0.83±0.69 positive pixels / μm2; n=5, p=0.33; Wilcoxon signed rank test of two unpaired samples). There was a non-significant increase in the occurrence of microhemorrhage in tumor ROI after sono-biopsy (4.54±3.08 positive pixels / μm2, n=5) compared to that after blood LBx (2.08±3.54 positive pixels / μm2; n=5, p=0.18; Wilcoxon signed rank test of two unpaired samples). Black bars indicate the mean. [Figure 28C] Figure 28C is a representative TUNEL staining image of a subject treated with sono-biopsy showing increased apoptotic signal in the tumor ROI. Arrows indicate apoptotic cells. [Figure 28D]FIG. 28D is a graph showing that there was no significant difference in TUNEL density of parenchymal tissue between blood LBx (0.20×10-3±0.22×10-3 positive cells / μm2, n=5) and sono-biopsy (0.47×10-3±0.22×10-3 positive cells / μm2; n=5, p=0.11; Wilcoxon signed rank test for two unpaired samples). There was no significant difference in TUNEL density of tumor ROI between blood LBx (1.82×10-3±0.62×10-3 positive cells / μm2, n=5) and sono-biopsy (1.97×10-3±1.22×10-3 positive cells / μm2; n=5, p=0.73; Wilcoxon signed rank test for two unpaired samples). Black bars indicate the mean. [Figure 29A] Figure 29A is an image of the hardware setup for MRI-guided sonoviopsy in a pig. The pig's head was stabilized by a head support. An MR-compatible motor allowed translation of the FUS transducer to a specific target location. [Figure 29B] FIG. 29B is an image showing the placement of the pig in the sono-biopsy device. [Figure 29C] Figure 29C is a pair of MRI images. CE T1 weighted MRI scan shows the tumor volume (bright spot at bottom left) and BBB disruption (bright area at bottom right) caused by FUS. [Figure 29D] Figure 29D is a graph showing a significant increase in CE volume from 348.70±358.02 mm3 to 799.50±501.19 mm3 (n=6; p=0.031; *p<0.05; Wilcoxon signed rank test for paired samples). Black bars indicate the mean. [Figure 30A] FIG. 30A is a pair of 1D amplitude plots for EGFRvIII detection in the plasma of each subject. [Figure 30B]Figure 30B is a graph showing that sono-biopsy significantly increased plasma levels of EGFRvIII ctDNA (n=7; p=0.016; *p<0.05; Wilcoxon signed rank test for paired samples) from 13.69±28.62 copies / mL to 3697.54±3780.61 copies / mL. Black bars indicate the mean. [Figure 30C] FIG. 30C is a pair of 1D amplitude plots for TERT C228T detection in the plasma of each subject. [Figure 30D] Figure 30D is a graph showing that sono-biopsy significantly increased plasma levels of TERT C228T ctDNA (n=10; p=0.022; *p<0.05; Wilcoxon signed rank test for paired samples) from 13.07±23.08 copies / mL to 112.25±150.75 copies / mL. Black bars indicate the mean. [Figure 30E] Figure 30E is a graph showing that using ddPCR, sono-biopsy is more sensitive than blood LBx, with detection rates of 100% for EGFRvIII and 71.43% for TERT C228T, compared to 28.57% and 42.86%, respectively, for blood LBx. ND: Not detected. [Figure 31A] Figure 31A shows an image of a representative horizontal slice stained with H&E. In some cases, microhemorrhages occur near the edge of the tumor (arrows). [Figure 31B] Figure 31B is a graph showing that microhemorrhage density was not significantly different between parenchymal tissue (0.33±0.13 positive cells / μm2, n=4) and tumor (1.28±0.79 positive cells / μm2, n=4, p=0.20; Wilcoxon signed rank test for two unpaired samples). Black bars indicate the mean. [Figure 31C] FIG. 31C is a representative image of TUNEL staining showing apoptotic cells (arrows). [Figure 31D]FIG. 31D is a graph showing that there was no significant difference between TUNEL density in tumors (110.40×10±112.25×10 positive cells / μm, n=4) compared to that in parenchymal tissues (51.34×10±56.12×10 positive cells / μm; (n=4, p=0.55; Wilcoxon signed rank test for two unpaired samples). Black bars indicate the mean. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0020] Those skilled in the art will appreciate that the drawings described below are for illustration purposes only and are not intended to limit the scope of the present disclosure in any way.

[0021] Focused ultrasound combined with microbubble-induced blood-brain barrier opening (FUS-BBBO) is a promising method for non-invasive and localized brain drug delivery, and a growing number of clinical studies are currently underway. In addition to delivering drugs, low-intensity FUS sonication of the brain can be used to enhance the release of brain disease biomarkers into the blood and CSF to enable non-invasive and reliable diagnosis of brain diseases. FUS sonication of the brain can enhance the release of brain disease biomarkers into the blood and CSF by producing mechanical and thermal effects in the brain.

[0022] A method for liquid biopsy of brain lesions assisted by microbubble cavitation In various aspects, the present application discloses methods for performing liquid biopsies to diagnose brain disorders. As previously described, the FUS-BBBO devices and methods enhance the release of biomarkers from the brain into the blood and CSF of a subject, thereby increasing the concentration of the biomarkers to levels that are more readily detectable using various biomarker assays and analytical methods.

[0023] In various embodiments, a liquid biopsy method includes injecting a predetermined amount of microbubbles into a subject, and then opening the BBB of the subject using a FUS-BBBO method and closed-loop feedback control of the microbubble-induced cavitation as described herein.

[0024] In various embodiments, after using the FUS-BBBO device and method to induce BBB opening, the method further comprises collecting a biological sample from the subject, comprising a biomarker. Non-limiting examples of suitable biological samples include a blood sample or a CSF sample. In various embodiments, blood samples and CSF samples are collected using any suitable existing method, without limitation.

[0025] In various embodiments, the liquid biopsy method further includes diagnosing the brain disorder based on one or more biomarkers isolated from the biological sample. Any suitable existing analytical systems, devices, and methods may be used to isolate and analyze the one or more biomarkers, without limitation. In various embodiments, the biomarkers include any suitable biomarkers known to be indicative of brain disorders, including, but not limited to, cytokines, cells, cell-free DNA, RNA, proteins such as beta amyloid protein, exosomes, and any combination thereof. Non-limiting examples of brain disorders that may be diagnosed using the disclosed liquid biopsy method include, without limitation, brain tumors, Alzheimer's disease, Parkinson's disease, and any other suitable brain disorders.

[0026] In various aspects, the efficacy of the disclosed liquid biopsy methods is enhanced by ensuring safe and effective opening of the BBB barrier using the FUS-BBBO systems, devices, and methods described herein. In one aspect, microbubble cavitation is safely and reliably controlled using an individualized closed-loop feedback method that accounts for individual differences in subject morphology, coupling of the FUS-BBBO sonication and cavitation detection elements to the subject's skull, and variability in microbubble composition and concentration for each individual and / or procedure.

[0027] A method for personalized closed-loop feedback control of microbubble cavitation In various aspects, the present application discloses systems, devices, and methods for individualized closed-loop feedback control of microbubble cavitation for safe and reliable FUS-BBBO. In various aspects, the disclosed FUS-BBBO feedback control method defines a target cavitation level (TCL) based on a baseline stable cavitation (SC) level for individual subjects who have undergone a "dummy" FUS sonication. Dummy FUS sonication defines a baseline cavitation level by applying FUS at a target brain location at low acoustic pressure for a short period of time in the presence of microbubbles, taking into account individual differences in detected cavitation emissions. FUS-BBBO is then performed using two sonication phases, an increase phase to reach a final phase that achieves the TCL, and continuing sonication in this final phase to maintain the SC level at the TCL. As described in the examples below, evaluations performed in wild-type mice demonstrated that the disclosed FUS-BBBO feedback control method achieves reliable and damage-free delivery of model drugs across the BBB at selected TCL levels.

[0028] As described in the Examples below, the individualized closed-loop feedback control method disclosed herein achieved reliable and safe FUS-BBBO. The disclosed control method defines the TCL based on a baseline SC level obtained for an individual subject, thereby avoiding overexposure to FUS during the subject's sonication associated with FUS-BBBO. As further described in the Examples below, when the TCL was increased 0.5 dB to 2 dB above the baseline SC level, drug delivery results increased without causing vascular damage. Increasing the TCL by more than 2 dB increased the probability of tissue damage.

[0029] Without being limited to any particular theory, FUS-BBBO is influenced by the interplay between at least several factors, including, but not limited to, the ultrasound energy delivered to the region, the concentration and structure of the microbubbles delivered to the region, and the individual cerebral vascular morphology. In various embodiments, the disclosed control method defines a TCL based on a cavitation signal generated by low-pressure FUS sonication over a period ranging from about 2 seconds to about 10 seconds or longer. As described in the Examples below, the TCL used for mice subjected to FUS-BBBO was based on about 5 seconds of cavitation signal generated by FUS sonication at a low pressure of 0.2 MPa measured in water. This corresponded to an estimated in situ acoustic pressure of about 0.16 MPa, assuming about 18% mouse skull attenuation.

[0030] In various embodiments, the dummy sonication level used to assess the individual TCL is less than the irradiation energy required to cause BBB opening. Without being limited to any particular theory, the assessment of the individual TCL as described herein simultaneously considers individual variations in the delivery of FUS, the concentration and structure of the microbubbles delivered to the area, and the cerebral vascular morphology of the individual subject. Without being limited to any particular theory, the acoustic emissions detected with the dummy sonication used to assess the individual TCL may be affected by one or more factors, including, but not limited to: · individual differences in skull thickness and the angle of incidence of the FUS beam, which affect the in situ acoustic pressure and the FUS signal reflected from the skull detected by the PCD; · variations in the concentration and size distribution of the injected microbubbles; and 3) heterogeneity in the spatial distribution of microbubbles near the BBB due to variations in blood vessel density, blood vessel size, and blood flow.

[0031] As described in the Examples below, experimental data detected individual variations in baseline SC levels, as summarized in FIG. 21. Due to differences in each subject's baseline SC levels, each subject's target absolute SC level was different (bottom right graph in FIG. 21). The closed-loop feedback control method disclosed herein maintained the SC level with high stability at TCL. As described in the Examples below, the disclosed closed-loop feedback control method achieved 58.2-78.6% stability for TCLs (FIG. 22B) over the range of 0.5-4 dB, as quantified by good burst rate. This is a stability level comparable to the best existing control methods.

[0032] In various embodiments, the selection of the optimal TCL should consider the stability of the feedback controller in addition to FUS-BBBO delivery outcome and safety. As described in the Examples, although no significant differences were detected among the five groups' successful burst rates, increasing the TCL was observed to be associated with a trend toward lower successful burst rates (FIG. 22B), indicating less stability / controllability between cavitation events. Furthermore, increasing the TCL within the range of 0.5 dB to 3 dB caused an approximately linear increase in Evans Blue fluorescence intensity (FIG. 23B), indicating more effective opening of the BBB and associated delivery of circulating compounds to the brain. However, higher TCL was associated with a higher probability of the presence of IC (FIG. 22D) and vascular damage (FIG. 24B). A TCL of 2 dB was identified as the optimal level for efficient and safe FUS-BBBO based on the relationship of TCL to IC and probability of vascular damage described above.

[0033] In some aspects, the disclosed feedback control method may further include monitoring IC and modulating the sonication pressure based at least in part on the change in detected IC and / or the estimated probability of IC. In one aspect, the feedback control algorithm may reduce the sonication pressure when IC is detected to avoid tissue damage. In various aspects, the disclosed closed loop feedback control method may be integrated into the operation of any suitable FUS-BBBO device or system, without limitation. In various aspects, the disclosed control method may be implemented in the form of instructions executable on at least one processor of a computing device, as described in additional detail below. In some aspects, the at least one processor resides in at least one computing device of a suitable FUS-BBBO device or system. In other aspects, the at least one processor resides in a separate computing device of a separate FUS-BBBO control system operatively connected to and communicating with the suitable FUS-BBBO system or device.

[0034] Transcranial cavitation detection system and method Existing techniques for 3D passive transcranial cavitation detection require the use of expensive and complex hemispherical phased arrays. In various aspects, devices and methods for 3D passive transcranial cavitation are disclosed that utilize a small number of sensors (e.g., four) for transcranial 3D localization of cavitation. The disclosed devices and methods further utilize differential microbubble cavitation (DMC) signal processing to obtain high quality cavitation signals for use in sensor-based cavitation localization.

[0035] In various embodiments, the disclosed transcranial cavitation device includes a minimum set of four sensors for 3D transcranial localization of microbubble cavitation. Differential microbubble cavitation (DMC) signals were obtained for each sensor by subtracting signals received without and with microbubbles under the same FUS sonication conditions. The DMC signals extract the acoustic emissions from the microbubbles, thereby effectively improving the signal-to-noise ratio and minimizing the effects of the skull. Then, the TDOA of the signals obtained from the different sensors was calculated by maximum cross-correlation (MCC). Finally, the 3D cavitation location was estimated using a TDOA algorithm. This method combines differential cavitation signal detection with a TDOA localization algorithm, and is also referred to in this application as the differential cavitation localization (DCL) method. The accuracy of DCL with and without the human skull and its dependence on the sensor position relative to the skull, FUS pressure, and number of FUS cycles were evaluated ex vivo in a water tank. Four miniaturized ultrasound sensors were used for transcranial detection of cavitation signals emitted from microbubbles flowing through a tube when sonicated by a FUS transducer in the water tank.

[0036] In various embodiments, a four-sensor network combined with a differential cavitation localization method for transcranial 3D cavitation localization is disclosed. As described in the Examples below, localization accuracy was found to be within 1.5 mm at the center of mass. In various embodiments, the four-sensor network may utilize a differential cavitation level (DCL) method to subtract signals acquired with and without microbubbles, improving the cavitation signal-to-noise ratio and minimizing the effect of the skull on the localization process.

[0037] Existing cavitation localization methods typically use delay-and-sum beamforming algorithms for cavitation location using 2D and 3D PCI. When absolute time-of-arrival is required, the anomalies in the received signal caused by the skull must be corrected. In various aspects, a time-of-arrival delay algorithm is used to transcranially localize the cavitation source based on the relative time-of-arrival difference of signals detected by two different sensors in a four-sensor network. Calculating the relative time difference eliminates the need to perform skull anomaly correction, which greatly simplifies the 3D localization algorithm. As demonstrated in the examples below, the accuracy of the disclosed transcranial cavitation localization method was robust even when the sensors were located at different locations around the skull.

[0038] Without being limited to any particular theory, the accuracy of the disclosed cavitation localization method depends on the acoustic pressure used to induce cavitation. As demonstrated in the examples below, when this pressure increased from 0.9 MPa to 1.3 MPa, the localization results increased and became unstable, because the side lobe beam from the FUS transducer used to induce cavitation was strong enough to sonicate the surrounding microbubbles on the side of the FUS focus. In various aspects, the differential cavitation level (DCL) signal may not come from a single source, and thus the ambiguity of the received signal increases when the FUS pressure is high, resulting in localization instability. As demonstrated in the examples below, for sonication using FUS pressures between 0.2 MPa and 0.9 MPa, a range typically used in BBB opening applications and research, the disclosed DCL cavitation localization method can perform stable and accurate transcranial localization of cavitation.

[0039] As demonstrated in the examples below, the accuracy of the disclosed DCL cavitation localization method decreased when the number of FUS cycles was increased. The localization accuracy decreased as the number of cycles increased, since a larger number of cycles corresponded to a longer signal length. For example, the spatial length of a 100-cycle pulse with a frequency of 500 kHz is about 30 cm, which may be more than three times longer than the length of the cavitation source relative to the sensor. Thus, the received signal of the microbubble cavitation may experience reflections and echoes from obstacles such as the sensor holder, the water tank, and the water surface. Thus, a longer pulse reduces the signal-to-noise ratio (SNR) of the received signal, which reduces the localization accuracy. One way to enable the disclosed DCL cavitation localization method to localize cavitation caused by a longer pulse is to increase the distance between the sensor and the source. However, typical applications of the FUS-induced cavitation method used a relatively small number of pulse cycles. For example, conventional histotripsy treatments typically use ultrasound cycles of 3-10 or as little as 1.5 cycles, whereas FUS-BBBD uses short bursts of 5 cycles to facilitate efficient and safe delivery of the drug to the brain.

[0040] The disclosed system and method for transcranial cavitation localization described herein has low computational resource requirements and low hardware costs. The data generated during localization using the disclosed method is small and the corresponding computational requirements are low, which facilitates real-time monitoring and characterization of transcranial cavitation. Due to the small number of sensors, the disclosed sensor array can be freely positioned around the skull according to actual needs. In some aspects, the disclosed DCL method can be incorporated into the data analysis algorithm of a wearable therapeutic device for the brain.

[0041] In some embodiments, the disclosed method is used to locate only one cavitation source per pulse. In other embodiments, the disclosed method is used to locate multiple simultaneous cavitation events. In these other embodiments, the time domain signal of each channel is divided into segments according to the location of the cavitation source. Sources within a limited range of each detector / channel corresponding to a particular time domain segment are calibrated, and the different segments generate a set of TDOAs based on the number of sources, thereby simultaneously locating multiple results based on time delay.

[0042] In various aspects, the size of the cavitation source may affect the localization accuracy. Without being limited to any particular theory, the cavitation sources localized using the disclosed apparatus and methods are typically of relatively small volume. Furthermore, the TDOA-based algorithms used in the disclosed localization methods have been derived to localize point sources or to localize distances between the sensor and the source that are much larger than the wavelength of the emitted waves, such as GPS problems. Cavitation caused using higher FUS pressure not only results in stronger source signals, but also causes larger sized cavitation sources. Thus, there is a balance between signal strength and cavitation source size. Using a higher strength FUS to cause cavitation improves localization accuracy due to the stronger signal, while larger sized sources reduce localization accuracy.

[0043] In various aspects, the disclosed transcranial cavitation localization method may be suitable for use in a wide variety of applications. By way of non-limiting example, 3D transcranial cavitation detection is critically required in several applications, such as concussion and blast-induced traumatic brain injury caused by microcavitations formed in the brain. The ability to perform transcranial cavitation detection is important for understanding the mechanism of brain trauma and for locating the site of injury.

[0044] By way of another non-limiting example, cavitation caused by focused ultrasound is the physical mechanism behind several emerging techniques in brain therapy. Accurately knowing the 3D location of cavitation in real time can improve treatment targeting accuracy and avoid damage to off-target tissue.

[0045] Further illustration of additional aspects of the disclosed transcranial cavitation localization method are provided in the Examples below.

[0046] The control or reference sample described herein may be a sample from a healthy subject. Instead of a control or reference sample, a reference value previously obtained from a healthy subject or from a group of healthy subjects may be used. The control or reference sample may also be a sample containing a known amount of a detectable compound or a spiked sample.

[0047] Computing systems and devices In various aspects, the disclosed FUS-BBBO and cavitation localization methods may be implemented using a computing system or device. FIG. 1 illustrates a simplified block diagram of a system for implementing the computer-assisted methods described herein. As shown in FIG. 1, a computing device 300 may be configured to implement at least a portion of the tasks associated with the disclosed methods described herein. The computer system 300 may include a computing device 302. In one aspect, the computing device 302 is part of a server system 304, which also includes a database server 306. The computing device 302 communicates with a database 308 via the database server 306. The computing device 302 is communicatively connected to a user computing device 330 and a FUS-BBBO system 334 via a network 350. The network 350 may be any network that allows for local or wide area communication between devices. For example, network 350 may provide for communicative connection to the Internet via at least one of a number of interfaces, including, but not limited to, at least one network such as the Internet, a local area network (LAN), a wide area network (WAN), an integrated services digital network (ISDN), a dial-up connection, a digital subscriber line (DSL), a cellular telephone connection, and a cable modem. User computing device 330 may be any device capable of accessing the Internet, including, but not limited to, a desktop computer, a laptop computer, a personal digital assistant (PDA), a cellular telephone, a smartphone, a tablet, a phablet, a wearable electronic device, a smart watch, or other web-based connectable appliance or mobile device.

[0048] In other aspects, the computing device 302 is configured to perform a number of tasks associated with the disclosed computer-assisted methods of performing FUS-BBBO and / or transcranial localization. In some aspects, the computing device 302, the user computing device 330, and / or the FUS-BBBO system 334 may be operatively connected via a network 350. FIG. 2 illustrates a component arrangement 400 of the computing device 402, which includes a database 410 along with other associated computing components. In some aspects, the computing device 402 is similar to the computing device 302 (shown in FIG. 1). A user 404 may access the components of the computing device 402. In some aspects, the database 410 is similar to the database 308 (shown in FIG. 1).

[0049] In one embodiment, the database 410 includes FUS-BBBO data 412, TDOA data 418, and cavitation localization data 420. The FUS-BBBO data 412 may include data for operating a FUS-BBBO system with personalized closed-loop feedback control of microbubble cavitation, as disclosed herein. Non-limiting examples of the FUS-BBBO data 412 include various measurements of cavitation signals, any parameters used to control the operation of the FUS-BBBO device, any parameters that define a mathematical formula or other algorithm used to implement personalized closed-loop feedback control of microbubble cavitation, as disclosed herein. The TDOA data 418 may include data used to perform transcranial localization of cavitation sources, as disclosed herein. Non-limiting examples of TDOA data 418 include measurements of background noise and / or cavitation signals, any parameters that define the equations and other algorithms used to implement the conversion of background noise and cavitation signals to differential cavitation signals as disclosed herein, and / or any parameters that define the equations and other algorithms used to implement the localization of cavitation sources using time difference of arrival (TDOA) methods as described herein.

[0050] The computing device 402 also includes a number of components that perform specific tasks. In an exemplary embodiment, the computing device 402 includes a data storage device 430, a cavitation localization component 440, a focused ultrasound blood-brain barrier opening (FUS-BBBO) component 450, and a communication component 460. The cavitation localization component 440 is configured to implement transcranial cavitation localization using differential cavitation signal determination and / or TDOA localization methods as described herein. The focused ultrasound blood-brain barrier opening (FUS-BBBO) component 450 is configured to implement individualized closed-loop feedback control of microbubble cavitation as disclosed herein. The data storage device 430 is configured to store data received or generated by the computing device 402, such as any of the data stored in the database 410, or any output of processing performed by any component of the computing device 402.

[0051] Communications component 460 is configured to enable communication between computing device 402 and other devices (e.g., user computing device 330 shown in FIG. 1 ) over a network, such as network 350 (shown in FIG. 1 ), or multiple network connections, using a predefined network protocol, such as TCP / IP (Transmission Control Protocol / Internet Protocol).

[0052] 3 illustrates a configuration of a remote or user computing device 502, such as user computing device 330 (shown in FIG. 1). Computing device 502 may include a processor 505 for executing instructions. In some aspects, executable instructions may be stored in memory area 510. Processor 505 may include one or more processing devices (e.g., in a multi-core configuration). Memory area 510 may be any device that allows information, such as executable instructions and / or other data, to be stored and retrieved. Memory area 510 may include one or more computer-readable media.

[0053] The computing device 502 may also include at least one media output component 515 for presenting information to the user 501. The media output component 515 may be any component capable of communicating information to the user 501. In some aspects, the media output component 515 may include an output adapter, such as a video adapter and / or an audio adapter. The output adapter may be operatively connected to the processor 505 and may be operatively connectable to an output device, such as a display device (e.g., a liquid crystal display (LCD), an organic light emitting diode (OLED) display, a cathode ray tube (CRT), or an "electronic ink" display) or an audio output device (e.g., speakers or headphones). In some aspects, the media output component 515 may be configured to present an interactive user interface (e.g., a web browser or a client application) to the user 501.

[0054] In some aspects, the computing device 502 may include an input device 520 for receiving input from the user 501. The input device 520 may include, for example, a keyboard, a pointing device, a mouse, a stylus, a touch-sensitive panel (e.g., a touchpad or touch screen), a camera, a gyroscope, an accelerometer, a position detector, and / or an audio input device. A single component, such as a touch screen, may function as both an output device for the media output component 515 and as an input device 520.

[0055] Computing device 502 may also include a communications interface 525, which may be communicatively coupled to a remote device. Communications interface 525 may include, for example, a wired or wireless network adapter or a wireless data transceiver for use with a cellular network (e.g., Global System for Mobile communications (GSM), 3G, 4G, or Bluetooth) or other mobile data network (e.g., Worldwide Interoperability for Microwave Access (WIMAX)).

[0056] Stored in memory area 510 are computer readable instructions for, for example, providing a user interface to user 501 via media output component 515, and optionally receiving and processing input from input device 520. The user interface may include a web browser and a client application, among other possibilities. The web browser allows user 501 to view and interact with media and other information typically embedded in web pages or websites from a web server. The client application allows user 501 to interact with server applications, for example associated with a vendor or business.

[0057] 4 illustrates an exemplary configuration of a server system 602. The server system 602 may include, but is not limited to, a database server 306 and a computing device 302 (both shown in FIG. 1). In some aspects, the server system 602 is similar to the server system 304 (shown in FIG. 1). The server system 602 may include a processor 605 for executing instructions. For example, the instructions may be stored in a memory area 625. The processor 605 may include one or more processing units (e.g., in a multi-core configuration).

[0058] The processor 605 may be operatively connected to a communications interface 615 such that the server system 602 may communicate with remote devices, such as user computing device 330 (shown in FIG. 1), or other server systems 602. For example, the communications interface 615 may receive requests from user computing device 330 over network 350 (shown in FIG. 1).

[0059] The processor 605 may be operatively connected to a storage device 625. The storage device 625 may be any computer-operated hardware suitable for storing and / or retrieving data. In some embodiments, the storage device 625 may be integrated into the server system 602. For example, the server system 602 may include one or more hard disk drives as the storage device 625. In other embodiments, the storage device 625 may be external to the server system 602 and may be accessed by multiple server systems 602. For example, the storage device 625 may include multiple storage devices, such as hard disks or solid state disks having a redundant array of inexpensive disks (RAID) configuration. The storage device 625 may include a storage area network (SAN) and / or a network attached storage (NAS) system.

[0060] In some aspects, processor 605 may be operatively connected to storage device 625 via storage interface 620. Storage device interface 620 may be any component capable of providing processor 605 with access to storage device 625. Storage device interface 620 may include, for example, an Advanced Technology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, a Small Computer System Interface (SCSI) adapter, a RAID controller, a SAN adapter, a network adapter, and / or any component that provides processor 605 with access to storage device 625.

[0061] Memory areas 510 (shown in FIG. 3) and 610 may include, but are not limited to, random access memory (RAM), such as dynamic RAM (DRAM) or static RAM (SRAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and non-volatile RAM (NVRAM). The memory types listed above are merely examples and thus are not limiting as to the types of memory usable for storage of computer programs.

[0062] The computer systems and computer-assisted methods described herein may include additional, reduced, or alternative operations and / or functionality, including those described elsewhere herein. The computer systems may include or be implemented with computer-executable instructions stored on a non-transitory computer-readable medium. The methods may be implemented with one or more local or remote processors, transceivers, servers, and / or sensors (e.g., processors, transceivers, servers, and / or sensors mounted on a vehicle or mobile device or associated with a smart infrastructure or a remote server) or with computer-executable instructions stored on one or more non-transitory computer-readable media.

[0063] The disclosed methods and algorithms may be incorporated into a controller or processor. Additionally, the disclosed methods and algorithms may be embodied as one or more computer-implemented methods for performing one or more computer-implemented methods, and may be embodied in the form of a tangible or non-transitory computer-readable storage medium containing a computer program or other machine-readable instructions (herein referred to as a "computer program"), which when loaded into and / or executed by a computer or other processor (herein referred to as a "computer"), causes the computer to become an apparatus for performing one or more methods. Such storage media containing computer programs include, for example, floppy disks and diskettes, compact disks (CD)-ROMs (both writable and non-writable), DVD digital disks, RAM and ROM memory, computer hard drives and backup drives, external hard drives, "thumb" drives, and other storage media readable by a computer. The method or methods may be embodied in the form of a computer program, which may be stored, for example, in a storage medium, transmitted through a transmission medium such as electrical conductors, optical fibers or other optical conductors, or by electromagnetic radiation, and when the computer program is loaded into and / or executed by a computer, the computer becomes an apparatus for performing the method or methods. The method or methods may be executed in a general-purpose microprocessor, or in a digital processor specifically configured to perform one or more operations. When a general-purpose microprocessor is used, the computer program code configures the circuitry of the microprocessor to create a specific logic circuit arrangement. A computer-readable storage medium includes a medium that is read by the computer itself, or a medium that is read by another machine that reads the computer instructions and provides those instructions to the computer to control its operation. Such a machine may include, for example, a machine for reading the storage medium described above.

[0064] In some aspects, a computing device is configured to perform machine learning such that the computing device "learns" to analyze, organize, and / or process data without being explicitly programmed. Machine learning may be implemented using machine learning (ML) methods and algorithms. In one aspect, a machine learning (ML) module is configured to implement the ML methods and algorithms. In some aspects, the ML methods and algorithms are applied to data inputs to generate machine learning (ML) outputs. The data inputs may include, but are not limited to, images or frames of video, object characteristics, and object classifications. The data inputs may further include sensor data, image data, video data, telematics data, authentication data, authorization data, security data, mobile device data, geolocation information, transaction data, personal identification data, financial data, usage data, weather pattern data, "big data" collections, and / or user preference data. The ML outputs may include, but are not limited to, tracked shape outputs, classification of objects, classification (segmentation) of regions in medical images, classification of types of motion, diagnosis based on object motion, object motion analysis, and trained model parameters. The ML output may further include speech recognition, image or video recognition, medical diagnosis, statistical or financial models, autonomous vehicle decision-making models, robotics behavior modeling, fraud detection analysis, user recommendations and personalization, gaming AI, skill acquisition, targeted marketing, big data visualization, weather forecasting, and / or information extracted regarding a computing device, a user, a home, a vehicle, or a party to a transaction. In some aspects, the data input may include a given ML output.

[0065] In some aspects, at least one of a number of ML methods and algorithms may be applied, which may include, but are not limited to, genetic algorithms, linear or logistic regression, instance-based algorithms, regularization algorithms, decision trees, Bayesian networks, cluster analysis, association rule learning, artificial neural networks, deep learning, dimensionality reduction, and support vector machines. In various aspects, the ML methods and algorithms implemented relate to at least one of a number of categories of machine learning, such as supervised learning, unsupervised learning, adversarial learning, and reinforcement learning.

[0066] The definitions and methods set forth herein are provided to better define the present disclosure and to guide those skilled in the art in practicing the present disclosure. Unless otherwise noted, terms should be understood according to common usage by those skilled in the relevant art.

[0067] In some embodiments, numbers expressing properties such as amounts of ingredients, molecular weights, reaction conditions, and the like, used to describe and claim certain embodiments of the present disclosure, should be understood to be modified in some instances by the term "about". In some embodiments, the term "about" is used to indicate that a value includes the standard deviation of the average for the device or method being employed to determine that value. In some embodiments, the numerical parameters set forth in the specification and appended claims are approximations that may vary depending on the desired properties sought to be obtained by the particular embodiment. In some embodiments, the numerical parameters should be construed in light of the large number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of some embodiments of the present disclosure are approximations, the numerical values ​​set forth in the specific examples are reported as precisely as practical. The numerical values ​​presented in some embodiments of the present disclosure can contain certain errors necessarily resulting from the standard deviations found in their respective testing measurements. References to ranges of values ​​herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each separate value is incorporated herein as if it were individually referred to herein. Reference to discrete values ​​is understood to include ranges between each value.

[0068] In some embodiments, the terms "a," "an," "the," and similar references used in the context of describing particular embodiments (particularly in certain contexts of the appended claims) may be construed to cover both the singular and the plural, unless specifically noted otherwise. In some embodiments, the term "or" as used in this application, including the claims, is used to mean "and / or," unless expressly stated to indicate only an alternative or unless the alternatives are mutually exclusive.

[0069] The terms "comprise," "have," and "include" are open-ended linking verbs. Any form or tense of one or more of these verbs, such as "comprises," "comprising," "has," "having," "includes," and "including," are also open-ended. For example, any method that "comprises," "has," or "includes" one or more steps is not limited to including only those one or more steps, but may cover other steps that are not recited. Similarly, any structure or device that "comprises," "has," or "includes" one or more features is not limited to including only those one or more features, but may cover other features that are not recited.

[0070] All methods described herein may be performed in any suitable order unless otherwise indicated herein or clearly contradicted by context. The use of any and all examples or exemplary phrases (e.g., "such as") provided in connection with a given embodiment herein is intended merely to better elucidate the disclosure and does not otherwise limit the scope of the disclosure as set forth in the claims. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the disclosure.

[0071] Groupings of alternative elements or embodiments of the disclosure disclosed herein are not to be construed as limitations. Each group member may be referred to or claimed individually or in any combination with other members of the group or other elements found in this application. One or more members of a group may be included in, or deleted from, a group for reasons of convenience or patentability. When any such inclusion or deletion occurs, the specification is deemed to include the groups as modified to satisfy the recitation of all Markush groups used in the appended claims herein.

[0072] All publications, patents, patent applications, and other references cited in this application are incorporated in their entirety for all purposes to the same extent as if each individual publication, patent, patent application, or other reference was specifically and individually indicated to be incorporated in its entirety for all purposes. Citation of any reference in this application shall not be construed as an admission that such is prior art to the present disclosure.

[0073] Having described the present disclosure in detail, it will be apparent that modifications, variations, and equivalent embodiments are possible without departing from the scope of the present disclosure as defined in the appended claims. Moreover, it should be recognized that all examples in the present disclosure are provided as non-limiting examples.

[0074] Working Example The following non-limiting examples are provided to further illustrate the present disclosure. It should be appreciated by those skilled in the art that the techniques disclosed in the following examples represent approaches that the inventors have discovered to work well in implementing the present disclosure, and therefore may be considered to constitute examples of modes for its implementation. However, those skilled in the art should appreciate in light of the present disclosure that numerous changes can be made in the specific embodiments disclosed and still achieve similar results without departing from the spirit and scope of the present disclosure.

[0075] Example 1 - Sensor network design and experimental setup In order to develop a suitable four-sensor network and apparatus for acquiring data suitable for use with the disclosed cavitation localization method, the following experiments were conducted.

[0076] A device for 3D cavitation localization using four sensors (S1, S2, S3, and S4) was designed and fabricated. The four sensors were four identical planar ultrasound transducers. Each transducer had a center frequency of 2.25 MHz, a 6 dB bandwidth of 1.39 MHz, and an aperture of 6 mm diameter (model V323-SM, Olympus America Inc., Waltham, MA, USA). As shown in FIG. 5A, sensors S2, S3, and S4 were equally distributed along a circle with a radius of 78 mm in the horizontal plane. Sensor S1 was located along the central axis of the circle and was vertically offset from the horizontal plane of sensors S2, S3, and S4 by a vertical distance of 45 mm. This sensor arrangement was adapted to fit the outer contour of an ex vivo adult skull cap used to evaluate the developed cavitation localization method, as shown in FIG. 5B. The center of sensor S1 was defined as the [0, 0, 0] coordinate for pressure measurement. FIG. 5C is a schematic diagram of a four-sensor network locating a microbubble sonicated by a single FUS transducer.

[0077] To replicate experimental conditions as representative as possible of in vivo conditions, the top half of a human skull was used in the experiments described herein. The skull cap was dried from air storage, immersed in water, and degassed in a vacuum chamber for at least one week to remove air bubbles trapped in the porous bone tissue before performing the experiments.

[0078] To evaluate the performance of the four-sensor network in 3D cavitation localization, a FUS transducer was used to sonicate microbubbles in a tube (inner diameter of 3 mm and outer diameter of 5 mm) located on top of the skull. Figure 6 provides a schematic diagram of the experimental setup. The sensor network, skull, and tube were placed in an acrylic tank filled with room temperature degassed water. Homemade microbubbles were diluted and injected into the tube using a syringe pump. Before dilution, the size and concentration of microbubbles were measured using an image-based cell counter (Countess® II FL, Thermo Fisher Scientific Inc., USA) and were found to have an average diameter of approximately 4.5 μm and a concentration of approximately 6.0 × 10 after dilution. 5 The FUS transducer (H204, Sonic concept, WA, USA) was driven by a function generator (Model 33500B, Keysight Technologies Inc., Englewood, CO, USA) connected to a 32 dB RF power amplifier (1020L, Electronics & Innovation, Rochester, NY, USA). The pressure at the focus of the transducer was calibrated by a hydrophone (HNC-0200, Onda, Sunnyvale, CA, USA), and all reported pressures were the measured peak negative pressure. A tube was connected to the FUS transducer using a 3D-printed frame, and the focus of the FUS transducer was aligned to the center of the tube under ultrasound imaging guidance. Ultrasound imaging guidance was performed using a 128-element linear ultrasound imaging probe (ATL L7-4, Philips Healthcare) inserted into the central aperture of the FUS transducer. The imaging probe was controlled by a Verasonics system (Verasonics, Inc., Redmond, WA, USA) acquiring standard B-mode images, and the location of the FUS focus was indicated by a crosshair superimposed on the B-mode image based on the hydrophone calibration of the FUS focus position, as shown in Figure 10A.

[0079] The position of the FUS focus with respect to the center of S1, the origin of the 3D coordinates, was calibrated by B-mode imaging. The tube was aligned along the imaging plane (XZ plane) of the ultrasound imaging probe. To register the position of the tube to the sensor network, the position of the tube with the FUS transducer was adjusted by a 3D motor such that the center position of S1 was aligned in the axial direction with respect to the FUS focus position. The distance between S1 and the FUS focus was measured based on the B-mode image, and the position of the FUS focus with respect to the origin of the coordinates was defined. Since the positions of S2-S4 with respect to S1 were known due to the geometry of the sensor holder, the coordinates of the center points of S2-S4 were also defined with respect to S1 / origin of the coordinates.

[0080] The cavitation signals detected by the four sensors were recorded by a 14-bit digital oscilloscope (Picoscope 5442D PICO Tech., UK) and stored in a computer for post-processing. Figures 7A and 7B show the effect of skull attenuation on the acquired signals in the time and frequency domains, respectively, for a representative signal. Statistical analysis was performed with GraphPad Prism (Version 8.3, La Jolla, CA, USA). The localization results of different groups were compared using an unpaired t-test (two independent groups) or a one-way analysis of variance (ANOVA) test (multiple independent groups). A P value of <0.05 was used to determine statistical significance.

[0081] Example 2 - Signal processing for cavitation localization Signal processing was performed using a two-stage approach using customized software implemented with MATLAB (Mathworks, Natick, MA, USA). The DMC signal was obtained by separating the cavitation signal from the background noise in the signal detected by the four-sensor array. Then, a TDOA algorithm was used to localize the cavitation source based on the DMC signal.

[0082] Strong attenuation of the pressure signal passing through the skull results in a weak harmonic content of the cavitation signal, which increases as the frequency of the pressure signal increases. To improve the signal-to-noise ratio (SNR) of the cavitation signal, the DMC signal was obtained by separating the cavitation signal from background noise associated with scattering from the tube reflections, echoes from the tube, holder, skull, and other intervening structures, and second harmonics generated in the FUS wave propagation. As shown in FIG. 8A, the signal from the FUS sonication was acquired without microbubbles to be used as a reference signal. Then, the signal from the FUS sonication was acquired with microbubbles using the same FUS settings. The reference signal (without microbubbles) was subtracted from the signal acquired with microbubbles to extract the DMC signal, which contained only the signal from microbubble cavitation, as shown in FIG. 8B.

[0083] The estimated TDOA used for the cavitation source location was estimated using a TDOA algorithm, which resulted in three nonlinear equations with three unknowns corresponding to the source location coordinates along the x, y, and z axes. Here, the source is assumed to be at an unknown location (x,y,z), while the sensor is assumed to be at a known location (x i ,,{i=1,2,3,4}), and therefore the squared distance r between the source and sensor i is i 2 is given by equation (1).

number

number

[0084] If c is the speed of sound in water estimated using the water temperature, then r 1,i may be obtained based on the time difference of arrival, and r 1,i =c(t1-t i , r i may be written as equation (2).

number

number

[0085] Equation (2) may be defined as a set of nonlinear equations, the solution of which gives (x, y, z), as shown in equation (3).

number

[0086] Inserting this intermediate result (x,y,z) from equation (3) into equation (1) at i=1 yields a quadratic equation in r1. Substituting the positive roots of r1 back into equation (3) yields the solution. In some cases, there may be two positive roots that yield two different answers. Any ambiguity in the solution may be resolved by restricting the transmitter to be within the region of interest. A Taylor series expansion gives r i,1 Linearizing and then solving iteratively is another way to obtain a solution, but this method may increase the computational complexity and the calculation may not converge to a solution.

[0087] Example 3 - Effect of the skull on the accuracy of DCL cavitation localization To evaluate the accuracy of cavitation location determined using the DCL method disclosed herein, the following experiment was performed.

[0088] The location accuracy of the disclosed DCL algorithm was calculated by the offset of the location estimated in 3D using the DCL method relative to the FUS focal position determined as described in Example 1. FIG. 10A shows the set location of the cavitation source, which served as ground truth to validate the DCL localization results. FIG. 10B shows the localization results obtained using the DCL method in the X-axis (width) and Z-axis (depth), with the Y-axis values ​​perpendicular to the display plane. FIG. 10C combines the results shown in FIG. 10A and FIG. 10B to evaluate the accuracy of the DCL method.

[0089] Four sets of localization experiments were performed to evaluate the accuracy of the DCL method and to investigate the influence of key parameters on its performance, including with and without skull influence, sensor position, FUS pressure, and number of pulse cycles.

[0090] To evaluate the effect of the skull on the accuracy of 3D cavitation localization, the FUS transducer and the microbubble tube were mounted on a three-axis positioner and moved along the X-axis, Y-axis, and Z-axis in 10 mm increments, respectively, for a total of 60 mm. At each position, the FUS transducer was agitated with 5 cycles of pulses with a center frequency of 500 kHz at a pulse repetition frequency (PRF) of 1 Hz to transmit a focused beam to the microbubble tube. The peak in situ negative pressure at the FUS focus was 0.4 MPa. Tracking of the cavitation source was performed over 19 positions, and each position was measured 30 times. All measurements were divided into three independent experiments conducted over a period of three days to ensure the reliability of the data obtained. Repeated experiments were performed with and without the skull to evaluate the effect of the skull on the accuracy of 3D cavitation localization using the disclosed DCL method. Cavitation localization with the skull was performed using a four-sensor network location as shown in Figure 10A.

[0091] The sensor and source locations were graphed in a 3D coordinate system as shown in FIG. 12A. A total of 19 sources were evenly distributed in a 60 mm×60 mm×60 mm cubic space. The accuracy of the DCL method without and with the skull was calculated as described above, and the results are presented in the bar graphs of FIG. 12B. FIG. 12C and FIG. 12D summarize the tracking accuracy of the transcranial cavitation sources in the XY and XZ planes, respectively; each lighter circle represents the average value over 10 iterations, and the error bars represent the deviation along the horizontal and vertical directions, respectively. The tracking accuracy without intervening skull tissue is summarized in the XY (FIG. 12E) and XZ (FIG. 12F) planes, respectively. The average accuracy was 1.91±0.96 mm with the skull present and 1.73±0.54 mm without intervening skull tissue. No statistically significant effect of skull texture on localization accuracy based on the mean localization results was demonstrated.

[0092] 13A, 13B, and 13C summarize the effect of the skull on the localization accuracy for a particular cavitation source location. 13A, 13B, and 13C are derived from the results summarized in 12C, 12D, 12E, and 12F by plotting the mean and standard deviation of the cavitation source localization with and without the skull as a function of position along the X-, Y-, and Z-axes, respectively. When the cavitation source is within 10 mm of the geometric center of the four-sensor network, the presence of the skull does not significantly affect the positioning results. However, for positions x=±30, y=±30, and z=±30, the accuracy without intervening skull tissue is significantly higher than the corresponding localization obtained through the skull. As the cavitation source moved progressively further away from the geometric center of the four-sensor network, the presence of the skull increasingly reduced the localization accuracy.

[0093] The results of these experiments did not find a statistically significant effect of skull tissue on localization accuracy based on the average cavitation source localization results described above.

[0094] Example 3 - Effect of sensor location on DCL cavitation localization accuracy To evaluate the effect of sensor placement on the accuracy of cavitation localization performed using the disclosed DCL method, the following experiment was performed.

[0095] As a criterion for selecting the positioning of the sensor, landmark structures in the skull (occipital and frontal crests) were used. The occipital and frontal crests are thicker than other regions of the skull, and the internal microstructure of these two regions is more complex than in other regions of the skull. Three representative locations for the sensor were selected, and in this study, S2 was located at the occipital crest (Figure 11A), the frontal crest (Figure 11B), and the part away from the crest (Figure 11C). The cavitation source was set at the geometric center of the sensor network, and the FUS parameters used in these experiments were consistent with those described in Example 2.

[0096] Figure 14 summarizes the transcranial cavitation source localization accuracy obtained using the disclosed DCL method for three different orientations of the sensors of the four-sensor network relative to the skull. The accuracy for the occipital crest (P1), frontal crest (P2), and away from the crest (P3) were 0.95 ± 0.13 mm, 1.04 ± 0.20 mm, and 1.03 ± 0.14 mm, respectively. Based on the statistical analysis test, the contact position of the sensor with the skull did not have a significant effect on the localization accuracy.

[0097] Example 4 - Effect of FUS pressure on the accuracy of DCL cavitation localization To evaluate the effect of FUS pressure on the accuracy of cavitation localization performed using the disclosed DCL method, the following experiment was performed.

[0098] The cavitation source was set at the geometric center of the sensor network as described in Example 3, FUS was maintained for 5 cycles, and the 4-sensor network was placed on the skull as shown in Figure 11 A. FUS peak negative pressures ranging from about 0.2 MPa to about 1.3 MPa were used to sonicate the microbubbles inside the microbubble tube phantom to evaluate the effect of pressure amplitude on DCL localization performance.

[0099] FIG. 15 summarizes the transcranial cavitation source localization accuracy obtained using the disclosed DCL method as a function of FUS pressure. The FUS pressures used in these experiments were 0.2, 0.4, 0.6, 0.9, 1.1, and 1.3 MPa, with corresponding localization accuracies of 1.06±0.09 mm, 0.95±0.13 mm, 0.65±0.06 mm, 0.91±0.79 mm, 2.94±0.91 mm, and 2.80±1.86 mm, respectively. The average accuracy slightly decreased when the FUS peak negative pressure increased from 0.2 to 0.6 MPa. For FUS peak negative pressures ranging from 0.9 MPa to 1.3 MPa, the localization results increased and became more and more unstable. Based on the statistical analysis of the above results, the localization accuracy at FUS pressures ranging from 0.2 MPa to 0.9 MPa was significantly different from the corresponding results for FUS pressures ranging from 1.1 MPa to 1.3 MPa.

[0100] Example 5 - Effect of FUS pressure cycles on the accuracy of DCL cavitation localization To evaluate the effect of the number of FUS pulse cycles on the accuracy of cavitation localization performed using the disclosed DCL method, the following experiment was performed.

[0101] For these experiments, the cavitation source was fixed at the center of the four-sensor network, the FUS peak negative pressure was maintained at 0.4 MPa, and the four-sensor network was located on the skull as shown in Figure 11 A. To sonicate the microbubbles and determine the optimal signal length, different cycle lengths of the FUS source, ranging from 5 cycles to 1000 cycles, were tested.

[0102] FIG. 16 summarizes the dependence of transcranial localization accuracy on FUS cycle number. The FUS cycle numbers tested in these experiments were 5, 10, 100, 500, and 1000 cycles, with corresponding localization accuracies of 0.95±0.13 mm, 1.52±1.14 mm, 14.61±11.96 mm, 135.2±52.07 mm, and 90.17±68.99 mm, respectively. With increasing cycle number, the localization accuracy of the DCL method decreased and became increasingly unstable. Statistical analysis of the above results showed that the localization accuracies at FUS cycle numbers 5 and 100 were significantly different from the corresponding localization accuracies at FUS cycle numbers 500 and 1000. Moreover, the localization accuracies at FUS cycle numbers 500 and 1000 were significantly different from each other.

[0103] Example 6 - Effect of FUS pressure cycles on the accuracy of DCL cavitation localization To investigate the feasibility of using a four-sensor network for transcranial 3D localization of microbubble cavitation, the following experiment was performed.

[0104] Cavitation is the dominant physical mechanism for cavitation-mediated brain therapy activated by focused ultrasound (FUS). Accurately knowing the 3D location of cavitation in real time brings the benefit of improving treatment targeting accuracy and avoiding off-target tissue damage. However, the skull induces strong phase and amplitude anomalies to the cavitation signal, presenting a major challenge for transcranial cavitation localization. Existing techniques for 3D cavitation localization use hemispherical multi-element arrays combined with passive beamforming and adaptive skull-specific correction algorithms. However, these techniques require expensive equipment and time-consuming computational methods, which limit the application of existing methods in real-time cavitation localization, which is urgently needed to ensure the safety and efficacy of FUS treatment.

[0105] A device for 3D cavitation localization was designed and fabricated (Figure 17A). The device consisted of four sensors (Olympus V323-SM) distributed in a hemisphere with a diameter of 18 cm. The performance of the device was evaluated using an ex vivo human skull setup (Figure 17B). In this setup, microbubbles (~1 × 10 6Microbubbles (1000 µg / mL) were injected into a tube phantom (3 mm inner diameter and 5 mm outer diameter) located inside the skull cavity (Figure 17C). The microbubbles were activated by a FUS transducer (1 Hz pulse repetition frequency, 75 cycles pulse length, and 1.5 MHz drive frequency) at an estimated in situ peak negative pressure of 6.5 MPa. The FUS transducer and tube were moved to different positions in the skull setup by a 3D stage. The FUS transducer was aligned coaxially with the ultrasound imaging probe. B-mode images were acquired before and after FUS sonication to determine the location of cavitation events based on changes in image contrast. Acoustic emissions from FUS-activated microbubbles were passively detected and stored for post-processing. The signals were filtered to retain only subharmonic frequencies (750 kHz with a bandwidth of 300 kHz) that were thought to contain the cavitation signal. The time delay between the signals received by the four sensors was measured by finding the maximum of the cross-correlation between them. The location of the cavitation source was calculated using a method similar to that commonly used in the Global Positioning System (GPS).

[0106] FIG. 18A shows a schematic diagram of the spatial positions of the four sensors of the 3D cavitation localization device described above, along with the movement of the cavitation source in the experimental setup. FIG. 18B shows the position errors along the x-, y-, and z-axes determined based on the cavitation signals detected with and without an intervening skull, with the error bars indicating the standard deviation of the measured positions. The position errors of the transcranial cavitation localization along the x-, y-, and z-axes were 1.7±1.2 mm, 1.6±1.7 mm, and 4.1±1.5 mm, respectively. For comparison, the position errors of the cavitation localization based on the signals detected without the skull were 1.2±1.8 mm, 0.9±1.6 mm, and 3.1±2.3 mm, respectively. FIG. 18C and FIG. 18D summarize the tracking accuracy of the transcranial cavitation localization in the XY and XZ planes, respectively. The lighter circle markers represent the average value over four replicates, and the bars represent the upper and lower standard deviations along the horizontal and vertical directions. Higher precision was achieved along the x and y axes compared to the z axis. Larger deviations were observed at positions further away from the center of the detector array compared to the corresponding deviations in measurements taken for cavitations closer to the center.

[0107] The results of these experiments confirmed the feasibility of using a four-sensor network for 3D transcranial cavitation localization. The disclosed method achieved average accuracies of 1.7 mm, 1.6 mm, and 4.1 mm along the x-, y-, and z-axes, respectively. The disclosed method determined 3D cavitation locations at low computational cost, enabling 3D real-time cavitation localization.

[0108] Example 7 - Evaluation of closed-loop feedback control of microbubble cavitation for FUS-BBBO To evaluate the safety and reliability of the closed-loop feedback control method for microbubble cavitation for focused ultrasound blood-brain barrier opening (FUS-BBBO) disclosed herein, the following experiment was conducted.

[0109] To deliver Evans Blue dye to the mouse brain, FUS-BBBO was used to open the BBB using the disclosed feedback control method.

[0110] Experimental animals To evaluate five different TCLs with the disclosed algorithm, 25 Swiss mice (8-10 weeks, ~25g body weight, female, Charles River Laboratory, Wilmington, MA, USA) were randomly assigned into five groups (n=5 for each group). Swiss mice (8-10 weeks, ~25g body weight, female, Charles River Laboratory, Wilmington, MA, USA) were housed in a room maintained at 22°C and 55% relative humidity with a 12h / 12h light / dark cycle and access to standard laboratory chow and water. To evaluate five different TCLs with the proposed algorithm, 25 Swiss mice were randomly assigned into five groups (n=5 for each group). Four Swiss mice were selected to evaluate existing closed-loop feedback control algorithms with TCLs defined based on subharmonic detection. During all experiments, mice were anesthetized with 1.5–2% isoflurane and stabilized using a stereotaxic apparatus (Kopf, Tujunga, CA, USA). A heating pad with temperature maintained at ~38°C was used to maintain mouse body temperature. Mice were prepared for FUS sonication by removing hair on the head with depilatory cream (Nair, Church & Dwight Co., NJ, USA) and connected to a water container with ultrasound gel. A catheter was placed in the tail vein to inject microbubbles and Evans blue.

[0111] FUS-BBBO system FUS-BBBO was performed on mice using the system shown in FIG. 19. A single-element FUS transducer with an aperture of 75 mm, a radius of curvature of 60 mm, and a central aperture of 25 mm in diameter was used to deliver FUS to mice. The FUS transducer was impedance-matched to operate at 1.5 MHz and driven by an arbitrary waveform generator (Agilent 33500B; Agilent Technologies, Loveland, CO, USA), which was connected to a 53 dB power amplifier (1020 L; E&I, Rochester, NY, USA). The FUS transducer was mounted on a 3D stage to facilitate targeting the transducer output. The acoustic pressure field generated by the FUS transducer was calibrated using a needle-type hydrophone (HNP-0200; Onda Inc., Sunnyvale, USA) in a degassed water tank. The axial and lateral full-width-at-half-maximum (FWHM) dimensions of the FUS transducer were 8.3 mm and 1.1 mm, respectively. The peak negative pressure of the FUS transducer at different voltage input levels was measured at the focal point of the transducer in the water tank. A 3D printed bar with a sharp tip was fabricated to facilitate precise targeting of specific brain locations. The tip of the bar was located near the top of the hydrophone when the FUS transducer was switched to the bar for use as a pointer. The pointer was then used to indicate the FUS focal point. The tip of the pointer was moved by the 3D stage to be aligned along lambda on the mouse skull, which is visible through the mouse skin. The pointer was then switched to the FUS transducer. The transducer was moved 1 mm laterally and 1 mm posteriorly, and 4 mm ventrally to target the brainstem, which was chosen to represent the targeted brain location.A single-element ultrasound transducer (I5P10, Guangzhou, China) with a central frequency of 4.7 MHz and a 6 dB bandwidth of ±1.9 MHz was inserted through the central hole of the FUS transducer, and both transducers were maintained in confocal alignment using a 3D-printed housing. For passive cavitation detection (PCD), a single-element ultrasound transducer was used to acquire cavitation emissions from microbubbles during FUS sonication. This PSD transducer was connected to a 22 dB preamplifier and a PicoScope (5244B, Pico Technology, Cambridgeshire, UK). The PicoScope was triggered by an arbitrary waveform generator to synchronize the FUS sonication with the PCD data acquisition. The signal acquired by the PCD was sampled at 40 MHz. All instruments were controlled by a PC using a custom MATLAB program.

[0112] FUS-BBBO under real-time closed-loop feedback control Microbubble contrast agent (Definity, Lantheus Medical Imaging, North Billerica, MA) was administered in sterile saline at a concentration of approximately 8 × 10 8The microbubble contrast agent was diluted to a final concentration of 1000 microbubbles. A bolus of the diluted microbubble contrast agent (volume = 30 μL) was injected into the vein of each mouse via the tail vein catheter. The injection was performed using a computer-controlled syringe pump (NE-1600; New Era Pump Systems Inc.). The microbubble injection was started 15 seconds before the FUS sonication to allow the microbubbles to flow through the tail vein catheter and reach the mouse brain. The injection continued until the end of the sonication at a constant rate of 12.8 μL / min. All mice were treated by FUS with the output pressure controlled in real time using the disclosed PCD-based closed-loop feedback control algorithm. The treatment procedure was performed in a two-stage process. In Figure 20, a representative example of the sonication sequence and the detected cavitation level is shown.

[0113] A baseline stable cavitation (SC) level was established for each mouse that underwent summy FUS sonication after microbubble injection. FUS sonication was performed with a pulse repetition frequency of 2 Hz, a pulse length of 6.7 ms bursts (i.e., duty ratio: 1.33%), and a sonication duration of 5 seconds. The FUS output pressure was 0.2 MPa (all reported pressures were peak negative pressures calibrated with water). This pressure was chosen because it was the lowest pressure at which the microbubble cavitation signal was higher than the noise level without microbubble injection and lower than the pressure required to cause BBB disruption. During sonication with each FUS pulse, the acoustic emission from the microbubbles was recorded by a PCD transducer and processed by a fast Fourier transform (FFT) algorithm. The SC level was calculated by summing the spectral magnitude within a ±0.02 MHz bandwidth at the third harmonic (i.e., 4.5 MHz) of the FUS transducer. The third harmonic radiation was chosen because it was at the center frequency of the PCD transducer. Ten PCD signals were acquired and the average of the SC levels calculated from these 10 signals was used to define the baseline SC level.

[0114] After establishing baseline SC levels as described above, mice were further subjected to FUS sonication using FUS-BBBO with real-time feedback control as disclosed herein. During FUS sonication with microbubble injection, cavitation was monitored in real time by the PCD, and a custom closed-loop feedback control algorithm was used to control the SC level to different TCLs defined as 0.5 dB, 1 dB, 2 dB, 3 dB, or 4 dB above the baseline SC level. The feedback control algorithm in these experiments consisted of an increase sonication phase followed by a maintenance sonication phase, as shown in FIG. 20. The increase phase started at 0 MPa and increased in steps of 0.013 MPa for each pulse until the SC level reached the TCL. Once the SC level reached the TCL, the control algorithm was switched to the maintenance phase. In the maintenance phase, the sound pressure was continuously adjusted to maintain the SC level within the target range (i.e., TCL ± tolerance) until the end of sonication. The tolerance was set to ±0.4 dB to reduce sensitivity to noise. If the SC level was within the TCL ± tolerance range, the FUS output pressure was kept the same. If the SC level was higher or lower than the TCL ± tolerance range, the FUS output pressure of the next pulse was immediately decreased or increased by a step size (0.013 MPa). The step size (0.013 MPa) was the minimum step size of the arbitrary waveform generator and was set to achieve fine adjustment.

[0115] Stability of the FUS-BBBO feedback control method The stability of the feedback control algorithm was determined by the good burst rate, which was calculated by the percentage of all measured SC levels in the maintenance phase that were within the TCL ± tolerance range. Higher stability represented more effective controllability during cavitation activity. IC levels were also quantified based on the acquired cavitation signals to act as a safety check. IC levels were calculated by summing the spectral magnitude within a ±0.02 MHz bandwidth at 3.3 MHz. These frequencies were chosen to quantify the level of broadband signals by avoiding harmonics. The presence of an IC event was defined if the IC level was higher than 1 dB relative to the baseline IC level, which was quantified based on the signal acquired during the dummy FUS sonication after microbubble injection. Inertial cavitation (IC) probability was calculated as the percentage of IC events that were present during the maintenance phase. A higher IC probability indicated an increased occurrence of IC events and an increased possibility of tissue damage.

[0116] GraphPad Prism (Version 9.0, La Jolla, CA, USA) was used to analyze the data. Differences between two groups were determined using an unpaired two-tailed Student's t test. A P value of <0.05 was used to determine statistical significance.

[0117] Figure 21 shows the baseline SC levels measured for each mouse in the five groups with sham ultrasound treatment. As expected, variation in baseline SC levels was observed between different subjects. The feedback control algorithm described above maintained the SC levels in the TCL at 0.5 dB, 1 dB, 2 dB, 3 dB, and 4 dB higher than the baseline SC levels for individual mice. As shown in the graph at the bottom right of Figure 21, the target absolute SC levels were different for each mouse.

[0118] The graph in Figure 22A shows the measured SC levels for each mouse in each group throughout the FUS-BBBO procedure. The plot of the average TCL for all groups in Figure 22A (bottom right graph) shows the successful control of FUS sonication to maintain SC at different levels using the feedback control algorithm disclosed herein. As summarized in Figure 22B, the disclosed feedback control algorithm achieved average stability of 78.6%, 74.0%, 65.9%, 58.2%, and 62.6% for TCL of 0.5 dB, 1 dB, 2 dB, 3 dB, and 4 dB, respectively. Figure 22C summarizes the measured average IC levels for each group. The average IC probability was 0%, 0%, 0%, 4.5%, and 37.0% for TCL of 0.5 dB, 1 dB, 2 dB, 3 dB, and 4 dB, respectively, as shown in Figure 22D. The IC probability was significantly higher than the other groups at 3 dB and 4 dB.

[0119] Assessment of drug delivery across the BBB To evaluate the efficacy of drug delivery to the brain using FUS-BBBO with the disclosed feedback control algorithm, Evans Blue, a widely used drug to evaluate changes in BBB permeability, was used. As previously described, 30 μL of 4% Evans Blue was intravenously injected into mice immediately after FUS sonication. Thirty minutes after sonication, the mice were sacrificed and perfused. The mouse brains were then harvested and fixed with 4% paraformaldehyde. The extracted whole brains were cut into 1 mm thick slices in the horizontal plane for Evans Blue imaging and examined by a Licor Pearl small animal imaging system (LI-COR Biosciences, Lincoln, NE) acquiring with the 700 nm channel. The exposure time for fluorescence imaging was kept the same to image all brain slices. The brain fluorescence intensity was then quantified using LI-COR Image Studio Lite software. Regions of interest (ROIs) were selected to cover the target brainstem regions, and quantification in all slices was normalized to the background ROI (i.e., tissue background signal). For each mouse, the normalized fluorescence intensity was used to quantify the effectiveness of Evans Blue delivery concentration in the target region (i.e., ROI) as an indication of FUS-BBBD drug delivery efficiency using the disclosed feedback control method. The spatial diffusion of Evans Blue was quantified to represent the FUS-BBBO drug delivery area. Fluorescence intensities higher than 10 dB were extracted against the background signal of brain tissue when the drug delivery area and quantification of these areas were calculated by a customized MATLAB program.

[0120] FIG. 23A shows photographs of representative brain slices and corresponding fluorescence images at each TCL (i.e., 0.5 dB, 1 dB, 2 dB, 3 dB, and 4 dB). For the 0.5 dB group, the fluorescence intensity of delivered Evans Blue increased by 2.4-fold, 5.3-fold, and 8.2-fold on average at 1 dB, 2 dB, and 3 dB, respectively, as summarized in FIG. 23B. Significant differences in fluorescence intensity were observed between different groups (0.5 dB vs. 1 dB, P=0.0046; 1 dB vs. 2 dB, P=0.0002; 2 dB vs. 3 dB, P=0.0178). As summarized in FIG. 23C, when the TCL was increased from 0.5 dB to 3 dB, the drug delivery area increased by 9.6 mm on average. 2 From 112.8 mm 2 (11.7-fold higher). There were significant differences in delivery area between each TCL group (0.5 dB vs. 1 dB, P = 0.0313; 1 dB vs. 2 dB, P = 0.0036; 2 dB vs. 3 dB, P = 0.0001). When the TCL was increased to 4 dB, the fluorescence intensity and drug delivery area were slightly decreased with respect to the corresponding values ​​observed at a TCL of 3 dB.

[0121] Safety assessment by histological analysis Histological examination was performed for all mice using hematoxylin and eosin (H&E) staining. Specifically, after fluorescence imaging, brain slices containing the target brainstem were fixed overnight in 4% paraformaldehyde and then cryoprotected with sucrose. The brain slices were cut horizontally into 10 μm sections and stained with H&E. Digital images of the tissue sections were acquired using an all-in-one microscope (BZ-X810, Keyence, Osaka, Japan). The hemorrhage area in the stained region was extracted based on the pixel hue by the built-in software of the BZ-X810. The total area of ​​red blood cell extravasation was calculated by summing all identified pixels in the FUS-targeted side of the brainstem. The contralateral brain area without FUS sonication was used as a control.

[0122] FIG. 24A shows a representative H&E staining image of the brainstem at the level where the FUS focus was targeted. FUS was targeted to the right side of the brainstem, and the contralateral side was used as a control. As shown by the upper and lower magnified images, no bleeding was observed in the cases of 0.5 dB, 1 dB, and 2 dB. Mild tissue damage was found in the case of 3 dB, and relatively severe tissue damage was found in the case of 4 dB. As summarized in FIG. 24B, group analysis revealed that there was no significant difference between the FUS-treated side and the contralateral untreated side in the 0.5 dB, 1 dB, and 2 dB groups, but large tissue damage was observed within the FUS-treated area in the 3 dB and 4 dB groups.

[0123] Comparison with existing feedback control algorithms The disclosed closed-loop feedback control was evaluated against an existing closed-loop feedback control approach in which TCL is defined based on the detection of subharmonic signals. As shown in FIG. 25A, pressure increase was applied until the SC level reached the threshold at which subharmonic signals (i.e., emission at 0.5f0, where f0 is the fundamental frequency of the driving frequency) were detected. The sound pressure was then controlled to maintain the SC level at 50% of the threshold throughout the remainder of the procedure. A total of four mice were used to test this existing approach following the same procedure as described above, and the mouse brains were processed in the same way for quantification of Evans Blue delivery results as described above.

[0124] Figure 25A shows an example of recorded SC levels of a closed-loop feedback control algorithm using a TCL defined based on the detection of subharmonic signals. The pressure increase phase required a pressure overshoot. As shown in Figure 25B, the correlation coefficient (R ) between the SC dose calculated by the area-under-the-curve of the SC level in the increase phase and the fluorescence intensity of delivered Evans blue was calculated. 2) was 0.992. As shown in FIG. 25C, the correlation coefficient (R 2 ) was 0.078. Comparing Figures 25B and 25C, microbubble cavitation activity in the growth phase, but not in the maintenance phase, was associated with FUS-BBBO drug delivery outcome. The results suggested that existing closed-loop control methods cannot reliably control the delivery outcome.

[0125] The results of these experiments demonstrated reliable and safe FUS-BBBO using the individualized closed-loop feedback control method disclosed herein at selected target cavitation levels. Establishing target cavitation levels using low pressure and short duration FUS sonication provided a way to account for individual differences in detected cavitation and avoid overexposure. The disclosed feedback control algorithm had high stability and successfully controlled FUS-BBBO drug delivery outcomes. The optimal target cavitation level for FUS-BBBO is influenced by both the performance of the cavitation controller and the delivery efficiency and safety. The results of these experiments highlighted the importance of controlling FUS exposure to achieve efficient and safe BBBO.

[0126] Example 8 - Sono-biopsy of circulating tumor DNA from glioblastoma in a mouse GBM model The following experiment was performed to demonstrate the capability of focused ultrasound (FUS) enabled liquid biopsy (sonobiopsy) in a mouse glioblastoma multiforme (GBM) model.

[0127] A mouse GBM model was established using human GBM cells (U87) with EGFRvIII overexpression (U87-EGFRvIII+) and containing the TERT C228T mutation. U87-EGFRvIII+-ZsGreen+ cells used for CTC detection were generated by transduction of U87-EGFRvIII+ cells with the lentiviral construct pCRoatan containing ZsGreen cDNA. Both cell sections were cultured as adherent monolayers in DMEM supplemented with 10% fetal bovine serum, 2 mmol / L l-glutamine, and 100 units / mL penicillin. Cells were maintained at 37°C in a humidified CO2 (5%) atmosphere, and the medium was changed as necessary. Before implantation, cells were dispersed in a 0.05% solution of trypsin / EDTA and adjusted to the concentration required for tumor implantation.

[0128] To generate the xenograft GBM model, immunodeficient mice (strain: NCI Athymic NCr-nu / nu, age: 6–8 weeks, Charles River Laboratory, Wilmington, MA, USA) were used. Briefly, mice were anesthetized and heads were fixed in a stereotaxic apparatus for tumor cell injection. Cells were injected and tumor growth was monitored using a dedicated 4.7T small animal MRI system (Agilent / Varian DirectDriveTM console, Agilent Technologies, Santa Clara, CA, USA). Starting at 7 days and continuing every 3 days thereafter, MRI scans were acquired to monitor tumor growth and neuroanatomical changes.

[0129] A mouse GBM model was used to detect EGFRvIII and TERT C228T mutations using sono-biopsy and using conventional blood-based LBx (blood LBx) analysis (control). Approximately 10-12 days after intracranial implantation, mice were assigned to blood LBx (blood collected without FUS) or sono-biopsy (blood collected immediately after FUS).

[0130] To perform sono-biopsy, a commercial MRI-compatible FUS system (Image Guided Therapy, Pessac, France) was set up in a small animal MRI scanner (Figure 26A). The system's MRI-compatible FUS transducer (Imasonics, Voray sur l'Ognon, France) consisted of a 7-element annular array with a central frequency of 1.5 MHz, an aperture of 25 mm, and a radius of curvature of 20 mm. The axial and lateral full width at half maximum (FWHM) of the FUS transducer were 5.5 mm and 1.2 mm, respectively. Pressure values ​​were reduced to account for the mouse skull attenuation of 18%. A catheter was placed in the mouse tail vein for intravenous injection of microbubbles as described below.

[0131] Coronal and axial T2-weighted MRI scans were acquired to image the mouse head and to position the geometric focus of the transducer (the same parameters were used as the previously described T2-weighted sequence to monitor tumor growth). MRI images were imported into a software program (ThermoGuide, Image Guided Therapy, Pessac, France) that positioned the transducer focus via three-point triangulation. The transducer was moved to the tumor center for FUS sonication. After intravenous injection of the MR contrast agent gadoterate meglumine (Gd-DOTA; Dotarem, Guerbet, Aulnay sous Bois, France) at a dose of 1 mL / kg diluted 1:1 with 0.9% saline, an axial T1-weighted MRI scan was performed before FUS to visualize the BBB permeability caused by the tumor (the same parameters were used as the previously described T1-weighted sequence to monitor tumor growth).

[0132] Mice were intravenously injected with Definity microbubbles (Lantheus Medical Imaging, North Billerica, MA, USA) at a dose of 100 μL / kg. FUS sonication was started 15 seconds before intravenous injection of the microbubbles (frequency: 1.5 MHz, pressure: 1.0 MPa, pulse repetition frequency: 5 Hz, duty ratio: 3.35%, pulse length: 6.7 ms, treatment duration: 3 min). To allow inclusion of the entire tumor volume, FUS sonication was performed at three points evenly spaced by 0.5 mm.

[0133] After sonication, Gd-DOTA was reinjected, and an axial T1-weighted MRI scan after FUS was performed (same parameters as the T1-weighted sequence before FUS) to quantify the changes in BBB permeability caused by FUS.

[0134] The mean tumor volumes for the blood LBx (n = 21) and sono-biopsy groups (n = 24) were 25.11 ± 16.25 mm 3 and 24.59±13.21mm 3 and was not significantly different (p=0.78; Wilcoxon signed-rank test for two unpaired samples). Contrast-enhanced (CE) T1-weighted MRI scans (Figure 26B) were acquired to assess tumor growth and evaluate BBB disruption caused by FUS. FUS significantly increased tissue volume and resulted in approximately 2-fold enhanced BBB permeability relative to the average (Figure 26C).

[0135] Terminal blood collection via cardiac puncture was performed 10 min after FUS sonication. Whole mouse blood (~500 μL) was collected via cardiac puncture. Within 4 h of collection, samples were centrifuged at 3000 × g for 10 min at 4 °C to separate plasma from hematocrit. Plasma aliquots were immediately placed on dry ice for flash freezing and then stored at -80 °C for later downstream analysis.

[0136] Plasma / Serum RNA / DNA Purification Mini Kit (Norgen Biotek, Thorold, ON, Canada) and Plasma / Serum cfc-DNA / cfc-RNA Advanced Fractionation Kit (Norgen Biotek, Thorold, ON, Canada) were used to extract cfDNA from mouse plasma per the manufacturer's protocol. cfDNA was eluted in 20 μL of each corresponding buffer and quantified using Qubit Fluorometric Quantitation (Thermo Fisher Scientific, Carlsbad, CA, USA). A Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA) was used to assess the size distribution and concentration of cfDNA extracted from plasma samples. Total cfDNA concentration was determined by the software as the area under the peak in the single nucleosome size range (140–230 bp).

[0137] An initial preamplification reaction was performed before ddPCR in case of very low DNA concentrations. cfDNA was preamplified with a pair of forward and reverse primers for EGFRvIII and TERT C228T (same primers used for ctDNA analysis) using Q5 hot start high-fidelity master mix (New England Biolabs, Beverly, MA, USA). Preamplification was performed using an Eppendorf Mastercycler: 98°C for 3 min; 12 cycles of 98°C for 30 s, 60°C for 1 min; final extension at 72°C for 5 min, 1 cycle of 4°C with no time limit. Preamplified products were used directly for further ddPCR reactions.

[0138] EGFRvIII and TERT C228T were detected using custom sequence-specific primers and fluorescent probes. ddPCR reactions were prepared with 2× ddPCR Supermix for probes (no dUTP) (Bio-Rad, Hercules, CA, USA), 2 μL of target DNA product, 0.1 μM forward and reverse primers, and 0.1 μM probe. For the TERT C228T reaction mix, 100 μM 7-deaza-dGTP (New England Biolabs, Beverly, MA, USA) was added to improve PCR amplification of GC-rich regions in the TERT promoter. A QX200 manual droplet generator (Bio-Rad, Hercules, CA, USA) was used to generate droplets. The PCR step was performed on a C1000 Touch Thermal Cycler (Bio-Rad, Hercules, CA, USA) by using the following program: The assay consisted of one cycle of 95°C for 10 min, 48 cycles of 95°C for 30 s, 60°C for 1 min, 98°C for 10 min, and 1 cycle of 12°C for 30 min, with one cycle of 4°C without time limit, all with a ramp rate of 2°C / s. All plasma samples were analyzed as artificial duplicates or triplicates based on sample availability. Data were acquired on a QX200 droplet reader (Bio-Rad, Hercules, CA, USA) and analyzed using QuantaSoft Analysis Pro (Bio-Rad, Hercules, CA, USA). All results were manually reviewed for false positives and background noise droplets based on negative and positive control samples. If more than three droplets exceeded the threshold fluorescence, the analysis was considered positive. Otherwise, the specimen was determined to have 0 copies / μl. EGFRvIII and TERT C228T ctDNA concentrations (copies / μl plasma) were calculated by multiplying the concentration (provided by QuantaSoft) by the elution volume and dividing by the input plasma volume used during DNA extraction.Subjects had positive detection of mutations if the level of mutant ctDNA was greater than 0 copies / μL. EGFRvIII and TERT C228T sensitivity was calculated as the true positive rate, i.e., the number of true positives divided by the sum of true positives and false negatives. 95% confidence intervals were calculated according to the well-known asymptotic Gaussian approximation 1.96√p(1p) / n, where p represents sensitivity and n was the sample size.

[0139] Analysis of plasma cell-free DNA (cfDNA) showed that sono-biopsy improved the release of cfDNA compared to conventional blood LBx. Plasma levels of mononucleosomal cfDNA (140-230 bp) were increased approximately 2-fold by sono-biopsy. Custom ddPCR primers and probes for detecting EGFRvIII and TERT C228T mutations were validated in vitro with cell lines with known mutation status. 1D amplitude plots showed detection of EGFRvIII for eight representative subjects in the blood LBx and sono-biopsy groups (Figure 27A). EGFRvIII ctDNA levels in the sono-biopsy group were significantly greater (920-fold) than the blood LBx group (Figure 27B). 1D amplitude plots showed detection of TERT C228T for eight representative subjects in the blood LBx and sono-biopsy groups (Figure 27C). There was a significant increase (10-fold) in the levels of TERT C228T ctDNA using sono-biopsy compared to blood LBx (Figure 27D). Sono-biopsy improved the diagnostic sensitivity from 7.14% to 64.71% for EGFRvIII and from 14.29% to 45.83% for TERT C228T (Figure 27E). In summary, sono-biopsy significantly improved the detection of brain tumor-specific mutations.

[0140] To evaluate the possibility of tissue damage in the parenchyma associated with sono-biopsy, the following experiments were performed. H&E staining was performed to quantify the extent of microhemorrhages caused by FUS, and TUNEL staining was used to evaluate the number of apoptotic cells. After blood collection, mice were perfused transcardially with 0.01 M phosphate-buffered saline (PBS) and then with 4% paraformaldehyde. Brains were harvested and prepared for cryosectioning. Brains were cut horizontally into 15 μm slices, and H&E staining was used to examine red blood cell extravasation and cell damage, or TUNEL staining was used to evaluate the number of apoptotic cells. Brain slices were digitally acquired with an Axio Scan.Z1 Slide Scanner (Zeiss, Oberkochen, Germany). QuPath v0.2.0 was used to detect areas of microhemorrhages and TUNEL expression. Imaged slices for mouse histological analysis were divided into tumor regions of interest (ROIs) that included the tumor mass and extended 0.5 mm around it, consistent with safety objectives from previous studies and potential damage caused by the outer and luminal diameters of the biopsy needle. Parenchymal tissue ROI was defined as the entire imaged slice without the tumor ROI. Tumor ROIs for porcine histological analysis included the tumor mass and a 3 mm margin.

[0141] After color deconvolution (hematoxylin vs. eosin), areas of microhemorrhages were detected using a positive pixel counting algorithm. Microhemorrhage density was calculated as the percentage of positive pixel area over the total stained area in each ROI. The number of apoptotic cells was detected using a positive cell detection algorithm. TUNEL density was calculated as the percentage of positive cells over the total stained cells in each ROI.

[0142] Sonobiopsy resulted in a non-significant increase in the detected microhemorrhages within the tumor region of interest (ROI) (Figures 28A and 28B), and no off-target damage was detected in the brain parenchyma.Sonobiopsy also did not alter TUNEL expression in the tumor ROI or brain parenchyma (Figures 28C and 28D).

[0143] Results of these experiments demonstrated the safety and efficacy of focused ultrasound (FUS)-enabled liquid biopsy (sonobiopsy) in a mouse glioblastoma multiforme (GBM) model.

[0144] Example 9 - Sono-biopsy of circulating tumor DNA from glioblastoma in a porcine GBM model The following experiment was performed to demonstrate the capability of focused ultrasound (FUS) enabled liquid biopsy (sonobiopsy) in a porcine glioblastoma multiforme (GBM) model.

[0145] A porcine model of GBM was developed that involved bilateral implantation of U87-EGFRvIII+ cells into the cortex of pigs as described in Example 8, followed by immunosuppressant treatment to prevent rejection of the implanted cells. Approximately 3×10 6 The cells were transplanted into pigs.

[0146] In the established protocol, pigs (breed: Yorkshire White, age: 4 weeks, sex: male, weight: 15 lbs, Oak Hill Genetics, Ewing, IL, USA) were implanted with tumor cells on day 0. After the pigs were sedated, the heads were shaved, prepared for aseptic surgery, and secured in a stereotaxic frame on the operating table. Bite and ear bars were positioned to secure the head and match the top of the skull to the operating table. A 2-3 cm midline cranial skin incision was made, and two 5 mm burr holes were drilled (Dremel, Racine, WI, USA) 5 mm posterior to the bregma and 7 mm to the right and left of the subject from the midline without breaking the dura. A 50 μL syringe (Hamilton, Reno, NV, USA) used for tumor cell injection was secured in the stereotaxic frame and positioned in the burr hole with the tip in the dura. The syringe was lowered 9 mm to the injection site and a Micro4 controller (World Precision Instruments, Sarasota, FL, USA) injected 40 μL at a rate of 44 nL / s. There was a 5 min delay before completion of the injection and removal of the needle to allow the cells to settle in the tissue and prevent backflow. The burr hole was filled with gel foam and the skin incision was closed with two layers of sutures. Cyclosporine oral solution (Neoral, Novartis Pharmaceuticals, East Hanover, NJ, USA) was administered via gavage (25 mg / kg) twice daily.

[0147] To verify tumor growth for 7 days after surgery, contrast-enhanced sagittal T1-weighted gradient-echo MRI scans (TR / TE: 23 / 3.03 ms; slice thickness: 0.9 mm; in-plane resolution: 0.94 × 0.94 mm) were performed on a 3T Siemens PRISMA Fit clinical scanner. 2MRI scans were performed using a 3D CT scanner (CT scanner, ...

[0148] The bilateral tumor model took advantage of the unique feature of the pig's large brain volume, providing the opportunity for sono-biopsy to establish two separate targets in separate pigs. Sono-biopsies were performed approximately 11 days after intracranial implantation. A customized MRI-guided FUS device was developed to sonicate each large animal tumor sequentially (with a 1-hour delay to minimize cross-contamination from biomarker release of the first sonication) in a clinical MRI scanner (Figures 29A and 29B).

[0149] A customized MRI-guided FUS device and established FUS procedure were used to achieve successful BBB disruption. The pig's head was fixed in a stereotactic head frame with a bite bar and head support and connected to the transducer. The FUS system (Image Guided Therapy, Pessac, France) contained an MR-compatible 15-element transducer with a central frequency of 650 kHz, an aperture of 65 mm, a radius of curvature of 65 mm, and an adjustable coupling bladder. The FUS system was mounted on an MR-compatible motor for improved targeting accuracy. FUS transducer calibration is provided in the Supporting Information. Briefly, the acoustic pressure in vivo was estimated on the top part of a harvested ex vivo pig skull. The axial and lateral FWHM of the transducer were 3.0 mm and 20.0 mm, respectively.

[0150] FUS was performed under MR guidance on a 1.5T Philips Ingenia clinical MR scanner (Philips Medical Systems, Inc., Cleveland, OH, USA). Treatment planning (TR / TE: 1300 / 130 ms; slice thickness: 1.2 mm; in-plane resolution: 0.58 × 0.58 mm) was performed. 2 Coronal and axial T2-weighted spin-echo MR images were acquired to examine the neuroanatomy with respect to the acoustic coupling medium (TR / TE: 710 / 23 ms; slice thickness: 2.5 mm; in-plane resolution: 0.98x0.98 mm). 2 Coronal and axial T2-weighted gradient-echo MR scans were used to visualize the presence of bubbles in the 3D CT images (T2-weighted gradient-echo MR scans; matrix size: 224x224; flip angle 18°). Targeting FUS was performed using the same ThermoGuide workflow as mouse sono-biopsy as described in Example 8. Gadobenate dimeglumine (Gd-BOPTA; Multihance, Bracco Diagnostics Inc., Monroe Township, NJ, USA) was injected intravenously at a dose of 0.2 mL / kg, and axial T1-weighted ultrafast spoiled gradient-echo MR scans were performed at baseline (TR / TE: 5 / 2 ms; slice thickness: 1.5 mm; in-plane resolution: 0.68x0.68 mm) before FUS. 2 ; matrix size: 320x320; flip angle 10°).

[0151] Definity microbubbles (Lantheus Medical Imaging, North Billerica, MA, USA) at a dose of 20 μL / kg were injected intravenously. FUS sonication was initiated 15 seconds prior to intravenous microbubble injection using the following parameters: frequency: 0.65 MHz, pressure: 3.0 MPa (measured in water; 2.0 MPa when measured using an ex vivo porcine skull), pulse repetition frequency: 1 Hz, duty ratio: 1%, pulse length: 10 ms, treatment duration: 3 min. Bolus injection was determined by precedent set by clinical documentation with similar injection paradigms and the observation that contrast enhancement via bolus was greater than that via injection. Three minutes of sonication was previously determined when all microbubbles had been exhausted, as observed by the lack of stable cavitation during passive cavitation detection. Treatment was repeated at four separate points spaced 3 mm apart to ensure tumor coverage.

[0152] After the FUS sonication was completed, Gd-BOPTA was injected intravenously and T1-weighted MR scans in the axial direction were acquired (same parameters as the T1-weighted sequence before FUS) to assess BBB permeability. T2-weighted images in the coronal direction were acquired (same parameters as the T1-weighted sequence before FUS) to assess possible tissue damage caused by FUS.

[0153] Contrast-enhanced T1-weighted MRI scans confirmed successful BBB disruption in both tumors (Figure 29C), where total CE volume was significantly increased after FUS (Figure 29D).

[0154] Blood samples (5 mL) were collected immediately before and 10 min after FUS sonication of each tumor. Porcine whole blood (~10 mL) was collected via a percutaneous catheter in a peripheral vein using BD Vacutainer K2 EDTA tubes (Becton Dickinson, Franklin Lakes, NJ, USA). Plasma aliquots were isolated and stored as described in Example 8. Circulating tumor DNA was extracted and quantified using ddPCR as described in Example 8.

[0155] ddPCR 1D amplitude plots demonstrate detection of EGFRvIII for all subjects in the blood LBx (before FUS) and sono-biopsy (after FUS) groups (Figure 30A). Sono-biopsy significantly improved the release of EGFRvIII ctDNA into the blood by 270-fold (Figure 30B). 1D fluorescence amplitude plots show detection of TERT C228T using ddPCR for all subjects in the blood LBx and sono-biopsy groups (Figure 30C). The level of TERT C228T ctDNA was significantly increased by 9-fold using sono-biopsy (Figure 30D). Sono-biopsy-induced release improved the diagnostic sensitivity from 28.57% to 100% for EGFRvIII and from 42.86% to 71.43% for TERT C228T (Figure 30E). Sonobiopsy was found to significantly improve the detection of brain tumor-specific mutations in a porcine GBM model.

[0156] To evaluate the safety of sono-biopsy in large animals, the following experiment was performed. Pig brains were harvested and fixed in 10% formalin. To detect microhemorrhages associated with sono-biopsy, histological staining with H&E and TUNE was performed as described in Example 8. H&E staining showed the presence of microhemorrhages near the edge of the tumor in some cases (Figure 31A). However, there was no significant difference in microhemorrhage density between sonicated tumor ROI and non-sonicated parenchymal tissue (Figure 31B). Furthermore, TUNEL staining suggested that there was no significant difference between the number of apoptotic cells in the parenchymal tissue compared to the tumor ROI (Figure 31D) (Figure 31C). MRI was used to evaluate acute tissue damage after FUS. Abnormalities in T2-weighted images, i.e., changes in signal intensity, were observed after FUS. The observed tissue damage was consistent with the reversible damage observed in clinical trials of BBB disruption caused by FUS for brain drug delivery.

[0157] Results of these experiments demonstrated the safety and efficacy of focused ultrasound (FUS)-enabled liquid biopsy (sonobiopsy) in a porcine glioblastoma multiforme (GBM) model.

Claims

1. A method of performing a liquid biopsy to diagnose a brain disorder in a subject, comprising: The above method is a. injecting a predetermined amount of microbubbles into the subject; b. Using a Focused Ultrasound Blood-Brain Barrier Opening (FUS-BBBO) device, (i) sonicating the subject's brain at a baseline sonication pressure to determine a baseline stable cavitation level, and (ii) sonicating the subject's brain at increasing sonication pressures until a Target Cavitation Level (TCL) is reached, thereby opening the subject's blood-brain barrier and releasing or increasing the concentration of at least one biomarker from the subject's brain into the subject's blood; c. obtaining a biological sample containing the at least one biomarker; method.

2. The baseline stable cavitation level is higher than signal noise and lower than a stable cavitation level sufficient to cause BBBO. The method of claim 1.

3. determining the baseline stable cavitation level and the target cavitation level further comprises detecting a microbubble cavitation signal generated by microbubbles in response to ultrasonic treatment by the FU-BBBO device; The method of claim 1.

4. The microbubble cavitation signal is processed using a Fast Fourier Transform (FFT) algorithm to generate the baseline stable cavitation level and TCL. The method of claim 3.

5. the target cavitation level comprises one of 0.5 dB, 1 dB, 2 dB, 3 dB, and 4 dB above the baseline stable cavitation level; The method of claim 1.

6. 1. A system for controlling operation of a focused ultrasound blood-brain barrier opening (FUBBO) device configured to perform FUBBO on a subject, comprising: the system comprises a computing device operatively connected to the FUS-BBBO device; the computing device comprises at least one processor; The at least one processor: a. using the FU-BBBO device to sonicate the subject at a baseline sonication pressure and to detect a baseline stable cavitation level from the subject that is above signal noise and below a stable cavitation level sufficient to cause BBBO; The subject is injected with microbubbles prior to sonication; The at least one processor: b. sonicating the subject at a series of incrementally increasing sonication pressures until a target cavitation level (TCL) is detected that is a predetermined amount above the baseline stable cavitation level, and detecting a corresponding series of cavitation levels; c. configured to continuously sonicate the subject to maintain the TCL and induce BBBO in the subject; system.

7. further comprising at least one passive cavitation detection (PCD) transducer for detecting the baseline stable cavitation level, the range of cavitation levels, and the target cavitation level. The system of claim 6.

8. detecting the baseline stable cavitation level, the series of cavitation levels, and the target cavitation level further comprises detecting a microbubble cavitation signal generated by microbubbles in response to ultrasonic treatment by the FU-BBBO device. The system of claim 6.

9. The microbubble cavitation signal is processed using a Fast Fourier Transform (FFT) algorithm to generate the baseline stable cavitation level, cavitation level, and TCL. The system of claim 8.

10. the microbubble cavitation signal comprises an acoustic signal having a frequency within a bandwidth of a center frequency of a PCD transducer; The system of claim 8.

11. the target cavitation level comprises one of 0.5 dB, 1 dB, 2 dB, 3 dB, and 4 dB higher than the baseline stable cavitation level; The system of claim 6.

12. 1. An apparatus for transcranial cavitation localization in a subject, comprising: The device comprises: a. two or more acoustic sensors for detecting cavitation signals inside the subject's skull, the two or more acoustic sensors positioned in a fixed pattern configured to conform to the subject's skull; b. a focused ultrasound (FUS) transducer for sonicating a volume of interest within the subject's skull; c. a computing device comprising at least one processor; The at least one processor: i. sonicating the volume of interest with the FUS transducer; ii. receiving, at the two or more acoustic sensors, a plurality of cavitation signals from within the subject's skull into which microbubbles have been injected; iii. Identifying at least three time delays based on the plurality of cavitation signals, the time delays comprising a difference in the arrival time of the cavitation signal at one of the acoustic sensors relative to one of the remaining acoustic sensors; iv. configured to identify a location of a cavitation signal source based on the at least three time delays; Device.

13. each time delay of the at least three time delays is identified based on a maximum value of a cross-correlation between a first sample of the cavitation signal detected at a first acoustic sensor and a second sample of the cavitation signal detected at a second acoustic sensor; 13. The apparatus of claim 12.

14. The cavitation signal source is located using a time difference of arrival (TDOA) method.

13. The apparatus of claim 12.

15. The method of claim 14, wherein opening the blood-brain barrier of the subject further comprises continuously sonicating the subject while maintaining the TCL. The method of claim 1.

16. The TCL is a predetermined amount higher than the baseline stable cavitation level. The method of claim 1.

17. The step of sonicating the subject's brain at increasing sonication pressures until the target cavitation level (TCL) is reached includes sonicating the subject at a series of incrementally increasing sonication pressures and detecting a corresponding series of cavitation levels. The method of claim 1.

18. The method of claim 1 further comprising: collecting a biological sample for liquid biopsy. The system of claim 6.

19. A method of performing a liquid biopsy to diagnose a brain disorder in a subject, comprising: The above method is a. injecting a predetermined amount of microbubbles into the subject; b. Determining the volume and location of a tumor of interest for liquid biopsy; c. sonicating the area surrounding the tumor covering the volume of the tumor with an FUS transducer, thereby opening the blood-brain barrier of the subject and releasing at least one biomarker; d. collecting a biological sample containing the at least one biomarker; method.

20. The method of claim 20, wherein determining the volume and location of the tumor of interest comprises: a. sonicating a volume of interest with the FUS transducer; b. receiving a plurality of cavitation signals from inside the subject's skull at two or more acoustic sensors; c) identifying at least three time delays based on the plurality of cavitation signals, the time delays comprising a difference in the arrival time of the cavitation signal at one of the acoustic sensors relative to one of the remaining acoustic sensors; d. determining the location of the cavitation signal source based on the at least three time delays.

20. The method of claim 19.

21. The biological sample comprises a blood sample or a CSF sample from the subject. The method of claim 1.

22. The method of claim 21, further comprising determining at least a portion of the biology of the brain disorder based on the at least one biomarker isolated from the biological sample. The method of claim 1.

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