Systems and methods for ultrasound-based assessment of cerebral autoregulation

Abstract

Methods and systems are herein provided for continuous ultrasound-based assessment of cerebral autoregulation. In one example, a method comprises continuously correlating cerebral blood flow data with systemic blood flow data to generate a flow index indicative of cerebral autoregulation status.

Description

SYSTEMS AND METHODS FOR ULTRASOUND-BASED ASSESSMENT OF CEREBRAL AUTOREGULATIONCROSS REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims priority to U.S. Provisional Application No. 63 / 581,932, entitled “SYSTEMS AND METHODS FOR ULTRASOUND-BASED ASSESSMENT OF CEREBRAL AUTOREGULATION”, and filed on September 11, 2023. The entire contents of the above identified application are hereby incorporated by reference for all purposes.FIELD

[0002] Embodiments of the subject matter disclosed herein relate to medical imaging, and more specifically to assessing cerebral autoregulation.BACKGROUND

[0003] Cerebral autoregulation is defined as the maintenance of a constant cerebral blood flow in the face of changing cerebral perfusion pressure. In health, this process protects the brain during transient changes in the arterial blood pressure from diminished or excessive blood flow. Traumatic brain injury, stroke, tumors, cardiovascular surgeries, sepsis, cardiac arrest, and others are examples of insults have been shown to impair cerebral autoregulation and have large-scale clinical impact. An impairment of autoregulation narrows the range of blood pressures at which flow is matched to metabolic needs of the brain. Optimal management of cerebral perfusion pressure for limiting brain tissue hypoxia at low cerebral perfusion pressure or edema at high cerebral perfusion pressure in these patients is critical but difficult to achieve because of limited monitoring capabilities. Monitoring of cerebral autoregulation, for example via a pressure reactivity index (PRx) which monitors correlation between intracranial pressure and arterial pressure, may aid in outcome prediction for patients with brain insults.BRIEF DESCRIPTION

[0004] In one embodiment, a method comprises continuously correlating cerebral blood flow with systemic blood flow to assess cerebral autoregulation. Systemic blood flow comprises one ofperipheral volumetric blood flow data and peripheral blood pressure data. Correlating cerebral blood flow with the systemic blood flow comprises calculating a flow index to indicate a status of autoregulation. Both cerebral blood flow and systemic blood flow are measured continuously, wherein cerebral blood flow is measured via a first ultrasound system. Peripheral volumetric blood flow is measured via a second ultrasound system and peripheral blood pressure is measured as mean arterial pressure either directly via an arterial line or non-directly such as through an estimation based on blood pressure cuff readings.

[0005] It should be understood that the brief description above is provided to introduce in simplified form a selection of concepts that are further described in the detailed description. It is not meant to identify key or essential features of the claimed subject matter, the scope of which is defined uniquely by the claims that follow the detailed description. Furthermore, the claimed subject matter is not limited to implementations that solve any disadvantages noted above or in any part of this disclosure.BRIEF DESCRIPTION OF THE DRAWINGS

[0006] The present disclosure will be better understood from reading the following description of non-limiting embodiments, with reference to the attached drawings, wherein below:

[0007] FIG. l is a block diagram of an ultrasound system according to an embodiment of the disclosure.

[0008] FIG. 2 is a block diagram of a computing system communicably coupled to the imaging system of FIG. 1.

[0009] FIG. 3 is a flowchart illustrating a method for correlating cerebral blood flow data and systemic blood flow.

[0010] FIG. 4 is a flowchart illustrating a method for determining blood flow through an artery.

[0011] FIG. 5 is a flowchart illustrating a method for determining a flow index.

[0012] FIG. 6 is an example of a pulse wave spectrum outputted by the ultrasound system ofFIG. 1.

[0013] FIG. 7 shows graphical representations of a pulse wave spectrum and velocity and integrated spectral power as functions of time.

[0014] FIG. 8 shows a block diagram of a scenario for assessing cerebral autoregulation.DETAILED DESCRIPTION

[0015] The following description relates to monitoring and assessment of cerebral autoregulation, specifically to methods for assessing cerebral autoregulation via monitoring of cerebral blood flow and / or cerebral blood flow surrogates and arterial pressure.

[0016] Blood flow to a brain (e.g., cerebral blood flow) is maintained to match cerebral metabolism within a range of systemic blood pressures. The mechanism by which this is achieved is called cerebral autoregulation. This process protects the brain during transient changes in the arterial blood pressure from diminished or excessive blood flow. Certain types of insults to the brain, such as ischemic injuries, traumatic brain injuries (TBIs), tumors, and the like, as well as other stressors to the body, such as undergoing a procedure, cardiac arrest, sepsis, among others, may affect and / or impair cerebral autoregulation. An impairment of autoregulation narrows the range of blood pressures at which flow is matched to metabolic needs of the brain. In some examples, impairment of cerebral autoregulation may have lasting and / or detrimental effects to a patient. Monitoring of cerebral autoregulation may aid in outcome prediction for patients with brain insults or other medical conditions as well as guide medical personnel in treatment / therapy decision making.

[0017] However, measuring and / or monitoring cerebral autoregulation is a difficult and often demands invasive monitoring techniques. A typical method of measuring cerebral autoregulation includes determination of a moving Pearson correlation coefficient between intracranial pressure (ICP) and arterial blood pressure, called a pressure reactivity index (PRx). In order to determine the correlation coefficient, both ICP and arterial blood pressure may be measured continuously. Continuously measuring ICP often demands an invasive procedure such as placement of an intraventricular catheter (IVC), which comprises inserting a catheter into a ventricle via a hole in the skull (e.g., a drilled burr hole).

[0018] Non-invasive methods, while available, are impractical for continuous monitoring purposes as is needed to assess cerebral autoregulation. Further, continuously monitoring of arterial blood pressure may also be invasive, for example being measured via an arterial line (e.g., a catheter placed into the lumen of a peripheral artery). Arterial pressure may be measured as mean arterial pressure (MAP), which is estimated directly from the systolic and diastolic arterial pressure measurements. MAP may alternatively be estimated non-invasively via blood pressure cuffreadings, but invasive measurement via an arterial line allows for direct calculation. When determining PRx of a patient, a moving Pearson correlation coefficient of the ICP and MAP is determined. Since PRx is a correlation coefficient, its value may range from -1 to 1. A PRx of zero may indicate that cerebral autoregulation is intact since ICP and MAP are uncorrelated, while a positive PRx may indicate impaired cerebral autoregulation as both ICP and MAP are increasing or decreasing at the same time.

[0019] Even when ICP and MAP are being continuously monitored, it is difficult to capture the data from monitors and apply the moving Pearson correlation to calculate PRx in real-time. Further, demand for invasive techniques in order to assess PRx may delay assessment of cerebral autoregulation as insertion of IVCs and arterial lines may not occur until well after an initial injury, in some examples hours or even days afterwards. A less invasive means to measure and assess cerebral autoregulation, especially at earlier time points, and continuous computation of an index corresponding to cerebral autoregulation, may increase efficiency of assessment and reduce delays in assessment and subsequent treatments.

[0020] Further, other currently available non-invasive methods of assessing cerebral autoregulation are based on velocity of blood flow, for example velocity of flow in the middle cerebral artery (e.g., MCAv) and either MAP or flow velocity in the extracranial internal carotid artery (ICA). In each case, using velocity ignores the potential for changes in vessel diameters occurring with or without changes in velocity. For example, certain medications or changes in cerebral perfusion pressure may influence artery diameter to an extent that would make flow index calculations based on maximum velocity or average velocity invalid.

[0021] Methods and systems are herein presented for ultrasound-based flow-pressure index for assessment of cerebral autoregulation. An ultrasound system may be configured for non- invasively measuring cerebral blood flow and / or surrogates of cerebral blood flow. Cerebral blood flow, as herein measured, may be a volumetric blood flow rather than simply velocity of the flow. Volumetric flow thus accounts for changes in diameter of vessels as a result of medications, changes in perfusion pressure, and the like. In some examples, cerebral blood flow may be measured via internal carotid artery (ICA) flow and / or middle cerebral artery (MCA) flow. Cerebral blood flow may be correlated to MAP to determine / calculate a flow pressure reactivity index (FPx) or to peripheral blood flow to determine / calculate a flow reactivity index (FRx). In this way, the invasive techniques demanded to calculate PRx may be mitigated, allowing for afaster, timelier assessment of cerebral autoregulation in acute, emergent, and / or post-operative settings. Further, invalidity that may result from using velocity of blood flow, by way of using measures of volumetric blood flow, may be mitigated. The systems may be adapted to continuously calculate FPx in real-time based on continuous measurements of cerebral blood flow and arterial pressure. In this way, cerebral autoregulation may be assessed and monitored continuously in real-time, allowing for reduced time spent by physicians and other care providers in determining cerebral autoregulation status.

[0022] Referring to FIG. 1, a schematic diagram of an example ultrasound system 100 in accordance with an embodiment of the disclosure is shown. The ultrasound system 100 is presented as a non-limiting example of an ultrasound imaging system that may be used to acquire flow data. The ultrasound system 100 includes a transmit beamformer 101 and a transmitter 102 that drives elements (e.g., transducer elements) 104 within a transducer array, herein referred to as probe 106, to emit pulsed ultrasonic signals (referred to herein as transmit pulses) into a body (not shown). According to an embodiment, the probe 106 may be a one-dimensional transducer array probe. However, in some embodiments, the probe 106 may be a two-dimensional matrix transducer array probe. As explained further below, the transducer elements 104 may be comprised of a piezoelectric material. When a voltage is applied to a piezoelectric crystal, the crystal physically expands and contracts, emitting an ultrasonic spherical wave. In this way, transducer elements 104 may convert electronic transmit signals into acoustic transmit beams.

[0023] After the elements 104 of the probe 106 emit pulsed ultrasonic signals into a body (of a patient), the pulsed ultrasonic signals are back-scattered from structures within an interior of the body, like blood cells or muscular tissue, to produce echoes that return to the elements 104. The echoes are converted into electrical signals, or ultrasound data, by the elements 104 and the electrical signals are received by a receiver 108. The electrical signals representing the received echoes are passed through a receive beamformer 110 that outputs ultrasound data. Additionally, transducer element 104 may produce one or more ultrasonic pulses to form one or more transmit beams in accordance with the received echoes.

[0024] According to some embodiments, the probe 106 may contain electronic circuitry to do all or part of the transmit beamforming and / or the receive beamforming. For example, all or part of the transmit beamformer 101, the transmitter 102, the receiver 108, and the receive beamformer 110 may be situated within the probe 106. The terms “scan” or “scanning” may also be used inthis disclosure to refer to acquiring data through the process of transmitting and receiving ultrasonic signals. The term “data” may be used in this disclosure to refer to either one or more datasets acquired with an ultrasound system. In one embodiment, data acquired via ultrasound system 100 may be used to train a machine learning model. A user interface 115 may be used to control operation of the ultrasound system 100, including to control the input of patient data (e.g., patient medical history), to change a scanning or display parameter, to initiate a probe repolarization sequence, and the like. The user interface 115 may include one or more of the following: a rotary element, a mouse, a keyboard, a trackball, hard keys linked to specific actions, soft keys that may be configured to control different functions, and a graphical user interface displayed on a display device 118.

[0025] The ultrasound system 100 also includes a processor 116 to control the transmit beamformer 101, the transmitter 102, the receiver 108, and the receive beamformer 110. The processor 116 is in electronic communication (e.g., communicatively connected) with the probe 106. For purposes of this disclosure, the term “electronic communication” may be defined to include both wired and wireless communications. The processor 116 may control the probe 106 to acquire data according to instructions stored on a memory of the processor, and / or memory 120. The processor 116 controls which of the elements 104 are active and the shape of a beam emitted from the probe 106. The processor 116 is also in electronic communication with the display device 118, and the processor 116 may process the data (e.g., ultrasound data) into images for display on the display device 118. The processor 116 may include a central processor (CPU), according to an embodiment. According to other embodiments, the processor 116 may include other electronic components capable of carrying out processing functions, such as a digital signal processor, a field- programmable gate array (FPGA), or a graphic board. According to other embodiments, the processor 116 may include multiple electronic components capable of carrying out processing functions. For example, the processor 116 may include two or more electronic components selected from a list of electronic components including: a central processor, a digital signal processor, a field-programmable gate array, and a graphic board. According to another embodiment, the processor 116 may also include a complex demodulator (not shown) that demodulates the RF data and generates raw data. In another embodiment, the demodulation can be carried out earlier in the processing chain. The processor 116 is adapted to perform one or more processing operations according to a plurality of selectable ultrasound modalities on the data. Inone example, the data may be processed in real-time during a scanning session as the echo signals are received by receiver 108 and transmitted to processor 116. For the purposes of this disclosure, the term “real-time” is defined to include a procedure that is performed without any intentional delay. The ultrasound system 100 may acquire 2D data of one or more planes at a significantly faster rate. However, it should be understood that the real-time frame-rate may be dependent on the length of time that it takes to acquire each frame of data for display. Accordingly, when acquiring a relatively large amount of data, the real-time frame-rate may be slower. Thus, some embodiments may have real-time frame-rates that are considerably faster than 20 frames / sec while other embodiments may have real-time frame-rates slower than 7 frames / sec. The data may be stored temporarily in a buffer (not shown) during a scanning session and processed in less than real-time in a live or off-line operation. Some embodiments of the invention may include multiple processors (not shown) to handle the processing tasks that are handled by processor 116 according to the exemplary embodiment described hereinabove. For example, a first processor may be utilized to demodulate and decimate the RF signal while a second processor may be used to further process the data, for example by augmenting the data as described further herein, prior to displaying an image. It should be appreciated that other embodiments may use a different arrangement of processors.

[0026] The ultrasound system 100 may continuously acquire data at a specified frame-rate. Images generated from the data may be refreshed at a similar frame-rate on display device 118. Other embodiments may acquire and display data at different rates. For example, some embodiments may acquire data at a frame-rate of less than 10 Hz or greater than 30 Hz depending on the size of the frame and the intended application. A memory 120 is included for storing processed frames of acquired data. In an exemplary embodiment, the memory 120 is of sufficient capacity to store at least several seconds’ worth of frames of ultrasound data. The frames of data are stored in a manner to facilitate retrieval thereof according to its order or time of acquisition. The memory 120 may comprise any known data storage medium.

[0027] In various embodiments of the present invention, data may be processed in different mode-related modules by the processor 116 (e.g., B-mode, Color Doppler, M-mode, Color M- mode, spectral Doppler, Elastography, TVI, strain, strain rate, and the like) to form 2D or 3D data. For example, one or more modules may generate B-mode, color Doppler, M-mode, color M-mode, spectral Doppler, Elastography, TVI, strain, strain rate, and combinations thereof, and the like. Asone example, the one or more modules may process color Doppler data, which may include traditional color flow Doppler, power Doppler, HD flow, and the like. The image lines and / or frames are stored in memory and may include timing information indicating a time at which the image lines and / or frames were stored in memory. The modules may include, for example, a scan conversion module to perform scan conversion operations to convert the acquired images from beam space coordinates to display space coordinates. A video processor module may be provided that reads the acquired images from a memory and displays an image in real time while a procedure (e g., ultrasound imaging) is being performed on a patient. The video processor module may include a separate image memory, and the ultrasound images may be written to the image memory in order to be read and displayed by display device 118. In some examples, the ultrasound system 100 may be adapted for continuously acquiring images, for example, the ultrasound system 100 may be a portable system that may be removably attached to a patient for continuous acquisition.

[0028] In various embodiments of the present disclosure, one or more components of ultrasound system 100 may be included in a portable, handheld ultrasound imaging device. For example, display device 118 and user interface 115 may be integrated into an exterior surface of the handheld ultrasound imaging device, which may further contain processor 116 and memory 120. Probe 106 may comprise a handheld probe in electronic communication with the handheld ultrasound imaging device to collect raw ultrasound data. In another example, the probe 106 may be in wireless communication with the processor / acquisition system and may be affixed to the patient for continuous acquisition. Transmit beamformer 101, transmitter 102, receiver 108, and receive beamformer 110 may be included in the same or different portions of the ultrasound system 100. For example, transmit beamformer 101, transmitter 102, receiver 108, and receive beamformer 110 may be included in the handheld ultrasound imaging device, the probe, and combinations thereof.

[0029] After performing a two-dimensional ultrasound scan, a block of data comprising scan lines and their samples is generated. After back-end filters are applied, a process known as scan conversion is performed to transform the two-dimensional data block into a displayable bitmap image with additional scan information such as depths, angles of each scan line, and so on. During scan conversion, an interpolation technique is applied to fill missing holes (e.g., pixels) in the resulting image. These missing pixels occur because each element of the two-dimensional block should typically cover many pixels in the resulting image. For example, in current ultrasoundsystems, a bicubic interpolation is applied which leverages neighboring elements of the two- dimensional block. As a result, if the two-dimensional block is relatively small in comparison to the size of the bitmap image, the scan-converted image will include areas of poor or low resolution, especially for areas of greater depth.

[0030] While the ultrasound system 100 is herein described as an imaging system, it should be understood that the ultrasound system 100 may be configured as a single element transducer system that utilizes pulse echo and range gating to select a portion of the echo signal to be processed to determine flow. In some examples, range gating selection may include acquiring images of a target vessel to determine the location of the target vessel. In other examples, the target vessel may be localized via other means and images may not be demanded. The ultrasound system 100 may be configured to obtain or otherwise determine a flow signal that is proportional to volumetric flow through the target vessel.

[0031] Referring to FIG. 2, an example of a computing system 200 is shown. In some embodiments, the computing system 200 is communicably coupled to one or more ultrasound system 236, such as the ultrasound system 100 of FIG. 1. In some embodiments, at least a portion of the computing system 200 is disposed at a device (e.g., an edge device or server) communicably coupled to the one or more ultrasound systems 236 via wired and / or wireless connections. In some embodiments, the computing system 200 is disposed at a separate device (e.g., a workstation) that can receive images from the computing system or from a storage device that stores the images generated by the one or more ultrasound systems 236. The computing system 200 may comprise an image processor 202, a user input device 232, and a display device 234. The computing system 200 may be in communication with the one or more ultrasound systems 236, as discussed, and with a blood pressure monitoring system 238.

[0032] The image processor 202 includes a processor 204 configured to execute machine readable instructions stored in non-transitory memory 206. The processor 204 may be single core or multi-core, and the programs executed by the processor 204 may be configured for parallel or distributed processing. In some embodiments, the processor 204 may optionally include individual components that are distributed throughout two or more devices, which may be remotely located and / or configured for coordinated processing. In some embodiments, one or more aspects of the processor 204 may be virtualized and executed by remotely-accessible networked computing devices configured in a cloud computing configuration. In some embodiments, the processor 204may include other electronic components capable of carrying out processing functions, such as a digital signal processor, a field-programmable gate array (FPGA), or a graphics board. In some embodiments, the processor 204 may include multiple electronic components capable of carrying out processing functions. For example, the processor 204 may include two or more electronic components selected from a plurality of possible electronic components, including a central processor, a digital signal processor, a FPGA, and a graphics board. In still further embodiments, the processor 204 may be configured as or with a graphical processing unit (GPU), including parallel computing architecture and parallel processing capabilities.

[0033] In the embodiment shown in FIG. 2, the non-transitory memory 206 stores a blood flow module 208, an arterial blood pressure module 210, and a flow index module 212. The blood flow module 208 may store instructions executable by the processor 204 to quantify cerebral and / or peripheral volumetric blood flow of a patient based on data of respective vessel(s) acquired with the one or more ultrasound systems 236. For example, a first ultrasound system of the one or more ultrasound systems 236 may acquire ultrasound data of the patient’s internal carotid artery (ICA) and volumetric cerebral blood flow may be determined based on data of the ICA. A second ultrasound system of the one or more ultrasound systems 236 may acquire ultrasound data of a peripheral artery of the patient and peripheral blood flow may be determined based on the data of the peripheral artery. The blood flow module 208 may store instructions for quantifying blood flow relatively and / or absolutely, as will be described with respect to FIG. 4. In this way, cerebral and / or peripheral blood flow may be quantified in a non-invasive manner, reducing time spent in determination as well as mitigating delays in determination that occur with invasive techniques.

[0034] The arterial blood pressure module 210 may store instructions executable by the processor 204 to obtain or otherwise determine MAP of the patient based on data from the blood pressure monitoring system 238. The blood pressure monitoring system 238 may be configured as a continuous monitor that measures blood pressure invasively (e.g., via an arterial line) or non- invasively (e.g., via a continuous blood pressure cuff). The arterial blood pressure module 210 may obtain data from the blood pressure monitoring system 238 for processing by the computing system 200.

[0035] The flow index module 212 may store instructions executable by the processor 204 to calculate a correlation index based on determined volumetric cerebral blood flow and either peripheral blood flow or arterial blood pressure from the blood flow module 208 and the arterialblood pressure module 210. The correlation index between cerebral blood flow and peripheral blood flow may be a flow reactivity index (e.g., a flow-flow index), abbreviated FRx. The correlation index between cerebral blood flow and arterial blood pressure may be a flow pressure reactivity index, abbreviated FPx. In some examples, the flow index module 212 may store instructions for applying a moving Pearson correlation coefficient to cerebral blood flow and arterial blood pressure / peripheral blood flow in order to determine a relationship between blood flow to the brain and systemic arterial blood pressure or peripheral blood flow, thereby indicating an index of cerebral autoregulation.

[0036] In some examples, when cerebral blood flow is monitored continuously and arterial blood pressure or peripheral blood flow is monitored continuously, FRx or FPx may be calculated in a continuous manner and updated in real-time as data of cerebral blood flow and arterial blood pressure or peripheral blood flow is acquired. As an example, the display device 234 may display FPx or FRx as an index in response to user input to the user input device 232. The displayed FPx or FRx may update in real-time in response to recalculations of FPx or FRx by the processor 204 in response to changes in cerebral blood flow and / or arterial blood pressure / peripheral blood flow as measured by the one or more ultrasound systems 236, the blood pressure monitoring system 238, and / or and the processor 204 via the blood flow module 208 and / or arterial blood pressure module 210, respectively. In this way, the computing system 200 may allow for users to more efficiently and readily assess cerebral autoregulation.

[0037] Turning now to FIG. 3, a flowchart illustrating a method 300 for correlating cerebral blood flow and systemic blood flow is shown. Method 300 may be carried out according to instructions stored in memory of a computing system and executed by one or more processors of the computing system, such as computing system 200 of FIG. 2, where the computing system is operably coupled to or included as part of an ultrasound system (e.g., ultrasound system 100 of FIG. 1 ). Method 300 may be executed to determine either a flow reactivity index or a flow pressure reactivity index, as will be further described.

[0038] At 302, method 300 includes obtaining cerebral blood flow data. As will be further described with respect to FIG. 4, blood flow may be assessed for a target vessel via flow signals (e.g., via image data, pulse echo and range gating, etc.) acquired of the vessel by a first ultrasound system. As such, cerebral blood flow may be determined from data acquired by the first ultrasound system of one or more cerebral arteries. As herein described, cerebral blood flow may be avolumetric measurement, e.g., an absolute measurement, or a relative measurement, as will be discussed with respect to FIG. 4. Cerebral blood flow may be monitored continuously, as previously discussed, wherein cerebral blood flows are determined in repeated, iterative manner. Cerebral blood flow data may be determined or otherwise obtained by the ultrasound system, for example by the processor 116 of ultrasound system 100, and may be stored in non-transitory memory of the ultrasound system, the computing system 200, or both.

[0039] At 304, method 300 includes obtaining systemic blood flow data. The systemic blood flow data may comprise peripheral blood flow data, volumetric (e g., absolute) or relative or peripheral arterial blood pressured data. In some examples, peripheral blood flow may be determined, as will be described in FIG. 4, by a similar process as is used to determine cerebral blood flow. For example, peripheral blood flow may be assessed for a target peripheral vessel via flow signals acquired of the vessel by a second ultrasound system. In this way, peripheral blood flow may be monitored continuously as well. In other examples, arterial blood pressure may be measured. Arterial blood pressure may be measured either invasively or non-invasively. For example, arterial blood pressure may be measured as MAP directly via an arterial line with a transducer, whereby MAP is determined by an outputted waveform from the transducer. MAP may alternatively be estimated non-invasively via a calculation based on blood pressure cuff reading(s) as is provided by equation (1):where DP is diastolic blood pressure and SP is systolic blood pressure. Both diastolic and systolic blood pressure may be measured continuously with a continuous blood pressure cuff in a non- invasive manner.

[0040] When measured either invasively or non-invasively, MAP may be monitored continuously, whereby a blood pressure cuff repeatedly takes blood pressures, or whereby the arterial line continuously outputs waveforms from the transducer. In this way, both blood flow to the brain as well as peripheral blood flow / pressure may be monitored continuously to allow for continuous, real-time assessment of cerebral autoregulation.

[0041] At 306, method 300 includes determining a flow index, as will be further described with respect to FIG. 5, as a correlation of the cerebral blood flow data and the systemic blood flow data. The flow index may be a flow reactivity index when peripheral blood flow is determined at 304. The flow index may be a flow pressure reactivity index when arterial blood pressure isdetermined at 304. When cerebral blood flow and peripheral blood flow or arterial blood pressure are monitored continuously, the determined flow index may be updated in real-time. In some examples, the determined flow index may be displayed on a display device communicatively coupled to the computing system so that care providers may visualize the flow index and assess cerebral autoregulation based on the flow index. Further, in some examples, the computing system may assess the flow index values and display an indication on the display device of cerebral autoregulation status, as will be further described with respect to FIG. 5.

[0042] Turning now to FIG. 4, a flowchart illustrating a method 400 for determining relative and volumetric blood flow is shown. The method 400 may be a portion of the method 300 presented in FIG. 3, specifically at 302 and in some examples 304. Method 400 may be carried out according to instructions stored in memory of a computing system and executed by one or more processors of the computing system, such as computing system 200 of FIG. 2, where the computing system is operably coupled to or included as part of one or more ultrasound systems (e.g., ultrasound system 100 of FIG. 1).

[0043] At 402, method 400 includes acquiring data of an artery with the ultrasound system. The data may be flow signal data, such as images acquired or pulse echo and range gating data, as previously described. The artery may be a cerebral artery or other cerebral feeding artery, in instances in which cerebral blood flow is being determined, or a peripheral artery, in instances in which peripheral blood flow is being determined. The cerebral or cerebral feeding artery may be an ICA, an MCA, or other ultrasound-accessible cerebral or cerebral feeding artery to appropriately represent blood flow to the brain. The peripheral artery may be a femoral artery, popliteal artery, radial artery, axillary artery, or any other suitable peripheral artery to appropriately represent systemic blood flow. In some examples, the ultrasound system may be an ultrasound system configured for measuring blood flow measurements, for example including velocity and spectral power.

[0044] At 404, method 400 includes generating a pulse wave spectrum of the data. The ultrasound system used to acquire the image(s) of the artery or arteries may output the pulse wave spectrum. The pulse wave spectrum may be generated by repeated pulses of ultrasound waves that are processed (e.g., by the processor 116) to measure phase shift between pulses. Phase shifts occur when there is a change in timing of a wave’s peaks and troughs. The phase shift between pulses is proportional to a component of the blood flow velocity in the direction of the ultrasound wavepropagation. This measurement provides a range of phase shifts for all velocities present in the targeted blood vessel and intersecting the ultrasound beam.

[0045] At 406, method 400 includes determining velocity based on phase shift of the pulse wave spectrum. As noted, the phase shift between pulses is proportional to velocity in the direction of propagation. The phase shift may be converted to a corresponding velocity based on selection of a given vessel within the image(s) and measurements determined across the vessel based on the range gating of the received signal. The determined velocity may be naturally measured in the process of acquiring image(s) without angle correction. In this way, knowledge of vessel direction is not needed to determine velocity as phase shift directly corresponds to the component of interest of the velocity.

[0046] In order to determine the mean velocity at a given point in time (7) from the pulse wave spectrum, a power- weighted average may be used to yield equation (2):where v is velocity and P is the spectral power and Vmax and vmin are the velocity limits in the spectrum. The numerator of equation (2) then corresponds to a relative volume flow Qrej(t) for the case where the noise in the pulse wave spectrum is equally distributed in positive and negative velocities such that its integrated value is 0.

[0047] At 408, method 400 optionally includes determining the peak velocities of the range of velocities to allow for the case of unequally distributed noise such that the integration of the spectrum is limited to the range of velocities containing actual flow signal. In order to determine peak velocities of the range of velocities, a cumulative sum of the pulse wave spectrum is generated, as noted at 410, based on equation (3):P(.Vcum. t) = J P(v, t)dV vmin where the variables are as described above and P(vcum, t) is accumulated spectral power up to the velocity vcumat time t. Based on the generated cumulative sum, two knee points may be determined, as noted at 412, corresponding to the highest and lowest velocities, Vhtgh and viow, of the actual flow. The knee points may be inflection points or peak points of a plot of cumulative sum spectral power. The peak velocities of the spectrum may be identified as the inflection points where the power at a given velocity is still contributing significantly to the cumulative sum. Acurve representing the cumulative sum may be fit with a least squares algorithm in order to determine the maximum (and minimum) peak in the curve, as will be further described with respect to FIG. 7.

[0048] The cumulative sum and knee point determinations may be repeated for each time point in the pulse wave spectrum. Repeating the cumulative sum and knee point determinations may result in the Vhigh (and viow) of the actual flow as a function of time, as will be shown in FIG. 7.

[0049] At 414, method 400 includes determining integrated spectral power based on the pulse wave spectrum using the velocity range of v / owto vhtgh. Integrated spectral power is the total integrated power from viow to Vhigh . Integrated spectral power is given by equation (4)where the variables are as described above. For a given pulse wave spectrum, gray scale level at each pixel in the spectrum is proportional to the instantaneous spectral power at a given velocity at a given point in time. The instantaneous spectral power at the given velocity and given point in time may be designated P( , t). The sum of the instantaneous spectral powers at a given point in time over a range of velocities is used to determine integrated power, PM, as a function of time, as is described in equation (4).

[0050] At 416, method 400 includes determining relative flow. Based on the integrated spectral power and the mean velocity as a function of time, a metric scaling with volumetric flow may be measured as a function of time. The metric scaling with volumetric flow may be a relative volume flow. Relative volume flow is given by equation (5): r Vhigb / \Qrel(i) = v(t)P v, t)dvVvlow where Qre(t) is relative volume flow as a function of time and the other variables are as described above. Note that equation (5) is a particular case of the numerator of equation (2) where the integration limits are modified to the velocity range of actual flow.

[0051] At 418, method 400 optionally includes normalizing relative flow to absolute volume (e.g., volumetric) flow. Normalization of relative volume flow may be accomplished by various methods. As an example, color flow imaging where mean velocity and signal power may be directly calculated from pulse echo data may be used to normalize the determined relative volume flow.

[0052] In this way, a standard output of imaging such as ultrasound imaging may be used to define relative volume flow and / or absolute volume flow of a target vessel. Using such a volume flow may thus account for the diameter of the target vessel, as opposed to using just the velocity, which may be invalid when diameter is affected by medications or other factors. In the application scenario herein presented, this may demonstrate a non-invasive method of determining a measure of cerebral blood flow and / or peripheral blood flow, depending on the vessel imaged. The measure of cerebral blood flow and / or peripheral blood flow may be used to assess one or more physiologic processes, including cerebral autoregulation. Cerebral autoregulation, which historically demands invasive measures that are time consuming, may be assessed in a more time efficient and less invasive manner. Determining an index to allow for assessment of cerebral autoregulation is described in detail with respect to FIG. 5.

[0053] It should be understood that any method that provides mean velocity and signal power of the blood flow from a selected artery may be suitable to calculate a metric proportional to volumetric flow (e.g., relative volume flow). Specifically, the method presented to determine mean velocity herein presented is a non-limiting example of such a method and the method presented to determine integrated spectral power herein presented is a non-limited example of such a method. As an example, velocity and power may be derived from data that does not include the wave spectrum. Further, accuracy of measurements herein may be subject to uniformity of the ultrasound beam of the imaging system and as such an imaging system may be used based on particular probe designs and other parameters that affect uniformity and weighting.

[0054] Turning now to FIG. 5, a flowchart illustrating a method 500 for determining an index indicating cerebral autoregulation status is shown. The method 500 may be a portion of the method 300 presented in FIG. 3, specifically at 302. Method 500 may be carried out according to instructions stored in memory of a computing system and executed by one or more processors of the computing system, such as computing system 200 of FIG. 2, where the computing system is operably coupled to or included as part of one or more ultrasound systems (e.g., ultrasound system 100 of FIG. 1). Method 500 may be executed to determine either a flow reactivity index or a flow pressure reactivity index, as will be further described.

[0055] At 502, method 500 includes obtaining cerebral blood flow data. As is described with respect to FIG. 4, cerebral blood flow data may be a relative or volumetric measure determined based on images or pulse echo and range gating data acquired with a first ultrasound system of theone or more ultrasound systems. The cerebral flow data may comprise relative blood flow or absolute volumetric blood flow in instances in which relative volumetric blood flow is normalized to absolute volumetric blood flow. The cerebral blood flow data may be obtained in a continuous manner by the first ultrasound system, whereby images are obtained of a first artery. The first artery may be a cerebral or cerebral feeding artery. The first ultrasound system may output and / or process data of the images acquired, including data of instantaneous velocity, mean velocity, integrated spectral power, and / or relative / volumetric flow as described with respect to FIG. 4.

[0056] At 504, method 500 includes determining whether peripheral blood flow has been measured. If peripheral blood flow has been measured, method 500 proceeds to 506. If peripheral flow has not been measured, MAP may have been measured or otherwise estimated and method 500 may proceed to 514. Peripheral blood flow may have been measured as is described with respect to FIG. 4, wherein images are acquired of a second artery, e.g., a peripheral artery, with a second ultrasound system to determine relatively and / or absolute blood flow through the peripheral artery.

[0057] At 506, method 500 includes obtaining peripheral flow data. As discussed, peripheral blood flow data may be acquired with the second ultrasound system, as is described with respect to FIG. 4. The peripheral flow data may be stored in non-transitory memory, either of a computing system communicably coupled to the second ultrasound system used to acquire images of the peripheral artery or of the second ultrasound system. The peripheral flow data may be obtained from non-transitory memory for processing by the computing system, for example based on instructions stored in the flow index module 212 of FIG. 2. As peripheral flow data may be acquired by the second ultrasound system and data processing system (e.g., computing system 200 of FIG. 2) in real-time, peripheral flow data may also be obtained in real-time.

[0058] At 508, method 500 includes determining flow reactivity index (FRx). FRx may be an index indicating correlation between cerebral blood flow and peripheral blood flow. In some examples, determining the FRx may comprise calculating a moving Pearson correlation coefficient value between cerebral blood flow and peripheral blood flow, as noted at 510. The moving Pearson correlation coefficient for FRx is given by equation (6) for peripheral relative flow or equation (7) for peripheral volumetric flow:where R is the Pearson correlation coefficient, n is the size of the sampling window, Q is the flow of the ICA, MCA, or other cerebral artery (e.g., cerebral blood flow), RVF is relative flow of the peripheral artery, and AVF is volumetric flow of the peripheral artery. Equations (6) and (7) may be identical with the exception of RVF vs AVF. Q may be either relative flow or volumetric flow.

[0059] The Pearson correlation coefficient may be calculated using a moving window of the data of each of the two respective measurements (e.g., cerebral blood flow and peripheral blood flow, either absolute or relative for both). The moving window may correspond to the n variable whereby n changes in real-time as data is acquired. In this way, the Pearson correlation coefficient may be calculated for a particular n and may be calculated iteratively in real-time as n changes. The Pearson correlation coefficient may be a value between -1 and 1. The two respective measurements may be positively correlated when the calculated R is 1, negatively correlated when the calculated R is -1, and may be uncorrelated when the calculated R is 0. Various thresholds may be stored in memory to indicate positive correlation, negative correlation, and non-correlation. As an example, a correlation may be considered positive when greater than 0.3 where such a cutoff has been shown to be a sensitivity and specificity for identifying between intact and impaired vascular reactivity of 80% and 79%, respectively (Lee JK, Kibler KK, Benni PB, Easley RB, Czosnyka M, Smielewski P, Koehler RC, Shaffner DH, Brady KM. Cerebrovascular reactivity measured by near-infrared spectroscopy. Stroke. 2009 May;40(5): 1820-6).

[0060] In some examples, the calculated FRx value may be displayed on a display device communicably coupled to or otherwise included in the computing system. For example, a graphical user interface may be displayed on the display device that includes one or more elements detailing vital signs or other metrics for the patient. The FRx value may be displayed as one such element within the interface such that users may easily view the calculated value in order to make assessments of cerebral autoregulation more efficient.

[0061] At 512, method 500 optionally includes assessing cerebral autoregulation based on calculated FRx. In some examples, the computing device may analyze a calculated FRx value anddetermine whether cerebral autoregulation is maintained or impaired, for example via a look up table of values / thresholds and corresponding cerebral autoregulation statuses. The assessment of cerebral autoregulation may be displayed on the display device, at least in some examples. As a non-limiting example, cerebral autoregulation may be considered impaired when FRx is greater than 0.3. In some examples, ranges of FRx values corresponding to statuses, such as maintained or impaired, may be predefined and stored in memory for processing by the processor when assessing cerebral autoregulation status based on the calculated FRx.

[0062] At 514, method 500 includes obtaining MAP data. The MAP data may be acquired directly (e.g., invasively) via an arterial line placed in the peripheral artery and equipped with a device to measure arterial flow through the peripheral artery or estimated based on equation (1), as is described above. The MAP data may be stored in non-transitory memory of the computing device similar to the cerebral blood flow data and / or the peripheral blood flow data. The MAP data may be obtained from the non-transitory memory for use in calculating flow index. As MAP data may be acquired continuously in real-time, it may also be obtained continuously in real-time.

[0063] At 516, method 500 includes determining flow pressure reactivity index (FPx) based on the cerebral blood flow data and the MAP data. FPx may be an index indicating correlation between cerebral blood flow and MAP. Determining the FPx may comprise calculating a moving Pearson correlation coefficient value between cerebral blood flow and MAP, as noted at 518. The moving Pearson correlation coefficient for FPx is given by equation (8):where the variables are as described above.

[0064] Similar to as described above with respect to determining FRx, the Pearson correlation coefficient for FPx may be determined with a moving window of n, allowing for FPx to be calculated in an iterative manner as data of cerebral blood flow and MAP is acquired and obtained. The Pearson correlation coefficient may be a value between -1 and 1. The two respective measurements (e.g., cerebral blood flow, relative or absolute, and MAP) may be positively correlated when the calculated R approaches 1 (e.g., is above a predefined threshold such as 0.3), negatively correlated when the calculated R approaches -1, and may be uncorrelated when the calculated R is approximately 0 (e.g., within a range from -0.3 to 0.3).

[0065] In some examples, the calculated FPx value may be displayed on the display device communicably coupled to or otherwise included in the computing system. For example, a graphical user interface may be displayed on the display device that includes one or more elements detailing vital signs and / or other metrics for the patient. The FPx value may be displayed as one such element within the interface such that users may easily view the calculated value in order to make assessments of cerebral autoregulation more efficient.

[0066] At 520, method 500 optionally includes assessing cerebral autoregulation based on the FPx. As described above, in some examples, the computing device may analyze a calculated FPx value and determine whether cerebral autoregulation is maintained or impaired, for example via a look up table of values and corresponding cerebral autoregulation statuses. The assessment of cerebral autoregulation may be displayed on the display device, at least in some examples. As an example, cerebral autoregulation may be considered maintained when FPx is between 0.3 and -0.3 and may be considered impaired when FPx is above 0.3. In some examples, ranges of FPx values corresponding to statuses, such as maintained or impaired, may be predefined and stored in memory for processing by the processor when assessing cerebral autoregulation based on the FPx.

[0067] In this way, based on whether peripheral blood flow data or MAP data is acquired of a patient whose cerebral autoregulation is to be monitored, a corresponding flow index may be calculated in real-time to provide more efficient assessment of cerebral autoregulation from continuously acquired data..

[0068] Turning now to FIG. 6, an example of a pulse wave spectrum 600 is shown. The pulse wave spectrum 600 may be outputted and / or generated based on images or pulse echo and range gating data acquired by an ultrasound system configured to measure blood flow / velocity, such as ultrasound system 100 of FIG. 1. The pulse wave spectrum 600 may be an example of a pulse wave spectrum used to determine velocity, mean velocity, and, together with integrated spectral power information derived from the spectrum, relative and / or volumetric flow.

[0069] The pulse wave spectrum 600 may comprise a plot 606 of velocities as a function of time, with an abscissa 602 indicating time, where time increases from left to right along the abscissa 602, and an ordinate 604 indicating velocity, where velocity increases further from the origin. The pulse wave spectrum 600 in FIG. 6 shows a positive and negative axis. Positive velocities may correspond to flow in a first direction while negative velocities may correspond to flow in a second opposite direction. Velocities as determined from / by the pulse wave spectrum600 for the purposes of determining relative and / or volumetric flow may be considered independent of flow direction.

[0070] The pulse wave spectrum 600 may further comprise a calculated mean velocity plot 608. The mean velocity plot 608 may be displayed as an overlay on the plot 606 of velocities. The mean velocity plot 608 may be plotted as a function of time similar to the plot 606 of velocities. The mean velocity plot 608 may be generated based on calculated mean velocity based on equation (2).

[0071] The pulse wave spectrum 600 may also include a cardiac cycle beginning marker 610 and a cardiac cycle ending marker 612, the space between therefore designated one full cardiac cycle. A peak systolic velocity marker 614 and an end diastolic velocity marker 616 may also be displayed in the pulse wave spectrum 600. The peak systolic velocity marker 614 may indicate a highest recorded velocity during systolic phase of the cardiac cycle and the end diastolic velocity marker 616 may indicate the velocity at the end of the cardiac cycle. The plot 606 of velocities may be displayed in grayscale. A grayscale level at each pixel in the plot 606 may be proportional to instantaneous spectrum power at a given velocity at a given point in time. The sum of these grayscale level values at a given point in time over a range of velocities may provide integrated spectral power as a function of time, as previously described. This summation may be over a range of velocities identified as described in 408. The pulse wave spectrum 600 may also include a calculated peak velocity plot 618 which may be the upper limit of the velocity range describe in 408. The peak velocity plot 618 may be displayed as an overlay on the plot 606 of velocities. The peak velocity plot 618 may be plotted as a function of time similar to the plot 606 of velocities.

[0072] FIG. 7 shows a graphical representation 700 of a pulse wave spectrum (e.g., pulse wave spectrum 600). A given time t may be selected and displayed within the graphical representation via vertical line 702. A cumulative sum of the instantaneous spectral power values in the pulse wave spectrum along the vertical line 702 is shown in graph 704. The cumulative sum, as described with respect to FIG. 4, may be provided by equation (3). The graph 704 plots cumulative power against velocity. A range of velocities designated along an abscissa 706 of the graph 704 may span from zero to a positive limit of the possible velocities in the spectrum. A resulting curve 708 of the graph 704 may have an inflection point 710, also herein called a knee point, where the peak velocity in the spectrum transitions to a noise floor. This inflection point 710may be where the highest velocity is still contributing to the cumulative sum. In some examples, power values for velocities greater than this peak velocity may be due to noise.

[0073] To have a reproducible and automated selection of the peak velocity by the system, the curve 708 may be linearly fit by a least squares algorithm, as described previously. A resulting line 712 may be generated. The graph 704 may be rotated to generate second graph 714. An amount of rotation of the graph 704 to generate the second graph 714 may be based on the resulting line 712, wherein in the second graph 714 the resulting line 712 is horizontal. The inflection point 710 may be a peak velocity in the second graph 714.

[0074] The process of determining peak velocity, as is described by the graphical representation 700, the graph 704, the second graph 714, and the inflection point 710 may be repeated for one or more time points across the pulse wave spectrum to determine peak velocity as a function of time. Peak velocity as a function of time is represented by third graph 716 with a curve 718 plotting peak velocity against time. The peak velocity may then be set as an upper integration limit vclimwhen calculating integrated spectral power via equation (3).

[0075] In this way, the pulse wave spectrum may be processed to generate both mean velocity and integrated spectral power, which, via equation (5), may provide for relative flow through the imaged artery. Because of this, relative volume flow, and in some examples absolute volumetric flow when relative flow is normalized, may be determined for cerebral arteries or cerebral feeding arteries in a non-invasive manner. Flow through cerebral arteries or cerebral feeding arteries may as such be used when determining whether cerebral autoregulation is maintained for patients who have sustained brain injuries, undergone an operation, are being treated for sepsis, etc. In this way cerebral autoregulation may be assessed quicker, sooner, and more efficiently as invasive measures are no longer demanded for assessment.

[0076] Turning now to FIG. 8, a scenario 800 for continuously monitoring cerebral autoregulation is shown. In the scenario 800, a patient 802 is being monitored. While in the scenario 800, the patient 802 is being monitored to assess cerebral autoregulation, it should be understood that other processes, including various vital signs and other biologic processes may also be monitored at the same time as cerebral autoregulation, while not explicitly discussed herein.

[0077] A first ultrasound system 804, such as a transcranial Doppler (TCD) ultrasound or other spectral Doppler ultrasound system, may be positioned to acquire images or pulse echo andrange gating data of a first artery. The first artery may be a cerebral or cerebral feeding artery, such as an ICA, MCA, or other. The first ultrasound system 804 may be configured to continuously acquire images of the first artery. In some examples, a first transducer 806 of the first ultrasound system 804 may be temporarily affixed to the patient 802 for the duration of monitoring. Data acquired by the first transducer 806 may be received and transmitted to a first processor 808. The first processor 808 of the first ultrasound system 804 may be configured to output a pulse wave spectrum, such as the pulse wave spectrum 600 shown in FIG. 6, and analyze the pulse wave spectrum to determine flow data via a method such as the method 400 described with respect to FIG. 4. The flow data may be continuously generated for the patient 802. The flow data for the first artery may thus be cerebral flow data. The flow data, as discussed with respect to FIG. 4, may be relative or volumetric, depending on whether the data has been normalized.

[0078] At the same time as the first ultrasound system 804 is acquiring data (e.g., flow signal data) and processing the data to generate flow data of the first artery, a second ultrasound system 810 may continuously acquire data (e.g., flow signal data such as images or pulse echo and range gating data) of a second artery. The second ultrasound system 810 may be configured as a spectral Doppler ultrasound system or other type of ultrasound system capable of measuring blood flow through an artery. The second artery may be a peripheral artery, such as a femoral artery, popliteal artery, or the like, that appropriately represents systemic blood flow. Data of the second artery may be acquired with a second transducer 812 of the second ultrasound system 810 and transmitted to a second processor 814 of the second ultrasound system 810 to generate blood flow data, in some examples via the method 400 described with respect to FIG. 4. The blood flow data for the second artery may be peripheral blood flow data and may be relative and / or volumetric, as previously described.

[0079] In some examples, the first and / or second transducers 806, 812 may be wirelessly coupled to the first and / or second processors 808, 814, respectively. In other examples, the first and / or second transducers 806, 812 may be communicably coupled to the first and / or second processors 808, 814, respectively, via wired connections.

[0080] Both the cerebral blood flow data and the peripheral blood flow data may be obtained by a computing system 816. The computing system 816 may be configured with one or more processors configured to execute instructions stored in non-transitory memory, similar to as described with respect to the computing system 200 of FIG. 2. The computing system 816 may beconfigured to process the cerebral blood flow data and the peripheral blood flow data to determine volumetric flow measurements, correlate cerebral flow and peripheral flow, and calculate FRx. The computing system 816 may repeatedly calculate FRx in real-time as data is continuously acquired from respective ultrasound systems at the same time.

[0081] The computing system 816 may be communicably coupled to a display device 818. The display device 818 may display, within a graphical user interface 820, one or more graphs, tables, and / or elements. As an example, the graphical user interface 820 may include a time aligned graph 822 of FRx values over a specified window of time, a first element 824 showing a current FRx value, and a second element 826 showing a cerebral autoregulation status determined based on the current FRx value. The cerebral autoregulation status shown in the second element 826 may be determined by the computing system 816, for example via a look up table stored in memory that includes ranges of FRx values corresponding to various statuses.

[0082] In this way, the scenario 800 as shown allows for continuous accurate monitoring of both cerebral and systemic blood flow in order to monitor and assess cerebral autoregulation with increased accuracy. Further, the scenario 800, which describes measurement of both cerebral and peripheral blood flow, may mitigate invasive measures for assessment of cerebral autoregulation, as opposed to a scenario in which peripheral blood pressure is measured via an arterial line, and can be performed without measuring via the arterial line. Additionally, measuring systemic blood flow via peripheral blood flow as described by the scenario 800 may allow for more accurate realtime data acquisition in which the data can be gathered via the specific medical devices described herein in real-time and processed efficiently as described herein. Non-invasive peripheral blood pressure measurement via blood pressure cuff readings may be slower to acquire data as repeated filling and release of the cuff takes time, thus not only slowing results, but requiring more processing and filtering to achieve similar accuracy. Utilizing peripheral blood flow data instead mitigates this issue as flow data may be acquired faster in real-time, allowing for more frequent and therefore more accurate real-time FRx calculation, while achieving the desired accuracy. However, it is recognized that there are noninvasive blood pressure (CNBP) devices that measure blood pressure continuously although their accuracy can vary. These CNBP devices may be used for FPx calculations, if desired, but in some cases may be eliminated.

[0083] The technical effect of the methods and systems herein provided is that cerebral autoregulation may be assess non-invasively, efficiently, and continuously. Correlating cerebralblood flow with systemic blood flow, either peripheral blood flow (relative and / or volumetric) or peripheral blood pressure, may allow for timelier assessment of cerebral autoregulation by mitigating need for placement of invasive devices, such as would be demanded for monitoring of ICP. Further, the system that includes one or more ultrasound systems configured for continuously monitoring blood flow through respective arteries communicably coupled to or otherwise incorporated as part of a computing system may allow for continuous calculation of the flow index for continuous monitoring of cerebral autoregulation. Additionally, using relative volume or other volumetric blood flow may mitigate invalid results that would be caused by using velocity of flow alone.

[0084] The disclosure also provides support for a method, comprising: continuously correlating cerebral blood flow with systemic blood flow to assess cerebral autoregulation. In a first example of the method, systemic blood flow is measured as one of peripheral blood pressure and / or peripheral blood flow to obtain systemic blood flow data. In a second example of the method, optionally including the first example, peripheral blood pressure is measured as mean arterial pressure. In a third example of the method, optionally including one or both of the first and second examples, cerebral blood flow is measured as at least one of cerebral relative blood flow and cerebral volumetric blood flow, and wherein peripheral blood flow is measured as at least one of peripheral relative blood flow and peripheral volumetric blood flow. In a fourth example of the method, optionally including one or more or each of the first through third examples, cerebral blood flow data is obtained continuously with a first ultrasound system configured for measuring blood flow. In a fifth example of the method, optionally including one or more or each of the first through fourth examples, correlating cerebral blood flow with systemic blood flow comprises correlating one of cerebral relative blood flow and cerebral volumetric blood flow with one of peripheral blood pressure and peripheral blood flow, wherein peripheral blood flow is either relative or volumetric. In a sixth example of the method, optionally including one or more or each of the first through fifth examples, correlating cerebral blood flow with systemic blood flow comprises determining a flow index. In a seventh example of the method, optionally including one or more or each of the first through sixth examples, the flow index is calculated as a Pearson correlation coefficient with a moving window. In a eighth example of the method, optionally including one or more or each of the first through seventh examples, cerebral autoregulation is assessed based on the calculated flow index. In a ninth example of the method, optionally includingone or more or each of the first through eighth examples, the method further comprises: obtaining cerebral blood flow data, wherein obtaining cerebral blood flow data comprises: acquiring one or more images of an artery with an ultrasound system, determining mean velocity of blood flow through the artery based on a pulse wave spectrum generated by the ultrasound system, determining integrated spectral power based on the pulse wave spectrum, and determining volumetric flow through the artery based on the mean velocity and the integrated spectral power.

[0085] The disclosure also provides support for a system comprising: one or more ultrasound systems configured to acquire flow signal data of blood vessels and measure blood flow through the blood vessels, a display device, and a computing system communicably coupled to the one or more ultrasound systems and the display device, the computing system comprising one or more processors and configured with instructions in non-transitory memory that when executed by the one or more processors, cause the computing system to: continuously acquire flow signals of a first artery with a first ultrasound system, continuously generate cerebral blood flow data based on the flow signals of the first artery, continuously generate systemic blood flow data, continuously calculate a flow index correlating the cerebral blood flow data with the systemic blood flow data, and output the flow index for display on the display device, wherein the displayed flow index is updated in real-time as the flow index is continuously calculated. In a first example of the system, the systemic blood flow data comprises one of peripheral blood pressure data and peripheral volumetric blood flow data. In a second example of the system, optionally including the first example, the flow index is a flow reactivity index when the systemic blood flow data comprises peripheral blood flow data and a flow pressure reactivity index when the systemic blood flow data comprises peripheral blood pressure data. In a third example of the system, optionally including one or both of the first and second examples, the cerebral blood flow data comprises one of continuously obtained relative flow data and continuously obtained volumetric flow data. In a fourth example of the system, optionally including one or more or each of the first through third examples, peripheral blood pressure data is measured as mean arterial pressure. In a fifth example of the system, optionally including one or more or each of the first through fourth examples, continuously generating the peripheral blood flow data comprises continuously obtaining flow signal data of a second artery with a second ultrasound system and determining peripheral blood flow based on the flow signal data.

[0086] The disclosure also provides support for a method, comprising: acquiring first flow signals of a first artery with a first ultrasound system, determining blood flow data of the first artery based on the first flow signals, acquiring second flow signals of a second artery with a second ultrasound system, determining blood flow data of the second artery based on the second flow signals, where the second flow signals are acquired at the same time as the first flow signals and blood flow data of the second artery is determined at the same time as blood flow data of the first artery, correlating blood flow of the first artery with blood flow of the second artery to calculate a flow index, determining, based on the flow index, a cerebral autoregulation status, and outputting the cerebral autoregulation status for display on a display device, wherein the first artery is one of an internal carotid artery (ICa) and middle cerebral artery (MCa), the second artery is a peripheral artery. In a first example of the method, determining blood flow through the first and second artery comprises determining mean velocity and integrated spectral power via a pulse wave spectrum generated by a corresponding ultrasound system. In a second example of the method, optionally including the first example, blood flow is one of a relative blood flow determined as a product of mean velocity and integrated spectral power and volumetric blood flow normalized from the relative blood flow. In a third example of the method, optionally including one or both of the first and second examples, the flow index ranges between 1 and -1, wherein a defined threshold defines a cutoff value between maintenance and impairment of cerebral autoregulation.

[0087] As used herein, an element or step recited in the singular and preceded with the word “a” or “an” should be understood as not excluding plural of said elements or steps, unless such exclusion is explicitly stated. Furthermore, references to “one embodiment” of the present invention are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features. Moreover, unless explicitly stated to the contrary, embodiments “comprising,” “including,” or “having” an element or a plurality of elements having a particular property may include additional such elements not having that property. The terms “including” and “in which” are used as the plain-language equivalents of the respective terms “comprising” and “wherein.” Moreover, the terms “first,” “second,” and “third,” etc. are used merely as labels, and are not intended to impose numerical requirements or a particular positional order on their objects.

[0088] This written description uses examples to disclose the invention, including the best mode, and also to enable a person of ordinary skill in the relevant art to practice the invention,including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined by the claims, and may include other examples that occur to those of ordinary skill in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal languages of the claims.

Claims

CLAIMS1. A method, comprising: continuously correlating cerebral blood flow with systemic blood flow to assess cerebral autoregulation.

2. The method of claim 1, wherein systemic blood flow is measured as one of peripheral blood pressure and / or peripheral blood flow to obtain systemic blood flow data.

3. The method of claim 2, wherein peripheral blood pressure is measured as mean arterial pressure.

4. The method of claims 1, wherein cerebral blood flow is measured as at least one of cerebral relative blood flow and cerebral volumetric blood flow, and wherein peripheral blood flow is measured as at least one of peripheral relative blood flow and peripheral volumetric blood flow.

5. The method of claim 1, wherein cerebral blood flow data is obtained continuously with a first ultrasound system configured for measuring blood flow.

6. The method of claim 1, wherein correlating cerebral blood flow with systemic blood flow comprises correlating one of cerebral relative blood flow and cerebral volumetric blood flow with one of peripheral blood pressure and peripheral blood flow, wherein peripheral blood flow is either relative or volumetric.

7. The method of claim 1, wherein correlating cerebral blood flow with systemic blood flow comprises determining a flow index.

8. The method of claim 7, wherein the flow index is calculated as a Pearson correlation coefficient with a moving window.

9. The method of claim 1, wherein cerebral autoregulation is assessed based on the calculated flow index.

10. The method of claim 1, further comprising obtaining cerebral blood flow data, wherein obtaining cerebral blood flow data comprises: acquiring one or more images of an artery with an ultrasound system; determining mean velocity of blood flow through the artery based on a pulse wave spectrum generated by the ultrasound system; determining integrated spectral power based on the pulse wave spectrum; and determining volumetric flow through the artery based on the mean velocity and the integrated spectral power.

11. A system comprising: one or more ultrasound systems configured to acquire flow signal data of blood vessels and measure blood flow through the blood vessels; a display device; and a computing system communicably coupled to the one or more ultrasound systems and the display device, the computing system comprising one or more processors and configured with instructions in non-transitory memory that when executed by the one or more processors, cause the computing system to: continuously acquire flow signals of a first artery with a first ultrasound system; continuously generate cerebral blood flow data based on the flow signals of the first artery; continuously generate systemic blood flow data; continuously calculate a flow index correlating the cerebral blood flow data with the systemic blood flow data; and output the flow index for display on the display device, wherein the displayed flow index is updated in real-time as the flow index is continuously calculated.

12. The system of claim 11, wherein the systemic blood flow data comprises one of peripheral blood pressure data and peripheral volumetric blood flow data.

13. The system of claims 11, wherein the flow index is a flow reactivity index when the systemic blood flow data comprises peripheral blood flow data and a flow pressure reactivity index when the systemic blood flow data comprises peripheral blood pressure data.

14. The system of claim 11, wherein the cerebral blood flow data comprises one of continuously obtained relative flow data and continuously obtained volumetric flow data.

15. The system of claim 11, wherein peripheral blood pressure data is measured as mean arterial pressure.

16. The system of claim 11, wherein continuously generating the peripheral blood flow data comprises continuously obtaining flow signal data of a second artery with a second ultrasound system and determining peripheral blood flow based on the flow signal data.

17. A method, comprising: acquiring first flow signals of a first artery with a first ultrasound system; determining blood flow data of the first artery based on the first flow signals; acquiring second flow signals of a second artery with a second ultrasound system; determining blood flow data of the second artery based on the second flow signals, where the second flow signals are acquired at the same time as the first flow signals and blood flow data of the second artery is determined at the same time as blood flow data of the first artery; correlating blood flow of the first artery with blood flow of the second artery to calculate a flow index; determining, based on the flow index, a cerebral autoregulation status; and outputting the cerebral autoregulation status for display on a display device; wherein the first artery is one of an internal carotid artery (ICA) and middle cerebral artery (MCA), the second artery is a peripheral artery.

18. The method of claim 17, wherein determining blood flow through the first and second artery comprises determining mean velocity and integrated spectral power via a pulse wave spectrum generated by a corresponding ultrasound system.

19. The method of claims 17, wherein blood flow is one of a relative blood flow determined as a product of mean velocity and integrated spectral power and volumetric blood flow normalized from the relative blood flow.

20. The method of claim 17, wherein the flow index ranges between 1 and - 1 , wherein a defined threshold defines a cutoff value between maintenance and impairment of cerebral autoregulation.

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