Apparatus and method for modular ultrasound and photoacoustic imaging and interactive guidance using a position-tracked modular imaging framework

Abstract

A method operable to track reconstruction of a tissue volume from disjoint acoustic sensors. The method includes placing modular acoustic elements individually at positions with respect to the tissue volume, tracking the positions of the modular acoustic elements individually, creating a volumetric distribution map of the tracked modular acoustic elements, registering the volumetric distribution map, and reconstructing an image of the tissue volume or transmit a focused ultrasound by providing the registered distribution map to a beamforming algorithm.

Description

Attorney Docket No.0184.0311-PCT / P18211-02 APPARATUS AND METHOD FOR MODULAR ULTRASOUND AND PHOTOACOUSTIC IMAGING AND INTERACTIVE GUIDANCE USING A POSITION- TRACKED MODULAR IMAGING FRAMEWORK GOVERNMENT FUNDING

[0001] This invention was made with government support under grant EB033758 awarded by the National Institutes of Health. The government has certain rights in the invention. CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. Provisional Patent Application No 63 / 641,195, filed on May 1, 2024, the disclosure of which is incorporated herein by reference. FIELD

[0003] This disclosure relates generally to medical sensing, and specifically to photoacoustic and ultrasound sensors. BACKGROUND

[0004] Medical sensing is widely used to reflect patient status, but continues to exhibit limitations that prevent its use in continuous monitoring. Near-infrared spectroscopy (NIRS) is limited by poor spatial resolution and high signal interference from mixed arterial / venous compartments in regions of interest, making standardization difficult. Portable magnetic resonance imaging (MRI) devices are limited to brain imaging. Bedside ultrasound (US) imaging requires physical contact of the transducer to the patient at specific positions and angles, requiring delicate skills by a trained operator. The clinical need for more continuous monitoring is particularly amplified for ill neonates because frequent re- interpretation of data is essential in the neonatal intensive care unit (NICU) environment, especially in the first few days of age when neonatal hemodynamic physiology is transitioning from intrauterine to extrauterine life. A flexible US array is a fixed array design disallowing any changes in overall aperture size and element pitch once fabricated.Moreover, state-of-the-art electronic systems do not provide feasible cost-effectiveness to handle individual control of thousands of elements in the flexible array. Therefore, continuous US imaging, accommodating variable body surface curvature and permitting on- demand customizability, would contribute substantial impact to health outcomes.

[0005] In traditional medical US imaging, piezoelectric transducers have predominantly taken the form of rigid probes, each tailored with specialized shapes based on specific use cases. Recent advancements in US technology have been devoted to the development of piezoelectric transducers that can better accommodate the diverse and intricate landscape of human anatomies, particularly in the context of longitudinal monitoring. Flexible US transceivers are designed to deform and adhere to the skin surface. Despite these strides, a critical challenge persists in the realm of image reconstruction, hinging on the requirement for fixed positions of transducer elements in relation to one another. Flexible transceivers have navigated this challenge through various means, including inbuilt shape sensing mechanisms. Yet, a degree of fixed positioning for the transducer elements remains essential. Also, clinical efficacy is limited by its predefined, fabricated form factor . Consequently, it is evident that while flexible arrays represent a significant breakthrough, there are still limitations in their applicability, particularly on highly contoured surfaces or across populations with substantial diversity in anatomical structures and scale. This underscores the need for more advanced and adaptable imaging solutions to suit the evolving demands of personalized and patient-centric healthcare.

[0006] Modular systems, wherein transducer elements are unfixed to each other, are a promising solution to the need for flexibility in US system shape. This approach not only facilitates seamless longitudinal monitoring across surfaces with arbitrary curvature but also affords the flexibility to customize the modular acoustic elements and number of modules according to the patient-specific scale and anatomical features. Through synthetic aperture focusing, these modules effectively act as one cohesive imaging unit. This form-factor can rely on a method of reconstruction that compensates for the lack of relative position information between the imaging elements that is inherent in fixed configurations. Even though the flexible US array technology is promising for continuous patient monitoring, it includes challenges because a fixed array design disallows changes once fabricated.

[0007] In the United States, 9-13% of all newborns require special care in the neonatal intensive care unit (NICU). However, current monitoring technologies for these illinfants are limited. Blood pressure, pulse oximetry, body temperature, and respiratory rate are common metrics, but they cannot differentiate of distress. Identifying the source of critical illness requires additional diagnostic tests, which can cause delays in initiating life- saving treatments. Ultrasound (US) imaging is an essential modality but incapable of continuous monitoring in its probe form-factor. It necessitates a trained sonographer, which is often restricted due to a lack of training, financial limits, and administrative / political constraints. Near-infrared spectroscopy (NIRS), continuous monitoring using X-ray, other tomographic imaging modalities (CT, MRI) exhibit various challenges. Therefore, there is an urgent demand for a non-invasive monitoring that provides spatiotemporal monitoring of multiple deep organs over time without any additional burden for clinicians.

[0008] Traditional US faces limitations of functionality as there is no inherent direct molecular contrast in tissue. Photoacoustic (PA) imaging is a hybrid modality which can provide the molecular contrast of light absorbance with US penetration depth and spatial resolution. In PA imaging, radio-frequency acoustic pressure is generated depending on the tissue light absorbance and thermo-elastic properties when the light energy at specific wavelengths is delivered to the target, which is obtained by a US transducer. Incorporating PA imaging into US systems can significantly expand the range of diagnostics beyond traditional acoustic clinical applications due to its unique capabilities, including the visualization of tissue composition and functional information such as blood oxygenation. Traditional US and PA devices are limited by their form factors, as the shape of US probes must be customized to the imaging task. To meet the needs of unique clinical challenges, what is needed is to adapt PA and US modalities to provide molecular and mechanical tissue characterization in a modular configuration for flexible wearable form factors.

[0009] Stroke represents a distinct form of neonatal brain injury that can be differentiated from other causes of brain injury. What is needed is a safe, rapid, noninvasive, inexpensive, and easy-to-use photoacoustic helmet (PAH) in a modular configuration that can 1) monitor and identify at-risk neonates, shortly after birth, allowing them to be triaged to therapy; 2) monitor the progress of therapy; and 3) provide prognostic information to the parents of newborns at risk for life-long brain injury.

[0010] Perinatal arterial ischemic stroke can be important in the differential diagnosis of infants presenting with seizures and encephalopathy in the newborn period. It is a major known cause for cerebral palsy, accounting for 30% of children affected with hemiplegiccerebral palsy. With an estimated incidence of 17-93 per 100,000 live births, the incidence of stroke in the perinatal period rivals the incidence of stroke in adults (17-23 per 100,000). Up to 90% of newborns with stroke present acutely in the first 72 hours of life with neurologic symptoms, most commonly seizures. It can be difficult to differentiate focal stroke from other forms of brain injury such as global hypoxic-ischemic encephalopathy (HIE) in the neonate, given the overlapping presentations. While hypothermia within 6 h of birth is used to treat HIE, there is no consensus regarding treatment of stroke aside from supportive therapy, in large part because definitive clinical trials of perinatal stroke require methods to accurately differentiate the stroke population from the global HIE population. Therefore, a device that could rapidly and reliably identify an area of focal cerebral ischemia or HIE in a newborn could be of importance for enabling stratification of newborns presenting with hypotonia into clinical trials to evaluate treatments specific for stroke. Furthermore, a device that could provide a diagnosis within an hour of birth could provide a therapeutic time window that could maximize efficacy. Cranial ultrasound, MRI imaging, bedside, low-field MRI each exhibit challenges with respect to infant diagnoses. What is needed is a device that can be deployed at the bedside in the NICU for early diagnosis of neonatal cortical ischemic stroke. By allowing appropriate, individualized management and enabling initiation of neuroprotection stroke trials, such a device could represent a paradigm shift for testing new therapies in the field of perinatal stroke. The total addressable market for monitoring this vulnerable neonatal population globally in the NICU in 2026 is expected to be $10.6 billion at the compound annual growth rate of 6.9%. A second major clinical use anticipated for commercialization is for infants who are on cardiopulmonary support with ECMO for days or weeks and are at risk for ischemic stroke. A monitoring device with real-time feedback of stroke onset for pediatric ECMO patients could be of clinical value so that therapy could be rapidly initiated before neurodegeneration proceeds. SUMMARY

[0011] To overcome technological challenges touched upon in the present disclosure, and other challenges, a non-invasive US imaging modality in accordance with embodiments of the present disclosure is described herein. The device includes a customizable number of US modules that configure a virtual aperture over a body surface to perform continuous USimaging of target organs, for example, but not limited to, brain, heart, lung, kidney, and intestines, during intensive care. The intended associated with the device allows for its use in diverse populations. For example, neonatal intensivists in particular canuse the device with minimal effort or prior training, regardless of gestational age and size of the neonate, spanning from premature to full term infants. A simulation-aided interactive guidance can simplify the clinical workflow to maximize the performance of a configuration of the device of the present disclosure.

[0012] Devices in accordance with embodiments of the present disclosure provide a modular combined ultrasound and photoacoustic imaging framework for user-independent biomedical diagnosis and monitoring of physiological and pathologic dynamics. The modular framework enables a wearable form factor that can be used to visualize dynamics in anatomical structure, hemodynamics, physiology, and mechanical properties in multiple organ systems through diverse body surfaces in a non-invasive manner. The field-of-view (FOV) of the system can permit both single-point tissue characterization using a single module and tomographic imaging by a synthetic modular aperture focusing technique (mSAF) with multiple modules as recognized by an external optical tracker.

[0013] Devices in accordance with embodiments of the present disclosure provide a multi-functional platform for healthcare applications, focusing on ease of use, safety, and automated diagnosis at multiple organs. Bone density and tissue oxygen saturation (sO2) tracking in musculoskeletal and vascular systems are possible.

[0014] The system and method of the present disclosure enable continuous and modular monitoring during critical care (ICU / NICU / OR) using a non-invasive US imaging modality, in which continuous wearability is achieved using a customizable number of imaging modules to provide morphological and tissue perfusion dynamics in internal organs (brain, heart, lung, and intestines) underneath any body surface. In neonates, the framework accommodates a wide range of weight and scale of the neonate in monitoring, spanning from extreme premature to term infants. Such technological versatility is important considering the correlation between prematurity and vulnerability for medical complications (e.g., congenital heart disease, respiratory distress syndrome, necrotizing enterocolitis, acute renal failure, etc.). This adaptability is currently unattainable by conventional US imaging and by the state- of-the-art flexible US array imaging.

[0015] A US device in accordance with embodiments of the present disclosure can monitor internal organs underneath neonatal body surfaces. In some configurations, each US module includes a single piezoelectric element, and the group of US modules is recognized in space by using a remote position tracking system. The spatial understanding enables the formation of a virtual aperture in the volume for a synthetic modular aperture focusing technique, where datasets from sequential US transmission events from each US module are recorded by the US modules and compounded with synthetic focusing delays. In the clinical workflow, the US module placement can be guided by interactive guidance software that estimates imaging quality in real time given the volumetric recognition of the US modules and their acoustic beam pattern overlap.

[0016] A system of one or more computers can be configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One or more computer programs can be configured to perform particular operations or actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions. One general aspect includes a method operable to track reconstruction of a tissue volume. The method includes enabling placement of modular acoustic elements individually at positions with respect to the tissue volume, tracking the positions of the modular acoustic elements individually, creating a volumetric distribution map of the tracked positions, registering the volumetric distribution map, and reconstructing an image of the tissue volume by providing the registered volumetric distribution map to a beamforming algorithm. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

[0017] One general aspect includes a method for detection of congenital heart disease in neonates. The method includes placing modular acoustic elements individually at positions with respect to a neonate heart, tracking the positions of the modular acoustic elements individually, grouping the modular acoustic elements by the tracked positions and estimated acoustic field, creating a volumetric distribution map of the grouped modular acoustic elements, registering the volumetric distribution map, reconstructing an image of the neonate heart by providing the registered volumetric distribution map to a beamforming algorithm, monitoring multiple organ systems simultaneously, classifying data from the modularacoustic elements based on the reconstructed image, and detecting the congenital heart disease based on the data from the classified data. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

[0018] One general aspect includes a method for detection of necrotizing enterocolitis in neonates. The method includes placing modular acoustic elements individually at positions with respect to a neonate abdomen, tracking the positions of the modular acoustic elements individually, grouping the modular acoustic elements by the tracked positions and estimated acoustic field, creating a volumetric distribution map of the grouped modular acoustic elements, registering the volumetric distribution map, reconstructing an image of the neonate abdomen by providing the registered volumetric distribution map to a beamforming algorithm, monitoring multiple organ systems simultaneously, classifying data from the modular acoustic elements based on the reconstructed image, and detecting the necrotizing enterocolitis in neonates based on the data from the classified data. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

[0019] One general aspect includes a method for transcranial ultrasound stimulation. The method includes placing modular acoustic elements individually at positions with respect to cranium, forming a wearable framework for transcranial focused ultrasound (tFUS), tracking the positions of the modular acoustic elements individually, grouping the modular acoustic elements by the tracked positions and estimated acoustic field, creating a volumetric distribution map of the grouped modular acoustic elements, registering the volumetric distribution map, reconstructing an image of a tissue volume by providing the registered volumetric distribution map to a beamforming algorithm, stimulating neural function in the tissue volume, monitoring the reconstructed image, and quantifying a response of the tissue volume responding to the stimulation based on the reconstructed image. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

[0020] One general aspect includes a method for discriminating between a malignant mass and a benign mass in a tissue volume. The method includes placing modular acousticelements individually at positions with respect to the tissue volume, tracking the positions of the modular acoustic elements individually, grouping the modular acoustic elements by the tracked positions and estimated acoustic field; creating a volumetric distribution map of the grouped modular acoustic elements, registering the volumetric distribution map, reconstructing an image of the tissue volume by providing the registered distribution map to a beamforming algorithm, monitoring the reconstructed image; and discriminating between the malignant mass and the benign mass in the tissue volume based on changes in sound propagation speed, density, attenuation, and morphology of the monitored reconstructed image. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

[0021] One general aspect includes a computer system operable to track reconstruction of a tissue volume. The computer system includes a hardware processor, and a non-volatile storage medium storing instructions that when executed by the hardware processor performs operations that may include placing modular acoustic elements individually at positions with respect to the tissue volume, tracking the positions of the modular acoustic elements individually, creating a volumetric distribution map of the tracked positions, registering the volumetric distribution map, and reconstructing an image of the tissue volume by providing the registered volumetric distribution map to a beamforming algorithm. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

[0022] One general aspect includes a computer program product operable to track reconstruction of a tissue volume. The computer program product includes a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computing device to cause the computing device to perform operations. The instructions include enabling placement of modular acoustic elements individually at positions with respect to the tissue volume, tracking the positions of the modular acoustic elements individually, creating a volumetric distribution map of the tracked positions, registering the volumetric distribution map, and reconstructing an image of the tissue volume by providing the registered volumetric distribution map to a beamforming algorithm. Other embodiments of this aspect include corresponding computer systems,apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and / or other aspects and advantages will become more apparent and more readily appreciated from the following detailed description of examples, taken in conjunction with the accompanying drawings, in which:

[0024] FIG.1 is a pictorial representation of the system in accordance with embodiments of the present disclosure;

[0025] FIG.2A is a pictorial illustration of a photoacoustic helmet for continuous monitoring in accordance with embodiments of the present disclosure;

[0026] FIG.2B is a schematic diagram of a photoacoustic module with light emitting diodes (LEDs) and optical fiber port for connection to a laser microphone;

[0027] FIG.2C is a flowchart of a clinical workflow in accordance with embodiments of the present disclosure;

[0028] FIG.3A is a linear regression between ground-truth and estimated brain oxygenation using control and optimized wavelength subsets with 21 and 2 wavelengths, respectively;

[0029] FIG.3B is a graph of mean and standard deviation among the goodness of fit, slope and y-intercept (n = 4) and ideal values;

[0030] FIGs.4A-4C are an equation and graph that describe weighted material decomposition based on photoacoustic intensity;

[0031] FIG.5 is a flowchart of a method in accordance with embodiments of the present disclosure;

[0032] FIGs.6A-6C are pictorial diagrams of transcranial photoacoustic imaging of photothrombotic stroke;

[0033] FIG.7A is a pictorial representation of the modular system in use;

[0034] FIG.7B shows classification for task-dependency;

[0035] FIG.7C is a position-tracked reconstruction and clinical workflow;

[0036] FIG.8 is flowchart of an US module and PA module workflow in accordance with embodiments of the present disclosure, showing optical tracking of the modular positions;

[0037] FIG.9A is a the calculation of relative modular position from a target voxel; and

[0038] FIG.9B is a graph of quantitative metrics to evaluate the SSA quality using covariance error ellipse; and

[0039] FIG.10 is a flowchart of a module synthetic aperture position calibration workflow. DETAILED DESCRIPTION

[0040] In order for the present disclosure to be more readily understood, certain terms are first defined below. Additional definitions for the following terms and other terms may be set forth throughout the specification. If a definition of a term set forth below is inconsistent with a definition in an application or patent that is incorporated by reference, the definition set forth in this application should be used to understand the meaning of the term.

[0041] As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. Thus, for example, a reference to “a method” includes one or more methods, and / or steps of the type described herein and / or which will become apparent to those persons skilled in the art upon reading this disclosure and so forth.

[0042] It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. Further, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. In describing and claiming the methods, systems, and computer readable media, the following terminology, and grammatical variants thereof, will be used in accordance with the definitions set forth below.

[0043] Referring now to FIG.1, a system and method in accordance with embodiments of the present disclosure provide a position-tracked reconstruction method for modular US and photoacoustic imaging and interactive guidance software. While traditional US reconstruction is premised on the fixed relative positions of transducer elements with respect to each other, a synthetic aperture focusing method allows for arbitrary positioning of these elements. Position tracking is used to create a volumetric distribution map of the modular acoustic elements. The distribution map is registered for use in a beamformingalgorithm for image reconstruction. This could be done by several methods, including, but not limited to, remote optical tracking of each module or surface tension-based surface recognition methods. Also, the framework may have multiple types of modules to enable multiple imaging modalities: ultrasound only, photoacoustic only, or ultrasound and photoacoustic imaging.

[0044] As an example of volumetric modular tracking, a stereo camera optical tracker and / or time-of-flight camera may be used to determine the cartesian coordinates of each module in a tracker-dependent reference frame, through identification of the rigid tags. This is then translated to a marker and anatomy-dependent reference frame to ensure reconstruction of the biologically relevant region. This step effectively creates a one-time registration of the modular locations, permitting the initial arbitrary positioning of the modules by the user. The tracking accuracy should be finer than the acoustic wavelength expected in ultrasound and / or photoacoustic imaging to guarantee the beamforming quality.

[0045] In cases where multiple organ systems are monitored with modular imaging simultaneously, a classification step to group modules by their position and estimated acoustic field may be introduced prior to creation of the final module map in the volume. The mapping is then used in the same manner as a rigid array transducer for acoustic propagation time-of-flight compensation during the beamforming process, facilitating synthetic aperture focusing and reconstruction of the ultrasound / photoacoustic image. The types of volumetric modular tracking methods, positional indicators, and reconstruction methods can be varied depending on user’s discretion and given clinical situation. The volumetric tracking can be repeated in various time intervals as needed to guarantee the accuracy of volumetric modular position tracking over time.

[0046] Potential applications of the modular imaging framework include internal organ monitoring of neonates. Multiple modular systems can cover the chest and abdominal regions for early detection of critical congenital heart disease (CHD) or necrotizing enterocolitis (NEC). Use of classification in the tracking step permits categorization of modules for heart imaging or intestinal imaging, considering light and acoustic divergence and coverage from each module to each voxel. Use of the ultrasound transmission module within this framework can also facilitate transcranial ultrasound stimulation applications. A wearable framework for transcranial focused ultrasound (tFUS) allows interrogation of neural function in biological tissues (brain, nerves, etc.), and subsequent ultrasound andphotoacoustic imaging can give an opportunity to quantify the response of the tissue responding to the controlled stimulation.

[0047] Use of the ultrasound transmission module within this framework can also enable bone density measurement by remotely inducing surface acoustic wave (SAW) and detect its propagation speed in a distance using other ultrasound reception modules. This can be especially useful for an application that needs consistent and user-independent bone density evaluation and monitoring.

[0048] The combination of flexible placement of transmission and sensing modules paves the way for transmission-mode ultrasound imaging that is essential in quantitative tissue characterization of mechanical properties. Determination of changes in sound propagation speed, density, attenuation, and morphology of a target tissue can aid in the discrimination of malignant and benign masses, for one. As modules do not fully cover the surface being imaged, gaps between modules can be used for immediate biopsy based on the volumetric imaging and real-time needle tip tracking. Ultimately, position-tracked reconstruction promotes a paradigm for ultrasound and photoacoustic imaging. The framework creates an adaptable modular form factor that not only addresses limitations posed by conventional rigid ultrasound probes but also aligns with the evolving demands of modern healthcare towards patient-centric care and accessibility of diagnostic technologies.

[0049] For modular placement, guidance software may be used to instruct a clinician to secure the best performance out of the given US modules. One may fully characterize the acoustic / optical beam profiles using a simulation or actual measurements using modules. Also, such understanding on physical interaction can be correlated to imaging performance metrics (e.g., spatial resolution, contrast, grating lobe artifacts). An interactive guidance workflow estimates the monitoring performance in real time to guide clinicians towards optimal monitoring quality. As an example of the interactive guidance software, one may design a workflow that starts with a pre-calibration phase that scans the neonatal body with a stereo and / or time-of-flight camera and marks the region for the target organ. The guidance software automatically simulates the modules at the surface in the most efficient way and estimates the beam overlap among them using their beam patterns already measured or simulated when the module was fabricated. In this case, the effective group of modules can be selected at each voxel, referred to herein as synthetic surface aperture (SSA). The information is used to form an angular histogram of the US modules at each voxel. Thecovariance error ellipse of the effective US modular positions in the angular histogram is configured to represent the SSA quality at each voxel. The ideal covariance error ellipse can be a circle as wide as possible with high modular inclusion, which is aligned at the origin of the angular histogram at the voxel. The quality of the SSA can be evaluated by four evaluation metrics: (1) SSA density represented by the number of effective US modules for the voxel included in the covariance error ellipse, defining grating lobe artifacts; (2) SSA width characterized by calculating the length of its long axis, indirectly indicating the spatial resolution at the voxel; (3) SSA roundness calculated by the difference between long and short axis lengths of the covariance error ellipse, indicating the uniformity of spatial resolution in the transverse imaging plane; (4) SSA centeredness by the distance between the covariance error ellipse’s center and the origin of the angular histogram, indicating the symmetry of the point spread function. The SSA evaluation metrics represent the resultant imaging quality. One may perform a calibrated phantom study or simulation to correlate how much imaging quality is obtained with certain combination of the SSA evaluation metrics. The user may specify target organs and the cost function to optimize the modular positions based on imaging quality evaluation in the target organ area. The guidance software may show the current expectation in imaging quality, and can suggest moving some of the modules to other places. It may present the revised imaging quality expectation as the clinician moves the modules.

[0050] The reconstruction method detailed here enables creation of images from the modular device system in accordance with embodiments of the present disclosure. The reconstruction method includes creating a mapping of the sensors using position-tracking. There are three basic modular configurations: (1) PA modules including light emitting diodes (LEDs), LED driver, optical tracking tag, and piezoelectric acoustic receive sensor optimized for acoustic recording; (2) US modules including piezoelectric acoustic elements optimized for balanced acoustic transmittance and reception; and (3) PA / US modules including LEDs, LED driver, optical tracking tag, and piezoelectric acoustic transmit and receive piezoelectric elements. Dual-modal US / PA imaging can be done either by using module configuration #3 alone or in combination with module configurations #1 and #2. Configurations #1 or #2 can be used alone for PA or US imaging, respectively. Steps for optically tracked reconstruction of a tissue volume include (1) photoacoustic modules are placed in desired locations on the anatomical surface (usually tiled across the surface and densely packed). This step differsfrom a traditional paradigm where individual array element positions are not selected but instead are applied to the surface probe. Steps also include (2) an optical tracker (or other sensing system) determines the x / y / z coordinates and rotation in space of each module in space from a tracker attached to the rear surface of each module. This is the non-standard step because when sensor / probe geometry is fixed, this step would not be needed. Steps also include (3) imaging including (a) PA imaging in which LEDs that are included in the module are pulsed to incite the photoacoustic effect, and an acoustic sensor receives the time-domain acoustic signal. In some configurations, the PA module prototype has 8 parallelized multi-wavelength LEDs and a central hole to put an optical fiber connected to an optical hydrophone system, providing individual control of various wavelength pulses and acoustic data acquisition. The system of the present disclosure provides a cohesive user experience for intensivists that simplifies monitoring of any target organ with minimal efforts or prior training, regardless of age or size of the patient.

[0051] Guidance software provides interactive and real-time estimation of the effective FOV with given US modular placements on arbitrary body surface of the neonate. The guidance software operates in real time, evaluating SSA quality metrics at each voxel to estimate the corresponding imaging quality and FOV maps. The workflow starts from the modular and surface recognitions using the multi-camera tracking system, and the SSA is estimated at each voxel. The volumetric maps of the SSA quality metrics (density, roundness, width, and centeredness) are established from the modular positions and orientations. The effective FOV is chosen from a simulation case that minimizes the mean squared errors (MSEs) of four SSA quality metrics, considering a tracking error function. The US modules are placed on the surface, and their locations are recognized by the tracking camera system. The SSA quality metrics are calculated at each voxel and an estimated FOV volume is overlayed on the 3D rendering of the tracking volume. In the phantom, US data acquisition and mSAF-based image reconstruction is performed. The FWHM is measured over the orthogonal direction of each hyperechoic wire target and overlaid with the FOV volume estimated. The effective FOV is estimated with the contour of sub-mm spatial resolution along with the wire targets. The experimental FOV is compared with the FOV estimated by the interactive guidance software. The mean and standard deviation of the area difference is calculated to represent the reliability of the FOV estimation. The frame rate to update the FOV estimation is measured as a metric of interaction between modules, in comparison torunning a whole acoustic simulation of the recognized volume. A supervised classification learner-based agent can be a solution to estimate the FOV. The agent can be trained by the previously obtained databases of the SSA quality and imaging metrics in a volume. Inclusion of any given voxel in the FOV can be determined by evaluating its SSA quality metrics. If tissue inhomogeneity causes errors in FOV estimation due to difference of sound propagation speeds in simulation and real-world data, an adaptive sound speed correction method can optimize the FOV estimation accuracy.

[0052] Referring now to FIGs.2A-2C, perinatal arterial ischemic stroke (PIS) is a prevalent subtype of pediatric stroke. Such strokes lead to lifelong developmental deficits and are one of the primary causes of cerebral palsy, contributing to over 30% of cases. The use of a modular photoacoustic imaging system fills the gap in existing neonatal neurocritical care by providing a method of longitudinally monitoring cortical oxygen saturation. A photoacoustic helmet (PAH) 201 (FIG.2A) in a modular configuration, allows for 1) continuous monitoring and rapid identification of at-risk patients; 2) continuous monitoring of the progress of therapy; and 3) providing prognostic information about the patient. Photoacoustic sensing module units 103 (detail shown in FIG.2B) can be equipped with rigid tags 203 (FIG.2B) for use in the reconstruction method described herein. The use of position-tracked reconstruction in a modular framework is suitable for patients in which a rigid helmet-like structure would not suit the massive diversity and constant change in the population’s skull shape. The modular framework permits monitoring of a target region of interest on the cortical surface with pre-selected spatial resolution such as, for example, but not limited to, 1cm. The configuration can enable identification of stroke-induced asymmetry of cortical oxygen saturation relative to the same region in contralateral cerebral cortex. In this specific example for neonatal brain monitoring, two module types can be used for maximal versatility. The first module is a multi-wavelength light transmission and acoustic reception module for photoacoustic sensing of oxygen saturation over neonatal brain cortex through the fontanelle and intact scalp / skull layers. The second module is an ultrasound transmission module, which can send acoustic signals to allow ultrasound imaging or neuromodulation. It can be implemented to have multiple focal points to control its acoustic intensity or beam divergency to achieve its performance goal. For example, a multi-ring array can be formed to control individual delays to change the module focal depth. Each of thesemodules contains a function-specific tag for identification and use in the reconstruction procedure.

[0053] PA imaging can provide the molecular contrast of light absorbance with acoustic penetration depth and spatial resolution. In PA imaging, radio-frequency (RF) acoustic pressure is generated depending on the light absorbance and thermo-elastic property of a target when the light energy at specific wavelength is delivered to a target. The generated acoustic pressure propagates the biological tissue and is obtained by an ultrasound transducer. Using this physical mechanism, venous and tissue blood oxygenation can be quantified using transcranial PA imaging in vivo. The form factor of modular PAH system 201 quantifies spatial markers of either regional hypoperfusion or HIE based on low tissue cerebral blood volume (CBV) and tissue O2saturation in the vulnerable neonatal brain in NICU. In conventional PA sensors, the detection signal-to-noise ratio (SNR) is limited and determined by the piezoelectric energy conversion efficiency and structural configuration including multiple interrogation of the vibrating, pressure-wave-interfacing diaphragm in a multi- bouncing light pathway. Each interrogation on the diaphragm by the laser beam imposes one unit of phase modulation onto the laser beam which can be decoded to retrieve the incident acoustic waves. Hence, by repeatedly interrogating the diaphragm, the net amount of phase modulation caused by the impinging the acoustic pressure wave, is effectively amplified, leading to a proportional increase in detected signal strength. The thermal noise floor is not impacted by the repeated light interrogation of the diaphragm, while elevation in the shot noise can be minimized, especially when good quality mirror coating is deployed on the diaphragm and relevant reflective surfaces. Hence, the signal amplification results in an improved SNR in sensing the impinging acoustic pressure waves. Improved sensitivity then allows the highly sensitive monitoring of neonatal cortex using safer light sources, overcoming low pulse energy. A translation of the PAH system using a hazard-free pulsed light-emitting diode (LED) technology, may not require safety equipment. A deep machine learning algorithm can overcome the limited energy density. A pre-selected configuration of LEDs 205 (FIG.2B) in a modular unit of the PAH system 201 is accomplished.

[0054] Referring now to FIG.2C, steps to accomplish continuous brain monitoring and data evaluation are shown. The steps can include, but are not limited to including, installing 251 a modular installation as described herein, tracking and registration 253 of the module units, and initiating 255 photoacoustic helmet monitoring. The steps can furtherinclude enhancing 257 images such as the O2 saturation 267 in a projected cerebral cortex image using a deep neural network. If 259 intervention is needed, the determination of which is made based on the enhanced image, the steps include raising an alert, minimizing 261 brain injury by providing treatment, and monitoring 263 progress by performing continuous therapy evaluation. If 259 no intervention is needed, the steps include monitoring 265 the brain and returning to enhancing incoming images. The advantages of the machine learning- enabled, LED-based PA imaging include, but are not limited to, (1) PA signal sensitivity with sub-mJ pulse energy. The machine learning-based, selective feature amplification enables enhancement of imaging contrast overcoming limited sensitivity. The advantages also include (2) temporal resolution on patient condition is available based on an improved PRF of the LED light source, for example, but not limited to ~4kHz. A high-speed sensing rate can lower the cost of the data acquisition system with time-multiplexed scheme. The advantages can include facilitation of clinical translation. The sub-mJ-scale energy density facilitates the clinical translation.

[0055] The PAH system 201 can accommodate various neonatal head shapes and sizes. The PAH system 201 allows arbitrary configurations by registering multiple modular systems. Spatial image reconstruction in the modular configuration is enabled by performing one-time rigid tag position tracking of the modular units, and then registering in a sparse beamforming. When the target monitoring region-of-interest is cerebral cortex at 1-cm spatial resolution, the system can enable identification of stroke-induced asymmetry of cortical O2saturation relative to the same region in the contralateral cerebral cortex. Although most hemorrhagic strokes occur deep in the germinal matrix and cerebral ventricles in premature newborns, clot embolization is thought to occur predominantly in large cerebral arteries and pial arteries supplying cerebral cortex. Thus, the system 201 can be used to identify embolic strokes in preterm and term delivered newborns.

[0056] Referring now to FIGs.3A and 3B, a least-square error (LSE) optimization for differentiating blood O2saturation selects the optimal wavelengths using the PA spectrum from 700 nm to 900 nm at 10 nm intervals obtained from neonatal piglets in vivo (n = 4). The most adverse wavelength is rejected at every iteration when its absence in the spectral unmixing maximizes the cost function “reliability” that includes goodness of fit (R2), slope, and y-intercept of the linear regression model between PA estimations and ground truth measurements of the sagittal sinus O2saturation. FIG.3A is an example of what is desired tobe measured as an end result, and what the system could be used for in a health care context. FIG.3B shows the comparison between results using control (21 wavelengths) and optimized wavelength sets (2 wavelengths) of 780 nm and 890 nm. The optimized set yielded better slope (i.e., 1.07 vs.1.00 %) and y-intercept (-1.04 vs. -0.76 %) than those using the full wavelength set, and an acceptable goodness of fit % (i.e., 90.82 vs.87.93 %). Therefore, a reliable yet faster sensing of blood O2saturation can be provided by two wavelengths, for example.

[0057] Referring now to FIGs.4A-4C, LED wavelength optimization is used to determine the LED configuration from available options. FIG.4A is an example of spectral mixing, how the signal is processed when the volume is reconstructed. This investigation includes further consideration of LED linewidth much broader than a conventional Nd:YAG laser (~20 nm vs.3-5 nm). This degrades the spectral selectivity for the target absorption spectrum. The simulation data of multi-spectral PA sensing reflects the realistic linewidth. For example, compounding spectral PA data from 830 nm to 870 nm with a standard Gaussian window, mimics the 850-nm LED excitation with ± 20nm of linewidth. The data set is generated at each of the LED wavelengths. From the simulation data set, the specific contrast extraction from the received PA intensity containing melanin or other sources is optimized to precisely compute the tissue O2saturation. The proposed weighted material decomposition (W-MD) further incorporates a weighting term to mitigate the effect of error from laser energy fluctuation or spectral tissue inhomogeneity. The weighting term is set through iterative optimization using simulation and phantom data. A tubing phantom experiment is conducted by placing the mixture of different concentrations of PA agents with known absorbance in a whole blood sample commercially available. The calculated hemoglobin concentrations (HbO2and HbR) are compared to the ground truth (FIG.4B). In some configurations, the W-MD approach secures a 67.6 % more accurate estimation compared to the non-optimized approach (FIG.4C).

[0058] A method for designing and fabricating a sensor-head housing the diaphragm to efficiently couple with tissue and to improve their operational stability is described herein. Specifically, the diaphragm and the base are bonded and integrated into a single unit, providing extended operating stability through a single-unit multi-bounce laser microphone sensor head. The diaphragm / base element is integrated with a second element housing an external mirror for multi-bounce signal amplification. The single-channel system is coupledwith an optical fiber and connected to multiple modular units through an optical switch for spatial scanning of neonate brain.

[0059] Referring now to FIG.5, a method 500 operable to track reconstruction of a tissue volume includes, but is not limited to including, placing 502 modular acoustic elements individually at positions with respect to the tissue volume, tracking 504 the positions of the modular acoustic elements individually, creating 506 a volumetric distribution map of the tracked modular acoustic elements, registering 508 the volumetric distribution map, and reconstructing 510 an image of the tissue volume by providing the registered distribution map to a beamforming algorithm. Tracking 504 can include remote optical tracking of the positions, and / or surface tension-based position recognition. Tracking 504 can also include determining Cartesian coordinates of the modular acoustic elements in a track-dependent reference frame through identification of rigid tags using stereo camera optical tracking, and creating a one-time registration of the positions by translating a marker and anatomy- dependent reference frame to reconstruct a biologically relevant region, or determining Cartesian coordinates of the modular acoustic elements in a track-dependent reference frame through identification of rigid tags using a time-of-flight camera, and creating a one-time registration of the positions by translating a marker and anatomy-dependent reference frame to reconstruct a biologically relevant region. Tracking 504 can further include determining a time of flight of a reflected acoustic signal or photoacoustic signals from a voxel in the tissue volume to a piezoelectric sensor of the modular acoustic elements based on a speed of sound in the tissue volume, wherein the time of flight is calculated as a Euclidean distance divided by an average propagation speed of sound through soft tissue or other phase aberration correction processes, and based on coordinates of the modular acoustic elements, and / or recognizing relative positions of the modular acoustic elements by deploying multiple tracking and calibration techniques simultaneously.

[0060] The modular acoustic elements can include photoacoustic imaging modules including one or more light source and one or more piezoelectric elements, and / or ultrasound imaging modules including one or more piezoelectric elements. The modular acoustic elements can also include one or more additional piezoelectric elements optimized for high- intensity acoustic transmittance for ultrasound neuromodulation or tissue ablation, electrodes for electrical patient sensing one or more light sources with optimal wavelength for light therapy of a patient, and one or more additional light sensors or optical fibers connected to anexternal device for sensing near-infrared spectroscopy, fluorescence emission, or spectral reflectance of an anatomical surface. The modular acoustic elements can also include stimulation modules including one or more piezoelectric elements optimized for acoustic transmittance at an intensity for ultrasound neuromodulation or tissue ablation.

[0061] Referring now to FIGs.6A-6C, shown are transverse PA images at 1-h post- stroke (FIG.7A), TTC staining at 1-day post-stroke, confirming infarction with pale staining (FIG.7B), tissue O2saturation, demonstrating >30% tissue O2saturation differences between ischemic (<30%) and normal (>50%) cortex (p < 0.0001, n = 17) (FIG.7C). FIGs.6A-6C illustrate the end of the process described herein, showing tissue reconstruction in stroke detection. In general, the occlusion of the middle cerebral artery decreases tissue Hb by 40% and tissue O2saturation falls to <30%. A photothrombotic stroke (PTS) is induced in piglets and the Nd:YAG OPO laser with 21 wavelengths is used to generate a tissue O2saturation in transverse image at 1 hour after stroke through the closed scalp and skull (n = 17). A linear array US detector is moved in 1-mm increments for the volumetric scanning. Distinct regions with tissue O2saturation <30% (FIG.6A, dotted contours) are evident that correspond to regions of infarction revealed by TTC vital dye staining 1 day later (FIG.6B). The tissue O2saturation provides statistical significance between PTS-affected and control regions (FIG. 6C, p < 0.0001). In human neonates with a ~10-cm length brain, these infarcts of ~1 cm diameter detected in piglets are clinically relevant. The modular unit design includes optimizing the acoustic pathway for neonatal brain imaging using acoustic simulation tools. Biophysical parameters of scalp, skull, and brain (i.e., acoustic impedance and sound propagation speed) are reflected in the simulation. Various dimensions, distribution, and number of the modular units are tested, considering uniformity in acoustic beam profile in brain tissue regions. The optimization is a heuristic evaluation of area ratio between sensing fiber and LED parts to correspondingly determine the sensor and energy density. The design specifications are spatial resolution (<10 mm in transverse plane). The LED configuration in the modular unit is designed based on Monte-Carlo simulation. The form factor of the pulsed LED light source is optimized to uniformly illuminate the cerebral cortex in the neonatal brain. LED elements at two optimal wavelengths are evenly distributed for accurate spectroscopic sensing. The device enables >5 mm imaging depth to reach cerebral cortex in neonate through intact thicknesses of scalp and skull. The device is an ultrahigh sensitive (~500 mP) multi-bounce laser microphone for biomedical sensing. The modular unit design supports 10 mm FWHM over 5 mm imaging depth in K-wave simulation.

[0062] Referring now to FIGs.7A-7C, shown is a position-tracked modular framework for neonatal monitoring. Potential applications of the modular imaging framework are in internal organ monitoring of neonates. The multiple modular systems can cover the chest and abdominal regions for early detection of critical congenital heart disease (CHD) or necrotizing enterocolitis (NEC). Use of classification in the tracking step permits categorization of modules for heart imaging or intestinal imaging, considering light and acoustic divergence and coverage from each module to each voxel. FIGs.7A-7C illustrate separate transmit and receive modules that are transmitting and receiving data at the same time.

[0063] Referring now to FIG.8, diagnostic and treatment pipelines in accordance with embodiments of the present disclosure are shown. Both diagnostic pipeline 807 and treatment pipeline 809 begin by simultaneously transmitting and receiving data from US and / or PA modules 825 / 823, calibrating the US and / or PA modules with respect to a target organ, and tracking the positions of the US and / or PA modules 825 / 823. When the received data are used in the diagnostic pipeline 807, images are reconstructed from the incoming data 803 and the position tracking data 801 using synthetic aperture focusing, and those data are provided to the volumetric feature scan 815. When the received data are used in the treatment pipeline 809, focused US transmission 813 provides data to a volumetric feature scan 815. In either pipeline, the volumetric feature scan 815 enables clinical parameter monitoring 817, and treatment guidance 819 feeds progress monitoring to the clinical parameter monitoring 817.

[0064] Non-invasive US imaging modality, in which a customizable number of US modules configures a virtual aperture over a body surface to perform continuous US imaging of target organs, for example, but not limited to brain, heart, lung, kidney, and intestines. Each US module can include a single piezoelectric element, and the group of US modules can be recognized in space by using remote stereo camera tracking system. The spatial understanding enables forming a virtual aperture in the volume for the synthetic modular aperture focusing technique, where datasets from sequential US transmission events from each US module are recorded by the US modules and compounded with synthetic focusing delays. In the clinical workflow, the US module placement can be guided by interactiveguidance software that estimates imaging quality in real time given the volumetric recognition of the US modules and their beam pattern overlap.

[0065] Referring now to FIGs.9A and 9B, shown is the evaluation of synthetic surface aperture (SSA) on an arbitrary surface. FIGs.9A and 9B illustrate a quantitative metric that forms a guide or placing sensors. Acoustic divergency of the US module is the first parameter to be optimized as a direct determinant of the synthetic surface aperture (SSA) defined by the group of modules on the surface that effectively transmit and receive US signals towards and from each voxel. Widening the acoustic divergency of the US module extends the SSA, which enhances spatial resolution and modular coherent compounding, while losing acoustic power as the diverging wave travels deeper. Using the raw beam profiles, effective beam threshold map will be defined as -6 dB contour from the peak at each depth, determining the inclusion of the US module to the SSA of each voxel. The US modules are placed on the surfaces of the chest / abdomen using the random circle-packing algorithm, mimicking clinical deployment by clinicians (FIG.9A), centered at the expected centers of the heart and intestine regions until filled. The SSA will be determined as a refinement of this initial condition, and optimized based on the beam profiles and field of view of the modules.

[0066] The SSA of the voxel is established by determination of whether each module’s beam threshold maps cover the target voxel. The information is used to form an angular histogram of the US modules at each voxel (FIG.9B). The covariance error ellipse of the effective US modular positions in the angular histogram of the voxel represent the SSA quality. The ideal covariance error ellipse can be a circle with a diameter as wide as possible and a high modular density, which is aligned at the origin of the angular histogram of the voxel. The quality of the SSA can be evaluated by four evaluation metrics: (1) SSA density, presenting the number of effective US modules included in the covariance error ellipse, determining grating lobe artifacts to affect imaging contrast; (2) SSA width, characterized by the length of its long / short axises, indicating the spatial resolution at the voxel; (3) SSA roundness, calculated by the difference between long and short lengths of the covariance error ellipse, indicating the uniformity of spatial resolution in the transverse imaging plane; (4) SSA centeredness, evaluated by the distance between the covariance error ellipse’s center and origin of the angular histogram, indicating the symmetry of point spread function.

[0067] Volumetric maps of these quantitative ‘SSA quality metrics’ are individually established over the neonatal torso volume. The total number of US modules is important in terms of translational practicality as it directly determines the cost of the system. The number of ‘active’ US modules per each transmit / receive event can be controlled by an extended aperture technique, which is time-multiplexed allocation of channel subsets for the US transmission and reception and their synthesis afterwards. The volumetric SSA quality metrics are correlated with the imaging quality metrics. 10 MHz acoustic sinusoidal pulse cycles are generated from one of the US modules, and reflected US wavefronts returning to the US modules are recorded. The procedure is repeated for other US modules until each US module takes their turn to transmit the US pulse. Volumetric image is reconstructed by using the method, synthesizing modular outcomes from multiple transmit / receive events with synthetic focusing delays calculated based on their ground-truth spatial positions in the volume. The full-width-half-maximum (FWHM) is measured at each point target.3D interpolation is utilized to fill the area among the point targets.

[0068] For the optimization of the US module, field-of-view (FOV) is the primary cost function to decide its inclusion of target organ to provide sub-mm spatial resolution. For each simulation setup, full-width-half-maximum (FWHM) is measured from each hyperechoic target. Potential target organ areas (e.g., heart, kidney, intestine, etc.) are placed as spheroids. The FOV coverage for each organ is calculated by percentage in each of the neonatal sizes tested. The optimal US module specifications are selected to maximize the FOV coverage in each of the neonatal sizes tested for each organ. Robustness against modular tracking error is measured by quantifying the changes in the FOV coverage. The corresponding SSA quality metrics are recorded to develop an interactive guidance software for clinicians. The modular design is optimized for better monitoring performance in more premature neonates, given with higher mortality and morbidity with complications. There is room for further versatility to allow much sparser US module placements with fewer total number of US modules. In some configurations, the modules fit near each other without any gap, while still permitting use of a flexible number of US modules depending on desired use case.

[0069] In some configurations, a US module includes optimized acoustic divergence that balances the spatial resolution and signal sensitivity with the optimized SSA over the neonatal skin surface. In some configurations, the distance between modules is fixed byhaving a hexagonal frame to embed a piezoelectric element at the center. A position indicator is included in a confined surface of the US module. The circuit board can include >three LED markers recognizable by optical tracker systems. The compact design includes a customized anode / cathode for the piezoelectric element, and signal output connectors are at the side of the circuit board for easier line management and better weight distribution. The modular system may also be waterproofed by a coating, shell or other system for ease of use.The attachment of the US modules is achieved via medical adhesive commonly used when attaching EKG electrodes, EEG electrodes, etc. The US modules are connected to a US research system for individual modular transmit and simultaneous data acquisition controls.

[0070] Referring now to FIG.10, a module synthetic aperture position calibration workflow 1000 is shown. An acoustic beam profile of a single module, target volume, a modular position map, and preliminary image quality metrics 1001 are used to calculate 1003 quality metrics of module distribution. The quality metrics are provided for evaluation 1005 of the impact of the modular positions on image quality. If 1011, image quality standards are met, US / PA treatment is begun 1009, and feature evaluation is conducted 1007 over a regular duration or at the times of substantial changes such as, for example, but not limited to patient postures and clinical procedures. If 1011 image quality standards are not met, movement of a subset of the modular acoustic elements into better positions for image quality are suggested 1013, and control is returned to calculate 1003 quality metrics.

[0071] While the invention has been described with reference to the exemplary embodiments thereof, those skilled in the art will be able to make various modifications to the described embodiments without departing from the true spirit and scope. The terms and descriptions used herein are set forth by way of illustration only and are not meant as limitations. In particular, although the method has been described by examples, some steps of the method can be performed in a different order than illustrated or simultaneously. Those skilled in the art will recognize that these and other variations are possible within the spirit and scope as defined in the following claims and their equivalents. All patents, patent applications, other publications or documents, and the like cited herein are incorporated by reference in their entirety for all purposes to the same extent as if each individual item were specifically and individually indicated to be so incorporated by reference.

Claims

CLAIMS 1. A method operable to track reconstruction of a tissue volume comprising: enabling placement of modular acoustic elements individually at positions with respect to the tissue volume; tracking the positions of the modular acoustic elements individually; creating a volumetric distribution map of the tracked positions; registering the volumetric distribution map; and reconstructing an image of the tissue volume by providing the registered volumetric distribution map to a beamforming algorithm.

2. The method of claim 1, wherein the tracking comprises: remote optical tracking of the positions.

3. The method of claim 1, wherein the tracking comprises: surface tension-based position recognition.

4. The method of claim 1, further comprising: recognizing relative positions of the modular acoustic elements by intercommunications among the modular acoustic elements.

5. The method of claim 1, further comprising: calibrating of the tracked positions by iterative optimization with imaging quality metrics.

6. The method of claim 1, wherein the tracking comprises: recognizing relative positions of the modular acoustic elements by deploying multiple tracking and calibration techniques simultaneously.

7. The method of claim 1, wherein the modular acoustic elements comprise: multiple types of the modular acoustic elements that enable multiple imaging modalities.

8. The method of claim 1, wherein the modular acoustic elements comprise: photoacoustic imaging modules including one or more light source and one or more piezoelectric elements.

9. The method of claim 1, wherein the modular acoustic elements comprise: ultrasound imaging modules including one or more piezoelectric elements.

10. The method of claim 1, wherein the modular acoustic elements comprise: dual-modal ultrasound and photoacoustic imaging modules including one or more light source and one or more piezoelectric elements with balanced acoustic transmission and reception efficiency.

11. The method of claim 1, wherein the modular acoustic elements comprise: one or more additional piezoelectric elements optimized for high-intensity acoustic transmittance for ultrasound neuromodulation or tissue ablation; electrodes for electrical patient sensing; one or more light sources with optimal wavelength for light therapy of a patient; and one or more additional light sensors or optical fibers connected to an external device for sensing near-infrared spectroscopy, fluorescence emission, or spectral reflectance of an anatomical surface.

12. The method of claim 1, wherein the modular acoustic elements comprise: stimulation modules including one or more piezoelectric elements optimized for acoustic transmittance at an intensity for ultrasound neuromodulation or tissue ablation.

13. The method of claim 1, wherein the tracking comprises: determining Cartesian coordinates of the modular acoustic elements in a tracker- dependent reference frame through identification of rigid tags using stereo camera optical tracking; and creating a one-time registration of the positions by translating a marker and anatomy- dependent reference frame to reconstruct a biologically relevant region.

14. The method of claim 1, wherein the tracking comprises: determining Cartesian coordinates of the modular acoustic elements in a track- dependent reference frame through identification of rigid tags using a time-of-flight camera; and creating a one-time registration of the positions by translating a marker and anatomy- dependent reference frame to reconstruct a biologically relevant region.

15. The method of claim 1, further comprising: monitoring multiple organ systems.

16. The method of claim 1, further comprising: monitoring multiple organ systems simultaneously; and classifying the modular acoustic elements by position and estimated acoustic field.

17. The method of claim 1, wherein tracking the positions of the modular acoustic elements comprises: determining a time of flight of a reflected acoustic signal or photoacoustic signals from a voxel in the tissue volume to a piezoelectric sensor of the modular acoustic elements based on a speed of sound in the tissue volume, wherein the time of flight is calculated as a Euclidean distance divided by an average propagation speed of sound through soft tissue or other phase aberration correction processes, and based on coordinates of the modular acoustic elements.

18. The method of claim 17, further comprising: applying an error correction to the Euclidean distance to correct for error in tracking.

19. The method of claim 18, wherein the time of flight is based at least on (1) transmit wave propagation time delays for a pre-selected pathway from the modular acoustic elements transmitting ultrasound or light energy to the voxel, and (2) receive wave propagation time delays from the voxel to a set of the modular acoustic elements for data acquisition.

20. The method of claim 19, further comprising: calculating the time of flight during photoacoustic imaging based on the receive wave propagation time delays from the voxel to the modular acoustic elements.

21. The method of claim 1, further comprising: determining an intensity of each voxel in the reconstructed image by summing magnitudes of the modular acoustic elements corresponding to times of flight, wherein a signal formed by the beamforming algorithm is a sum of admultiplied by rdmultiplied by the a between n and τtof ,where adis an apodization coefficient, rdis a received signal for a dthphotoacoustic modular acoustic element, and time of flight is a receive time delay of the dthphotoacoustic modular acoustic element.

22. The method of claim 1, further comprising: repeating the tracking of module positions at various time intervals to update the volumetric distribution map.

23. A method for detection of congenital heart disease in neonates comprising: enabling placement of modular acoustic elements individually at positions with respect to a neonate heart; tracking the positions of the modular acoustic elements individually; grouping the modular acoustic elements by the tracked positions and estimated acoustic field; creating a volumetric distribution map of the grouped modular acoustic elements; registering the volumetric distribution map; reconstructing an image of the neonate heart by providing the registered volumetric distribution map to a beamforming algorithm; monitoring multiple organ systems simultaneously; classifying data from the modular acoustic elements based on the reconstructed image; and detecting the congenital heart disease based on the data from the classified data.

24. A method for detection of necrotizing enterocolitis in neonates comprising:enabling placement of modular acoustic elements individually at positions with respect to a neonate abdomen; tracking the positions of the modular acoustic elements individually; grouping the modular acoustic elements by the tracked positions and estimated acoustic field; creating a volumetric distribution map of the grouped modular acoustic elements; registering the volumetric distribution map; reconstructing an image of the neonate abdomen by providing the registered volumetric distribution map to a beamforming algorithm; monitoring multiple organ systems simultaneously; classifying data from the modular acoustic elements based on the reconstructed image; and detecting the necrotizing enterocolitis in neonates based on the data from the classified data.

25. A method for transcranial ultrasound stimulation comprising: enabling placement of modular acoustic elements individually at positions with respect to cranium, forming a wearable framework for transcranial focused ultrasound (tFUS); tracking the positions of the modular acoustic elements individually; grouping the modular acoustic elements by the tracked positions and estimated acoustic field; creating a volumetric distribution map of the grouped modular acoustic elements; registering the volumetric distribution map; reconstructing an image of a tissue volume by providing the registered volumetric distribution map to a beamforming algorithm; stimulating neural function in the tissue volume; monitoring the reconstructed image; and quantifying a response of the tissue volume responding to the stimulation based on the reconstructed image.

26. A method for discriminating between a malignant mass and a benign mass in a tissuevolume, the method comprising: enabling placement of modular acoustic elements individually at positions with respect to the tissue volume; tracking the positions of the modular acoustic elements individually; grouping the modular acoustic elements by the tracked positions and estimated acoustic field; creating a volumetric distribution map of the grouped modular acoustic elements; registering the volumetric distribution map; reconstructing an image of the tissue volume by providing the registered volumetric distribution map to a beamforming algorithm; monitoring the reconstructed image; and discriminating between the malignant mass and the benign mass in the tissue volume based on changes in sound propagation speed, density, attenuation, and morphology of the monitored reconstructed image.

27. A computer system operable to track reconstruction of a tissue volume comprising: a hardware processor; a non-volatile storage medium storing instructions that when executed by the hardware processor perform operations comprising: enabling placement of modular acoustic elements individually at positions with respect to the tissue volume; tracking the positions of the modular acoustic elements individually; creating a volumetric distribution map of the tracked positions; registering the volumetric distribution map; and reconstructing an image of the tissue volume by providing the registered volumetric distribution map to a beamforming algorithm.

28. The computer system of claim 27, wherein the tracking comprises: remote optical tracking of the positions.

29. The computer system of claim 27, wherein the tracking comprises: surface tension-based position recognition.

30. The computer system of claim 27, further recognizing relative positions of the modular acoustic elements by intercommunications among the modular acoustic elements.

31. The computer system of claim 27, further comprising: calibrating of the tracked positions by iterative optimization with imaging quality metrics.

32. The computer system of claim 27, wherein the tracking further comprises: recognizing relative positions of modular acoustic elements by deploying multiple tracking and calibration techniques simultaneously.

33. The computer system of claim 27, wherein the modular acoustic elements comprise: multiple types of the modular acoustic elements that enable multiple imaging modalities.

34. The computer system of claim 27, wherein the modular acoustic elements comprise: photoacoustic imaging modules including one or more light source and one or more piezoelectric elements.

35. The computer system of claim 27, wherein the modular acoustic elements comprise: ultrasound imaging modules including one or more piezoelectric elements.

36. The computer system of claim 27, wherein the modular acoustic elements comprise: dual-modal ultrasound and photoacoustic imaging modules including one or more light source and one or more piezoelectric elements with balanced acoustic transmission and reception efficiency.

37. The computer system of claim 27, wherein the modular acoustic elements comprise: one or more additional piezoelectric elements optimized for high-intensity acoustic transmittance for ultrasound neuromodulation or tissue ablation;electrodes for electrical patient sensing; one or more light sources with optimal wavelength for light therapy of a patient; and one or more additional light sensors or optical fibers connected to an external device for sensing near-infrared spectroscopy, fluorescence emission, or spectral reflectance of an anatomical surface.

38. The computer system of claim 27, wherein the modular acoustic elements comprise: stimulation modules including one or more piezoelectric elements optimized for acoustic transmittance at an intensity for ultrasound neuromodulation or tissue ablation.

39. The computer system of claim 27, wherein the tracking comprises: determining Cartesian coordinates of the modular acoustic elements in a track- dependent reference frame through identification of rigid tags using stereo camera optical tracking; and creating a one-time registration of the positions by translating a marker and anatomy- dependent reference frame to reconstruct a biologically relevant region.

40. The computer system of claim 27, wherein the tracking comprises: determining Cartesian coordinates of the modular acoustic elements in a track- dependent reference frame through identification of rigid tags using a time-of-flight camera; and creating a one-time registration of the positions by translating a marker and anatomy- dependent reference frame to reconstruct a biologically relevant region.

41. The computer system of claim 27, further comprising: monitoring multiple organ systems.

42. The computer system of claim 27, further comprising: monitoring multiple organ systems simultaneously; and classifying the modular acoustic elements by position and estimated acoustic field.

43. The computer system of claim 27, wherein tracking the positions of the modular acousticelements comprises: determining a time of flight of a reflected acoustic signal or photoacoustic signals from a voxel in the tissue volume to a piezoelectric sensor of the modular acoustic elements based on a speed of sound in the tissue volume, wherein the time of flight is calculated as a Euclidean distance divided by an average propagation speed of sound through soft tissue or other phase aberration correction processes, and based on coordinates of the modular acoustic elements.

44. The computer system of claim 43, further comprising: applying an error correction to the Euclidean distance to correct for error in tracking.

45. The computer system of claim 44, wherein the time of flight is based at least on (1) transmit wave propagation time delays for a pre-selected pathway from the modular acoustic elements transmitting ultrasound or light energy to the voxel, and (2) receive wave propagation time delays from the voxel to another set of the modular acoustic elements for data acquisition.

46. The computer system of claim 45, further comprising: calculating the time of flight during photoacoustic imaging based on the receive wave propagation time delays from the voxel to the modular acoustic elements.

47. The computer system of claim 27, further comprising: determining and intensity of each voxel in the reconstructed image by summing magnitudes of the modular acoustic elements corresponding to times of flight, wherein a signal formed by the beamforming algorithm is a sum of admultiplied by rdmultiplied by the a between n and τtof ,where adis an apodization coefficient, rdis a received signal for a dthphotoacoustic modular acoustic element, and time of flight is a receive time delay of the dthphotoacoustic modular acoustic element.

48. The computer system of claim 27, further comprising: repeating the tracking at various time intervals.

49. A computer program product operable to track reconstruction of a tissue volume, the computer program product comprising readable storage medium having program instructions embodied therewith, the program instructions executable by a computing device to cause the computing device to perform operations comprising: enabling placement of modular acoustic elements individually at positions with respect to the tissue volume; tracking the positions of the modular acoustic elements individually; creating a volumetric distribution map of the tracked positions; registering the volumetric distribution map; and reconstructing an image of the tissue volume by providing the registered volumetric distribution map to a beamforming algorithm.

Images