Volumetric m-mode ultrasound for automated detection and monitoring of deep vein thrombosis

Abstract

A method for autonomous monitoring for deep vein thrombosis (DVT) in a subject, the methods including: emitting ultrasound waveforms toward the subject by an ultrasound transducer; by an ultrasound receiver, and in a common ultrasound acquisition step, obtaining a first ultrasound dataset comprising a first volumetric ultrasound image and a first volumetric Doppler dataset of a target region of a target blood vessel of the subject; determining, based on the volumetric ultrasound image, at least one anatomic-parameter; determining, based on the volumetric Doppler dataset, at least one flow-parameter; and diagnosing a presence or a development of a DVT clot at a clot location in the target blood vessel based on the at least one anatomic-parameter or the at least one flow-parameter; wherein the target region is at a predetermined location that is selected without a priori knowledge of the clot location. Systems configured to implement the methods therein.

Description

VOLUMETRIC M-MODE ULTRASOUND FOR AUTOMATED DETECTION AND MONITORING OF DEEP VEIN THROMBOSISCROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims the benefit of U.S. Application No. 63 / 746,662, filed on January 17, 2025, the disclosure of which is hereby incorporated by reference in its entirety.FIELD OF THE INVENTION

[0002] The present application relates generally to methods and systems for automated detection and / or monitoring for deep vein thrombosis (DVT) m a subject.BACKGROUND

[0003] Deep vein thrombosis (DVT) is a major health burden that results in high morbidity and mortality in both the outpatient and hospital settings. In the United States, an estimated 900,000 people are diagnosed with venous thromboembolism annually with 100,000 attributable deaths and a treatment cost of $10 billion. Vascular ultrasound (VUS) has become a clinically essential imaging tool given its portability, absence of ionizing radiation, and diagnostic accuracy.

[0004] Each year, 150,000 Americans are diagnosed with Deep Vein Thrombosis, a condition where blood clots form in the deep veins. If untreated, DVT can lead to severe complications, including Pulmonary Embolism — a life-threatening blockage of blood flow to the lungs ----- and Post-Thrombotic Syndrome, which causes chronic leg pain and increases the risk of further clotting.

[0005] The current diagnostic process for DVT is lengthy and complex, requiring clinical assessments, imaging, and lab tests. This often delays diagnosis, heightening the risk of severe secondary conditions.

[0006] VUS performed by a registered vascular technologist (RVT) is considered standard of care in diagnosing DVT, typically in the arms or legs, and is the most common indication for VUS performed in the hospital. To diagnose DVT with VUS, the operator subjectively assesses the flow, respiratory variation, compressibility, and morphological characteristics of major veins.

[0007] VUS performed by an RVT is costly and resource intensive, requiring significant infrastructure of a non-invasive VUS laboratory and trained staff. An additional limitationof VUS is high intra- and inter-operator variability, particularly for point-of-care ultrasound (POCUS), which is ultrasound (US) performed by an expert operator who is not an RVT (e g.. a physician with US training). POCUS training is time and cost intensive – even limited physician uses such for traumatic hemorrhage (i.e., the Focused Assessment with Sonography for Trauma, or 'FAST' exam) or vascular access require significant training and repetition to achieve basic competence. Moreover, POCUS can worsen the mismatch between access to imaging and access to trained interpretation.

[0008] Nevertheless, POCUS has become an important tool in pre-hospital (e.g., resource-limited and outpatient) and hospital (e.g., emergency room or intensive care unit (ICU)) settings to perform vascular access, diagnose DVT. and assess for hemorrhage in order to dramatically expand access in clinical situations that would otherwise depend on RVT VUS. Early detection or prediction of DVT has the potential to reduce the need for invasive treatments (e.g., long-term anticoagulation or thrombolysis) or the risk of complications such as pulmonary embolism or death,

[0009] In limited-resource settings without VUS expertise, specific needs related to VUS include diagnosis of venous thrombosis, early detection of hemorrhage in the pre-hospital emergency setting, obtaining IV access, management of outpatient dialysis access, and home monitoring of conditions such as heart or renal failure

[0010] Existing scanning techniques include B-mode, M-mode, Doppler incorporated (i.e., spectral and color flow) modes, as well as newer 3D modes, and proprietary modes for specific disease states and organs However, there is a need to balance the temporal resolution needed for long-term and reproducible measurements with restricted 3D spatial coverage (to only the clinically relevant portion of the anatomy of interest) to make storage and computing tasks more efficient.

[0011] Accordingly, there is a long-felt need in the art for methods and systems for improved VUS access and improved VUS reproducibility.SUMMARY

[0012] To address these and related challenges, the present disclosure provides, in an aspect, methods for autonomous monitoring for deep vein thrombosis (DVT) in a subject, the method including: emitting ultrasound waveforms toward the subject by an ultrasound transducer; by an ultrasound receiver, and in a common ultrasound acquisition step, obtaining a first ultrasound dataset including a first volumetric ultrasound image and a first volumetric Doppler dataset of a target region of a target blood vessel of the subject;using a controller operatively coupled to the ultrasound transducer and to the ultrasound receiver, the controller including at least one processor and a computer-readable medium having computer-executable instructions stored thereon, executing the computerexecutable instructions by the at least one processor to: determine, based on the volumetric ultrasound image, at least one anatomic-parameter; determine, based on the volumetric Doppler dataset, at least one flow-parameter; and diagnose a presence or a development of a DVT clot at a clot location in the target blood vessel based on the at least one anatomic-parameter or the at least one flow-parameter; and automatically send a signal to an alarm indicator to provide an alarm interface when the presence or the development of the DVT clot in the target blood vessel is diagnosed, wherein the target region is at a predetermined location that is selected without a priori knowledge of the clot location.

[0013] In some embodiments, monitoring for DVT in the subject comprises detecting a developed DVT clot.

[0014] In some embodiments, monitoring for DVT in the subject comprises predicting a development of a DVT clot.

[0015] In some embodiments, the methods further include obtaining a second ultrasound dataset including a second volumetric ultrasound image and a second volumetric Doppler dataset of the target region, wherein the second ultrasound dataset is acquired after the first ultrasound dataset is acquired.

[0016] In some embodiments, monitoring for DVT comprises tracking, over a monitoring duration, a change between the first ultrasound dataset and the second ultrasound dataset.

[0017] In some embodiments, wherein the monitoring duration is between about 1 minute and about 3 days.

[0018] In some embodiments, the first volumetric ultrasound image is: a single-element transducer array-derived 3D ultrasound image; a 3D M-mode ultrasound image including a depth, a lateral position, and a time; a 4D M-mode ultrasound image including the depth, the lateral position, the time, and a tilt or a rotation; or a 5D M-mode ultrasound image including the depth, the lateral position, the time, the tilt, and the rotation.

[0019] In some embodiments, when the first volumetric ultrasound image is a 4D M-mode ultrasound image or a 5D M-mode ultrasound image, the first volumetric Doppler dataset is a Pulsed Wave Doppler, a Continuous Wave Doppler, or a Color Doppler.

[0020] In some embodiments, the at least one anatomic parameter is selected from a group consisting of a morphology of the target region, a respiratory variation of diameter of the target region, a cardiac variation of diameter of the target region, and a lumen echogenicity of the target region.

[0021] In some embodiments, the at least one flow-parameter is selected from a group consisting of a volume flow of blood through the target region, a respiratory variation of the flow through the target region, a cardiac variation of the flow through the target region, and a turbulence of flow through the target region.

[0022] In some embodiments, the predetermined location is: for an arm, a position along a subclavian or an axillary vein proximate a bicep muscle of the subject; and for a leg, a position near a calf muscle of the subject, a thigh muscle of the subject, or a pelvic muscle of a subject.

[0023] In some embodiments, diagnosing the presence or the development of the DVT clot in the subject is based on both the at least one anatomic-parameter and the at least one flow-parameter.

[0024] In some embodiments, a normal vein is indicated by one or more of: the morphology being normal, the lumen echogenicity being anechoic throughout, the respiratory variation being phasic, the cardiac variation being phasic, the volume flow being normal, and / or the flow being laminar: a pre-DVT vein is indicated by one or more of: the morphology being normal, the lumen echogenicity being a dynamic echogenicity, the respiratory variation being phasic or phasic with diminished variation, the cardiac variation being phasic or phasic with diminished variation, the volume flow being decreased, and / or the turbulence being transitional to turbulent; and an acute DVT vein is indicated by one or more of: the morphology being enlarged, the lumen echogenicity being hypoechoic, the respiratory’ variation being absent, the cardiac variation being absent, the volume flow being absent, and the turbulence being absent.

[0025] In some embodiments, the respiratory variation of the target region is determined based on changes in measured diameter of the target region.

[0026] In some embodiments, the respiratory variation of the target region is determined based on flow velocity' variation with the respiratory cycle.

[0027] In some embodiments, the cardiac variation of the target region is determined based on flow velocity variation with the cardiac cycle.

[0028] In some embodiments, the lumen echogenicity of the target region is determined based on changes in acoustic scattering in the blood of the region, gray-scale data of the volumetric ultrasound image, and combinations thereof.

[0029] In some embodiments, the clot location is at a distance D from the target region.

[0030] In some embodiments, the Doppler dataset is a spectral Doppler dataset, a continuous-wave Doppler dataset, a pulsed Doppler dataset, a Color Doppler dataset, and combinations thereof,

[0031] In an aspect, the present disclosure provides systems for automated monitoring of deep vein thrombosis (DVT) in a subject, the systems including: an ultrasound transducer configured to emit a probe ultrasound signal toward a target region of a target blood vessel of the subject: an ultrasound receiver configured to receive a response ultrasound signal from the target region of the subject; and a controller including a non-transitory computer- readable medium having computer-executable instructions stored thereon that, if executed by one or more processors of a computing device, cause the computing device to perform steps including: emitting ultrasound waveforms toward the subject by the ultrasound transducer; by the ultrasound receiver, and in a common ultrasound acquisition step, obtaining a first ultrasound dataset including a first volumetric ultrasound image and a first volumetric Doppler dataset of the target region; determining, based on the volumetric ultrasound image, at least one anatomic-parameter; determining, based on the volumetric Doppler dataset, at least one flow-parameter; diagnosing a presence or a development of a DVT clot at a clot location in the target blood vessel based on the at least one anatomic-parameter or the at least one flow-parameter; and automatically sending a signal to an alarm indicator to provide an alarm interface when the presence or the development of the DVT clot in the target blood vessel is diagnosed, wherein a placement of the ultrasound transducer and the ultrasound receiver is a predetermined placement selected without a priori knowledge of the clot location.

[0032] In some embodiments, the system further includes obtaining a second ultrasound dataset including a second volumetric ultrasound image and a second volumetric Doppler dataset of the target region, wherein the second ultrasound dataset is acquired after the first ultrasound dataset is acquired.

[0033] In some embodiments, the volumetric ultrasound image is: a single-element transducer array-derived 3D ultrasound image; a 3D M-mode ultrasound image including a depth, a lateral position, and a time; a 4D M-mode ultrasound image including the depth,the lateral position, the time, and a tilt or a rotation; or a 5D M-mode ultrasound image including the depth, the lateral position, the time, the tilt, and the rotation.

[0034] In some embodiments, diagnosing the presence or the development of the DVT clot in the subject is based on both the at least one anatomic-parameter and the at least one flow-parameter.

[0035] This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This summary is not intended to identify key features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The foregoing aspects and many of the attendant advantages of this disclosure will become more readily appreciated as the same become better understood by reference to the following detailed description, when taken in conjunction with the accompanying drawings.

[0037] FIGURE 1 is a schematic diagram of an ultrasound system, according to embodiments of the present disclosure.

[0038] FIGURES 2A, 2B and 2C show imaging ultrasound planes in several rotation and tilt angles, and in a combination of rotation and tilt angles, according to embodiments of the present disclosure.

[0039] FIGURE 3A depicts an IVC visualization with the VMM method, according to embodiments of the present disclosure.

[0040] FIGURE 3B depicts the tilting of the scanhead indicated by the + and - signs wnth 0 being a neutral position over the IVC. according to embodiments of the present disclosure.

[0041] FIGURES 4A and 4B depict 4D Volumetric M-mode display of the IVC for a volunteer normal human subject (bottom) with a plot of the corresponding ultrasound scanhead tilt angle (top) as recorded by a spatial tracking, according to embodiments of the present disclosure.

[0042] FIGURE 5 depicts a conceptual map of automated DVT monitoring, according to embodiments of the present disclosure.

[0043] FIGURE 6 depicts parameters for DVT monitoring, according to embodiments of the present disclosure.

[0044] FIGURE 7 A depicts swept spectral Doppler tilt scan of the carotid artery and jugular vein of a volunteer subject, formatted as a 3D volume and showing a Spectral Doppler waveform acquired from the carotid artery'. according to aspects of the present disclosure.

[0045] FIGURE 7B depicts swept spectral Doppler tilt scan of the carotid artery’ and jugular vein of a volunteer subject, formatted as a 3D volume and showing a Spectral Doppler waveform acquired from the jugular vein, according to embodiments of the present disclosure.

[0046] FIGURE 8 depicts simultaneous Doppler spectral waveforms from the carotid artery' (positive flow) and jugular vein (negative flow) acquired while manually sweeping the image plane across the vessels in a longitudinal view, according to embodiments of the present disclosure.

[0047] FIGURES 9A and 9B depict Doppler spectral waveforms from the carotid artery and jugular vein acquired while manually sweeping the image plane across the vessels in a longitudinal view in CW Doppler mode, according to embodiments of the present disclosure.

[0048] FIGURES 10 A and 10B depict Doppler range gate depth change, according to embodiments of the present disclosure.

[0049] FIGURES 11A-11E depict the change in Spectral Doppler signal in the jugular vein during a shock / resuscitation experiment in a pig model, according to embodiments of the present disclosure.

[0050] FIGURE 12 depicts a schematic of DVT monitoring in a human subject, according to embodiments of the present disclosure;

[0051] FIGURE 13 depicts a flow chart diagram of a method, according to embodiments of the present disclosure.

[0052] FIGURES 14A and 14B depict single element transducer arrays for obtaining ultrasound datasets, according to embodiments of the present disclosure.DETAILED DESCRIPTION

[0053] The detailed description set forth below in connection with the appended drawings, where like numerals reference like elements, is intended as a description of various embodiments of the disclosed subject matter and is not intended to represent the only embodiments. Embodiments described in this disclosure are provided merely asexamples or illustrations and should not necessarily be construed as preferred or advantageous over other embodiments. The illustrative examples provided herein are not intended to be exhaustive or to limit the claimed subject matter to the precise forms disclosed.

[0054] As used herein, "common ultrasound acquisition step" refers to an acquisition procedure where a first volumetric ultrasound image and a first volumetric Doppler dataset are acquired at substantially the same time or during substantially the same data acquisition run. For instance, in some embodiments, the ultrasound transducer may be configured to acquire the ultrasound image and the Doppler dataset with the same scanning protocol, such that the same ultrasound emission from the ultrasound transducer acquires both the ultrasound image and the Doppler dataset. In some embodiments, the ultrasound transducer may be configured to perform a two-phase scanning protocol, such that the ultrasound image is acquired followed by the Doppler dataset, or such that the Doppler dataset is acquired followed by the ultrasound image, where a gap between the two phases is sufficiently short such that the clinical parameters of the patient substantially do not change (e.g.. less than 10 seconds, less than 5 seconds, less than 4 seconds, less than 3 seconds, less than 2 seconds, less than 1 second, or less than 0,5 seconds).

[0055] Methods for Autonomous Monitoring for Deep Vein Thrombosis (DVT)

[0056] In an aspect, the present disclosure provides methods for autonomous monitoring for deep vein thrombosis (DVT) in a subject, the method including: emitting ultrasound waveforms toward the subject by an ultrasound transducer; by an ultrasound receiver, and in a common ultrasound acquisition step, obtaining a first ultrasound dataset including a first volumetric ultrasound image (e.g., volumetric M-mode (VMM) image; single-element transducer array-derived volumetric image) and a first volumetric Doppler dataset of a target region of a target blood vessel of the subject; using a controller operatively coupled to the ultrasound transducer and to the ultrasound receiver, the controller including at least one processor and a computer-readable medium having computer-executable instructions stored thereon, executing the computer-executable instructions by the at least one processor to: determine, based on the volumetric ultrasound image, at least one anatomic- parameter; determine, based on the volumetric Doppler dataset, at least one flow¬ parameter; and diagnose a presence or a development of a DVT clot at a clot location in the target blood vessel based on the at least one anatomic-parameter or the at least one flow-parameter: and automatically send a signal to an alarm indicator to provide an alarminterface when the presence or the development of the DVT clot in the target blood vessel is diagnosed, wherein the target region is at a predetermined location that is selected without a priori knowledge of the clot location.

[0057] VMM Ultrasound Dataset Acquisition

[0058] For illustrative purposes and for clarity, ultrasound dataset acquisition will first be described with reference to the VMM techniques, though it should be understood that other methods of acquiring volumetric ultrasound are within the scope of the present disclosure, including single-element transducer array-derived volumetric ultrasound described further herein.

[0059] Accordingly, in some embodiments, the first ultrasound dataset includes VMM ultrasound datasets combined with Doppler datasets acquired in the common ultrasound acquisition step. The scanning method of VMM ultrasound removes the need for an experienced operator by intermittently sampling a target blood vessel. The result of this dual measurement approach is an intermittent flow signal sorted and associated with volumetric anatomical measurements. Furthermore, locating the ultrasound transducer (probe) directly over the suspected location of the clot is not necessary with the inventive technology, i.e., locating the ultrasound transducer in a general operational proximity of the clot will suffice. This significantly reduces the skill training requirements for a technician and the diagnostic inconvenience for a patient.

[0060] In this regard, in some embodiments, to overcome the high intra- and inter¬ operator variability of vascular ultrasound (VUS), and to overcome the need for an experienced operator to perform both registered vascular technologist (RVT) VUS and point of care ultrasound (POCUS). the present disclosure relates to volumetric ultrasound images acquired via VMM ultrasound techniques

[0061] VMM has high spatial and temporal resolution and favorable data storage requirements. VMM combined with Doppler advantageously provides for volumetric data acquisition with limited probe movement to allow for multi-dimensional imaging of major vasculature over time with minimal variability or dependence on an experienced operator.

[0062] In VMM. acquisition of 2D ultrasound images during continuous spatial scanning of the anatomic region with the vein of interest produces a multi-dimensional image data set. The data are formatted as a 3D volume (depth I lateral position / time) with an additional spatial scanning variable (tilt and / or rotation).

[0063] In this regard, and to aid in better illustrating VMM acquisition, FIGURE 1 is a schematic diagram of an ultrasound system 100 in accordance with some embodiments of the present disclosure. However, it should be noted that ultrasound system 100 is only one embodiment of systems within the scope of the present disclosure provided for illustrative purposes. Other configurations of an ultrasound system, including different arrangements of components, and / or with more or fewer components than illustrated, are within the scope of the present disclosure

[0064] In some embodiments, the system 100 may be used for gathering 2D scans together with position and orientation of the ultrasound probe, thus enabling subsequent interpretation of the ultrasound images. System 100 may include the ultrasound probe 50 and an ultrasound scanner 55.Operation of the system 100 may be controlled by a computer (also referred to as a personal computer, a controller, or a smart device) 75 that may, in some embodiments, include a display 59. The ultrasound probe may include a housing that contains an ultrasound transducer and an ultrasound receiver. The integration of an ultrasound transducer and ultrasound receiver into an ultrasound probe 50 is well within the scope of one having ordinary skill in the art.

[0065] Various configurations of ultrasound probe 50 may be used depending on clinical application and signal acquisition considerations. For example, some embodiments of the present disclosure may rely on a customized ultrasound probe 50, which has a flatter profile than the conventional handheld probes. For example, a disc-shaped casing of the ultrasound probe 50 further simplifies coupling of the probe to the patient, because a force applied orthogonally to the patient is more easily applied at the flat backside of the probe. The custom probe also keeps the working area around the patient less obstructed if the patient continues to receive care while being monitored. Once the probe 50 is fitted, generally no further operator involvement is needed for the IVC measurements. The resulting ultrasound images may be observable on a display 59. While this example of probe 50 is provided for clarity, it should be understood that other shapes may be used. Additionally, in some embodiments, a conventional ultrasound display is not included, and instead the internal processing according to the methods described herein may result in an output of a DVT risk score or DVT state indicator (an "alarm") without any display of the actual ultrasound data.

[0066] FIGURES 2A–2C show ultrasound planes transmitted in several rotation and tilting angles in accordance with an embodiment of the present technology. In particular,FIG. 2A illustrates ultrasound 54 transmitted in different rotational directions (rotational planes) 50R. FIG. 2B illustrates ultrasound 54 transmitted in different tilt planes 50T. In some embodiments, direction of the transmitted and / or received ultrasound may be controlled by phased array elements or mechanical motors of the ultrasound probe 50. FIG.2C shows that tilt (also referred to as a pitch) and rotation can be combined to expand the field of view.

[0067] An example of a data set according to embodiments of the present disclosure for the Inferior Vena Cava (IVC) is shown in FIGURES 3A and 3B. While this data depicts datasets for the IVC, it should be understood that analogous datasets may be acquired for any venous target in the subject.

[0068] In particular, FIGURE 3A depicts an IVC visualization with the VMM method, according to embodiments of the present disclosure. The IVC appears intermittently in the long-axis vertical plane (depth-time plane) as the tilt scan passes across the vessel The lateral-depth plane (x-y axis) shows a 2D B-mode image at a selected time. The horizontal lateral-time plane (x-t axis) visualizes the data at a selected depth.

[0069] FIGURE 3B depicts the tilting of the scanhead indicated by the + and - signs with 0 being a neutral position over the IVC, according to embodiments of the present disclosure. The bottom part of this display shows the vertical plane (i.e. depth-time plane). Throughout the tilting of the scanhead from + positioning to - positioning the hepatic and portal veins can be seen, respectively.

[0070] A display that correlates the tilt angle to explicitly indicate the spatial scanning component is shown in FIGURES 4A and 4B. FIGURES 4A and 4B depict 4D Volumetric M-mode display of the IVC for a volunteer normal human subject (FIGURE 4B) with a plot of the corresponding ultrasound scanhead tilt angle (FIGURE 4A) as recorded by a spatial tracking device (Flock of Birds, Ascension Technology Inc., Burlington VT), according to embodiments of the present disclosure. The tilt angle has been mapped over the depth-time plane, showing the tilt angle corresponding to the anatomy captured over time. In this regard, FIGURES 4A and 4B are provided to demonstrate how variations in tilt angle may provide additional information not acquired when tilt angle is not varied.

[0071] For volume status assessment, the added dimensions in the M-mode data enable the use of an ultrasound imaging method that does not rely on the expertise of a trained ultrasound examiner to practice the inventive technology. Instead, simple automated guidance signals based on ultrasound data direct an operator (also referred to herein as a"user") to place the device on the skin over a vein of interest (also referred to as a "target blood vessel", such as on a "patient" or "subject"). The automated repetitive volume scan then provides spatial coverage to capture the vein size data within the integrated field of view. The spatial scanning protocol eliminates the need to precisely place a single 2D image plane with an optimized view of the vessel of interest. By running for an extended period of time (on the order of minutes), the vein is interrogated repeatedly in the scan volume at different points in the respiratory cycle even though the placement of the ultrasound probe may be away from the target blood vessel (i.e., not directly above the target blood vessel).

[0072] After acquiring data for a given time period of interest, the 2D ultrasound image data are formatted in a 3D volume to provide a time series that captures the changes in vein size with respiration. The volumetric data acquisition ensures the vein is interrogated multiple times in the scan sequence and at varying time points in the respiratory cycle. The M-mode format highlights the changes in size of the vein over time, while also recording the neighboring anatomic structures for reference. This multi-dimensional data set may then be analyzed by automated feature tracking to record the vein dimensions and their variation with respiration. This is an important parameter that is then compared over extended time periods (on the order of hours or even days) to, for example, monitor the need to administer fluids.

[0073] Detecting DVT with VMM and Doppler

[0074] In some embodiments, the methods and systems of the present disclosure generally expand the spatial coverage of standard M-mode (motion mode) imaging to include the lateral direction of an ultrasound image plane, plus incorporate cy clical spatial scanning of the imaging plane. This data acquisition method may be used to provide early detection of Deep Vein Thrombosis (DVT), as described herein.

[0075] Accordingly, the methods described herein may be used to monitor venous physiology associated with the formation of DVT. The scanning method allows for placement of a monitoring device that does not require precise placement with a specific view of the vein of interest, that is, the monitoring device (ultrasound probe) may be placed away from a target region of the vein of interest (i.e., the device does not require precise initial placement at the location where a DVT may form), while still being able to obtain clinically relevant results over a period of time, as explained herein. In this regard. FIGURE 5 depicts a schematic overview of methods according to some embodiments ofthe present disclosure. Here, one or more ultrasound sensors are placed on the skin to monitor veins of interest. It should be understood that, while FIGURE 5 depicts two ultrasound sensors, in some embodiments, the one or more ultrasound sensors may include one ultrasound sensor, two ultrasound sensors, three ultrasound sensors, four ultrasound sensors, five or more ultrasound sensors, ten or more ultrasound sensors, twenty or more ultrasound sensors, or fifty or more ultrasound sensors. Processing of multiple parameters from the ultrasound data provide a measure of the development or presence of a DVT. As illustrated in FIGURE 5, an ultrasound transducer 50 may be placed against a patient or subject, such as leg L, to acquire an ultrasound dataset. Through the methods described herein, the ultrasound dataset may then be processed to identify one or more pathophysiological signatures. As described further below, these pathophysiological signatures inform an operator or user of the ultrasound systems of a DVT diagnosis.

[0076] Specifically, the present disclosure provides monitoring for pre-DVT and DVT physiologic signatures using, in some embodiments, VMM, Both anatomic (gray-scale B- mode ultrasound) and Doppler (blood flow) information are acquired and may be used to provide early indication of DVT development in patients at risk. The parameters of interest are listed in TABLE 1 and shown schematically in FIGURE 6. Changes in the set of measurements are tracked over time and together they provide a means of classifying the status of the vein.

[0077] Table 1: Parameters for Monitoring DVTNormal vein Pre-DVT Acute DVT Morphology Normal Normal Enlarged Hypoechoic but 2) Lumen Anechoic Dynamic echogenicityincreased and stable echogenicity throughout ('smoke')relative to pre-DVT Cardiac pulse3) variation (flow Phasic or phasic butPhasic Absent and / or diminished variationdiameter)Respiratoryvariation (flow Phasic or phasic butPhasic Absent and / or diminished variationdiameter)5) Volume flow Normal Decreased No flow Transitional (laminar6) SpectralLaminar plus turbulent) to No flow broadening7turbulent

[0078] DVT is understood by the causal framework of Virchow's triad: hypercoagulability, endothelial injury, and static blood flow. This causal triad is reflected in subtle changes in the VUS parameters that begin to appear before a thrombus begins to form. That there are predictable changes in VUS parameters is clinically known (see TABLE 2) but these parameters have not been systematically studied during in vivo DVT formation. One advantage of VMM is its ability to detect these subtle changes in real-time. An analysis of these early changes yields characteristic 'pre-DVT signatures' that could provide an opportunity for preventative intervention in high-risk patients.

[0079] Table 2: VUS Parameters for DVT Prediction and Diagnosis1. Volume Flow (mL / min) Doppler US is used to measure blood flow velocity within the vein. A reduction in flow velocity or volume flow, particularly when combined with other changes in venous morphology, is an indicator of thrombus development. Scanning in Doppler mode captures changes in velocity across the vein’s crosssection, allowing for early detection of flow disruptions caused by a developing thrombus. Continuous flow velocity monitoring is advantageously used for detecting early signs of stasis and venous obstruction, especially in high-risk patients.2. Phasicity and Pulsatility Phasicity (changes with the respiratory' cycle) and pulsatility’ (% change in peak velocity) (changes with the cardiac cycle) are normal in healthy venous flow. The absence of these variations, particularly in the presence of other indicators such as reduced flow velocity, is a strong diagnostic marker for DVT. Scanning in Doppler mode tracks these variations over time, providing continuous monitoring of both respiratory' and cardiac influences on venous flow. Absent phasicity or pulsatility in venous Doppler signals correlates with thrombosis, making these parameters important for early diagnosis.3. Spectral Broadening As venous flow becomes turbulent due to thrombus formation, (cm / s) the Doppler signal shows increased spectral broadening. This is a result of chaotic flow patterns, which are a hallmark of thrombus formation. The loss of a clear spectral window in the Doppler w aveform is another indicator of turbulent flow and thrombus development. The ability to detect turbulence and changes in flow patterns is important for identifying areas of localized blood stasis, which contribute to thrombus formation.4. Morphology: Vein size Changes in vein size (diameter, cross-sectional area) are (cm / cm2) important markers of thrombus formation. Occlusive thrombus will cause venous dilation, whereas non-occlusive thrombus may cause venous contraction VMM continuously measures venous diameter and cross-sectional area over time, providingreal-time insights into the mechanical effects ofthrombogenesis These structural changes, when combined with changes in flow velocity, are important indicators of venous obstruction due to DVT.5. Diameter Change with In addition to the cyclic flow variations described above (entry Respiration and Cardiac 2), diameter changes with the respiratory and cardiac cycle can Cycle (cm) also be detected. The decrease or absence of respiratory' and cardiac variations in vein diameter can indicate thrombus development.6. Echogenicity (grayscale As a thrombus forms, the echogenicity of the vein's lumen brightness 0 to 255) increases, moving from hypoechoic (dark) to hyperechoic (bright) as the thrombus matures. Acute thrombi are typically hypoechoic due to their higher water content, but more chronic thrombi are echogenic due to increased fibrin and collagen content. Studies have confirmed the correlation between thrombus echogenicity and its age and composition, makingthis a reliable parameter for thrombus development.

[0080] The parameters to be quantified in the major veins (for example common femoral vein) using the VMM plus Doppler approach of the present disclosure are described below:

[0081] 1, Volume flow rate for a segment of vein. Volume flow is calculated from the mean blood velocity and the cross-sectional area (derived from the diameter) to quantify the volume of blood passing through the vessel. A decrease in volume flow rate with no change in vessel diameter could indicate a developing obstruction at some location (upstream or downstream) in the vein,

[0082] 2. Change in Doppler flow velocities with the respiratory cycle (phasicity or respirophasic). Change in flow with the cardiac cycle (pulsatility ) may also be measured. Absent phasicity or pulsatility in venous Doppler signals correlates with thrombosis, making these parameters important for early diagnosis.

[0083] 3. Measure of flow characteristics in the vein. Flow patterns may change from normal laminar flow (flow parallel to the vessel walls and axisymmetric) to transitional flow (flow not parallel to the vein walls) and finally to turbulent flow (chaotic flow) as a DVT develops. Flow patterns across the diameter of the vein (different Doppler measurement depths) may also vary, with changes from symmetric parabolic flow to asymmetric flow (highest velocity shifts toward the near or far wall). These changes may be detected by analysis of the Doppler spectral waveforms, including measurements of spectral broadening, spectral window size, and statistical measures of the spectrumbrightness distribution (mean, median, mode, maximum, minimum). Flow patterns at the DVT site are evaluated both upstream and downstream from the DVT.

[0084] 4. Maximum diameter and / or cross-sectional area for a segment of vein. Occlusive thrombus will cause venous dilation, whereas non-occlusive thrombus may cause venous contraction.

[0085] 5. There may also be detectable change in cross-sectional area (variation in maximum to minimum diameter) over time with the respiratory cycle and with the cardiac cycle. The decrease or absence of respiratory and cardiac variations in vein diameter can indicate thrombus development.

[0086] 6. Quantifiable measure of brightness in the vein over a segment of vein and at different depths (center vs. periphery) of the vein. The blood in the normal vein is anechoic (black or close to black). As a DVT forms there can be a flowing 'smoke' appearance (associated with Rouleaux formation). This stage is hypoechoic (some echoes observed, but brightness lower than that of the tissue) and also dynamic, with detectable motion patterns. At the acute DVT stage the vein lumen is still hypoechoic, but there is no flow and the echo level is higher than the pre-DVT level. Over a longer time frame the vein lumen will be isoechoic (similar to normal tissue) or hyperechoic (bright) with no flow (chronic venous occlusion), and potentially hyperechoic with some central flow (re¬ canalized chronic venous occlusion).

[0087] Both image and Doppler data may be used to obtain information about the parameters discussed herein, in the manner described below.

[0088] Image Data

[0089] In some embodiments, VMM tracks change in vein size (diameter, cross-sectional area) over an extended time of interest (minutes / hours / days) as a DVT develops. VMM's ability to capture and reformat data into 3D volumes permits precise tracking of venous structure, providing insights into vein behavior under different physiological conditions.

[0090] Vein size and diameter change with respiration and cardiac cycle are also measured from the 3D-formatted M-mode data. These are dynamic measurements measured over periods of seconds. The magnitude of these cyclic size changes can then be compared over extended time periods (minutes / hours / days) as a DVT develops.

[0091] Grayscale B-mode images also provide blood echogenicity data, allowing changes in venous lumen characteristics to be assessed. As thrombus formation progresses.observable variations in acoustic scattering offer early visual indications of thrombus presence. By monitoring these changes alongside size and diameter metrics, VMM provides a robust anatomical profile of the vessel during DVT onset and progression.

[0092] Doppler Data

[0093] Venous flow parameters are measured using Spectral Doppler data collected during the sweeps performed for the Volumetric M-Mode acquisition (FIGURES 7 A and 7B), In FIGURE 7 A, each 2D plane in the time direction (video capture series) shows a spectral waveform captured at a different location as the tilt angle changes. The selected plane shows the arterial waveform in the carotid artery. (Note: the 2D image above the waveform is frozen and does not show' the anatomy for the Doppler beam location). In FIGURE 7B, the selected plane shows the transition from the carotid arterial waveform to the venous waveform in the jugular vein.

[0094] For several cycles of the Volumetric M-Mode tilt scans, the instrument may be run in Spectral Doppler mode. As the Doppler beam sweeps across the major blood vessels, Doppler spectral waveforms are captured to record blood velocity as a function of time. Waveforms for venous and arterial segments are distinguished by their direction and flow patterns. These may be separated even when the Doppler sample volume includes both an artery and a vein.

[0095] In several cycles of tilt sweeps, the VMM system operates in spectral Doppler mode to capture detailed blood-flow data. As the Doppler beam sweeps across major blood vessels, spectral waveforms are recorded, showing blood velocity as a function of time. These waveforms differentiate between venous and arterial segments based on flow direction and patterns, even when both vessel types are present in the sample volume.

[0096] Spectral Doppler technique measures blood flow at high time resolution at a relatively physical small location. Rather than imaging a 2D slice, Spectral Doppler detects motion along a single acoustic beam. The detected Doppler shifts are displayed as a frequency spectrum, which is directly related to target (red blood cell) velocity This analysis is updated at a rate that is sufficient to display velocity as a function of time over the cardiac cycle (referred to as a ’spectral waveform').

[0097] Continuous-Wave Doppler measures motion at all depths simultaneously along the transmit beam, while Pulsed Doppler measures motion at a specific depth of interest along this beam. The Volumetric M-Mode scan technique can be applied in either of these modes. For automated monitoring, Continuous Wave (CW) may be preferred so thatvessels at all depths are measured. In either case, Doppler shift data are recorded continuously as the beam is swept from side to side over the region of interest.

[0098] An example of a Pulsed Doppler waveform acquired during a swept scan is shown in FIGURE 8. FIGURE 8 depicts simultaneous Doppler spectral waveforms from the carotid artery (positive flow) and jugular vein (negative flow) acquired while manually sweeping the image plane across the vessels in a longitudinal view, according to embodiments of the present disclosure. The time periods without spectral waveforms represent locations during the sweep when the vessels are not in the field of view. In this case Pulsed Wave Doppler mode is run with a large sample volume (depth range) that includes both the artery (deeper vessel) and the vein (shallower vessel). Tn the illustrated example, the depth of interest (referred to as the 'sample volume') was expanded to the maximum allowed in order to sample as large a depth as possible. This increases the region sampled for blood flow by the transducer sweep. During this scan the carotid artery and jugular vein were sampled at the same spatial location during the sweep,

[0099] An example of a CW waveform acquired during a swept scan is shown m FIGURES 9A and 9B. During this scan the carotid artery and jugular vein were sampled at different spatial locations during the sweep. FIGURES 9A and 9B depict Doppler spectral waveforms from the carotid artery and jugular vein acquired while manually sweeping the image plane across the vessels in a longitudinal view in CW Doppler mode, according to embodiments of the present disclosure. In FIGURE 9A, the CW Doppler beam intersects the carotid artery' at one spatial location as the beam is swept back and forth across the neck. In FIGURE 9B, the CW Doppler beam intersects the jugular vein at a different spatial location from that in FIGURE 9A as the beam is swept back and forth across the neck. The sweep speed is slow enough to capture flow' changes associated with the cardiac and respiratory cycles, (Note: in this case the 2D image at lop is frozen and does not show the anatomy for the Doppler beam location).

[0100] Additional adjustments to the Doppler scanning parameters may be made automatically and / or autonomously. In Pulsed Doppler the range gate size may be varied between measuring a small point of interest or a point of interest expanded to cover a w ide range of depths For a small range gate the depth location could be varied so that the examination point moves across the vessel, providing a comparison of flow between the center of the vessel and the near and far walls (FIGURES 10 A and 10B). FIGURES 10A and 10B depict Doppler range gate depth change, according to embodiments of the presentdisclosure. A small sample gate size (1 mm) is set in the depth direction. The depth of the sample gate is then varied over time to record Doppler spectral waveforms at precisely-defined locations. The sample gate is close to the near wall of the jugular vein (1.1 cm depth for FIGURE 10A and 1.4 cm depth for FIGURE 10B). In both CW and Pulsed Doppler modes the Doppler beam steering and the lateral location could be varied to provide additional spatial sampling variation.

[0101] Spectral Doppler may be acquired by an imaging transducer that typically also records an image of the field of view through which the Doppler beam is transmitted. This image may provide a record of the anatomy as the beam is swept through blood vessels. This swept-sampling method may also be simplified to run with a non-imaging transducer that only acquires the Doppler shifts along the transmit beam, in the manner described below under " Single-Element Transducer Array-Derived Volumetric Ultrasound." The blood vessels that are measured may be inferred from the expected anatomy and the specific Doppler waveforms,

[0102] Data Analysis for VMM-based Data

[0103] The data acquired from VMM are processed to extract hemodynamic information. Doppler spectral waveforms are analyzed to provide measurements of peak velocity, mean velocity, and velocity range (spectral broadening). Spectral Doppler data also reveal patterns such as phasicity and pulsatility, which are key diagnostic markers for DVT. Angle correction is applied to the Doppler data to account for the orientation of the US beam relative to the vessel, ensuring that velocity measurements are accurate.

[0104] Changes in Doppler velocity over time are analyzed to identify turbulence, with increased spectral broadening indicating disturbed flow and thrombus development. The brightness distribution of the spectral waveform is also analyzed, offering further quantification of venous turbulence. Volume flow is calculated from the vessel cross-sectional area and the Doppler velocity measurements, allowing for continuous assessment of blood flow dynamics. This helps in detecting reductions in flow that could signal partial venous obstruction by a thrombus. In some embodiments, turbulence and volume flow are examples of a volumetric Doppler dataset.

[0105] Grayscale imaging data are used to assess vein size and lumen echogenicity, providing anatomical context to the functional flow data. Spectral Doppler recordings show clear shifts in venous flow patterns in response to physiological changes, confirming the utility of Doppler data for tracking flow’ disturbances during different clinicalconditions. The same principles apply to DVT monitoring, where early disturbances in venous flow are detectable before thrombus formation. In some embodiments, venous flow patterns are examples of a volumetric Doppler dataset

[0106] The Doppler spectral waveforms provide detailed flow measurement including peak velocity, mean velocity, and velocity range (spectral broadening). The anatomic data for the vessels from the Volumetric M-Mode imaging scans provide the vessel orientation relative to the Doppler transmit beam so that angle correction may be applied for velocity calculations. Angle correction compensates for changes in the Doppler frequency shifts due to the angle between the transmit beam and the flow direction.

[0107] Analysis of the velocity changes over time of venous waveforms may be used to identify patterns such as phasicity (change with respiration) or pulsatility (change with cardiac cycle). Analysis of the brightness distribution of the waveform along the velocity axis may be used to quantify spectral broadening, with increased spectral broadening indicating the presence of turbulence. The imaging data is used to calculate volume flow based on vessel cross-sectional area and the blood velocity measurements. The imaging data are also used to measure vein size and changes in vein diameter associated with respiration and cardiac pulse.

[0108] An example of changes in venous flow in the jugular vein for a hemorrhage / resuscitation experiment in a pig model is shown in FIGURES 11 A- HE. In FIGURES 11A-11E, the jugular vein spectral waveform is below the baseline (negative Doppler shift): the carotid artery waveform is above the velocity' baseline. Frames are extracted from a series of images acquired during a continuous tilt scan, where FIGURE 11A depicts a pre-hemorrhage state, FIGURE 11B-11D depict states between a pre¬ hemorrhage and resuscitation state, and FIGURE 11E depicts a resuscitation state. The image sequence shows the changes in the venous spectral waveform from baseline (prehemorrhage) to shock and then in response to fluid resuscitation. The images are single frames extracted from a continuous tilt sequence as the image plane passed through the jugular vein (negative flow') and carotid artery' (positi ve flow),

[0109] Color Doppler data may also be analyzed to identify blood flow patterns. Color Doppler measures flow velocities in a 2D plane rather than along a single transmit beam. The time resolution is lower for Color Doppler compared to Spectral Doppler, and Color Doppler measures mean velocity at a given time point rather than the range of velocitiesrecorded by spectral analysis. However, the 2D field of view acquired by Color Doppler may capture spatial flow patterns that may be of use in DVT monitoring.

[0110] Changes in Doppler parameters with activity or intervention may be used to identify pre-DVT conditions or acute DVT. For example, the patient may be instructed to perform cyclical tilting motion of the foot. An example of an external intervention is compression of the calf with a cuff with monitoring of Doppler signals in the thigh.

[0111] According to embodiments of the present disclosure, the VMM may be applied to diagnose DVT in an automated and / or autonomous fashion, without the need for an expert human operator. The VMM scanning method allows for deployment of a monitoring device that does not require precise placement with a specific view of the vein of interest. This scanning technique may be used to acquire both anatomic (gray-scale B-mode images) and blood flow- (Doppler) information to provide continuous monitoring of DVT development in patients at risk.

[0112] Anatomic and physiologic parameters acquired over extended time periods are analyzed to identify changes m the vein that are related to DVT formation.

[0113] Anatomic parameters of interest include vein size, vein size change with respiration and cardiac cycle, and blood echogenicity; physiologic parameters include flow velocity, flow turbulence, and flow variation with respiration and cardiac cycle. In addition to identifying acute DVT formation, the techniques of the present disclosure may diagnose the 'pre-DVT' state, providing a prediction of DVT formation prior to full development of a thrombus m the vein. Diagnosis of pre-DVT may improve the timing of treatment for DVT and decrease subsequent morbidity and mortality from both treatment (anticoagulation medication) as well as clinical sequelae of DVT (pulmonary embolus and death).

[0114] Process Steps for Autonomous DVT Detection

[0115] To better illustrate the methodological steps described above, FIGURE 12 provides a schematic of a human subject, and FIGURE 13 provides a flow chart diagram of process 200 according to embodiments of the present disclosure.

[0116] Referring initially to FIGURE 12, the human subject is depicted to include a DVT clot at a clot location 120, while an ultrasound probe 50 is positioned at a predetermined location to scan a target region of a target blood vessel. A distance D is defined between the clot location 120 and the ultrasound probe 50 at the predetermined location. In thisregard, FIGURE 12 is an example of a possible placement of an ultrasound probe for conducting any of the methods described herein.

[0117] Referring to FIGURE 13, the dash line arrows represent optional process steps and / or process steps that may proceed in the alternative. In different embodiments, the process 200 may include additional steps that are not illustrated in the flowchart, or the process 200 may include fewer steps than illustrated in the flowchart.

[0118] In this regard, process 200 begins with block 202 where ultrasound waveforms are emitted toward the subject by an ultrasound transducer. These ultrasound waveforms interact with a portion of tissue of the subject (described herein as a target blood vessel).

[0119] Subsequently, process 200 involves block 204 where an ultrasound receiver, and in a common ultrasound acquisition step, obtains a first ultrasound dataset including a first volumetric ultrasound image and a first volumetric Doppler dataset of a target region of a target blood vessel of the subject. As used herein, "target region” refers to the region of the target blood vessel that is captured in the volumetric ultrasound image and the volumetric Doppler dataset. The "target region" thus is related to the "predetermined location," discussed further below, in that the placement of the ultrasound transducer at the "predetermined location" will determine w'here in the target blood vessel the "target region" will be located.

[0120] After acquiring the first volumetric ultrasound image and the first volumetric Doppler dataset, process 200 involves process block 206, where the process determines, based on the volumetric ultrasound image, at least one anatomic-parameter; and process block 208, where the process determines, based on the volumetric Doppler dataset, at least one flow-parameter. The determining steps at process block 206 and 208 may occur using a controller operatively coupled to the ultrasound transducer and to the ultrasound receiver, the controller including at least one processor and a computer-readable medium having computer-executable instructions stored thereon, executing the computer¬ executable instructions by the at least one processor to perform steps shown in block 206 and 208 (as well as 210, 212, and 214, described below).

[0121] With the at least one anatomic-parameter and at least one flow-parameter determined, the process 200 next involves diagnosing a presence or a development of a DVT clot at a clot location in the target blood vessel based on the at least one anatomic-parameter or the at least one flow-parameter. In this regard, the diagnosing step may involve using only the flow-parameter or only the anatomic-parameter, such as where theflow-parameter or anatomic-parameter provides a characteristic signal of a presence or a development of a DVT clot in the subject.

[0122] Then, process 200 includes block 212 where the process automatically sends a signal to an alarm indicator to provide an alarm interface when the presence or the development of the DVT clot in the target blood vessel is diagnosed. In some embodiments, the alarm indicator may include a visual alarm on an alarm interface, such as a text box or other text-based message on display 59 or an illumination of a light source (like an LED) which indicates to a user that the presence or the development of the DVT clot in the subject has been diagnosed. In some embodiments, the alarm indicator may include an audible alarm on an alarm interface, such as a buzzer, beeper, or other alarm sound emitted from a speaker system. In some embodiments, the alarm indicator may be transmitted electronically and / or wirelessly to a secondary device, such as a smartphone, pager, or similar device in proximity to a care provider. While the above non-exhaustive examples of an alarm indicator are provided for illustrative purposes, it should be understood that other alarm indicators may be utilized and are within the scope of the present disclosure.

[0123] As is discussed further herein, an advantage of the methods of the present disclosure is that ultrasound transducer placement does not involve the same high level of skill required in determining a proper placement in other methods of using ultrasound to diagnose the presence of the DVT clot. Accordingly, in the methods of the present disclosure, the location of the ultrasound transducer may be at a predetermined location that is not necessarily proximate to (e.g., directly above and / or within the scanning scope of the transducer) a clot location in the subject. As used herein, "predetermined location" refers to a relative position on the subject or patient that is clinically relevant based on the risk factors present in the subject or patient.

[0124] In this regard, the "predetermined location" relates to the "target region" discussed above, in that once the ultrasound transducer is placed at the "predetermined location," the region of the target blood vessel that is imaged will constitute the "target region." The target region is thus at a predetermined location selected without a priori knowledge of the clot location. In other words, the target region is at a distance D from the clot location, such that the ultrasound image data collected of the target region may not include an image of the DVT clot

[0125] For example, referring again to FIGURE 12, the "predetermined location" may refer to a distance D between the ultrasound transducer 50 and a clot location 120 that mimics a DVT, where the distance D is determined in the horizontal plane of the drawings projection (i.e., the plane of paper). As a result, the DVT (either in its inception state or its fully developed state) may be detectable without traversing the ultrasound transducer along the vein of interest, because a placement of the ultrasound transducer 50 within a reasonable distance from the DVT will still identify and locate the DVT. Such "reasonable distance" will in practice be determined by the resolution and clarity of the ultrasound measurement at a given distance between the ultrasound transducer 50 and the DVT at clot location 120,

[0126] Additionally, it should be understood that the "predetermined location" is not selected based on the location where a DVT forms. The clot formation location is inherently a stochastic process and may occur at different locations within a blood vessel. In this regard, while the "predetermined location" is selected with no ex ante knowledge of where a clot will actually form (the "clot location"), in certain circumstances the "predetermined location" may coincide with where the DVT forms, even though the "predetermined location" is selected irrespective of where the DVT may eventually form, in some cases because certain regions of the subject are more likely to develop a DVT clot than others. Thus, a "predetermined location" selected with knowledge of the probabilities of a "clot location" forming is, in some embodiments, still one selected without a priori know ledge of where the clot will actually form.

[0127] Because the "predetermined location" is related to the "target region", the clot location may be at a distance D from the target region. In this regard, when the clot location is at a distance D from the target region in the target blood vessel, the volumetric ultrasound image data and the Doppler data may not include any direct imaging of the DVT clot. However, even though the "predetermined location" and "target region" are selected without knowledge of the clot location, in some embodiments, the clot location coincides with the "target region", even though the "target region" w'as imaged without a priori knowledge of the clot location.

[0128] Process 200 may also include optional process block 214 directed to obtaining a second ultrasound dataset including a second volumetric ultrasound image and a second volumetric Doppler dataset of the target blood vessel, wherein the second ultrasound dataset is acquired after the first ultrasound dataset is acquired. It should be noted that,while "first" and "second" are used with respect to the ultrasound dataset, volumetric ultrasound image, and volumetric Doppler dataset, methods according to the present disclosure may include any number of additional process steps for acquiring an ultrasound dataset. In this regard, the methods of the present disclosure may include acquiring a third ultrasound dataset, a fourth ultrasound dataset, a fifth ultrasound dataset, and the like. In some embodiments, each of these subsequent ultrasound datasets are examples of the first and second ultrasound datasets described with respect to process blocks 204 and 214.

[0129] In some embodiments, monitoring for DVT in the subject includes detecting a developed DVT clot. In other embodiments, monitoring for DVT in the subject includes predicting a development of a DVT clot. In this regard, the methods of the present disclosure advantageously may be used not only to identify the presence of the DVT clot once it has occurred in order to accelerate treatment for the subject or patient, but the methods of the present disclosure also provide the user of the methods described herein information when a DVT is developing, thereby allowing for proactive action to be taken to treat the patient before the developed DVT causes further harm.

[0130] In some embodiments, monitoring for DVT includes tracking, over a monitoring duration, a change between the first ultrasound dataset and the second ultrasound dataset. For instance, the tracking may be performed by placing an ultrasound transducer at a location of the patient and left in place for a determined length of time (30 minutes, 1 hour, 2 hours, 6 hours, etc.) based on the risk factors for the patient. The monitoring duration may be the same as the determined length of time, or the monitoring duration may be shorter than the determined length of time. For instance, the monitoring duration, in some embodiments, may be as short as 10 minutes, 20 minutes, 30 minutes, 60 minutes, 2 hours, 4 hours, 6 hours, 8 hours, 10 hours, or 12 hours. In some embodiments, the determined length of time may be, for example, as long as 76 hours, 48 hours, 36 hours, 24 hours, or 12 hours, or some other intermediary duration. In this regard, changes in the anatomic-parameter and flow-parameter may be tracked over the clinically appropriate length of time to diagnose changes indicative of DVT onset.

[0131] Moreover, while the above example includes a determined length of time for the ultrasound transducer to be left on the patient, it should be understood that the ultrasound transducer may also be applied and removed from the patient in between acquisition of the first and second ultrasound datasets, such as where a single ultrasound transducer is being used to monitor two or more patients concurrently. In such examples, markings may bemade on or near the patient to approximate placement of the ultrasound transducer on a same spot for the first ultrasound dataset acquisition as for the second ultrasound dataset acquisition.

[0132] In some embodiments, the monitoring duration is between about 1 minute and about 3 days, between about 5 minutes and about 6 hours, between about 5 minutes and about 3 hours, between about 5 minutes and about 1 hour, or between about 5 minutes and about 30 minutes.

[0133] In some embodiments, the volumetric ultrasound image is a single-element transducer array-derived 3D ultrasound image: a 3D M-mode ultrasound image including a depth, a lateral position, and a time; a 4D M-mode ultrasound image including the depth, the lateral position, the time, and a tilt or a rotation; or a 5D M-mode ultrasound image including the depth, the lateral position, the time, the tilt, and the rotation. In some embodiments, when the first volumetric ultrasound image is a 4D M-mode ultrasound image or a 5D M-mode ultrasound image, the first volumetric Doppler dataset is a Pulsed Wave Doppler, a Continuous Wave Doppler, or a Color Doppler.

[0134] In some embodiments, the at least one anatomic parameter is selected from a group consisting of a morphology of the target region, a respiratory variation of diameter of the target region, a cardiac variation of diameter of the target region, and a lumen echogenicity of the target region.

[0135] In some embodiments, the at least one flow-parameter is selected from a group consisting of a volume flow of blood through the target region, a respiratory’ variation of the flow through the target region, a cardiac variation of the flow through the target region, and a turbulence of flow through the target region.

[0136] In some embodiments the predetermined location is: for an arm, a position along a subclavian or an axillary vein proximate a bicep muscle of the subject; and for a leg, a position near a calf muscle of the subject, a thigh muscle of the subject, or a pelvic muscle of a subject Without wishing to be bound by any particular theory, the above predetermined locations may be selected based at least in part on a statistical understanding of a likelihood of developing DVT based on a patient's clinical situation. For instance, if the patient is fully immobilized, the monitoring on a section of the arm may be appropriate. In a similar manner, if the patient is immobilized only from the waist down, then a leg location may be appropriate for monitoring

[0137] In some embodiments, diagnosing the presence or the development of the DVT clot in the subject is based on an both the at least one anatomic-parameter and the at least one flow-parameter.

[0138] In some embodiments, a normal vein is indicated by one or more of: the morphology being normal, the lumen echogenicity being anechoic throughout, the respiratory variation being phasic, the cardiac variation being phasic, the volume flow being normal, and / or the flow being laminar; a pre-DVT vein is indicated by one or more of: the morphology being normal, the lumen echogenicity being a dynamic echogenicity’, the respiratory variation being phasic or phasic with diminished variation, the cardiac variation being phasic or phasic with diminished variation, the volume flow being decreased, and / or the turbulence being transitional to turbulent; and an acute DVT vein is indicated by one or more of: the morphology being enlarged, the lumen echogenicity being hypoechoic, the respiratory variation being absent, the cardiac variation being absent, the volume flow being absent, and the turbulence being absent.

[0139] In some embodiments, the respirator}’ variation of the target blood vessel is determined based on changes in measured diameter of the target blood vessel.

[0140] In some embodiments, the respiratory variation of the target region is determined based on flow velocity variation with the respiratory cycle.

[0141] In some embodiments, the cardiac variation of the target region is determined based on flow velocity variation with the cardiac cycle.

[0142] In some embodiments, the lumen echogenicity of the target region is determined based on changes in acoustic scattering in the blood of the region, gray-scale data of the volumetric ultrasound image, and combinations thereof.

[0143] In some embodiments, the clot location is at a distance D from the target region.

[0144] In some embodiments, the Doppler dataset is a spectral Doppler dataset, a continuous-wave Doppler dataset, a pulsed Doppler dataset, a Color Doppler dataset, and combinations thereof.

[0145] Single -Element Transducer Array-Derived Volumetric Ultrasound

[0146] While the above description generally describes the acquisition of ultrasound datasets where volumetric ultrasound data is acquired using VMM, it should be understood that other methods of acquiring ultrasound datasets are within the scope of the present disclosure and that the present disclosure is not limited to the use of VMM. In this regard,in some embodiments, the first ultrasound dataset includes single-element transducer array-derived volumetric ultrasound datasets combined with Doppler datasets acquired in the common ultrasound acquisition step. The single-element transducer array acquires data from a target blood vessel, with certain elements providing flow data for a sampled volume of the target blood vessel where signals relevant to the methods described herein are present. This combination may effectively provides automated coverage without the need for an experienced operator, without sweeping a 2D image plane to acquire the data,

[0147] In some embodiments, the present disclosure relates to volumetric ultrasound images acquired via the single-element transducer array-derived volumetric ultrasound techniques.

[0148] An example of this design is depicted in FIGURE 14A-14B, which depict a single-element transducer array 300 which includes an array of single element transducers 310. The single element transducers 310 emit Doppler beams 320. The illustrated example includes 64 single element transducers 310 for illustrative purposes, though it should be understood that any number of single element transducers 310 may be used and thus fall within the scope of this disclosure. This design uses an array of single-element transducers that can switch between Continuous Wave (CW) Doppler, Pulsed Wave (PW) Doppler, Color Doppler and Amplitude-mode (A-mode) ultrasound. Rather than scan a set of 2D planes, the array 300 emits a plurality of Doppler beams 320 to interrogate a volume of tissue and indicate the general location of the blood vessel in the volumetric field of view. Both CW and PW would distinguish arteries and veins based on Doppler spectral waveform analysis. Color Doppler measures flow at all depths along a transmit beam and can be used for guidance of the initial placement of the device over the vein of interest. Color Doppler indicates flow velocity and direction to distinguish arteries and veins. The depth measurement of flow could be used to set the sample gate location for PW Doppler, A-mode ultrasound measures echo strength and can be used to measure the vein diameter and the blood echogenicity. Details for each of these modes are provided below.

[0149] Color Doppler processing on each array element identifies the presence of a blood vessel. The array provides coverage of a 3D volume within the tissue. Flow direction and velocity measurements for multiple elements are used to indicate placement over a vein Comparison of signals over the array elements indicates alignment with the longitudinal axis of the target vein. Color Doppler measures the presence or absence of flow at all depths along the transmit beam so that the depth of the vein is determined.

[0150] After placement of the device the array elements can be run in either CW or PW mode. Both modes provide Spectral Doppler measurements that produce detailed flow measurements with high temporal resolution. The depth measurement from the Color Doppler mode can guide the depth and size settings for the PW sample volume.

[0151] A-mode ultrasound can be run intermittently with the Doppler modes to provide anatomic measurements of the vein. A-mode measures echo strength as a function of depth along the transmit beam. Analysis of the echo strength provides measurement of the vein diameter and the blood echogenicity in the vein lumen.

[0152] Systems for Automated Monitoring of Deep Vein Thrombosis (DVT)

[0153] In an aspect, the present disclosure provides systems for automated monitoring of deep vein thrombosis (DVT) in a subject, the systems including: an ultrasound transducer configured to emit a probe ultrasound signal toward a target region of a target blood vessel of the subject; an ultrasound receiver configured to receive a response ultrasound signal from the target region of the subject; and a controller including a non-transitory computer-readable medium having computer-executable instructions stored thereon that, if executed by one or more processors of a computing device, cause the computing device to perform steps including: emitting ultrasound waveforms toward the subject by the ultrasound transducer; by the ultrasound receiver, and in a common ultrasound acquisition step, obtaining a first ultrasound dataset including a first volumetric ultrasound image and a first volumetric Doppler dataset of the target region; determining, based on the volumetric ultrasound image, at least one anatomic-parameter; determining, based on the volumetric Doppler dataset, at least one flow-parameter; diagnosing a presence or a development of a DVT clot at a clot location in the target blood vessel based on the at least one anatomic- parameter or the at least one flow-parameter; and automatically sending a signal to an alarm indicator to provide an alarm interface when the presence or the development of the DVT clot in the target blood vessel is diagnosed, wherein a placement of the ultrasound transducer and the ultrasound receiver is a predetermined placement selected without a priori knowledge of the clot location.

[0154] In this regard, the systems provided by the present disclosure may be configured to perform any of the methods described herein. In some embodiments, system 100 is an example of any of the systems described in the present disclosure. Accordingly, it should be understood that the controller in the systems described herein may be configured toperform any of the methodological steps described herein above with respect to the methods of the present disclosure.

[0155] In some embodiments, the systems further includes obtaining a second ultrasound dataset including a second volumetric ultrasound image and a second volumetric Doppler dataset of the target region, wherein the second ultrasound dataset is acquired after the first ultrasound dataset is acquired.

[0156] In some embodiments, the volumetric ultrasound image is: a single-element transducer array-derived 3D ultrasound image: a 3D M-mode ultrasound image including a depth, a lateral position, and a time: a 4D M-mode ultrasound image including the depth, the lateral position, the time, and a tilt or a rotation: or a 5D M-mode ultrasound image including the depth, the lateral position, the time, the tilt, and the rotation. Accordingly, in some embodiments, the ultrasound transducer and the ultrasound receiver, either together or individually, comprise a single-element transducer array, a 3D-M-mode ultrasound system, a 4D-M-mode ultrasound system, or a 5D M-mode ultrasound system.

[0157] In some embodiments, diagnosing the presence or the development of the DVT clot in the subject is based on both the at least one anatomic-parameter and the at least one flow-parameter.

[0158] The detailed description set forth above in connection with the appended drawings, where like numerals reference like elements, are intended as a description of various embodiments of the present disclosure and are not intended to represent the only embodiments. Each embodiment described in this disclosure is provided as a representative example or illustration and should not be construed as preferred or advantageous over other embodiments. The representative examples provided herein are not intended to be exhaustive or to limit the disclosure to the precise forms disclosed. Similarly, any steps described herein may be interchangeable with other steps, or combinations of steps, in order to achieve the same or substantially similar result. Generally, the embodiments disclosed herein are non-limiting, and the inventors contemplate that other embodiments within the scope of this disclosure may include structures and functionalities from more than one specific embodiment shown in the figures and described in the specification. That is, the present disclosure includes embodiments that combine features from different embodiments.

[0159] In the foregoing description, specific details are set forth to provide a thorough understanding of exemplary embodiments of the present disclosure. It will be apparent toone skilled in the art, however, that the embodiments disclosed herein may be practiced without embodying all the specific details. In some instances, well-known process steps have not been described in detail in order not to unnecessarily obscure various aspects of the present disclosure. Further, it will be appreciated that embodiments of the present disclosure may employ any combination of features described herein.

[0160] In the detailed description herein, references to "one embodiment," "an embodiment." "an example embodiment," "some embodiments," "one or more embodiments," etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but even’ embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. In addition, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described. After reading the description, it will be apparent to one skilled in the relevant art(s) how to implement the disclosure in alternative embodiments. Thus, it will be appreciated that embodiments of the present disclosure may employ any combination of features described herein. All such combinations or sub-combinations of features are within the scope of the present disclosure.

[0161] In view of the limitations of the processing techniques available in the field, the terms "approximately", "substantially", and "about" reflect a certain inability (or uncertainty ) to precisely control the exact dimensions of certain features and measurements described herein. Depending on the level of precision that can be achieved using the commercially available processing and measurement tools available at the time, the terms "approximately", "substantially", and "about" may be used to mean within ±5% of a target value for some features. The terms "approximately", "substantially", and "about" may include the target value.

[0162] Throughout this specification, terms of art may be used. These terms are to take on their ordinary meaning in the art from which they come, unless specifically defined herein or the context of their use would clearly suggest otherwise

[0163] The drawings in the FIGURES are not to scale. Similar elements are generally denoted by similar references in the FIGURES. For the purposes of this disclosure, the same or similar elements may bear the same references. Furthermore, the presence ofreference numbers or letters in the drawings cannot be considered limiting, even when such numbers or letters are indicated in the claims.

[0164] Terms such as "a," "an," "the," and "said" are used to indicate the presence of one or more elements and components. The terms "comprise," "include," "have," "contain." and their variants are used to be open ended and may include or encompass additional elements, components, etc., in addition to the listed elements, components, etc., unless otherwise specified. The terms "first," "second," etc. may be used as differentiating identifiers of individual or respective components among a group thereof, rather than as a descriptor of a number of the components, unless clearly indicated otherwise.

[0165] Although relative terms such as "on," "below," "upper," "lower," "top," "bottom," "right," and "left" may be used to describe the relative spatial relationships of certain structural features, these terms are used for convenience only, as a direction in the examples. Thus, if a structure is turned upside down, the "upper" component will become a "lower" component. When a structure or feature is described as being "on" (or formed on) another structure or feature, the structure can be positioned directly on ( / .<?., contacting) the other structure, without any other structures or features intervening between the structure and the other structure. When a structure or feature is described as being "over" (or formed over) another structure or feature, the structure can be positioned over the other structure, with or without other structures or features intervening between them.

[0166] When two components are described as being "coupled to" each other, the components can be electrically coupled to each other, with or without other components being electrically coupled and intervening between them. When two components are described as being "directly coupled to" each other, the components can be electrically coupled to each other, without other components being electrically coupled between them.

[0167] The present application may also reference quantities and numbers. Unless specifically stated, such quantities and numbers are not to be considered restrictive, but exemplary of the possible quantities or numbers associated with the present application. Also in this regard, the present application may use the term "plurality " to reference a quantity or number. In this regard, the term "plurality " is meant to be any number that is more than one, for example, two, three, four, five, etc. The term "based upon" means "based at least partially upon "

[0168] Embodiments disclosed herein may utilize circuitry in order to implement technologies and methodologies described herein, operatively connect two or more components, generate information, determine operation conditions, control an appliance, device, or method, and / or the like. Circuitry of any type can be used. In an embodiment, circuitry includes, among other things, one or more computing devices such as a processor (e.g., a microprocessor), a central processing unit (CPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or the like, or any combinations thereof, and can include discrete digital or analog circuit elements or electronics, or combinations thereof.

[0169] As used herein, the executable instructions for autonomous monitoring for DVT may additionally or instead include instructions for executing and / or training one or more artificial intelligence models. In some examples, training of the artificial intelligence model(s) and use of the artificial intelligence models to identify regions of interest may be performed using a same computer system. In other examples, one or more of the artificial intelligence model(s) may be trained using a different computer system, and data encoding the trained artificial intelligence model may be stored in the computer readable media of the computer system and may be used to identify positive margins in samples described herein. Training of the artificial intelligence model(s) may be performed using any of a variety of techniques including, but not limited to, supervised learning, weakly supervised learning, unsupervised learning, clustering, and / or reinforcement learning. The artificial intelligence model(s) may be implemented using one or more machine classifiers, such as one or more deep learning models, neural networks (e.g., patch-based neural networks), machine learning models, including but not limited to one or more decisions trees. Examples of machine learning models may include support vector machines (e.g, radial basis function (RBF) kernel support vector machine) and / or random forest classifiers.

[0170] Many embodiments of the technology described above may take the form of computer- or controller-executable instructions in a non-volatile memory, including routines executed by a programmable computer or controller. Those skilled in the relevant art will appreciate that the technology can be practiced on computers other than those shown and described above. The technology can be embodied in a special purpose computer (such as one that integrates ultrasound scanner 55, display 59, and computer 75), controller or data processor that is specifically programmed, configured or constructed to perform one or more of the computer-executable instructions described above.Accordingly, the terms "computer" and "controller" as generally used herein refer to any data processor and can include Internet appliances and hand-held devices (including palmtop computers, wearable computers, cellular or mobile phones, multi-processor systems, processor-based or programmable consumer electronics, network computers, mini computers and the like).

[0171] In one or more embodiments, a system of the disclosure may comprise a computing device. The computing device includes one or more processors, a non-transitory computer readable medium, a communication interface, a display, and a user interface. Components of the computing device are linked together by a system bus, network, or other connection mechanism.

[0172] The one or more processors can be any type of processor(s), such as a microprocessor, a digital signal processor, a multicore processor, etc., coupled to the non-transitory computer readable medium.

[0173] The non -transitory computer readable medium can be any type of memory or data store, such as volatile memory' (e.g., Random Access memory (RAM). Dynamic Random Access memory' (DRAM), static random-access memory' (SRAM), or the like), non-volatile memory (e.g., Read-Only memory' (ROM), Electrically Erasable Programmable Read-Only memory' (EEPROM), Compact Disc Read-Only memory' (CD-ROM), or the like), persistent memory', or the like. Further non-limiting examples of one or more data stores include Erasable Programmable Read-Only memory' (EPROM), flash memory', or the like The one or more data stores can be connected to, for example, one or more computing devices by one or more instructions, data, or power buses.

[0174] In some embodiments, circuitry' includes a computer-readable media drive or memory slot configured to accept signal-bearing medium (e.g., computer-readable memory' media, computer-readable recording media, or the like). In an embodiment, a program for causing a system to execute any of the disclosed methods can be stored on, for example, a computer-readable recording medium (CRMM), a signal-bearing medium, or the like. Non-limiting examples of signal-bearing media include a recordable type medium such as any form of flash memory, magnetic tape, floppy' disk, a hard disk drive, a Compact Disc (CD), a Digital Video Disk (DVD), Blu-Ray Disc, a digital tape, a computer memory, or the like, as well as transmission type medium such as a digital and / or an analog communication medium (e.g., a fiber optic cable, a waveguide, a wired communications link, a wireless communication link (e.g., transmitter, receiver.transceiver, transmission logic, reception logic, etc.). Further non-limiting examples of signal-bearing media include, but are not limited to, DVD-ROM, DVD-RAM, DVD+RW, DVD-RW, DVD-R. DVD+R. CD-ROM, Super Audio CD. CD-R. CD+R, CD+RW, CD-RW, Video Compact Discs, Super Video Discs, flash memory, magnetic tape, magneto¬ optic disk, MINIDISC, non-volatile memory card, EEPROM, optical disk, optical storage, RAM, ROM, system memory, web server, or the like.

[0175] Additionally, the non-transitory computer readable medium can be configured to store instructions. The instructions are executable by the one or more processors to cause the computing device to perform any of the functions or methods described herein.

[0176] The non-transitory computer readable medium can also be configured to store a computational model. The computational model can take the form of a convolutional neural network or any other type of artificial neural network. The computational model can take other forms as well.

[0177] The communication interface can include hardware to enable communication within the computing device and / or between the computing device and one or more other devices. The hardware can include transmitters, receivers, and antennas, for example. The communication interface can be configured to facilitate communication with one or more other devices, in accordance with one or more wired or wireless communication protocols. For example, the communication interface can be configured to facilitate wireless data communication for the computing device according to one or more wireless communication standards, such as one or more Institute of Electrical and Electronics Engineers (IEEE) 801.11 standards, ZigBee standards, Bluetooth standards, etc. As another example, the communication interface can be configured to facilitate wired data communication with one or more other devices. The communication interface can also include analog-to-digital converters (ADCs) or digital-to-analog converters (DACs) that the computing device can use to control various components of the monitoring system

[0178] The display can be any type of display component configured to display data. As one example, the display can include a touchscreen display As another example, the display can include a flat-panel display, such as a liquid-crystal display (LCD) or a light¬ emitting diode (LED) display.

[0179] The user interface can include one or more pieces of hardware used to provide data and control signals to the computing device. For instance, the user interface can include a mouse or a pointing device, a keyboard or a keypad, a microphone, a touchpad,or a touchscreen, among other possible ty pes of user input devices. Generally, the user interface can enable an operator to interact with a graphical user interface (GUI) provided by the computing device (e.g., displayed by the display).

[0180] The principles, representative embodiments, and modes of operation of the present disclosure have been described in the foregoing description. However, aspects of the present disclosure, which are intended to be protected, are not to be construed as limited to the particular embodiments disclosed. Further, the embodiments described herein are to be regarded as illustrative rather than restrictive. It will be appreciated that variations and changes may be made by others, and equivalents employed, without departing from the spirit of the present disclosure Accordingly, it is expressly intended that all such variations, changes, and equivalents fall within the spirit and scope of the present disclosure as claimed.NON-LIMITING EXAMPLES

[0181] While general features of the disclosure are described and shown and particular features of the disclosure are set forth in the claims, the following non-limiting embodiments relate to features, and combinations of features, that are explicitly envisioned as being part of the disclosure. The following non-limiting embodiments contain elements that are modular and can be combined with each other in any number, order, or combination to form a new' non-limiting example, which can itself be further combined with other non-limiting examples.

[0182] Example 1. A method for autonomous monitoring for deep vein thrombosis (DVT) in a subject, the method including: emitting ultrasound waveforms toward the subject by an ultrasound transducer; by an ultrasound receiver, and in a common ultrasound acquisition step, obtaining a first ultrasound dataset including a first volumetric ultrasound image and a first volumetric Doppler dataset of a target region of a target blood vessel of the subject; using a controller operatively coupled to the ultrasound transducer and to the ultrasound receiver, the controller including at least one processor and a computer-readable medium having computer-executable instructions stored thereon, executing the computer-executable instructions by the at least one processor to: determine, based on the volumetric ultrasound image, at least one anatomic-parameter, determine, based on the volumetric Doppler dataset, at least one flow-parameter; and diagnose a presence or a development of a DVT clot at a clot location in the target blood vessel based on the at least one anatomic-parameter or the at least one flow-parameter; andautomatically send a signal to an alarm indicator to provide an alarm interface when the presence or the development of the DVT clot in the target blood vessel is diagnosed, wherein the target region is at a predetermined location that is selected without a priori knowledge of the clot location.

[0183] Example 2. The method of Example 1 or any other Example, wherein monitoring for DVT in the subject comprises detecting a developed DVT clot.

[0184] Example 3. The method of Examples 1-2 or any other Example, monitoring for DVT in the subject comprises predicting a development of a DVT clot.

[0185] Example 4. The method of Examples 1-3 or any other Example, further including obtaining a second ultrasound dataset including a second volumetric ultrasound image and a second volumetric Doppler dataset of the target region, wherein the second ultrasound dataset is acquired after the first ultrasound dataset is acquired.

[0186] Example s. The method of Examples 1-4 or any other Example, wherein monitoring for DVT comprises tracking, over a monitoring duration, a change between the first ultrasound dataset and the second ultrasound dataset.

[0187] Example 6. The method of Examples 1-5 or any other Example, wherein the monitoring duration is between about 1 minute and about 3 days, between about 5 minutes and about 6 hours, between about 5 minutes and about.3 hours, between about 5 minutes and about 1 hour, or between about 5 minutes and about 30 minutes.

[0188] Example 7. The method of Examples 1-6 or any other Example, wherein the first volumetric ultrasound image is: a single-element transducer array-derived 3D ultrasound image; a 3D M-mode ultrasound image including a depth, a lateral position, and a time; a 4D M-mode ultrasound image including the depth, the lateral position, the time, and a tilt or a rotation; or a 5D M-mode ultrasound image including the depth, the lateral position, the time, the tilt, and the rotation.

[0189] Example 8. The method of Examples 1-7 or any other Example, wherein, when the first volumetric ultrasound image is a 4D M-mode ultrasound image or a 5D M-mode ultrasound image, the first volumetric Doppler dataset is a Pulsed Wave Doppler, a Continuous Wave Doppler, or a Color Doppler.

[0190] Example 9. The method of Examples 1-8 or any other Example, wherein the at least one anatomic parameter is selected from a group consisting of a morphology of thetarget region, a respiratory variation of diameter of the target region, a cardiac variation of diameter of the target region, and a lumen echogenicity of the target region.

[0191] Example 10. The method of Examples 1-9 or any other Example, wherein the at least one flow-parameter is selected from a group consisting of a volume flow of blood through the target region, a respiratory variation of the flow through the target region, a cardiac variation of the flow through the target region, and a turbulence of flow through the target region.

[0192] Example 11. The method of Examples 1-10 or any other Example, wherein the predetermined location is: for an arm, a position along a subclavian or an axillary vein proximate a bicep muscle of the subject; and for a leg, a position near a calf muscle of the subject, a thigh muscle of the subject, or a pelvic muscle of a subject.

[0193] Example 12. The method of Examples 1-11 or any other Example, wherein: diagnosing the presence or the development of the DVT clot in the subject is based on both the at least one anatomic-parameter and the at least one flow -parameter.

[0194] Example 13. The method of Examples 1-12 or any other Example, wdierem: a normal vein is indicated by one or more of: the morphology being normal, the lumen echogenicity being anechoic throughout, the respiratory variation being phasic, the cardiac variation being phasic, the volume flow being normal, and / or the flow being laminar; a pre-DVT vein is indicated by one or more of: the morphology being normal, the lumen echogenicity being a dynamic echogenicity, the respiratory variation being phasic or phasic with diminished variation, the cardiac variation being phasic or phasic with diminished variation, the volume flow being decreased, and / or the turbulence being transitional to turbulent; and an acute DVT vein is indicated by one or more of: the morphology being enlarged, the lumen echogenicity being hypoechoic, the respiratory variation being absent, the cardiac variation being absent, the volume flow being absent, and the turbulence being absent.

[0195] Example 14. The method of Examples 1-13 or any other Example, wherein the respiratory variation of the target region is determined based on changes in measured diameter of the target region,

[0196] Example 15. The method of Examples 1-14 or any other Example, wherein the respiratory variation of the target region is determined based on flow velocity variation with the respiratory cycle.

[0197] Example 16. The method of Examples 1-15 or any other Example, wherein the cardiac variation of the target region is determined based on flow velocity variation with the cardiac cycle.

[0198] Example 17. The method of Examples 1-16 or any other Example, wherein the lumen echogenicity of the target region is determined based on changes in acoustic scattering in the blood of the region, gray-scale data of the volumetric ultrasound image, and combinations thereof

[0199] Example 18. The method of Examples 1-17 or any other Example, wherein the clot location is at a distance D from the target region.

[0200] Example 19. The method of Examples 1-18 or any other Example, wherein the Doppler dataset is a spectral Doppler dataset, a continuous- wave Doppler dataset, a pulsed Doppler dataset, a Color Doppler dataset, and combinations thereof.

[0201] Example 20. A system for automated monitoring of deep vein thrombosis (DVT) in a subject, the system including: an ultrasound transducer configured to emit a probe ultrasound signal toward a target region of a target blood vessel of the subject; an ultrasound receiver configured to receive a response ultrasound signal from the target region of the subject; and a controller including a non-transitory computer-readable medium having computer-executable instructions stored thereon that, if executed by one or more processors of a computing device, cause the computing device to perform steps including: emitting ultrasound waveforms toward the subject by the ultrasound transducer; by the ultrasound receiver, and in a common ultrasound acquisition step, obtaining a first ultrasound dataset including a first volumetric ultrasound image and a first volumetric Doppler dataset of the target region; determining, based on the volumetric ultrasound image, at least one anatomic-parameter; determining, based on the volumetric Doppler dataset, at least one flow-parameter; diagnosing a presence or a development of a DVT clot at a clot location in the target blood vessel based on the at least one anatomic- parameter or the at least one flow-parameter; and automatically sending a signal to an alarm indicator to provide an alarm interface when the presence or the development of the DVT clot in the target blood vessel is diagnosed, wherein a placement of the ultrasound transducer and the ultrasound receiver is a predetermined placement selected without a priori knowledge of the clot location.

[0202] Example 21. The system of Example 20 or any other Example, further including obtaining a second ultrasound dataset including a second volumetric ultrasound image anda second volumetric Doppler dataset of the target region, wherein the second ultrasound dataset is acquired after the first ultrasound dataset is acquired.

[0203] Example 22. The system of Examples 20-21 or any other Example, wherein the volumetric ultrasound image is: a single-element transducer array-derived 3D ultrasound image; a 3D M-mode ultrasound image including a depth, a lateral position, and a time; a 4D M-mode ultrasound image including the depth, the lateral position, the time, and a tilt or a rotation; or a 5D M-mode ultrasound image including the depth, the lateral position, the time, the tilt, and the rotation.

[0204] Example 23. The system of Examples 20-22 or any other Example, wherein diagnosing the presence or the development of the DVT clot in the subject is based on an both the at least one anatomic-parameter and the at least one flow -parameter.

[0205] LISTING OF DRAWING ELEMENTS50 ultrasound probe 200 process5 OR rotational plane 202 block50T tilt plane 204 block54 ultrasound 206 block55 ultrasound scanner 208 block59 display 210 block75 computer 212 block100 system 214 block120 clot location 300 single element transducer array L leg 310 single element transducerD distance 320 ultrasound signal

[0206] While illustrative Examples have been illustrated and described, it will be appreciated that various changes can be made therein without departing from the spirit and scope of the disclosure.

Claims

AMENDED CLAIMSreceived by the International Bureau on 18 March 2026 (18.03.2026) Claims

1. A method for autonomous monitoring for deep vein thrombosis (DVT) in a subject, the method comprising:emitting ultrasound waveforms toward the subject by an ultrasound transducer;by an ultrasound receiver, and in a common ultrasound acquisition step, obtaining a first ultrasound dataset comprising a first volumetric ultrasound image and a first volumetric Doppler dataset of a target region of a target blood vessel of the subject;using a controller operatively coupled to the ultrasound transducer and to the ultrasound receiver, the controller including at least one processor and a computer-readable medium having computerexecutable instructions stored thereon, executing the computerexecutable instructions by the at least one processor to: determine, based on the volumetric ultrasound image, at least one anatomic-parameter;determine, based on the volumetric Doppler dataset, at least one flowparameter; anddiagnose a presence or a development of a DVT clot at a clot location in the target blood vessel based on the at least one anatomic-parameter or the at least one flow-parameter; andautomatically send a signal to an alarm indicator to provide an alarm interface when the presence or the development of the DVT clot in the target blood vessel is diagnosed,wherein the target region is at a predetermined location that is selected without a priori knowledge of the clot location.

2. The method of claim 1, wherein monitoring for DVT in the subject comprises detecting a developed DVT clot.

3. The method of claim 1, wherein monitoring for DVT in the subject comprises predicting a development of a DVT clot.

4. The method of claim 1, further comprising obtaining a second ultrasound dataset comprising a second volumetric ultrasound image and a second volumetric Doppler dataset of the target region, wherein the second ultrasound dataset is acquired after the first ultrasound dataset is acquired.

5. The method of claim 4, wherein monitoring for DVT comprises tracking, over a monitoring duration, a change between the first ultrasound dataset and the second ultrasound dataset.

6. The method of claim 5, wherein the monitoring duration is between about 1 minute and about 3 days, between about 5 minutes and about 6 hours, between about 5 minutes and about 3 hours, between about 5 minutes and about 1 hour, or between about 5 minutes and about 30 minutes.

7. The method of claim 1, wherein the first volumetric ultrasound image is:a single-element transducer array-derived 3D ultrasound image;a 3D M-mode ultrasound image comprising a depth, a lateral position, and a time;a 4D M-mode ultrasound image comprising the depth, the lateral position, the time, and a tilt or a rotation; ora 5D M-mode ultrasound image comprising the depth, the lateral position, the time, the tilt, and the rotation.

8. The method of claim 7, wherein, when the first volumetric ultrasound image is a 4D M-mode ultrasound image or a 5D M-mode ultrasound image, the first volumetric Doppler dataset is a Pulsed Wave Doppler, a Continuous Wave Doppler, or a Color Doppler.

9. The method of claim 1, wherein the at least one anatomic parameter is selected from a group consisting of a morphology of the target region, a respiratory variation of diameter of the target region, a cardiac variation of diameter of the target region, and a lumen echogenicity of the target region.

10. The method of claim 1, wherein the at least one flow-parameter is selected from a group consisting of a volume flow of blood through the target region, a respiratory variation of the flow through the target region, a cardiac variation of the flow through the target region, and a turbulence of flow through the target region.

11. The method of claim 1, wherein diagnosing the presence or the development of the DVT clot in the subject is based on both the at least one anatomic -parameter and the at least one flow-parameter.

12. The method of claim 11, wherein:a normal vein is indicated by one or more of: the morphology being normal, the lumen echogenicity being anechoic throughout, therespiratory variation being phasic, the cardiac variation being phasic, the volume flow being normal, and / or the flow being laminar;a pre-DVT vein is indicated by one or more of: the morphology being normal, the lumen echogenicity being a dynamic echogenicity, the respiratory variation being phasic or phasic with diminished variation, the cardiac variation being phasic or phasic with diminished variation, the volume flow being decreased, and / or the turbulence being transitional to turbulent; andan acute DVT vein is indicated by one or more of: the morphology being enlarged, the lumen echogenicity being hypoechoic, the respiratory variation being absent, the cardiac variation being absent, the volume flow being absent, and the turbulence being absent.

13. The method of claim 8, wherein the respiratory variation of the target region is determined based on changes in measured diameter of the target region.

14. The method of claim 9, wherein the respiratory variation of the target region is determined based on flow velocity variation with the respiratory cycle.

15. The method of claim 9, wherein the cardiac variation of the target region is determined based on flow velocity variation with the cardiac cycle.

16. The method of claim 8, wherein the lumen echogenicity of the target region is determined based on changes in acoustic scattering in the blood of the region, gray- scale data of the volumetric ultrasound image, and combinations thereof.

17. The method of claim 1, wherein the clot location is at a distance D from the target region.

18. The method of claim 1, wherein the Doppler dataset is a spectral Doppler dataset, a continuous-wave Doppler dataset, a pulsed Doppler dataset, a Color Doppler dataset, and combinations thereof.

19. A system for automated monitoring of deep vein thrombosis (DVT) in a subject, the system comprising:an ultrasound transducer configured to emit a probe ultrasound signal toward a target region of a target blood vessel of the subject;an ultrasound receiver configured to receive a response ultrasound signal from the target region of the subject; anda controller comprising a non-transitory computer-readable medium having computer-executable instructions stored thereon that, ifexecuted by one or more processors of a computing device, cause the computing device to perform steps comprising:emitting ultrasound waveforms toward the subject by the ultrasound transducer;by the ultrasound receiver, and in a common ultrasound acquisition step, obtaining a first ultrasound dataset comprising a first volumetric ultrasound image and a first volumetric Doppler dataset of the target region;determining, based on the volumetric ultrasound image, at least one anatomic-parameter;determining, based on the volumetric Doppler dataset, at least one flow-parameter;diagnosing a presence or a development of a DVT clot at a clot location in the target blood vessel based on the at least one anatomic-parameter or the at least one flow-parameter; andautomatically sending a signal to an alarm indicator to provide an alarm interface when the presence or the development of the DVT clot in the target blood vessel is diagnosed,wherein a placement of the ultrasound transducer and the ultrasound receiver is a predetermined placement selected without a priori knowledge of the clot location.

20. The system of claim 19, further comprising obtaining a second ultrasound dataset comprising a second volumetric ultrasound image and a second volumetric Doppler dataset of the target region, wherein the second ultrasound dataset is acquired after the first ultrasound dataset is acquired.

Images