System and method for doppler ultrasound guided peripheral vascular manipulation

Abstract

Systems and methods are disclosed herein for providing guidance and feedback during an operation during a vascular intervention by Doppler ultrasound during the operation on one or more distal peripheral vessels affected by the vascular intervention. In accordance with various exemplary embodiments, Doppler ultrasound is performed during operation to detect flow characteristic changes and / or abnormal vascular flow characteristics of the interventional-affected peripheral vascular system, such as detection of adverse vascular events. The detected blood flow characteristic changes are communicated during operation to guide and / or assist in clinical decisions, and / or are used to automatically send control signals to interrupt or adjust treatment. In other embodiments, guidance and / or feedback during the operation is automatically provided during the neuromodulation operation via Doppler ultrasound of one or more peripheral vessels whose blood flow is modulated by the neuromodulation operation, and the neuromodulation device may be optionally controlled based on detected changes in blood flow.

Description

[0001] Cross-references to related applications

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 667,935, filed July 5, 2024, entitled “System and Method for Performing Doppler Ultrasound-Guided Peripheral Vascular Manipulation,” the entire contents of which are incorporated herein by reference. Background Technology

[0003] This disclosure relates to diagnostic ultrasound. More specifically, this disclosure relates to Doppler ultrasound for detecting vascular pathology.

[0004] When peripheral artery disease is diagnosed, surgical intervention is often attempted to open the proximal vessel, thereby improving blood flow to the wound, promoting wound healing, and avoiding amputation. Typically, interventional physicians use fluoroscopy to image the limb arteries and perform interventional procedures such as angioplasty (balloon), plaque resection, or stent implantation to open the narrowed vessel. After the intervention, they use fluoroscopy again to determine the extent to which the intervention improved distal perfusion. This technique is not ideal because fluoroscopy is not a quantitative measure of perfusion. Furthermore, fluoroscopy readings may not predict desired outcomes, such as wound healing.

[0005] Following the initial intervention, interventional physicians face the decision of whether to perform additional interventions. Additional interventions may be performed on the same vessel or on other vessels. While additional interventions may improve perfusion and outcomes, they introduce additional risks and require additional time in the catheterization lab and / or operating room. More importantly, interventional physicians often cannot determine which interventions or how many interventions will yield positive results. Invention Overview

[0007] This document discloses systems and methods for providing guidance and feedback during vascular intervention via Doppler ultrasound during manipulation of one or more distal peripheral vessels affected by a vascular intervention. According to various exemplary embodiments, Doppler ultrasound during manipulation is used to detect changes in flow characteristics and / or abnormal vascular flow characteristics in the peripheral vascular system affected by the intervention, such as the detection of adverse vascular events. Detected changes in flow characteristics are communicated during manipulation to guide and / or assist clinical decision-making, and / or are used to automatically send control signals to interrupt or adjust treatment. In other embodiments, guidance and / or feedback during neuromodulation manipulation is automatically provided via Doppler ultrasound of one or more peripheral vessels (whose blood flow is modulated by neuromodulation manipulation), and the neuromodulation device can optionally be controlled based on detected changes in blood flow.

[0008] A further understanding of the functionality and benefits of this disclosure can be achieved by referring to the following detailed description and accompanying drawings.

[0009] Brief description of the attached figures

[0010] The embodiments will be described in conjunction with the accompanying drawings. In the drawings, the same reference numerals may denote the same or similar functional elements.

[0011] Figure 1A , 1B 1C illustrates an exemplary automated processing workflow involving the use of Doppler ultrasound during the procedure to assess the flow characteristics and / or vascular flow features of peripheral vessels affected by the intervention, in order to provide feedback and / or control over the intervention in the procedure.

[0012] Figure 1D and 1E An exemplary automated processing workflow is shown, involving the use of Doppler ultrasound during operation to assess the flow properties and vascular flow characteristics of peripheral vessels affected by neuromodulation interventions, in order to provide feedback and / or control over the intervention in the procedure.

[0013] Figure 2A An exemplary system is shown for using intraoperative Doppler ultrasound to assess the flow characteristics and vascular flow features of peripheral vessels affected by intervention to facilitate feedback and / or interventional control during the procedure.

[0014] Figure 2B An exemplary convolutional neural network architecture for processing spectral Doppler ultrasound data to detect vascular event features is shown.

[0015] Figure 3A and 3B Color flow and spectral Doppler images of intravascular emboli (which appear as bright white vertical "stripes" on ultrasound Doppler waveforms) are displayed respectively.

[0016] Figure 4A , 4B 4C and 4C images show color flow and spectral Doppler images of the embolus, respectively. (Compared to...) Figure 3A and 3B Compared to the previous diagram, emboli appear more frequently in these diagrams.

[0017] Figure 5A and 5B An example of vasospasm is shown. The change in peak systolic velocity is clearly visible as blood flow increases over time.

[0018] Figure 6 An example of Doppler spectrum showing blood vessel retraction is displayed.

[0019] Figure 7A and7B It showed that there was stimulation ( Figure 7A ) and non-irritating ( Figure 7B The Doppler waveform of the spectrum at time ). Invention Details

[0021] Various embodiments and aspects of this disclosure will be described with reference to the details discussed below. The following description and accompanying drawings are illustrative and should not be construed as limiting the scope of this disclosure. Numerous specific details are described to provide a thorough understanding of various embodiments of this disclosure. However, in some cases, to avoid making the discussion of embodiments of this disclosure too lengthy, well-known or conventional details have not been described.

[0022] As used herein, the terms "comprising" and "including" should be understood as inclusive and open-ended, not exclusive. Specifically, when used in the specification and claims, the terms "comprising" and "including," and variations thereof, mean to include the specified features, steps, or components. These terms should not be construed as excluding the presence of other features, steps, or components.

[0023] As used herein, the term "exemplary" means "used as an example, instance, or illustration" and should not be construed as being preferred or advantageous relative to other configurations disclosed herein.

[0024] As used herein, the terms "about" and "approximately" are intended to cover variations that may exist within the upper and lower limits of a numerical range, such as variations in properties, parameters, and dimensions. Unless otherwise stated, the terms "about" and "approximately" mean plus or minus 25% or less.

[0025] It should be understood that, unless otherwise stated, any specified scope or group is a shorthand for each member within that scope or group and for each possible subscope or subgroup covered therein, and similarly relates to any subscope or subgroup therein. Unless otherwise stated, this disclosure relates to and explicitly incorporates each particular member and each combination of subscopes or subgroups.

[0026] As used herein, when used in conjunction with a quantity or parameter, the phrase "of a magnitude" refers to a range between approximately one-tenth and ten times the quantity or parameter.

[0027] As used herein, the phrase "spectral Doppler dataset" refers to a dataset characterized by changes in blood flow velocity over time, measured via Doppler ultrasound. Each time point in a spectral Doppler dataset has an associated velocity distribution that characterizes the distribution of velocity values ​​within the sampling area (Doppler window). A spectral Doppler dataset can also be referred to as a Doppler spectrogram.

[0028] As used in this article, the phrase "spectral Doppler waveform" refers to a graphical representation of at least a portion of a spectral Doppler dataset.

[0029] As used herein, the phrase "real-time" refers to a refresh rate, latency, or lag of less than 100 milliseconds in an action (such as processing data, displaying results, or otherwise communicating results). As used herein, the phrase "near real-time" refers to a refresh rate, latency, or lag of less than 2 seconds in an action (such as processing data, displaying results, or otherwise communicating results).

[0030] As used herein, when used in conjunction with vascular intervention, the phrases "distal peripheral vessels" and "distal peripheral vascular system" refer to one or more peripheral vessels located downstream of the site of the intervention and affected by the intervention. In this context, the intervention may be performed to target an obstruction or pathology to improve or restore perfusion of the distal peripheral vessels and / or the distal peripheral vascular system. The intervention may be a peripheral vascular intervention or a non-peripheral (e.g., coronary) intervention.

[0031] As used herein, the term "period of procedure" includes the period during which a medical procedure is performed (intraoperative) and the period after which the patient continues to receive procedure-related care, such as while the patient is in the operating room, recovery room, or intensive care unit.

[0032] Various exemplary embodiments of this disclosure relate to using peripheral vascular Doppler ultrasound to provide feedback and guidance during vascular interventions (e.g., but not limited to vascular procedures employing balloon angioplasty (balloon inflation / deflation), plaque resection, intravascular lithotripsy, and stent implantation, including peripheral and / or coronary vascular procedures). Conventionally, operative decisions during peripheral vascular procedures are made via imaging modalities, including whether intraoperative wound visualization occurs during intraoperative angiography, defined as an area of ​​high contrast agent concentration around the distal wound region. This approach relies on injecting contrast agent into the vessel to show whether the contrast agent reaches the distal vascular system. Its main drawback is its subjectivity, as the operator observes the contrast agent visualization and subjectively visually assesses the amount of contrast agent. Another drawback is that it depends on the amount of contrast agent used and does not measure any objective data on flow to the wound, but only flow to the target general area. Most methods, such as the ankle-brachial index, toe-brachial index, and TcP02, are bulky, time-consuming, unsuitable for the intraoperative environment, and limited to clinics and diagnostic centers. Furthermore, these methods typically fail to provide the clear, direct, and sufficiently rapid feedback needed to facilitate decision-making during the procedure.

[0033] The inventors have discovered that during vascular interventions, intraoperative Doppler ultrasound can be performed on the distal peripheral vascular system affected by the intervention (e.g., one or more vessels affected by the peripheral vascular intervention) to assess the blood flow characteristics and abnormal vascular flow features of the affected peripheral vascular system, thereby providing useful intraoperative feedback that can guide and / or assist clinical decision-making and positively influence the outcome of the procedure. In particular, many exemplary embodiments disclosed herein will demonstrate how intraoperative processing of spectral Doppler ultrasound data can guide and / or facilitate intraoperative decision-making when performing vascular interventions aimed at correcting vascular system pathology.

[0034] The content of this disclosure is structured as follows. First, it broadly describes methods for assessing the peripheral vascular system during various vascular procedures using intraoperative Doppler ultrasound to facilitate intraoperative feedback and / or guidance. Doppler data acquired during the operation, characterizing flow in one or more peripheral vessels, such as spectral Doppler waveforms and / or other forms of Doppler data, is dynamically and intraoperatively processed to infer and / or detect changes in vascular flow characteristics in the one or more peripheral vessels, such as the detection of peripheral vascular events, including, for example, events that occur and / or persist during the operation. In some exemplary embodiments, feedback is provided to the user to alert them to the detected change upon detection of a change in vascular flow characteristics. In some exemplary embodiments, upon detection of a change in vascular flow characteristics, a control signal is automatically generated and delivered (e.g., transmitted, relayed) to an operation-related therapeutic medical device or therapeutic medical system, wherein the control signal is configured to control or instruct the medical device to interrupt or modulate (adjust) the therapeutic intervention. These and other exemplary implementations will be described below in the context of using Doppler ultrasound to detect peripheral vascular events, including detecting emboli in peripheral blood vessels, detecting peripheral vascular spasm, detecting peripheral vascular retraction, and detecting changes in blood flow in peripheral blood vessels during neuromodulation procedures (e.g., spinal cord stimulation).

[0035] Compared to commercial Doppler ultrasound systems that enable interventional physicians to observe the presence of vascular flow characteristics by looking at a display (e.g., a spectral Doppler waveform or a power M-mode Doppler display) or to hear these events via audio output, the systems and methods provided by exemplary embodiments of this disclosure involve autonomous processing of Doppler ultrasound data for continuous (longitudinal in the time dimension) detection and monitoring of changes in vascular flow characteristics during therapeutic interventions.

[0036] Now for reference Figure 1AAn exemplary method is illustrated, in which feedback and / or guidance during vascular manipulation, such as identifying flow features associated with the presence of adverse vascular events, is provided based on detecting changes in vascular flow characteristics through processing ultrasound Doppler data characterizing blood flow within a distal peripheral vessel during the manipulation. In step 100, Doppler ultrasound data is acquired from the distal peripheral vessel during the vascular manipulation. This data is dynamically and processed during the manipulation, e.g., in real-time or near real-time, as shown in step 110, to detect the presence of changes in vascular flow characteristics related to the vascular intervention, such as the presence of vascular events.

[0037] In step 120, the occurrence of detected changes in vascular flow characteristics (e.g., flow characteristic detection associated with adverse vascular events) is communicated to the interventional physician (e.g., user, clinician, nurse). This communication can be achieved through various methods and means, such as, but not limited to, visual displays on a user interface, audible alarms, and combinations thereof.

[0038] In one exemplary implementation Figure 1A The illustrated workflow can be performed after first securing the Doppler ultrasound device to an anatomical region suitable for detecting the target peripheral vessels (e.g., the foot), and this step can be performed before initiating treatment. Before treatment initiation, a baseline ultrasound spectral Doppler dataset (e.g., for at least 5 seconds) can be collected. The spectral Doppler dataset is autonomously analyzed to determine, for example, the waveform envelope, and optionally, Doppler indices, such as, but not limited to, maximum velocity, mean velocity, pulsatility index, and resistance index. An "energy" waveform can also be calculated, where, for each time interval (e.g., every 50 milliseconds or other suitable time interval), the sum of pixel values ​​(or the squares of pixel values, or another sum of sums of corresponding pixel values ​​based on the frequency (or velocity) axis) is calculated. This initial, preoperative energy waveform is labeled as the baseline energy waveform. Once treatment begins, Doppler waveforms are continuously measured at the same location, and the above measurements are repeated during the procedure. Depending on the application, indicators characterizing changes in flow patterns are extracted at intermittent or periodic intervals and displayed to the interventional physician during the procedure.

[0039] Now for reference Figure 1B Another exemplary method is shown, wherein steps 100 and 110 are based on Figure 1A The method shown is followed, and except Figure 1AIn addition to or as an alternative to step 120, step 130 is executed automatically, such that the detection of changes in vascular flow characteristics (e.g., flow characteristic detection associated with vascular events) automatically triggers the generation of control signals or instructions, and delivers (e.g., via communication or transmission) them to a therapeutic medical device or therapeutic medical system used to deliver treatment during operation. For example, the control signals can be configured to control or instruct the medical device or the system controlling the medical device to interrupt or modulate the therapeutic intervention, such as... Figure 1B As shown in step 130.

[0040] Now for reference Figure 1C Another exemplary method is shown, wherein steps 100 and 110 are based on Figure 1A The method shown is performed, and as Figure 1A Step 120 and / or Figure 1B As a supplement or alternative to step 130, step 140 is executed automatically, such that the detection of changes in blood flow characteristics (e.g., flow characteristic detection associated with adverse vascular events) automatically triggers the generation and delivery (e.g., communication, transmission) of a control signal, which is delivered to a device or system configured to deliver an additional therapeutic intervention that mitigates the effects of the detected changes in vascular flow characteristics (e.g., reducing the impact, severity, and / or risk of the detected adverse vascular event). Examples of such therapeutic interventions include vascular robotics, surgical, and endovascular interventions, including but not limited to vascular interventions performed in the aorta, iliac arteries, femoral arteries, tibial arteries, and foot vessels, as well as spinal cord stimulation devices for peripheral and / or chronic pain, and cardiopulmonary bypass membrane oxygenation (ECMO) devices and / or ventricular assist devices (VADs) for the treatment of shock. The device or system used to mitigate changes in vascular flow characteristics (and / or adverse vascular events) may be the same as the device or system used to deliver the primary treatment associated with the vascular intervention (provided that such device or system has the capability to perform the additional intervention), or a separate device or system may be controlled to deliver the additional therapeutic intervention.

[0041] exist Figures 1A to 1C In the exemplary automated workflow shown, the detected changes in vascular flow characteristics can be adverse vascular events that are associated with a deterioration or potential deterioration of the subject's vascular health, and / or that the occurrence of the detected vascular event is associated with or predicts comorbidities. However, in some other exemplary embodiments, the detected changes in flow characteristics are not necessarily associated with a deterioration in vascular pathology, but can be improvements in vascular perfusion that are related to the outcome of therapeutic interventions.

[0042] In some exemplary embodiments, after an adverse vascular event is detected, additional background information related to the occurrence of the adverse vascular event is determined, at least in part, by employing changes in one or more Doppler indices (assessed before and after the detection of the adverse vascular event), such as, but not limited to, the severity of the adverse vascular event. One or more Doppler indices may be determined based on processing spectral Doppler data. Various examples of this exemplary embodiment, described below in conjunction with adverse vascular events including emboli, vasospasm, and vasoconstriction, are employed to provide additional multimodal background information that helps assess the severity and / or risk associated with the adverse vascular event. The estimated or inferred severity or risk may be used when generating feedback related to the adverse vascular event. In some exemplary embodiments, the change in a given Doppler index is determined based on Doppler index measurements prior to the occurrence of the adverse vascular event and Doppler index measurements during the vascular event. In some exemplary embodiments, the change in a given Doppler index is determined based on Doppler index measurements prior to the occurrence of the adverse vascular event and Doppler index measurements after the vascular event has ended.

[0043] Non-limiting examples of Doppler indices used when automatically assessing the severity and / or risk of detected vascular events during operation include peak systolic velocity, end-diastolic velocity, mean velocity, pulsatility index, acceleration time (which can be used to assess distal perfusion and detect improvement or damage after intervention, such as foot acceleration time when the peripheral vessel being tested is a foot vessel), and resistance index (a measure of downstream resistance, which may optionally provide an indirect indicator of vasospasm, retraction, or tissue-level ischemia). In some exemplary embodiments, at least one Doppler indice is acceleration time and / or resistance index. In some exemplary embodiments, the peripheral vessel is a foot vessel, and at least one Doppler indice is foot acceleration time, and resistance index may also be included.

[0044] It should be understood that in some exemplary embodiments, the Doppler index includes one or more standard Doppler indices known in the art, while in other exemplary embodiments, the Doppler index additionally or alternatively includes one or more non-standard or unknown Doppler parameter values ​​or measures obtained by processing spectral Doppler data.

[0045] In some exemplary embodiments, the severity or risk of an adverse vascular event may be determined in part by employing one or more non-Doppler exponential indicators (characterizing the adverse vascular event) obtained by processing one or more spectral Doppler waveforms corresponding to the adverse vascular event. For example, such indicators may be combined with changes in the aforementioned Doppler index to provide an estimate of the severity and / or risk associated with the adverse vascular event. In the case of embolic adverse vascular events, non-limiting examples of such indicators include indicators characterizing the number and / or type of emboli, embolus duty cycle, and / or detected embolic clusters. In the case of adverse vascular events involving vasospasm, non-limiting examples of such indicators include indicators characterizing the intensity and / or duration of vasospasm. In some exemplary embodiments, feedback during operation is generated by processing at least one indicator associated with the adverse vascular event using a machine learning algorithm (examples of which are described below), and at least one additional indicator associated with changes in at least one Doppler index before and after the occurrence of the adverse vascular event.

[0046] In some exemplary embodiments, after the occurrence or occurrence of an adverse vascular event is identified by processing Doppler data (e.g., a spectral Doppler dataset), the Doppler data can be further monitored to detect the completion of the vascular event. This can be achieved, for example, by continuously or repeatedly acquiring and monitoring (processing) Doppler data until no flow characteristics associated with the adverse vascular event are detected. Feedback indicating that the adverse vascular event has ended can be provided when or after it is determined that the event has concluded.

[0047] While many of the exemplary embodiments disclosed herein involve the acquisition of Doppler ultrasound data during the vascular interventional portion of a clinical procedure, it should be understood that Doppler data acquisition may additionally or alternatively be performed post-procedurally, after the vascular intervention is completed, such as while the patient is still receiving procedural care, for example in the operating room, recovery room, or intensive care unit. Indeed, while some adverse vascular events may occur during the interventional (intraoperative) phase of a medical procedure, some vascular events (such as vasoconstriction and / or vasospasm) may occur immediately or later after the intraoperative phase of the procedure.

[0048] Furthermore, while some exemplary automated workflows described herein involve detecting changes in blood flow characteristics via processing ultrasound Doppler data characterizing blood flow within distal peripheral vessels located downstream of the vascular intervention site in the vascular system, it should be understood that some exemplary embodiments of this disclosure involve using Doppler ultrasound to detect changes in the flow characteristics of one or more peripheral vessels in response to non-vascular therapeutic interventions (and / or to detect adverse vascular events). For example, as will be described in detail below, the inventors have found that the application of neural stimulation (e.g., spinal nerve stimulation) can lead to changes in blood flow characteristics in the peripheral vascular system, and the detection of such peripheral blood flow changes can be used as a feedback mechanism to monitor and / or control neuromodulation operations.

[0049] For example, now refer to Figure 1D This paper illustrates an alternative exemplary method in which feedback and / or guidance are provided during a neuromodulation (e.g., neurostimulation) procedure based on Doppler ultrasound characterization of blood flow in one or more peripheral vessels during the procedure. As shown in step 150, Doppler ultrasound data is acquired from the peripheral vessels during the neuromodulation procedure, wherein the neuromodulation treatment results in changes in peripheral blood flow characteristics. This data is processed dynamically and during the procedure, e.g., in real-time or near real-time, as shown in step 160, to detect changes in blood flow characteristics by processing ultrasound Doppler data characterizing blood flow in the peripheral vessels during the procedure. In step 170, the occurrence of changes in blood flow characteristics in the examined peripheral vessels is communicated to the interventional physician (e.g., user, clinician, nurse), thereby providing the interventional physician with feedback suitable for monitoring and / or controlling the neuromodulation procedure.

[0050] Now for reference Figure 1E This illustrates another exemplary method involving neural modulation, wherein steps 150 and 160 are based on Figure 1D The method shown is followed, and except Figure 1D In addition to or as an alternative to step 170, step 180 is automatically executed such that the detection of changes in peripheral vascular flow characteristics (e.g., velocity and / or flow rate changes) automatically triggers the generation and delivery (e.g., communication, transmission) of a control signal, which is delivered to a neurostimulation device or system used to deliver neural stimulation during operation. For example, the control signal can be configured to control or instruct the medical device to interrupt or modulate (adjust) neuromodulation interventions, such as... Figure 1E Step 180 is shown.

[0051] exist Figures 1A to 1EIn the exemplary embodiments shown, Doppler ultrasound data characterizing blood flow in one or more peripheral vessels acquired during operation can be acquired, dynamically processed, and processed during operation according to various acquisition, preprocessing, and postprocessing algorithms to infer and / or detect the presence of changes in blood flow characteristics in peripheral vessels (e.g., vascular events). For example, in some exemplary embodiments described in detail below, Doppler ultrasound data acquired during operation can be acquired and preprocessed to generate a spectral Doppler dataset, which is then processed to detect changes in blood flow characteristics.

[0052] Therefore, in some exemplary embodiments, spectral Doppler datasets are processed during operation to automatically detect changes in vascular blood flow characteristics. Spectral Doppler waveforms are currently the standard data output format for commercially available Doppler ultrasound scanners and are accessible to any end user via DICOM or screen capture. Many exemplary embodiments of this disclosure facilitate the intraoperative detection of peripheral vascular events by adapting standard Doppler ultrasound scanners to process spectral Doppler datasets generated by such scanners according to the methods disclosed herein. Therefore, many exemplary embodiments herein can be implemented in a hardware-independent manner, adaptable to any existing spectral Doppler ultrasound system capable of generating spectral Doppler ultrasound datasets with sufficiently low latency for continuous intraoperative processing and feedback (e.g., as a modification or enhancement) according to the systems and methods described herein.

[0053] In other exemplary embodiments, the systems and methods of this disclosure can be implemented by processing other Doppler ultrasound dataset formats, such as raw Doppler time-domain signals, which are optionally processed according to a preprocessing and postprocessing framework, wherein the Doppler ultrasound signals in the time domain are compared at time points before and after a vascular event.

[0054] It should be understood that the detection of vascular events (peripheral vascular events) can be based on a variety of thresholds, criteria, and / or comparative metrics. In some exemplary embodiments, when one or more conditions indicating the presence of vascular features in a time-dependent Doppler dataset (e.g., a time-dependent spectral Doppler ultrasound dataset or a raw time-domain time-series Doppler ultrasound dataset) are met, it can be determined that changes in vascular blood flow characteristics and / or a specific vascular event have occurred.

[0055] For example, vascular features associated with a specific vascular event can be characterized by one or more thresholds associated with one or more features of a Doppler ultrasound dataset, such as one or more parameters representing a sudden change in velocity distribution, peak velocity, or signal intensity. For instance, a vascular event can be determined to have occurred when a sudden change in the velocity distribution of a spectral Doppler waveform within a given time window meets predetermined criteria (e.g., one or more aggregate measures). In another example, a vascular event can be determined to have occurred when one or more parameters representing a sudden change in the slope of a time-dependent characteristic of a spectral Doppler waveform (e.g., peak systolic velocity (PSV) or end-diastolic velocity (EDV)) meet certain conditions.

[0056] In some exemplary embodiments, in addition to simply detecting changes in vascular blood flow characteristics or the occurrence of vascular events, or alternatively, the severity, intensity, frequency, or other measures associated with changes in vascular blood flow characteristics and / or specific types of vascular events may be determined dynamically and during operation. In such cases, these measures may be communicated to the interventional physician and / or used to automate the delivery of vascular or non-vascular treatments.

[0057] In some exemplary embodiments, the detection of vascular events may represent discrete vascular events occurring over a short period of time during a vascular procedure, such as the presence of an embolus. In other exemplary embodiments, the detection of vascular events may represent blood flow characteristics extending over time intervals. For example, the detection of vascular events may be associated with the occurrence of a vascular condition, such as vasospasm or vasoconstriction. This vascular condition may persist for a period of time during or after the vascular intervention. Examples of detecting such persistent vascular events are described in detail below.

[0058] While many of the exemplary embodiments disclosed herein relate to assessing or characterizing blood flow within peripheral vessels, it should be understood that Doppler ultrasound data can be collected and processed from two or more vessels.

[0059] In embodiments of this disclosure relating to procedures involving the use of Doppler ultrasound data acquired during operations involving distal peripheral vessels affected by vascular interventions, it should be understood that the vascular procedure may involve any vascular intervention capable of affecting blood flow within the distal peripheral vessel, such as, but not limited to, coronary artery interventions including coronary stenting, balloon angioplasty, intravascular lithotripsy, valve intervention / replacement, thrombectomy, and peripheral vascular interventions including stenting, balloon angioplasty, drug-eluting balloon angioplasty, intravascular lithotripsy, deep vein arterialization, and other interventions such as intra-aortic aneurysm repair, aortic stenting, ECMO, left ventricular assist devices, hemodialysis / ultrafiltration.

[0060] It should be understood that Figures 1A to 1EThe workflow shown can be implemented fully automatically, provided that the ultrasound Doppler probe is properly positioned to detect Doppler ultrasound signals from the target peripheral blood vessel. Now refer to... Figure 2A An exemplary system suitable for performing the methods disclosed herein is illustrated. This exemplary system includes an ultrasound device 330 comprising an ultrasound transducer array 340, a transmit beamformer 300 with pulse generator-receiver circuitry 320, a receive beamformer 310, and control and processing hardware 200 (e.g., a controller, computer, or other computing system). The ultrasound device can be secured to a subject during intervention, for example via adhesive supports and / or one or more straps, thereby enabling continuous or intermittent measurement of Doppler ultrasound data during the procedure.

[0061] The control and processing hardware 200 may also include or be able to connect to an interventional therapy system 350, such as an endovascular therapy device (e.g., an endovascular therapy catheter or its associated subsystem), which can be controlled to perform endovascular interventions, such as, but not limited to, balloon angioplasty (balloon inflation and deflation), plaque resection, endovascular lithotripsy, and stent implantation. As shown in the figure, the control and processing hardware 200 may also include or be connected to an additional treatment device or system 360 capable of delivering additional treatment to mitigate detected adverse treatment events.

[0062] Control and processing hardware 200 is used to control the transmit beamformer 300 and the receive beamformer 310, and to process the received signal after beamforming. For example... Figure 2A As shown, in one embodiment, the control and processing hardware 200 may include a processor 210, a memory 220, a system bus 205, one or more input / output devices 230, and a number of optional additional devices, such as a communication interface 260, a display 240, an external memory 250, and a data acquisition interface 230.

[0063] This exemplary method relates to controlling an ultrasound transducer array 340 to perform hemodynamic measurements (e.g., detecting Doppler ultrasound data), which can be implemented via a processor 210 and / or a memory 220. Figure 2A As shown, the control of the ultrasonic transducer array 340 can be achieved by the control and processing hardware 200 via executable instructions represented by the computing module 290. The control and processing hardware 200 may include and execute scan conversion software (e.g., real-time scan conversion software).

[0064] In some exemplary embodiments, the transducer array 340 is controlled to acquire one or more Doppler ultrasound datasets, as shown in the Doppler processing module 280. In some exemplary embodiments, the transducer array can be controlled to scan the ultrasound beam and generate ultrasound images, for example, as controlled via the image processing module 285. Each Doppler dataset may correspond to a different location, such as a location within an image acquired by the ultrasound transducer array 340. The ultrasound image may be a Doppler ultrasound image.

[0065] In some exemplary embodiments, control and processing hardware 200 may be used in a program to process a given Doppler ultrasound dataset to detect changes in blood flow characteristics during program operation (including detection of adverse vascular events), as schematically illustrated by peripheral blood flow assessment module 290. The blood flow assessment module may also perform calculations to determine one or more peripheral hemodynamic measures, including but not limited to acceleration time (i.e., foot acceleration time in the case of ultrasound detection of peripheral blood vessels in the foot), flow rate, peak velocity, beats per minute, and velocity slope from the start to the end of systole.

[0066] In some exemplary embodiments, the ultrasound beam emitted by the ultrasound transducer array 340 can be scanned to identify one or more regions associated with areas having sufficiently high Doppler signals, such as locations corresponding to target arterial vessels. For example, the transmitted beamformer 300 can be controlled to scan the ultrasound beam over a 1D or 2D angular range (2D if the transducer array is a 2D transducer array), and the Doppler signals can be processed to determine the angle corresponding to the maximum signal, as schematically illustrated by the scanning module 295. In some exemplary embodiments, a preferred angle can be determined by processing the collected Doppler signals according to a machine learning algorithm (e.g., a neural network) trained with Doppler signals having a desired shape and / or signal-to-noise ratio.

[0067] The functionality described herein can be implemented in part via hardware logic in processor 210 and in part using instructions stored in memory 220. Some implementations can be implemented via processor 210 without additional instructions stored in memory 220. Some implementations are implemented using instructions stored in memory 220, which are executed by one or more general-purpose microprocessors. In some exemplary implementations, a custom processor, such as an application-specific integrated circuit (ASIC) or a field-programmable gate array (FPGA), may be employed. Therefore, this disclosure is not limited to specific hardware and / or software configurations.

[0068] It should be understood that Figure 2AThe exemplary system shown is not intended to limit the components that may be used in a given implementation. For example, the system may include one or more additional processors. Furthermore, one or more components of the control and processing hardware 200 may be provided as external components that interface with the processing device. For example, as shown, any one or more of the transmit beamformer 300 and receive beamformer 310 may be included as components of the control and processing hardware 200 (as shown within the dashed lines) or may be provided as one or more external devices.

[0069] While some implementations can be carried out in fully functional computers and computer systems, various implementations can be used as computing products distributed in various forms and can be applied regardless of the specific type of machine or computer-readable medium used to implement that distribution.

[0070] At least some of the aspects disclosed herein can be partially embodied in software. That is, these techniques can be executed in a computer system or other data processing system in response to its processor (e.g., a microprocessor) executing a sequence of instructions contained in memory (e.g., read-only memory (ROM), volatile random access memory (RAM), non-volatile memory, cache memory, or remote storage device).

[0071] Computer-readable storage media can be used to store software and data that, when executed by a data processing system, cause the system to perform various methods. Executable software and data can be stored in a variety of locations, including, for example, ROM, volatile RAM, non-volatile memory, and / or cache. Parts of the software and / or data can be stored in any of these storage devices. As used herein, the phrases "computer-readable material" and "computer-readable storage medium" refer to all computer-readable media except for the transient propagation of a signal itself.

[0072] While many exemplary embodiments disclosed herein employ the processing of Doppler ultrasound data acquired from one or more peripheral vessels during operation to detect changes in vascular events and / or peripheral blood flow characteristics based on thresholds or criteria associated with time-dependent features or parameters, it should be understood that the foregoing exemplary processing methods are intended to be non-limiting and are illustrative of a broad range of exemplary methods.

[0073] In some exemplary alternative implementations, machine learning algorithms can be used to process Doppler ultrasound data during operation to detect the presence of vascular events. For example, adverse vascular events can be detected via a machine learning algorithm configured to receive spectral Doppler waveform segments as input and generate an output that includes a classification of the adverse vascular event type among various types of adverse vascular events. In this exemplary implementation, the machine learning algorithm can be trained based on training spectral Doppler waveform data that associates waveform features with different types of labeled adverse vascular events.

[0074] In one exemplary implementation, multiple flow streams analyze ultrasound Doppler spectra to extract different Doppler indices. For example, a first processing stream detects the presence or absence of an embolus, a second processing stream calculates a resistance index after extracting the peak velocity distribution, and a third processing stream analyzes the spectra to calculate acceleration time (e.g., foot acceleration time in the case of detecting foot vessels). Optionally, the peak velocity distribution may be extracted first, and the resistance index and acceleration time may be measured. After these measures are calculated, they may be fed into a machine learning classifier to perform multimodal classification, where the inputs are the different Doppler indices. In one exemplary implementation, the output is a binary outcome variable indicating whether the patient should continue with the intervention. Non-limiting examples of machine learning classifiers include random forests, logistic regression, fully connected neural networks, and XGBoost. In some exemplary applications, additional metrics are used as inputs. For example, venous arterialization monitoring may be implemented using additional features such as peak systolic velocity, end-diastolic velocity, resistance index, and flow rate.

[0075] In another exemplary implementation, Doppler ultrasound data referencing clinical judgment (e.g., spectral Doppler data) can be labeled with expert user tags that associate the reference data with known vascular events, such as the presence of emboli, vasospasm, and / or vascular retraction. The reference dataset can also be labeled to indicate changes in treatment (e.g., increases or decreases) adopted to mitigate the vascular event. If such labeled datasets are available, machine learning algorithms can be trained to process unlabeled Doppler data acquired in the procedure and output one or more metrics, such as the presence / absence of emboli, the number of peripheral emboli, the duty cycle associated with peripheral emboli, the presence / absence of retraction and / or vasospasm, the severity associated with retraction and / or vasospasm, determination of whether an intervention was interrupted, determination of appropriate treatment modifications, and determination of appropriate additional therapeutic interventions (and optionally the extent of such interventions) to mitigate the detected vascular event.

[0076] In a non-limiting example, the spectral Doppler waveform can be represented by a 2D image having velocity along the vertical axis and time along the horizontal axis, which can be used as input to a convolutional neural network (CNN), such as... Figure 2B As shown. Examples of CNNs include VGG-16, DenseNet, AlexNet, etc. Typically, CNNs require a predetermined input image size. Therefore, the spectrogram will be resampled to the expected size using image interpolation (e.g., 224×224 for VGG-16). Furthermore, many CNNs are designed for 3-channel RGB images. For grayscale images (like spectrograms), they can be copied to all three channels.

[0077] Figure 2B An example of a standard CNN architecture is shown. The input on the left can be a Doppler spectrogram (replicated across 3 channels). The input spectrogram can be of a fixed time length, such as 5 seconds. CNNs can be used to classify spectral Doppler waveforms to determine the presence or absence of one or more vascular events.

[0078] In another exemplary implementation, the softmax layer and output layer of the CNN can be replaced with a regression layer that provides continuous outputs. In one exemplary implementation, the regression output layer may contain multiple outputs, each of which is a scalar value. For example, in the case of training a CNN to perform embolus detection, one scalar output may provide a determination of the number of emboli detected, and a second scalar output may provide a determination of the duty cycle associated with the presence of emboli within the time range of the input Doppler dataset.

[0079] In this example, the training data can be composed of spectrograms of Doppler signals obtained when emboli are present, manually reviewed by expert clinicians or sonographers. Each time an embolus occurs, the dataset is labeled with its occurrence time and duration by placing a cursor at the location on the displayed spectrogram. The total number of emboli and the total duration are recorded. The duty cycle is calculated as the ratio of the total embolus time to the dataset time interval (e.g., 5 seconds). The recorded number of emboli and duty cycle are used as the ground truth for the aforementioned CNN.

[0080] In this example scenario, the CNN is trained to predict the scalar output described above. During inference, the input is a spectral Doppler dataset, and the output will be numerical. Employing a data-driven approach using machine learning allows the system to operate under various conditions, including low signal-to-noise ratios and artifacts. Furthermore, to increase the system's robustness, augmentations can be used during training to increase the number of input spectral samples. In this case, noise can be injected into the spectral Doppler waveform while maintaining the same corresponding true value output.

[0081] The foregoing exemplary embodiments and variations thereof contemplated by this disclosure can be beneficial in addressing issues related to the need for skilled sonographers during vascular ultrasound procedures. In fact, the embodiments provided herein enable autonomous processing of Doppler ultrasound data acquired during the procedure from one or more peripheral blood vessels, thereby autonomously providing feedback, guidance, and / or control of the treatment device during treatment, significantly reducing the skill level and experience required by the user.

[0082] While some of the systems and methods described and illustrated herein relate to the application of foot wound and foot ultrasound vascular diagnostic assessment during treatment procedures, it should be understood that the embodiments described herein may be adopted or modified to employ Doppler ultrasound to acquire peripheral vascular systems in other anatomical regions during the procedure, such as, but not limited to, the hand.

[0083] Intraoperative Doppler ultrasound detection of emboli in peripheral blood vessels during peripheral vascular procedures.

[0084] In some exemplary embodiments, the aforementioned systems and methods are modified to facilitate the detection of emboli in one or more peripheral blood vessels (e.g., peripheral blood vessels in the foot) during a therapeutic intervention. Prior to this disclosure, clinicians were unable to determine whether embolization of the foot vascular system occurred during peripheral vascular interventions such as balloon angioplasty (inflation and deflation), plaque resection, endovascular lithotripsy, and stent implantation, as well as the incidence and volume of emboli. Foot embolization is presumed to affect blood flow and wound healing in the foot vessels, but currently there is no automated, real-time method to quantify emboli. Emboli are generated by unnatural processes resulting from therapeutic interventions, rather than the body's natural response; they can be, for example, atherosclerotic plaques or thrombi impacted or moved by a device, which are released downstream and may cause obstruction in the arch of the foot or microcirculation, often leading to adverse outcomes.

[0085] Therefore, in some exemplary embodiments, the foregoing embodiments are modified to facilitate the detection of embolic events resulting from interventional treatment (e.g., targeting within a blood vessel to a proximal-to-Doppler ultrasound sensor), wherein the disclosed exemplary embodiments are modified to facilitate, for example, the characterization of the presence or load of emboli via flow disturbance analysis, including the detection of transient high-frequency components or specific waveform patterns.

[0086] In some exemplary embodiments, systems and methods are provided to determine the number of embolic signals, particle signal size, and the distinction between artifacts (non-real signals) and real embolic signals during operation (e.g., real-time or near real-time). In some exemplary embodiments, detected emboli can be classified according to various emboli morphologies by processing Doppler ultrasound data, for example, using thresholds related to measurements obtained from processing spectral Doppler data within a specific time window, and / or detecting embolic features associated with emboli of different sizes (e.g., large embolic events and smaller / micro-embolic events) based on the aforementioned machine learning methods (as described above). Peripheral embolism detection and optional emboli identification during operation, compared to micro-embolic events, can facilitate recognition of the impact of clinical deterioration (or its likelihood) on wound healing perfusion through larger emboli. For example, such systems can detect the presence of "embolic rain" or a large number of embolic events, which may prompt clinicians to discontinue treatment or administer medications such as tissue plasminogen activator (TPA, a blood thinner) to dissolve thrombus particles. In some exemplary embodiments, the exemplary method and system may provide continuous automated ultrasound monitoring of statistical and / or summary measures related to peripheral emboli, such as, but not limited to, the total number of embolic signals that have occurred throughout the procedure, thereby providing clinicians with useful in-procedure insights.

[0087] In a non-limiting example implementation of a procedure involving the detection of emboli in peripheral vessels during a therapeutic intervention using Doppler ultrasound, the following steps may be performed. First, a baseline Doppler image is acquired and the PSV and EDV are recorded. When treatment is performed (e.g., balloon angioplasty, plaque resection, vascular laser therapy, or stent implantation), a "real-time" continuous Doppler dataset (waveform) is activated. Spectral Doppler waveforms (optionally occupying approximately 3 / 4 of the spectrum) are collected, optionally at a scan rate reflecting 3 to 4 cardiac cycles. The waveform gain can be configured to detect embolic features. Embolic features are characterized as bright echo vertical lines above the baseline, determined based on the automatic processing of spectral Doppler data over multiple time windows and the application of a suitable embolic detection threshold. The number of embolic signals is automatically counted during treatment (e.g., throughout the entire procedure) and communicated to the clinician. The PSV and EDV after treatment are recorded and compared with the baseline values.

[0088] In a non-limiting example implementation of a procedure involving the detection of emboli in peripheral vessels during a therapeutic intervention using Doppler ultrasound, the following steps are performed. A spectral Doppler waveform is calculated by performing an FFT on the ultrasound Doppler signal, as described, for example, in Ubeyli, E. et al., “Applications of FFT and ARMA Spectral Analysis in Arterial Doppler Signals,” *Mathematics and Computational Applications*, 8, 311 (2003). Typically, the FFT length is 128, and the FFT calculation is repeated over an overlap window of 25 ms with a 50% overlap. The amplitude of the FFT is then calculated and squared to produce the spectral power. This produces a 2D matrix where the vertical axis is the frequency range and the horizontal dimension is a time step of 12.5 ms. For each step, the values ​​in the vertical dimension (i.e., frequency) are summed, resulting in an “energy” waveform characterized by a summation (scalar) value at each time interval. In the aforementioned calculations, the underlying data being evaluated is a function of the Fourier spectrum of the Doppler frequency shift over time (equivalent to a spectrogram in signal processing). Therefore, at each time interval, what is plotted is the spectral energy density—each pixel represents the energy in a frequency range: the brighter the pixel, the higher the energy in that frequency range. Thus, the summation provides a measure of energy within each time interval. These scalar values ​​can be combined into a time series. Two such time series can be created: one for the preoperative phase and another for the treatment and postoperative phases. Typically, each summed time series will be several seconds long to encompass multiple cardiac cycles.

[0089] As an optional initial step, it can be determined whether an artifact exists in each time window. For example, this can be done as follows: (i) summing along the positive side of the waveform to obtain a first value, (ii) summing along the negative side of the waveform to obtain a second value, (iii) subtracting the negative value from the positive value to obtain the difference, and (iv) identifying it as an artifact if the difference falls within a range known to be associated with artifacts.

[0090] If the time window is not determined to represent an artifact, the energy metric within the time interval is further processed to determine if it indicates the presence of an embolus. This can optionally be performed by comparison with the preoperative waveform. In an example of such a comparison method, the start and end of each cardiac cycle are calculated from the waveform. This is performed by extracting the waveform envelope (using a tracing algorithm from the literature). The end-diastolic phase of each cycle is calculated from the envelope. This process is repeated on the baseline energy waveform as described above. For the baseline energy waveform, the average energy distribution across 5 cycles is calculated. The energy distribution for each cycle within the treatment / post-treatment timeframe is then calculated. In an optional step, the correlation coefficient of the energy envelope between the post-treatment and baseline traces is calculated. If the correlation coefficient is higher than a preselected threshold (e.g., 0.9), it indicates that the two waveforms are now comparable for further evaluation.

[0091] Embolism detection is then performed on a per-time-window basis. For example, in this exemplary comparison method, the difference between the postoperative period of each time window and the baseline mean waveform (per cycle) can be calculated. If the difference exceeds a threshold, the time window is identified as corresponding to the presence of an embolic event.

[0092] The number of embolic events per cardiac cycle can be displayed as an output trace. When the number of embolic events exceeds a threshold set by the clinician, an alarm can be generated to notify the interventional physician to stop treatment. Other parameters can also be calculated, such as, but not limited to, the embolic duty cycle (the percentage of time emboli occupied by a time window). Such parameters can optionally be used to determine when to interrupt the procedure. For example, when the embolic duty cycle exceeds 20%, an alarm can be generated to alert the clinician to interrupt treatment, or a control signal can be generated and delivered to the treatment device to automatically interrupt treatment.

[0093] Some of the aforementioned methods involve processing spectral Doppler waveforms. Alternatively, if the raw time-domain spectral Doppler gate signal is available, other time-domain techniques, such as cross-correlation estimation of time delays at two different locations along the vessel, can be used to determine whether it is an artifact. Unlike transcranial Doppler (TCD), where the spectral gate is typically placed at different depths, the gate in this exemplary embodiment can be placed laterally, and the ultrasound beam can be switched between two lateral locations along the vessel for Doppler acquisition. Therefore, in other exemplary embodiments that do not involve processing spectral Doppler data, the raw time-domain Doppler gate signal (before FFT) can be processed using time-domain techniques, such as, but not limited to, cross-correlation at two different locations along the vessel, which would involve employing two Doppler windows at different locations along the vessel.

[0094] Now for reference Figure 3A and 3B The images show color flow and spectral Doppler images of emboli within blood vessels (represented by bright white vertical "stripes" on the ultrasound Doppler waveform). Figure 4A , 4B 4C and 4C respectively show color flow and spectral Doppler images of the embolus. Figure 3A and 3B Compared to the example shown, Figures 4A-4C Blood flow distribution in the body is mainly composed of emboli, while Figure 3A and 3B This shows the combination of blood flow and emboli.

[0095] In some exemplary embodiments, after the occurrence and / or presence of an embolus is detected, additional background information related to the presence of the embolus, such as the severity of the adverse vascular event, is determined by employing changes in one or more Doppler indices (which are assessed before and after the detection of the adverse vascular event). This additional Doppler-based background information can be used, according to a multimodal processing workflow, to estimate or infer the severity and / or risk associated with the presence of the embolus, and may optionally be used to infer whether distal perfusion is impaired.

[0096] For example, in some exemplary embodiments, changes in acceleration time (e.g., dorsalis pedis artery acceleration time) and resistance index before and after embolism are used to generate a measure of severity and / or risk associated with the presence of emboli. Prolongation of acceleration time (e.g., dorsalis pedis artery acceleration time) and / or change in resistance index are used as clinically important indicators (e.g., weighted as being associated with increased severity when generating a composite severity measure) because they indicate potentially impaired downstream perfusion and can inform, guide, or recommend immediate clinical intervention. This assessment can be used for therapeutic interventions such as thrombolysis or aspiration. Conversely, in some exemplary embodiments, emboli that do not show a corresponding change in acceleration time (e.g., dorsalis pedis artery acceleration time) or resistance index, or whose changes are below a predetermined threshold, are considered potentially subclinical and are processed (e.g., weighted) to reduce the severity and / or risk assessment. In such cases, this lower risk classification allows clinicians to de-prioritize or monitor emboli without further therapeutic intervention.

[0097] Doppler ultrasound monitoring during peripheral vascular procedures to detect vasospasm and vasoconstriction in peripheral vessels.

[0098] Vasospasm, also known as spasm, is a sudden, involuntary contraction of the smooth muscle in the blood vessel wall, resulting in narrowing or constriction of the vessel. This phenomenon can occur during or after vascular interventions such as angioplasty, stent placement, or catheter insertion. Vasospasm can significantly impact the success of interventions and patient outcomes by reducing blood flow to the affected vessel.

[0099] Vascular retraction refers to the natural tendency of a blood vessel to revert to its original diameter after being dilated or dilated during vascular interventions such as angioplasty. Vascular retraction essentially involves the tightening of an artery. For example, in radial or tibial artery approaches with a diameter of 2-3 mm, vascular retraction can occur once the sheath is removed, with the arterial media (muscular wall) reacting and "clamping." When this occurs, intra-arterial medication or a balloon is delivered to open the artery. This phenomenon is an important consideration in vascular interventions because it can affect the long-term success of vascular procedures. During angioplasty, a balloon catheter is used to dilate blood vessels that have narrowed or become blocked due to atherosclerosis. However, once the balloon is deflated and removed, the vessel may partially collapse or "retract" back to its pre-dilation state. This can lead to a reduction in the lumen diameter, which can impair blood flow and may result in restenosis (vascular restenosis). If complete arterial closure occurs, vascular retraction can be a devastating outcome.

[0100] If vasospasm or recoil is detected during the procedure, appropriate treatment can be delivered before the procedure is completed and the patient is transferred to the recovery room. Once in the recovery room, there are very few tools available to reopen a closed artery, aside from medication. In example cases where an artery supplies the foot and there is significant recoil, the use of a balloon or stent can prevent severe ischemia, potentially avoiding future amputation.

[0101] While vasoconstriction and vasospasm (vasospasm) are known to be common during vascular interventions, there is currently no automated solution to perform autonomous procedures for detecting vasospasm and / or vasoconstriction. Therefore, there is also no available solution to automatically detect the occurrence of vasospasm / vasoconstriction, the severity of vasoconstriction, the duration of vasoconstriction, or the relief of vasospasm / vasoconstriction. Furthermore, prior to this disclosure, the inventors were not aware of the potential utility of monitoring peripheral blood vessels (e.g., within the foot system window) during the procedure in determining the occurrence or presence of vasospasm and vasoconstriction.

[0102] Furthermore, it should be noted that while transcranial Doppler (TCD) has been used to perform vasospasm detection, the conventional TCD vasospasm detection workflow involves measuring different arteries separately at different times to detect vasospasm, only after vasospasm has occurred due to increased velocity in the monitored artery. For example, in conventional TCD-based vasospasm detection methods, blood flow in the middle cerebral artery is compared with blood flow distal to the internal carotid artery to obtain a ratio (Lindegaard ratio), which is then evaluated to infer the presence of vasospasm. In stark contrast to this conventional approach, the modified method for vasospasm detection disclosed herein involves monitoring blood flow characteristics in peripheral vessels using Doppler ultrasound during procedures involving vascular interventions to detect the occurrence of vasospasm during the procedure. This allows for treatment of vasospasm using intraoperative therapy shortly after its occurrence, potentially improving the overall outcome of the procedure.

[0103] The inventors have discovered that vasospasm and vasoconstriction can be detected during the procedure by Doppler ultrasound monitoring of peripheral vessels distal to the endovascular intervention. This is achieved by detecting spectral Doppler waveform characteristics that indicate the occurrence of vasospasm or vasoconstriction, as described in further detail below. Furthermore, this exemplary method facilitates continuous assessment of the duration of vasospasm and vasoconstriction to guide the treatment window and facilitates confirmation of relief, helping to determine treatment efficacy. Therefore, embodiments of this disclosure can be modified to facilitate the detection of vasospasm and vasoconstriction during the procedure and can be further modified, for example, to quantify the percentage of flow changes associated with vasospasm. This percentage of change in blood flow, measured as the difference before and after the occurrence of vasospasm, can be used by interventional physicians to determine whether to administer medication such as nitroglycerin to dilate the affected vessels.

[0104] like Figure 5A and 5B As shown, the characteristic of vasospasm in spectral Doppler waveforms is a gradual increase in peak velocity throughout a continuous cardiac cycle, resulting in a progressively stronger vascular flow characteristic. This flow characteristic can be detected using various processing algorithms; examples of non-restricted algorithms are described below.

[0105] According to an exemplary workflow, spectral Doppler data is acquired during vascular intervention based on the detection of distal vessels, and the spectral Doppler dataset is processed over a time span including several consecutive cardiac cycles. The envelope can be calculated using methods established in the literature, such as those described by Kathpalia, A. et al., "Adaptive Spectral Envelope Estimation for Doppler Ultrasound," IEEE Transactions on Ultrasound, Ferroelectrics & Frequency Control 63, 1825-1838 (2016).

[0106] According to this exemplary vasospasm detection method, a peak detection algorithm is applied to the envelope to calculate the peak systolic velocity (PSV). After knowing the peak value for each cardiac cycle, the end-diastolic velocity is calculated. This operation is repeated for each cardiac cycle. From this PSV value history, a linear fit can be calculated for the PSV values ​​of a set of consecutive cardiac cycles (e.g., 3, 4, 5, or 6 cardiac cycles). The slope of the fit to the PSV values ​​can be evaluated and compared to a threshold, such that if the slope exceeds the threshold, vasospasm is determined to have occurred, while if the slope of consecutive PSV values ​​is below the threshold, normal blood flow is inferred. In another exemplary embodiment, beat-by-beat PSV variation is calculated, and if the difference in PSV values ​​exceeds a threshold for N consecutive cardiac cycles, vasospasm is determined to have occurred. In other exemplary vasospasm detection embodiments, machine learning algorithms can be employed to detect the presence of gradually increasing blood flow features in spectral Doppler data, which can be implemented, for example, according to the exemplary machine learning method described above.

[0107] Now, considering modifications of the method disclosed herein for detection during vascular retraction, it should be noted that retraction is typically associated with the cessation of blood flow after several cardiac cycles. Therefore, the retraction characteristic in the spectral Doppler waveform is generally associated with a decrease in PSV to near zero, and the detectable diastolic portion of the waveform disappears compared to the baseline spectral Doppler waveform before intervention. After vascular retraction occurs, the spectral Doppler waveform diminishes to a cut-off characteristic, characterized by a series of "peaks" on the spectrogram with high distal resistance, for example in… Figure 6 As shown in the figure, vasoconstriction results in a weakened spectral Doppler waveform that lacks detectable diastolic flow.

[0108] Therefore, recoil can be identified during operation by monitoring spectral Doppler data acquired from distal peripheral vessels affected by vascular intervention, allowing detection of recoil when pulse-weighted PSV changes significantly decrease or blood flow / waveform disappears (at least during diastole). Such spectral Doppler flow characteristics can be detected using various methods, including monitoring PSV reductions exceeding a set threshold within a defined cardiac cycle, or machine learning-based detection methods, such as those detecting the aforementioned punctuated recoil flow characteristics.

[0109] In some exemplary embodiments, after the occurrence and / or presence of vasospasm or vasoconstriction is detected, additional background information related to the occurrence of the adverse vascular event, such as the severity of the adverse vascular event, is determined by employing changes in one or more Doppler indices (assessed before and after the detected adverse vascular event). This additional Doppler-based background information can be used, according to a multimodal processing workflow, to estimate or infer the severity and / or risk associated with the presence of the vascular event, and optionally to infer whether intervention is necessary. For example, in an example case of a detected vasospasm event, an elevated resistance index accompanied by a correspondingly prolonged acceleration time (e.g., dorsalis pedis artery acceleration time) may suggest a more severe vasospasm requiring or potentially benefiting from treatment (e.g., use of a vasodilator). In an example case of a detected vasoconstriction event, an acute or significant (e.g., meeting pre-established criteria or thresholds) change in the resistance index or acceleration (e.g., dorsalis pedis artery acceleration time) after angioplasty may suggest elastic recoil, and the determined severity may inform the need for or benefit of using a therapeutic intervention (e.g., balloon dilation).

[0110] In some exemplary implementations, the severity of vascular retraction can be measured based on preoperative baseline PSV and waveform. For example, if the intraoperative PSV decreases to below a selected threshold (e.g., 5 cm / sec or lower), severe retraction can be determined, while other thresholds can be established and used to measure or infer other levels of retraction severity.

[0111] Doppler ultrasound monitoring of peripheral blood vessels guides spinal cord stimulation and neuromodulation.

[0112] Spinal cord stimulators deliver focused electrical currents to the spine to treat neuropathic pain. The inventors have discovered that such spinal cord stimulation therapy has a direct effect on peripheral vascular blood flow. Based on clinical studies, the inventors have been able to determine that activation of spinal cord stimulation leads to increased peripheral vascular blood flow, as experimentally confirmed by Doppler ultrasound assessment of peripheral vascular perfusion in the foot during the procedure. In particular, the inventors have found that the application of spinal cord stimulation can modify Doppler-derived hemodynamic parameters, such as dorsalis pedis artery acceleration time, peak and end-diastolic flow velocities, drag index, and foot vascular flow. Experiments have shown that adjusting the electrical dose results in immediate, real-time changes in foot vascular blood flow.

[0113] Therefore, in some exemplary embodiments, changes in blood flow characteristics are detected based on ultrasound Doppler data characterizing blood flow in peripheral vessels processed during the operation, providing feedback and / or guidance during neuromodulation procedures (e.g., spinal cord stimulation procedures). Figure 1D and 1EAs shown. For example, detected changes in vascular flow can be used to automatically guide the dosage or intensity of neuromodulation therapy (e.g., spinal cord stimulation) based on the detected vascular blood flow characteristics. For example, neuromodulation therapy can be modified using a predetermined relationship between the nerve stimulation dose and / or intensity and one or more peripheral vascular flow measurements detectable via peripheral Doppler ultrasound. For example, the spinal stimulation device has adjustable settings for pain control, and these settings can be adjusted based on peripheral vascular blood flow characteristics detected via Doppler ultrasound.

[0114] In a non-limiting example workflow, changes in blood flow characteristics are detected based on ultrasound Doppler data characterizing blood flow within peripheral vessels, processed during the operation, to provide feedback and / or guidance during neuromodulation procedures (e.g., spinal cord stimulation). The method follows an example approach. After positioning an ultrasound transducer to detect peripheral vessels (e.g., peripheral vessels in the foot), a baseline arterial spectral Doppler waveform is measured before applying neuromodulation (e.g., spinal cord stimulation). One or more Doppler hemodynamic indices, such as acceleration time (e.g., dorsalis pedis artery acceleration time), PSV, and EDV, may be measured before applying neuromodulation. As shown in the right-hand figure, the waveform will feature low PSV, low resistance index, and large PAT values. These values ​​can be stored, and a historical record with recorded values ​​can be created for each cardiac cycle within the initial waveform.

[0115] As described above, the inventors have discovered that the application of spinal cord stimulation leads to increased peripheral blood flow due to vasodilation. This can result in significant differences in Doppler blood flow characteristics and flow patterns, and the blood flow characteristics can be characterized using the aforementioned Doppler data processing methods to detect changes caused by the applied stimulation. In some exemplary embodiments, such as Figure 7A and Figure 7B As shown, indices including PSV, EDV, RI, PI, and PAT (foot acceleration time) can be measured and used to detect and / or quantify changes in blood flow characteristics induced by stimulation application. Figure 7B The display shows the spectral Doppler waveform before stimulation, while Figure 7A (Showing the spectral Doppler waveform after stimulation). It should be noted that the stronger blood flow in the former due to stimulation... Figure 7A acceleration time of dorsalis pedis artery in the middle Figure 7B This represents a significant reduction. In such implementations, the difference between hemodynamic (flow characterization) values ​​for each index can be calculated and displayed as a rolling waveform. This multi-channel display can be provided to interventional physicians throughout the procedure.

[0116] Furthermore, changes in one or more aspects of the detected blood flow characteristics (e.g., parameters or indices, such as acceleration time (e.g., dorsalis pedis artery acceleration time), peak systolic velocity (PSV), and resistance index (RI)) exceeding predetermined thresholds may be of clinical concern, for example, due to a sudden increase in vascularization. In such cases, reducing the treatment energy may be appropriate or beneficial, and this reduction can be recommended and / or automatically controlled based on the ratio between the pre-stimulation and during-stimulation Doppler indices. As the stimulation energy is reduced, quantitative information from the Doppler indices is used to adjust the treatment.

[0117] While this exemplary embodiment describes the processing of Doppler ultrasound data characterizing blood flow in peripheral vessels during operation to monitor and / or adjust spinal stimulation, it should be understood that this exemplary embodiment can be modified for various neuromodulation devices, including but not limited to electrospinal stimulation, spinal drug delivery, deep brain stimulation, vagus nerve stimulation, peripheral nerve stimulation, transcutaneous electroneuropathy, transcranial magnetic stimulation, and other neuromodulation devices, provided that Doppler ultrasound measurements are performed on peripheral vessels whose blood flow characteristics respond to neuromodulation therapy. Furthermore, it should be understood that while this exemplary embodiment uses peripheral vascular Doppler ultrasound as a diagnostic method to monitor and / or control neuromodulation, neuromodulation methods can take many forms, including but not limited to electrical, acoustic (ultrasound), magnetic, and optical (e.g., infrared) neurostimulation methods. Those skilled in the art can determine, through routine experiments, which neuromodulation methods result in detectable changes in peripheral vascular blood flow characteristics and which peripheral vessels are suitable for Doppler ultrasound detection for a given neuromodulation method.

[0118] In some exemplary embodiments involving the use of Doppler ultrasound to detect changes in peripheral vascular blood flow characteristics induced by neuromodulation, a user interface may be provided that facilitates control of neuromodulation and also displays the detected blood flow measurements, and optionally includes one or more automatic feedback modules that control / limit the applied neuromodulation based on the detected blood flow measurements.

[0119] In some exemplary embodiments, a threshold for autonomous interruption based on changes in peripheral vascular blood flow characteristics caused by neuromodulation detected via Doppler ultrasound, and / or a predetermined relationship between one or more flow indices or parameters generated by processing ultrasound Doppler data in the procedure and the applied stimulus, can be tailored to a specific patient. For example, different patients will have different degrees of pathological severity, different pain thresholds, different physiological responses to a given neuromodulation intervention, and / or other related differences, which can lead to such customization being beneficial to the patient.

[0120] It should be understood that medical procedures involving neuromodulation (e.g., neurostimulation) can take many forms without departing from the intended scope of this disclosure. For example, some neuromodulation procedures involve the use of an external neuromodulation device (e.g., but not limited to, transcranial magnetic stimulation and transcutaneous electroneuropathy, external vagus nerve stimulation, and photobiomodulation) that is temporarily in contact with or located proximally to the patient during neuromodulation therapy delivery. In other example cases, neuromodulation may be performed using implantable devices (e.g., spinal cord stimulators and deep brain stimulators), and in such cases, the neuromodulation procedure may involve any procedure that includes the implanted device or subsequent use or modification of the implanted device. For example, ultrasound-Doppler-assisted neuromodulation medical procedures involving implanted neuromodulation devices may include one or more of the following: (i) medical procedures involving the initial implantation of the device, (ii) any therapeutic use of the device, (iii) procedures involving adjustment, calibration, or other modifications, and (iv) procedures involving the removal of the device, provided that the Doppler ultrasound is used to monitor peripheral blood flow.

[0121] Peripheral vascular flow was monitored during the peripheral vascular balloon procedure using Doppler ultrasound.

[0122] In another exemplary embodiment, the aforementioned exemplary methods and workflows can be adapted for peripheral vascular interventional procedures such as proximal artery balloon dilation. Accurate measurement of vessel size is crucial in balloon angioplasty. If the balloon size is too small, it may not provide clinical benefit, while if the balloon size is too large, it may lead to dissection. Therefore, in some exemplary embodiments, distal peripheral vessels downstream of the balloon angioplasty placement site are assessed via Doppler ultrasound during the procedure to detect changes in blood flow characteristics during balloon angioplasty treatment. For example, when the balloon is delivered to a given artery (e.g., an anterior tibial artery balloon), the relevant downstream peripheral vessels (e.g., the dorsalis pedis artery or arcuate artery) are monitored according to the methods described above. If automated processing of the Doppler data results in the determination that blood flow has stopped during balloon dilation, a warning is communicated and / or a control signal is automatically generated and sent to the balloon catheter to stop further dilation. If no change in blood flow or only a reduction (according to a suitable predetermined threshold) is detected, an alarm suggesting that the balloon size may be inappropriate is communicated.

[0123] List of implementation methods

[0124] Implementation Method 1. A method for monitoring and detecting adverse vascular events by examining the peripheral vascular system using Doppler ultrasound, the method comprising: During medical procedures involving vascular intervention, Doppler ultrasound data from peripheral blood vessels are acquired during the procedure, where blood flow within the peripheral blood vessels is downstream of the location associated with the vascular intervention. Doppler ultrasound data is automatically processed during operation to identify flow characteristics associated with adverse vascular events; Detecting the occurrence of adverse vascular events; and Generate operational feedback related to adverse vascular events.

[0125] Implementation 2. The method according to Implementation 1 further includes determining the severity of the adverse vascular event after detecting its occurrence, wherein the severity of the adverse vascular event is determined at least in part by employing changes in one or more Doppler indices before and after the occurrence of the adverse vascular event; Feedback during the procedure is generated based on the severity of adverse vascular events.

[0126] Implementation 3. The method according to Implementation 2, wherein one or more Doppler indices are generated based on the processing of spectral Doppler data.

[0127] Implementation 4. The method according to Implementation 2, wherein flow characteristics are detected based on the processing of spectral Doppler data.

[0128] Implementation 5. The method according to Implementation 2, wherein the change in at least one Doppler index is assessed based on a first value of the Doppler index obtained before the adverse vascular event and a second value of the Doppler index obtained during the adverse vascular event.

[0129] Implementation 6. The method according to Implementation 2, wherein the change of at least one Doppler index is assessed based on a first value of the Doppler index obtained before the adverse vascular event and a second value of the Doppler index obtained after the adverse vascular event.

[0130] Implementation 7. The method according to any one of Implementations 2 to 6 further includes processing the spectral Doppler waveform associated with adverse vascular events to determine a quantitative indicator characterizing the adverse vascular events, and further using the quantitative indicator to infer the severity of the adverse vascular events.

[0131] Implementation Method 8. The method according to any one of Implementation Methods 1 to 7, wherein adverse vascular events are detected by a machine learning algorithm configured to receive spectral Doppler waveform segments as input and generate an output including a classification of adverse vascular event types among multiple types of adverse vascular events, said machine learning algorithm having been trained on training spectral Doppler waveform data that associates waveform features with different types of labeled adverse vascular events.

[0132] Implementation 9. The method according to any one of Implementations 2 to 7, wherein feedback during operation is generated by processing at least one indicator related to adverse vascular events using a machine learning algorithm, and at least one additional indicator related to changes in at least one Doppler index before and after the occurrence of an adverse vascular event.

[0133] Implementation 10. The method according to any one of Implementations 2 to 9, wherein one or more Doppler indices include one or more of peak systolic velocity and end-diastolic velocity.

[0134] Implementation Method 11. The method according to any one of Implementation Methods 2 to 9, wherein one or more Doppler indices are selected from acceleration time and drag indices.

[0135] Implementation Method 12. The method according to any one of Implementation Methods 2 to 9, wherein one or more Doppler indices include acceleration time and drag indices.

[0136] Implementation 13. The method according to any one of Implementations 2 to 9, wherein the peripheral blood vessels are foot blood vessels, and one or more Doppler indices include foot acceleration time.

[0137] Implementation 14. The method according to implementation 13, wherein one or more Doppler indices further include a resistance index.

[0138] Implementation Method 15. The method according to any one of Implementation Methods 1 to 14, wherein the flow characteristics indicate the occurrence of adverse vascular events.

[0139] Implementation Method 16. The method according to any one of Implementation Methods 1 to 9 further includes: The completion of the detection of adverse vascular events; and Provides additional feedback indicating that an adverse vascular event has been completed.

[0140] Implementation 17. The method according to any one of Implementations 2 to 6, wherein an adverse vascular event is associated with the detection of one or more emboli.

[0141] Implementation 18. The method according to Implementation 17, wherein an embolic event is identified by detecting a bright echo vertical line that meets the embolism detection criteria within a given time window of the spectral Doppler waveform.

[0142] Implementation 19. The method according to Implementation 17, wherein an embolic event is identified by processing a spectral Doppler waveform to generate an energy waveform and comparing the energy waveform with a previously measured preoperative energy waveform known to be embolic.

[0143] Implementation 20. The method according to Implementation 17, wherein embolus detection is performed via a machine learning algorithm configured to receive a spectral Doppler waveform segment as input and generate an output including at least one scalar measure characterizing the detection of an embolus.

[0144] Implementation 21. The method according to implementation 20, wherein the machine learning algorithm is configured such that the output includes determining the number of emboli within a spectral Doppler waveform segment.

[0145] Implementation 22. The method according to implementation 21, wherein the feedback during operation includes the total number of emboli detected.

[0146] Implementation 23. The method according to implementation 20, wherein the machine learning algorithm is configured such that the output includes determining the embolus duty cycle within a spectral Doppler waveform segment.

[0147] Implementation 24. The method according to implementation 20, wherein the machine learning algorithm is configured such that the output includes distinguishing between real emboli and artifacts.

[0148] Implementation 25. The method according to implementation 20, wherein the machine learning algorithm is configured such that the output includes classifying emboli according to emboli size.

[0149] Implementation 26. The method according to any one of Implementations 17 to 25, wherein one or more Doppler indices are selected from acceleration time and drag indices.

[0150] Implementation 27. The method according to any one of Implementations 17 to 25, wherein one or more Doppler indices include acceleration time and drag indices.

[0151] Implementation 28. The method according to any one of Implementations 17 to 25, wherein the peripheral blood vessels are foot blood vessels, and one or more Doppler indices include partial acceleration time.

[0152] Implementation 29. The method according to implementation 28, wherein one or more Doppler indices further include a resistance index.

[0153] Implementation 30. The method according to any one of Implementations 2 to 6, wherein an adverse vascular event is associated with the detection of vasospasm.

[0154] Implementation 31. The method according to implementation 30, wherein vasospasm is detected based on identifying time-dependent spectral Doppler waveforms having flow characteristics characterized by an increasing peak flow velocity in a continuous cardiac cycle.

[0155] Implementation 32. The method according to implementation 31, wherein flow features associated with vasospasm are identified using a machine learning algorithm.

[0156] Implementation 33. The method according to any one of Implementations 30 to 32, wherein the peripheral blood vessels are foot blood vessels, and one or more Doppler indices include foot acceleration time.

[0157] Implementation Method 34. The method according to any one of Implementation Methods 30 to 32, wherein one or more Doppler indices are selected from acceleration time and drag indices.

[0158] Implementation 35. The method according to implementation 34, wherein the severity of vasospasm is determined based on an increase in acceleration time and an increase in the resistance index.

[0159] Implementation 36. The method according to any one of Implementations 2 to 6, wherein adverse vascular events are associated with the detection of vascular retraction.

[0160] Implementation 37. The method according to Implementation 36, wherein the recoil is detected based on identifying a time-dependent spectral Doppler waveform having flow characteristics characterized by a peak systolic velocity decreasing to near zero over a continuous cardiac cycle, and having no detectable diastolic portion compared to the pre-intervention baseline spectral Doppler waveform.

[0161] Implementation 38. The method according to implementation 37, wherein flow characteristics associated with vascular retraction are identified using a machine learning algorithm.

[0162] Implementation 39. The method according to any one of Implementations 36 to 38, wherein the peripheral blood vessels are foot blood vessels, and one or more Doppler indices include foot acceleration time.

[0163] Implementation 40. The method according to any one of Implementations 36 to 38, wherein one or more Doppler indices are selected from acceleration time and drag indices.

[0164] Implementation 41. The method according to Implementation 40, wherein the severity of vasoconstriction is determined based on the change in acceleration time and resistance index.

[0165] Implementation 42. The method according to any one of Implementations 1 to 41, wherein feedback during operation includes alarms.

[0166] Implementation Method 43. The method according to any one of Implementation Methods 1 to 42, wherein a vascular intervention device is used to perform the vascular intervention, the method further comprising sending a control signal to the vascular intervention device to interrupt or adjust the vascular intervention to mitigate adverse vascular events.

[0167] Implementation 44. The method according to any one of Implementations 1 to 42, wherein a vascular intervention is performed using a vascular interventional device, the method further comprising sending control signals to an additional treatment device to provide treatment suitable for mitigating adverse vascular events.

[0168] Implementation Method 45. The method according to any one of Implementation Methods 1 to 44, wherein Doppler ultrasound data are acquired while performing vascular intervention.

[0169] Implementation Method 46. The method according to any one of Implementation Methods 1 to 45, wherein Doppler ultrasound data are acquired after a vascular intervention is performed.

[0170] Implementation 47. An ultrasound system, comprising: A Doppler ultrasound detection subsystem is configured to detect Doppler ultrasound data from peripheral blood vessels during medical procedures involving vascular intervention; and A control and processing circuit, operatively coupled to the Doppler ultrasound subsystem, includes a processor and associated memory, the memory including instructions executable by the processor for performing operations including: Acquiring Doppler ultrasound data from peripheral blood vessels; Automatically process Doppler ultrasound data to identify flow characteristics associated with adverse vascular events; Detecting the occurrence of adverse vascular events; and Generate feedback related to adverse vascular events.

[0171] Implementation 48. The system according to implementation 47, wherein the control and processing circuitry is further configured to perform operations including the following: After detecting the occurrence of an adverse vascular event, the severity of the adverse vascular event is determined, wherein the severity of the adverse vascular event is determined at least in part by employing one or more Doppler indices to measure changes before and after the occurrence of the adverse vascular event. Feedback is generated based on the severity of adverse vascular events.

[0172] Implementation method 49. A method comprising: During the neuromodulation procedure, Doppler ultrasound data from peripheral blood vessels are acquired during the procedure, and the blood flow in the peripheral blood vessels is regulated according to the neuromodulation procedure. The procedure automatically processes Doppler ultrasound data to detect changes in vascular flow characteristics in peripheral blood vessels induced by neuromodulation; and Provides feedback during operation that indicates changes in vascular flow characteristics.

[0173] Implementation 50. The method according to Implementation 49, wherein a neuromodulation device is used to perform neuromodulation operations, and the method further includes sending a control signal to the neuromodulation device based on changes in vascular flow characteristics to interrupt or adjust the neuromodulation treatment.

[0174] Implementation 51. The method according to Implementation 49, wherein Doppler ultrasound data is processed to identify flow characteristics associated with the presence of adverse vascular events, and the method further includes detecting the occurrence of adverse vascular events.

[0175] Implementation 52. The method according to Implementation 51, wherein a neuromodulation device is used to perform the neuromodulation operation, the method further comprising sending a control signal to the neuromodulation device to interrupt or adjust the neuromodulation operation to mitigate adverse vascular events.

[0176] Implementation Method 53. A neural modulation system, comprising: Neuromodulation device; The Doppler ultrasound detection subsystem is configured to detect Doppler ultrasound data from peripheral blood vessels during neuromodulation procedures performed using a neuromodulation device; and A control and processing circuit, operatively coupled to the Doppler ultrasound subsystem, includes a processor and associated memory, the memory including instructions executable by the processor for performing operations including: The procedure automatically processes Doppler ultrasound data to detect changes in vascular flow characteristics in peripheral blood vessels induced by neuromodulation; and Provides feedback during operation that indicates changes in vascular flow characteristics.

[0177] Implementation 54. The neuromodulation system according to Implementation 53, wherein the neuromodulation device is a neurostimulation device.

[0178] Implementation method 55. A method comprising: Doppler ultrasound data is acquired from peripheral vessels during medical procedures involving vascular intervention, where blood flow within the peripheral vessels is downstream of the location associated with the vascular intervention and is therefore affected by the intervention. The system automatically processes Doppler ultrasound data during operation to detect changes in vascular flow characteristics in peripheral blood vessels; and Provides feedback during operation that indicates changes in vascular flow characteristics.

[0179] The specific embodiments described above have been shown by way of example, and it should be understood that these embodiments may be readily modified and alternatively formed. It should be further understood that the claims are not intended to limit to the specific forms disclosed, but rather to cover all modifications, equivalents, and alternatives falling within the spirit and scope of this disclosure.

Claims

1. A method for monitoring and detecting adverse vascular events by examining the peripheral vascular system using Doppler ultrasound, the method comprising: During medical procedures involving vascular intervention, Doppler ultrasound data from peripheral blood vessels are acquired during the procedure, where blood flow within the peripheral blood vessels is downstream of the location associated with the vascular intervention. The Doppler ultrasound data is automatically processed during operation to identify flow characteristics associated with adverse vascular events; Detecting the occurrence of the adverse vascular events; and Generate operational feedback related to the adverse vascular events.

2. The method of claim 1, further comprising determining the severity of the adverse vascular event after detecting its occurrence, wherein the severity of the adverse vascular event is determined at least in part by employing changes in one or more Doppler indices before and after the occurrence of the adverse vascular event; The feedback during the operation is generated based on the severity of adverse vascular events.

3. The method of claim 2, wherein one or more Doppler indices are generated based on the processing of the spectral Doppler data.

4. The method of claim 2, wherein flow characteristics are detected based on the processing of spectral Doppler data.

5. The method of claim 2, wherein a change in at least one Doppler index is assessed based on a first value of the Doppler index obtained prior to the adverse vascular event and a second value of the Doppler index obtained during the adverse vascular event.

6. The method of claim 2, wherein a change in at least one Doppler index is assessed based on a first value of the Doppler index obtained before the adverse vascular event and a second value of the Doppler index obtained after the adverse vascular event.

7. The method according to any one of claims 2 to 6, further comprising processing the spectral Doppler waveform associated with the adverse vascular event to determine a quantitative indicator characterizing the adverse vascular event, and further using the quantitative indicator to infer the severity of the adverse vascular event.

8. The method according to any one of claims 1 to 7, wherein adverse vascular events are detected by a machine learning algorithm configured to receive spectral Doppler waveform segments as input and generate an output comprising a classification of the adverse vascular event type among multiple types of adverse vascular events, the machine learning algorithm having been trained on training spectral Doppler waveform data that associates waveform features with labeled different types of adverse vascular events.

9. The method according to any one of claims 2 to 7, wherein the feedback during the operation is generated by processing at least one indicator related to adverse vascular events using a machine learning algorithm, and at least one additional indicator related to changes in at least one Doppler index before and after the occurrence of the adverse vascular event.

10. The method according to any one of claims 2 to 9, wherein the one or more Doppler indices include one or more of peak systolic velocity and end-diastolic velocity.

11. The method according to any one of claims 2 to 9, wherein the one or more Doppler indices are selected from acceleration time and drag indices.

12. The method according to any one of claims 2 to 9, wherein the one or more Doppler indices include acceleration time and drag indices.

13. The method according to any one of claims 2 to 9, wherein the peripheral blood vessels are foot blood vessels, and the one or more Doppler indices include foot acceleration time.

14. The method of claim 13, wherein the one or more Doppler indices further include a resistance index.

15. The method according to any one of claims 1 to 14, wherein the flow characteristics indicate the occurrence of adverse vascular events.

16. The method according to any one of claims 1 to 9, further comprising: Completion of adverse vascular events detection; as well as Provides additional feedback indicating the completion of adverse vascular events.

17. The method according to any one of claims 2 to 6, wherein the adverse vascular event is associated with the detection of one or more emboli.

18. The method of claim 17, wherein an embolic event is identified by detecting a bright echo vertical line that meets the embolism detection criteria within a given time window of the spectral Doppler waveform.

19. The method of claim 17, wherein the embolic event is identified by processing a spectral Doppler waveform to generate an energy waveform and comparing the energy waveform with a previously measured pre-intervention energy waveform known to be embolic.

20. The method of claim 17, wherein embolus detection is performed via a machine learning algorithm configured to receive a spectral Doppler waveform segment as input and generate an output including at least one scalar metric characterizing the detection of an embolus.

21. The method of claim 20, wherein the machine learning algorithm is configured such that the output includes determining the number of emboli within a spectral Doppler waveform segment.

22. The method of claim 21, wherein feedback during operation includes the total number of emboli detected.

23. The method of claim 20, wherein the machine learning algorithm is configured such that the output includes determining the embolus duty cycle within a spectral Doppler waveform segment.

24. The method of claim 20, wherein the machine learning algorithm is configured such that the output includes distinguishing between real emboli and artifacts.

25. The method of claim 20, wherein the machine learning algorithm is configured such that the output includes classifying emboli according to emboli size.

26. The method according to any one of claims 17 to 25, wherein the one or more Doppler indices are selected from acceleration time and drag indices.

27. The method according to any one of claims 17 to 25, wherein the one or more Doppler indices include acceleration time and drag indices.

28. The method according to any one of claims 17 to 25, wherein the peripheral blood vessels are foot blood vessels, and the one or more Doppler indices include foot acceleration time.

29. The method of claim 28, wherein the one or more Doppler indices further include a resistance index.

30. The method according to any one of claims 2 to 6, wherein the adverse vascular event is associated with the detection of vasospasm.

31. The method of claim 30, wherein vasospasm is detected based on identifying time-dependent spectral Doppler waveforms having flow characteristics characterized by an increasing peak flow rate during consecutive cardiac cycles.

32. The method of claim 31, wherein the flow features associated with vasospasm are identified using a machine learning algorithm.

33. The method according to any one of claims 30 to 32, wherein the peripheral vessels are foot vessels, and one or more Doppler indices include foot acceleration time.

34. The method according to any one of claims 30 to 32, wherein the one or more Doppler indices are selected from acceleration time and drag indices.

35. The method of claim 34, wherein the severity of the vasospasm is determined based on an increase in acceleration time and an increase in the resistance index.

36. The method according to any one of claims 2 to 6, wherein the adverse vascular event is associated with the detection of vascular retraction.

37. The method of claim 36, wherein the recoil is detected based on identifying a time-dependent spectral Doppler waveform having flow characteristics characterized by a peak systolic velocity decreasing to near zero over a continuous cardiac cycle, and having no detectable diastolic portion compared to the pre-intervention baseline spectral Doppler waveform.

38. The method of claim 37, wherein the flow characteristics associated with vascular retraction are identified using a machine learning algorithm.

39. The method according to any one of claims 36 to 38, wherein the peripheral blood vessels are foot blood vessels, and the one or more Doppler indices include foot acceleration time.

40. The method according to any one of claims 36 to 38, wherein the one or more Doppler indices are selected from acceleration time and drag indices.

41. The method of claim 40, wherein the severity of the vascular retraction is determined based on the change in acceleration time and resistance index.

42. The method according to any one of claims 1 to 41, wherein the feedback during the operation includes alarms.

43. The method according to any one of claims 1 to 42, wherein a vascular interventional device is used to perform the vascular intervention, the method further comprising sending a control signal to the vascular interventional device to interrupt or adjust the vascular intervention to mitigate adverse vascular events.

44. The method of any one of claims 1 to 42, wherein a vascular interventional device is used to perform the vascular intervention, the method further comprising sending control signals to an additional treatment device to provide treatment suitable for mitigating the adverse vascular event.

45. The method according to any one of claims 1 to 44, wherein Doppler ultrasound data are acquired simultaneously with the vascular intervention.

46. ​​The method according to any one of claims 1 to 45, wherein Doppler ultrasound data are acquired after a vascular intervention is performed.

47. An ultrasound system comprising: A Doppler ultrasound detection subsystem configured to detect Doppler ultrasound data from peripheral blood vessels during medical procedures involving vascular intervention; as well as A control and processing circuit, operatively coupled to the Doppler ultrasound subsystem, includes a processor and associated memory, the memory including instructions executable by the processor for performing operations including: Acquiring Doppler ultrasound data from peripheral blood vessels; Automatically process Doppler ultrasound data to identify flow characteristics associated with adverse vascular events; Detecting the occurrence of adverse vascular events; and Generate feedback related to adverse vascular events.

48. The system of claim 47, wherein the control and processing circuitry is further configured to perform operations including: After an adverse vascular event is detected, the severity of the adverse vascular event is determined, wherein the severity of the adverse vascular event is determined at least in part by employing one or more Doppler indices to measure changes before and after the occurrence of the adverse vascular event. Feedback is generated based on the severity of adverse vascular events.

49. A method comprising: During the neuromodulation procedure, Doppler ultrasound data from peripheral blood vessels are acquired during the procedure, and the blood flow in the peripheral blood vessels is regulated according to the neuromodulation procedure. The Doppler ultrasound data is automatically processed during operation to detect changes in vascular flow characteristics in peripheral blood vessels caused by neuromodulation manipulation; and Provides feedback during operation that indicates changes in vascular flow characteristics.

50. The method of claim 49, wherein a neuromodulation device is used for neuromodulation operation, the method further comprising sending a control signal to the neuromodulation device based on changes in vascular flow characteristics to interrupt or adjust the neuromodulation treatment.

51. The method of claim 49, wherein processing Doppler ultrasound data to identify flow characteristics associated with the presence of adverse vascular events, the method further comprising detecting the occurrence of adverse vascular events.

52. The method of claim 51, wherein a neuromodulation device is used to perform the neuromodulation operation, the method further comprising sending a control signal to the neuromodulation device to interrupt or adjust the neuromodulation operation to mitigate adverse vascular events.

53. A neural modulation system, comprising: Neuromodulation device; A Doppler ultrasound detection subsystem is configured to detect Doppler ultrasound data from peripheral blood vessels during neuromodulation operations performed using a neuromodulation device; as well as A control and processing circuit, grounded to the Doppler ultrasound subsystem, includes a processor and associated memory, the memory including instructions executable by the processor for performing operations including: The procedure automatically processes Doppler ultrasound data to detect changes in vascular flow characteristics in peripheral blood vessels induced by neuromodulation; and Provides feedback during operation that indicates changes in vascular flow characteristics.

54. The neuromodulation system according to claim 53, wherein the neuromodulation device is a neurostimulation device.

55. A method comprising: Doppler ultrasound data is acquired from peripheral vessels during medical procedures involving vascular intervention, where blood flow within the peripheral vessels is downstream of the location associated with the vascular intervention and is therefore affected by the intervention. The system automatically processes Doppler ultrasound data during operation to detect changes in vascular flow characteristics in peripheral blood vessels; and Provides feedback during operation that indicates changes in vascular flow characteristics.

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