Muscle contraction state triggering in quantitative medical diagnostic ultrasound

Abstract

For quantitative ultrasound imaging using a medical diagnostic ultrasound scanner (20), muscle tissue is scanned (10) to determine (12) a contraction state. Once a desired state is identified, QUS imaging (14) is triggered to quantify or measure tissue properties while the muscle is in the desired state. The value of the tissue property at the known contraction state may be more diagnostically useful or informative. Comparison (16) of tissue properties across time and / or location may more accurately reflect the diagnostic information resulting from the triggering (14).

Description

Background Art

[0001] The present embodiment relates to medical diagnostic ultrasound. In particular, ultrasound is used to provide quantitative ultrasound (QUS) imaging. In QUS imaging, the ultrasound information is further processed to quantify the biomarkers or characteristics of the tissue being imaged. Rather than simply providing a B-mode image of the tissue, the characteristics of the tissue are imaged. For example, the shear wave velocity in the tissue is calculated using ultrasound imaging. Other examples include strain, attenuation, or backscatter measurements.

[0002] QUS imaging enables the evaluation and comparison of tissue properties under different conditions. One example includes shear wave velocity or backscatter changes during cycles of muscle contraction and relaxation. Another example includes elasticity, fat fraction, shear wave viscosity, modulus, or backscatter coefficient measured for muscle or between different tissues. For imaging muscle, inconsistencies in quantification arise from measuring and / or comparing quantities from unknown contractile states of the muscle. Summary of the invention

[0003] By way of introduction, the preferred embodiments described below include methods, systems, computer readable media, and instructions for quantitative ultrasound imaging using a medical diagnostic ultrasound scanner. Muscle tissue is scanned to determine a state of contraction. Once the desired state is identified, QUS imaging is triggered while the muscle is in the desired state to quantify or measure tissue properties. The values ​​of tissue properties at a known state of contraction may be more diagnostically useful or informative. Comparison of tissue properties across time and / or location may more accurately reflect diagnostic information due to the triggering.

[0004] In a first aspect, a method for quantitative ultrasound imaging using a medical diagnostic ultrasound scanner is provided. The medical diagnostic ultrasound scanner scans muscle tissue of a patient using ultrasound. An image processor detects a contraction state of the muscle tissue using ultrasound data from the scan. The image processor triggers quantitative ultrasound imaging performed by the medical diagnostic ultrasound scanner. Quantitative ultrasound imaging is triggered in response to detecting the contraction state. A quantitative ultrasound image from the triggered quantitative ultrasound imaging is displayed.

[0005] In a second aspect, a non-transitory computer-readable storage medium has stored therein data representing instructions executable by a programmed processor for quantitative ultrasound imaging. The storage medium includes instructions for: triggering the quantitative ultrasound imaging in response to detecting a muscle contraction state; comparing a first quantity from the triggered quantitative ultrasound imaging with a second quantity; and generating an image showing the comparison.

[0006] In a third aspect, a system for triggering quantitative ultrasound imaging is provided. A transmit beamformer is configured to transmit ultrasound in a patient. A receive beamformer is configured to output samples from ultrasound echoes. An image processor is configured to determine a state of a muscle based on the output samples or data detected from the output samples of a first scan performed by the transmit and receive beamformers, is configured to cause the transmit and receive beamformers to perform a second scan based on the state of the muscle, the second scan being used for quantitative ultrasound imaging, and is configured to determine a value of a tissue property based on the quantitative ultrasound imaging. A display is configured to display the value of the tissue property.

[0007] The present invention is defined by the appended claims, and nothing in this section should be taken as limiting those claims.Other aspects and advantages of the present invention are discussed below in conjunction with the preferred embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The components and drawings are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention. Moreover, in the drawings, like reference numerals designate corresponding parts throughout the different drawings.

[0009] Figure 1 is a flow chart of one embodiment of a method for quantitative ultrasound imaging using a medical diagnostic ultrasound scanner; and

[0010] Figure 2 is a block diagram of one embodiment of a system for triggered quantitative ultrasound imaging. DETAILED DESCRIPTION

[0011] The muscle contraction state is automatically detected and triggers shear wave and / or backscatter acquisition. The detection of contraction state is based on: radio frequency (RF) and / or image analysis to estimate tissue motion, deformation or strain (speckle tracking, normalized cross correlation, etc....). Trained artificial intelligence algorithms (machine learning models) can detect contraction states from RF, image and / or video (e.g., sequence) data. Triggered QUS uses a specialized (e.g., fixed) sequence triggered based on the detected muscle contraction state. The analysis tool provides measurement information of tissue properties based on shear waves and / or backscatter during known muscle contractions or load cycles.

[0012] Figure 1 One embodiment of a flow chart of a method for QUS imaging using a medical diagnostic ultrasound scanner is shown. Muscle contraction states are automatically detected. Shear wave, backscatter and / or other QUS acquisitions are triggered based on the detection of a given contraction state.

[0013] This method is Figure 2The ultrasound imaging system 20, the image processor 25, or a different system and / or processor may be used to perform the QUS. For example, the ultrasound imaging system 20 scans, detects, triggers itself to perform QUS, compares the quantities, and displays the relationship from the comparison.

[0014] Figure 1 The actions of may be performed in the order shown (from top to bottom) or in a different order. For example, the QUS image may be displayed in action 18 prior to the comparison of the quantities in action 16.

[0015] Can be used with Figure 1 The actions shown are additional, different, or fewer than those shown. For example, action 16 is not performed. As another example, an action for scanning and generating a B-mode or other ultrasound image to identify a region of interest may be added.

[0016] In act 10, a medical diagnostic ultrasound scanner scans muscle tissue of a patient. A transducer is positioned relative to the patient. The ultrasound scanner scans a planar region or volumetric region of the patient, including muscle tissue. For example, the transducer is positioned by an arm or leg. A user activates scanning. Acoustic energy is emitted into the patient, and acoustic echoes are received. The echoes are beamformed to sample the scanned region.

[0017] Any type of scanning can be used. For example, B-mode scanning is used. A B-mode image is generated to view the patient's body, allowing a user to indicate a region of muscle tissue as a region of interest. Alternatively, an image processor detects the muscle tissue region from beamformed samples, detected data, and / or image data.

[0018] Scanning occurs once, or a sequence of scans is performed. For example, scanning is repeated in an ongoing or interleaved manner. Any frame rate can be used, such as 20 Hz or higher.

[0019] In action 12, the image processor detects the contraction state of the muscle tissue using ultrasound data from the scan. The ultrasound data used for detection comes from the scan. Data from beamforming before detection (e.g., before B-mode or intensity detection) can be used. For example, radio frequency or in-phase and orthogonal data of samples used for beamforming can be used. Detected or image data, such as B-mode data, can be used. The image data can be after detection (e.g., B-mode or intensity detection) and before scan conversion and / or display mapping. Image data after scan conversion and / or display mapping can be used.

[0020] The detection is from a data frame, such as that which represents a patient at a particular time or period. In other embodiments, a sequence of frames is used (e.g., frames of video data or ultrasound data over time). Changes over time or changes between ultrasound data frames are used to detect contraction states.

[0021] In one embodiment, the detection of contraction state is based on tissue motion. The patient is asked to move. A scan of the muscle is performed during the movement. The movement of the tissue during the movement is detected to determine the contraction state. For example, speckle tracking, normalized cross correlation, correlation or other similarity metrics are used to determine the change in position, strain, expansion or reduction in muscle tissue over time. The similarity of one or more local regions over time is determined.

[0022] One-dimensional, two-dimensional or three-dimensional tissue motion tracking can be used. For example, correlation along a scan line detected in one dimension is used to track tissue motion.

[0023] Changes over time indicate the state of muscle contraction. For example, maximum expansion corresponds to a loaded or contracted state, while minimum expansion corresponds to an unloaded or relaxed state.

[0024] In other embodiments, image processing is used. For example, the muscle is segmented. The shape or change in shape of the muscle can be used to determine the contraction state, for example by template matching.

[0025] In another embodiment, a sequence of ultrasound data frames from repeated executions of a scan is used to detect the contraction state. The sequence is input to a machine learning model. The machine learning model is trained from training data to detect the contraction state and / or identify when the contraction state has a given value in the sequence, such as relaxation or load. The machine learning model is a neural network, support vector machine, or other machine learning model. The model is machine trained using training data of many samples of the sequence having a ground truth contraction state for each frame of the sequence. Once trained, the machine learning model indicates or detects the contraction state based on the input of ultrasound data for the patient's sequence. In an alternative embodiment, the machine learning model is trained to output the contraction state given an input of one frame of ultrasound data.

[0026] The contraction state is detected as contracted or loaded, relaxed, or in between. The level or amount of contraction, for example ranging from relaxed to fully loaded, can be determined as the contraction state. In one embodiment, a binary determination is made between contraction and relaxation. The contraction state can be determined over time.

[0027] In action 14, the image processor triggers QUS imaging in response to detection of a muscle contraction state. QUS will be performed at a specific contraction state, such as quantifying tissue properties when the muscle is under load and / or relaxed. Alternatively, QUS is performed over time to measure values ​​of tissue properties, wherein the values ​​are associated with various contraction states over time.

[0028] The medical diagnostic ultrasound scanner is triggered to perform QUS. In response to detecting a contraction state from correlation or speckle tracking of radiofrequency ultrasound data or image ultrasound data, in response to detection by a machine learning model, or other detection of a contraction state, the scanner is triggered to perform QUS.

[0029] The trigger is immediate or within a short period of time (e.g., 0.5 seconds). The QUS is performed before the muscle contraction state changes or changes a lot. Alternatively, the trigger is delayed, such as using previous cycles of contraction state changes to predict when the muscle will be in the desired contraction state. In another embodiment, the trigger confirms the contraction change so as to trigger a continued QUS (i.e., without stopping).

[0030] Once triggered, the ultrasound scanner transmits and receives acoustic energy and calculates one or more values ​​of one or more tissue properties. Tissue properties are measured for a location, along a line, over an area, or throughout a volume. QUS is performed on any region of interest, such as a region of interest of muscle.

[0031] Any type of QUS can be performed. For example, shear wave imaging is performed. An acoustic radiation force pulse is emitted. In response, shear waves are generated in the tissue. The shear waves propagate from an origin. Ultrasonic scanning is used to track tissue displacement based on the shear waves. The shear wave velocity in the tissue is determined based on the displacement. Other tissue properties such as Young's modulus, viscoelastic properties and / or attenuation can be determined based on the shear waves and / or shear wave velocity. As another example, elasticity, attenuation or strain imaging is performed.

[0032] In one embodiment, backscatter imaging is performed as a triggered QUS. For backscatter coefficient or attenuation quantification, a B-mode scan is performed. Instead of performing B-mode detection (e.g., detecting the power or intensity of the return signal), the beamformed samples prior to detection are processed. A power spectrum of the return signal as a function of time is created using a fast Fourier transform. Depth variations give attenuation, such as based on an exponential fit. Attenuation compensation is then applied to determine the signal power at the target as the backscatter coefficient. The backscatter coefficient as a function of frequency can be used to determine scattering.

[0033] QUS imaging can rely on setup specific calibration or information used in QUS imaging. For example, using a given intensity of ARFI or calibration from phantom imaging. To avoid errors due to changes, the scan sequence and / or imaging processing of QUS can be set. The sequence for triggering the scan is based on the muscle contraction state. The sequence is specific to that state. Rather than allowing the user to change, the triggered scan is set or fixed. In an alternative embodiment, the user can set one or more scan or image processing settings.

[0034] In act 16, the image processor compares the quantity from the triggered QUS with another quantity. A relationship between the quantities is determined. The relationship may be a difference, a ratio, or other function. The relationship is calculated.

[0035] This relationship is an analysis tool that provides measurement information of tissue properties during muscle contraction or load cycling or under another muscle state.Online or offline analysis of QUS measurements (eg, QUS images or values) is provided.

[0036] Comparisons are performed for quantities at different locations, times, and / or under different conditions. In the case of two zones, the relationship compares the quantity for one zone with the quantity for another zone, such as different parts of a muscle, different muscles, muscles in different states of contraction, or muscle with other types of tissue. The relationship between three or more zones can be determined as different sets of relationships or a single relationship between three or more zones. The relationship between quantities at different frequencies can be determined. The relationship between quantities at different times can be determined. For example, the relationship between the backscatter coefficients of the same muscle tissue during contraction and when relaxed is determined. For backscattering, the relationship can be between spectra.

[0037] In action 18, an image processor generates an image. The image is a QUS image, such as a shear wave velocity or backscatter coefficient image. The QUS image includes values ​​of one or more quantitative parameters. For example, shear wave velocity as a function of one-dimensional, two-dimensional, or three-dimensional position is included in the QUS image. The image is used to reflect ultrasound data, which can be used to form a display image or ultrasound data that is formatted for display or has been displayed. Shear wave, backscatter, or other QUS imaging can determine the value of a tissue property at one or more locations in one, two, or three dimensions. In another example, the QUS image includes a quantitative value for the entire ROI or a location (e.g., a point).

[0038] The QUS image may include other information. For example, QUS values ​​may be used for the region of interest, and positions in the field of view outside the region of interest may be formed from the B-mode image.

[0039] The QUS image can show the result of the comparison. For example, the value of the ratio or difference is displayed as part of the image, with or without showing the two quantities. The result can be an annotation, a graph, a highlight, or a map. For example, muscle tissue with a quantity and / or comparison result above or below a threshold is highlighted or distinguished from other tissues to indicate that the quantity or comparison result is above or below the threshold.

[0040] The comparison results, one or more quantities and / or information derived therefrom can be displayed as annotations (e.g., alphanumeric text), charts and / or color to pixel mappings. The image can also show the contraction state detected for the muscle. For example, annotations are provided as markings indicating the contraction state and quantities measured from QUS.

[0041] The image is on a display, such as that of an ultrasound scanner. The image may be transmitted over a computer network and / or stored in a memory, such as a patient electronic health record or a picture archiving and communication system database.

[0042] Figure 2 One embodiment of a medical system 20 for triggered QUS imaging is shown. The medical system 20 is an ultrasound scanner for detecting the contraction state of a muscle and responding with QUS imaging or measurement based on the (triggered and / or marked) contraction state. The medical system 20 performs Figure 1 method or another.

[0043] The medical system 20 includes a transmit beamformer 22, a transducer 23, a receive beamformer 24, an image processor 25, a memory 26, and a display 27. Additional, different, or fewer components may be provided. For example, the medical system 20 includes a B-mode or other detector separate from the image processor 25. As another example, the image processor 25, memory 26, and / or display 27 are provided without front-end components, such as the transmit and receive beamformers 12, 16. In yet another example, a user interface including a user input (e.g., a mouse, trackball, keyboard, buttons, knobs, sliders, and / or touchpad) may be provided for user indication of a region of interest or other input.

[0044] In one embodiment, medical system 20 is a medical diagnostic ultrasound system. In an alternative embodiment, system 20 is a computer or workstation.

[0045] The transducer 23 is an array of multiple elements. The elements are piezoelectric or capacitive film elements. The array is configured as a one-dimensional array, a two-dimensional array, a 1.5D array, a 1.25D array, a 1.75D array, a circular array, a multi-dimensional array, an oscillator array, a combination thereof, or any other array now known or later developed. The transducer elements transduce between acoustic energy and electrical energy. The transducer 23 is connected to the transmit beamformer 22 and the receive beamformer 24 through a transmit / receive switch, but separate connections may be used in other embodiments.

[0046] The transmit and receive beamformers 22, 24 are beamformers for scanning using the transducer 23. The transmit beamformer 22 transmits one or more beams using the transducer 23 to scan an area. Vector®, sector, linear or other scanning formats may be used. Receive lines and / or transmit beams are distributed in the scan area. The receive beamformer 24 samples the receive beams at different depths.

[0047] The transmit beamformer 22 is a processor, delay, filter, waveform generator, memory, phase rotator, digital-to-analog converter, amplifier, a combination thereof, or any other transmit beamformer component now known or later developed. In one embodiment, the transmit beamformer 22 generates envelope samples digitally. Using filtering, delay, phase rotation, digital-to-analog conversion, and amplification, the desired transmit waveform is generated. Other waveform generators, such as switching pulses or waveform memories, may be used.

[0048] The transmit beamformer 22 is configured as a plurality of channels for generating an electrical signal of a transmit waveform for each element of the transmit aperture on the transducer 23. The waveform is a monopole, bipole, step, sinusoidal or other waveform having a desired center frequency or frequency band of one, multiple or fractional number of cycles. The waveform has relative delays and / or phasing and amplitude for focusing the acoustic energy. The transmit beamformer 22 includes a controller for changing the aperture (e.g., the number of active elements), the apodization distribution (e.g., the type or center of mass) over the plurality of channels, the delay distribution over the plurality of channels, the phase distribution over the plurality of channels, the center frequency, the frequency band, the waveform shape, the number of cycles and / or a combination thereof. The amplitude may be changed or set. The transmit beam origin, orientation and focus are generated based on these beamforming parameters.

[0049] The receive beamformer 24 is a preamplifier, filter, phase rotator, delay, adder, baseband filter, processor, buffer, memory, combination thereof or other now known or later developed receive beamformer components. The receive beamformer 24 is configured into multiple channels for receiving electrical signals representing echoes or acoustic energy impinging on the transducer 23. The channels from each element of the receive aperture within the transducer 23 are connected to amplifiers and / or delays. The analog-to-digital converter digitizes the amplified echo signal. The digital RF receive data is demodulated to the baseband frequency. Then, any receive delay, such as dynamic receive delay and / or phase rotation, is applied by the amplifier and / or delay. A digital or analog adder combines data from different channels of the receive aperture to form one or more receive beams. The adder is a single adder or a cascade of adders. In one embodiment, the beamforming adder is configured to sum the in-phase and quadrature channel data in a complex manner so that the phase information of the formed beam is maintained. In an alternative embodiment, the receive beamformer sums the radio frequency data.Other receive beamformers may be used.

[0050] The receive beamformer 24 is configured to form receive beams in response to the transmit beams. For example, the receive beamformer 24 receives one, two, or more receive beams in response to each transmit beam. Phase rotators, delays, and / or adders may be repeated for parallel receive beamforming. One or more of the parallel receive beamformers may share portions of the channel, such as sharing initial amplification. The receive beams are collinear, parallel, and offset or non-parallel with the corresponding transmit beams.

[0051] The receive beamformer 24 is configured to output samples for one location or different locations in the patient. The receive beamformer 24 outputs spatial samples representing different spatial locations of the scan area or samples representing one location. Once the channel data is beamformed or otherwise combined to represent a spatial location along the scan line 21, the data is converted from the channel domain to the image data domain. The field of view is scanned by scanning with transmit and receive beamforming in any mode (e.g., sector, vector, or linear).

[0052] The transmit and receive beamformers 22, 24 are configured to scan in various modes. For example, a B-mode scan is performed to detect a contraction state. As another example, a transmit and receive sequence is performed for QUS. The beamformer controller configures the beamformers 22, 24 to scan in a given mode, such as repeating a B-mode scan until a muscle contraction in a given state is detected, and then performing a QUS scan alone or interleaved with a B-mode scan while the muscle is in one or more desired states.

[0053] The image processor 25 is a digital signal processor, a general purpose processor, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), an artificial intelligence processor, a control processor, a digital circuit, an analog circuit, a graphics processing unit, a combination thereof, or other now known or later developed devices for calculating quantities and determining relationships between quantities in QUS. The image processor 25 is configured by hardware, firmware and / or software, such as operating according to instructions provided in the memory 26 or a different memory. In one embodiment, the image processor 25 is a digital signal processor, ASIC or FPGA specifically used to apply Fourier transforms, and another device (e.g., a calculator or processor) calculates the backscatter coefficient based on the output of the transform device. In other embodiments, the image processor 25 is a programmable device that performs image processing for shear wave, strain, elastic and / or other QUS imaging.

[0054] The image processor 25 is configured to determine the state of the muscle from (1) the output samples or (2) data from detection of the output samples. The image processor 25 determines the state from beamformed samples (e.g., radio frequency or in-phase and quadrature information) or from image data after detection (e.g., after B-mode detection). The state is determined by speckle tracking, correlation, or other similarity metrics along one, two, or three dimensions. Other detections may be used. Shapes (e.g., segments) may be identified and contraction states detected based on the shapes. In one embodiment, a machine learning detector determines contraction states and / or identifies one or more times or frames of data in a sequence representing a muscle in a given contraction state in response to input of ultrasound data frames (e.g., image frames) or a sequence of such frames.

[0055] The image processor 25 is configured to cause the transmit and receive beamformers 22, 24 to perform a scan based on the state of the muscle. The image processor 25 controls the scan so as to provide QUS to the muscle when it is in the desired state. The QUS scan is triggered based on the detection.

[0056] The image processor 25 is configured to determine a value of a tissue property from the QUS imaging. Beamformed samples are acquired at one or more locations. Prior to detection, the samples are used to calculate a quantity. In an alternative embodiment, the quantity is calculated from image data after detection.

[0057] Quantify one or more values ​​of any tissue property. For example, calculate strain, elasticity, backscatter coefficient, scattering, attenuation, viscoelasticity, Young's modulus, shear wave velocity, or another type of tissue property. Determine a value for a location or for a set of locations. Values ​​can be calculated for different times and / or locations.

[0058] In one embodiment, the image processor 25 is configured to determine a relationship between a value of a tissue property and another value of the tissue property from a different time or location. A ratio, difference or other relationship between quantities over time and / or location is found. For spectral information, a ratio of spectra may be determined. A frequency-dependent relationship may be determined.

[0059] Ultrasound data and / or quantities can be used to generate images. A B-mode detector, flow estimator (e.g., Doppler processor) or other detector can be provided to detect characteristics of samples from receive beamforming. The B-mode detector detects the intensity or power of acoustic backscatter. The flow estimator detects the velocity, energy or variance of a moving object (e.g., tissue or fluid). The detection can be used to generate an image from which a region of interest is selected. QUS provides one or more values ​​of a region of interest. The one or more values ​​can be displayed separately from other imaging modalities, or can be superimposed, such as a color overlay or annotation on a B-mode image.

[0060] The image processor 25 is configured to generate an image. The image includes the value of the amount of tissue properties, multiple such values ​​from different positions or times, and / or the relationship between the values ​​(e.g., ratio). For example, a graph of the backscatter coefficient as a function of time is generated as an image. As another example, alphanumeric text is generated as an image, such as alphanumeric text providing the tissue property values ​​of the muscle from the QUS. A table can be generated. In another embodiment, the tissue properties as a function of the position in one or more regions of interest are mapped to a color or grayscale, and its mapped values ​​are used to generate a one-dimensional, two-dimensional, or three-dimensional distribution of values ​​in the muscle tissue.

[0061] The memory 26 is a video random access memory, random access memory, removable media (such as a magnetic disk or optical disk), a hard drive, a database, or other memory device for storing samples, spectra, values ​​of quantities representing tissue properties, relationships between quantities, and / or images. The memory 26 is used by the image processor 25 for QUS or Figure 1 Other actions described.

[0062] Instructions for implementing the above-described processes, methods and / or techniques (e.g., QUS imaging triggered based on the contraction state of the muscle) are provided on a computer-readable storage medium or memory, such as a cache, a buffer, a RAM, a removable medium, a hard drive, or other computer-readable storage media such as represented by memory 26. Computer-readable storage media include various types of ritual and non-volatile storage media. Functions, actions, or tasks shown in the drawings or described herein are performed in response to one or more sets of instructions stored in or on a computer-readable storage medium. The function, action, or task is independent of a specific type of instruction set, storage medium, processor, or processing strategy, and can be performed by software, hardware, integrated circuits, firmware, microcode, etc. operating alone or in combination. Similarly, processing strategies may include multi-processing, multi-tasking, parallel processing, etc. In one embodiment, instructions are stored on a removable media device for reading by a local or remote system. In other embodiments, instructions may be stored in a remote location for transmission over a computer network or over a telephone line. In yet other embodiments, instructions are stored in a given computer, CPU, GPU, or system.

[0063] Display 27 is a CRT, LCD, plasma, projector, monitor, printer, touch screen or other display device now known or later developed. Display 27 receives RGB, other color values ​​or other values ​​and outputs an image. The image can be a grayscale or color image. The image shows information as a function of QUS, such as one or more values ​​showing the tissue properties of the muscle in a given state. One or more values ​​of the relationship of values ​​from different times and / or locations can be displayed. An alphanumeric, graphical, annotated or other representation of one or more values ​​is displayed in the image on display 27. The image may or may not additionally represent the patient area scanned by beamformer 22, 16 and transducer 23.

[0064] In one embodiment, an image representing at least one region of interest is annotated or color coded to indicate the level of the value relative to a baseline. For example, different ranges of tissue properties represent different stages of a muscle disease. The image of the region of interest is color coded with a color, shade, or brightness representing a given stage. Different colors, shades, and / or brightness represent different disease stages. The stage of the patient is indicated by the color or other modulation of the region pixels. Textual indications may be used.

[0065] Although the present invention has been described above with reference to various embodiments, it will be appreciated that many changes and modifications may be made without departing from the scope of the invention. Therefore, it is intended that the foregoing detailed description should be considered illustrative rather than restrictive, and it will be appreciated that the following claims, including all equivalents, are intended to define the spirit and scope of the present invention.

Claims

1. A method for quantitative ultrasound imaging using a medical diagnostic ultrasound scanner (20), the method comprising: Scanning (10) the patient's muscle tissue using ultrasound by the medical diagnostic ultrasound scanner (20); detecting (12) a contraction state of the muscle tissue using ultrasound data from the scan (10) by an image processor; triggering (14) by the image processor the quantitative ultrasound imaging performed by the medical diagnostic ultrasound scanner (20), the quantitative ultrasound imaging being triggered in response to the detection of the contraction state; and A quantitative ultrasound image from the triggered quantitative ultrasound imaging is displayed.

2. The method according to claim 1, wherein: Detecting (12) includes detecting (12) the contraction based on tissue movement using ultrasound data, wherein the ultrasound data includes beamformed data prior to detection.

3. The method according to claim 1, wherein: Detection (12) includes detection using speckle tracking or similarity metrics.

4. The method according to claim 1, wherein: Detecting (12) includes performing detection (12) in a sequence from repeated execution of the scanning (10) to a machine learning model that has been trained to detect the contraction state based on input of the ultrasound data.

5. The method according to claim 1, wherein: Detecting (12) includes detecting (12) the contracted state as contracted, and wherein triggering (14) includes triggering (14) the quantitative ultrasound imaging of the muscle in the contracted state.

6. The method according to claim 1, wherein: Detecting (12) includes detecting (12) the contracted state as relaxed, and wherein triggering (14) includes triggering (14) the quantitative ultrasound imaging of the muscle in the relaxed state.

7. The method according to claim 1, wherein: The triggering (14) includes triggering (14) shear wave imaging or backscatter imaging, and wherein the displaying includes displaying the shear wave image or the backscatter image, respectively.

8. The method of claim 1, further comprising calculating (16) a relationship between the first quantity and the second quantity from the triggered quantitative imaging, and wherein, Displaying the quantitative ultrasound image includes displaying the relationship, wherein the first quantity and the second quantity are at different locations, times, and / or under different conditions.

9. A non-transitory computer-readable storage medium having stored therein data representing instructions executable by a programmed processor for quantitative ultrasound imaging, the storage medium comprising instructions for: triggering (14) the quantitative ultrasound imaging in response to detecting a muscle contraction state; comparing a first quantity from the triggered quantitative ultrasound imaging to a second quantity (16), wherein the first quantity and the second quantity are at different locations, times, and / or under different conditions; and An image showing the comparison is generated.

10. The non-transitory computer-readable storage medium of claim 9, wherein: The triggering (14) includes triggering (14) upon detection from a similarity metric or speckle tracking from the radio frequency ultrasound data or the image ultrasound data.

11. The non-transitory computer-readable storage medium of claim 9, wherein: Triggering (14) includes triggering (14) upon detection by a machine learning model.

12. The non-transitory computer-readable storage medium of claim 9, wherein: Triggering (14) includes triggering (14) shear wave or backscatter imaging as the quantitative ultrasound imaging, and wherein generating the image includes generating an image of tissue properties based on the shear wave or backscatter imaging.

13. The non-transitory computer-readable storage medium of claim 9, wherein: The comparing (16) includes comparing (16) a first quantity at a first location to a second quantity at a second location, and / or comparing (16) a first quantity at a first time to a second quantity at a second time.

14. A system for triggering (14) quantitative ultrasound imaging, the system comprising: a transmit beamformer (22) configured to transmit ultrasound within a patient; a receive beamformer (24) configured to output samples of echoes from the ultrasound; an image processor configured to determine a state of a muscle based on the output samples or data from detection of the output samples of a first scan (10) by the transmit and receive beamformer, configured to cause the transmit and receive beamformer to perform a second scan (10) based on the state of the muscle, the second scan (10) being used for the quantitative ultrasound imaging, and configured to determine a value of a tissue property based on the quantitative ultrasound imaging; and A display (27) is configured to display the value of the tissue property.

15. The system of claim 14, wherein: The image processor is configured to determine the state based on speckle tracking, a similarity metric, or a machine learning detector, and wherein the image processor is configured to determine a relationship between the value of the tissue property and another value of the tissue property from a different time or location, and wherein the display is configured to display the relationship.

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