WO2022271601A1 - Systèmes et procédés de suppression d'artéfacts de fouillis de réverbération dans une imagerie ultrasonore - Google Patents

Systèmes et procédés de suppression d'artéfacts de fouillis de réverbération dans une imagerie ultrasonore Download PDF

Info

Publication number
WO2022271601A1
WO2022271601A1 PCT/US2022/034182 US2022034182W WO2022271601A1 WO 2022271601 A1 WO2022271601 A1 WO 2022271601A1 US 2022034182 W US2022034182 W US 2022034182W WO 2022271601 A1 WO2022271601 A1 WO 2022271601A1
Authority
WO
WIPO (PCT)
Prior art keywords
signals
reverberation
data
tissue
ultrasound imaging
Prior art date
Application number
PCT/US2022/034182
Other languages
English (en)
Inventor
Shigao Chen
Ping Gong
U Wai Francisco LOK
Joshua D. Trzasko
Original Assignee
Mayo Foundation For Medical Education And Research
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Mayo Foundation For Medical Education And Research filed Critical Mayo Foundation For Medical Education And Research
Priority to EP22744000.5A priority Critical patent/EP4359816A1/fr
Publication of WO2022271601A1 publication Critical patent/WO2022271601A1/fr

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
    • G01S7/52017—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00 particularly adapted to short-range imaging
    • G01S7/52077—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00 particularly adapted to short-range imaging with means for elimination of unwanted signals, e.g. noise or interference
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/08—Detecting organic movements or changes, e.g. tumours, cysts, swellings
    • A61B8/0883—Detecting organic movements or changes, e.g. tumours, cysts, swellings for diagnosis of the heart
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/44—Constructional features of the ultrasonic, sonic or infrasonic diagnostic device
    • A61B8/4483—Constructional features of the ultrasonic, sonic or infrasonic diagnostic device characterised by features of the ultrasound transducer
    • A61B8/4494—Constructional features of the ultrasonic, sonic or infrasonic diagnostic device characterised by features of the ultrasound transducer characterised by the arrangement of the transducer elements
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/46—Ultrasonic, sonic or infrasonic diagnostic devices with special arrangements for interfacing with the operator or the patient
    • A61B8/467—Ultrasonic, sonic or infrasonic diagnostic devices with special arrangements for interfacing with the operator or the patient characterised by special input means
    • A61B8/469—Ultrasonic, sonic or infrasonic diagnostic devices with special arrangements for interfacing with the operator or the patient characterised by special input means for selection of a region of interest
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/52—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/5207—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of raw data to produce diagnostic data, e.g. for generating an image
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/52—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/5269—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving detection or reduction of artifacts
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/88—Sonar systems specially adapted for specific applications
    • G01S15/89—Sonar systems specially adapted for specific applications for mapping or imaging
    • G01S15/8906—Short-range imaging systems; Acoustic microscope systems using pulse-echo techniques

Definitions

  • Increasing fat content for some subjects may lead to liver steatosis, which may progress to fibrosis, cirrhosis, liver failure, or even hepatocellular carcinoma.
  • One of the methods to evaluate fat content in the liver is proton density fat fraction (PDFF) acquired with magnetic resonance imaging (MRI) which was often used as a benchmarking standard.
  • PDFF proton density fat fraction
  • MRI magnetic resonance imaging
  • Ultrasound attenuation coefficient has also been shown to have the potential to quantify fat content in the human liver.
  • Ultrasound attenuation coefficient estimation (ACE) has been reported using different approaches, such as spectral shift technique, reference phantom-based methods, and the reference frequency method (RFM).
  • the ultrasound pulses undergo multi-path reflections (for example, within the fat and muscle layers of body wall) that will reveal reverberation clutter artifacts at different locations.
  • the reverberation clutters superimposed on liver echoes may bias the value of the attenuation coefficient, thus effective reverberation clutter suppression is needed for ACE.
  • a robust principal component analysis may be used to separate a static (e.g. low dimension) background from sparse moving (e.g. high dimension) objects with the presence of outliers.
  • Principal component analysis may include computing a set of linearly uncorrelated variables which is called principal components based on the covariance characteristics of the data. While PCA may be used to suppress reverberation clutter signals adaptively, PCA may be sensitive to data outliers, and thus degrades the reverberation clutter suppression capability.
  • RPCA by contrast, may effectively suppress reverberation clutter signals in the presence of outliers.
  • RPCA may assume the sparsity nature of the high dimensional moving objects signals, and a transformation may be used to ensure the tissue signal satisfies the sparsity property.
  • the tissue signal may be transformed to the wavelet domain to fulfill the sparsity condition.
  • the use of the RPCA combined with wavelet kernels may be used to suppress reverberation clutter signals to achieve robust ACE.
  • a method for reverberation signal suppression in ultrasound imaging of a subject.
  • the method includes accessing or acquiring ultrasound imaging data of a subject that includes a plurality of frames at different times and including tissue signals and reverberation signals.
  • the method also includes generating a region of interest (ROI) frames subset by determining a ROI for each frame in the plurality of frames and generating a spatiotemporal matrix from the ROI frames subset.
  • the method also includes separating tissue signals from reverberation signals in the spatiotemporal matrix using an adaptive method.
  • the method also includes generating an image of the subject with the reverberation signals suppressed by subtracting the separated reverberation signals from the ultrasound imaging data.
  • ROI region of interest
  • a system for reverberation signal suppression in ultrasound imaging of a subject.
  • the system includes a computer system configured to: i) access ultrasound imaging data of a subject that includes a plurality of frames at different times and including tissue signals and reverberation signals; ii) generate a region of interest (ROI) frames subset by determining a ROI for each frame in the plurality of frames; iii) generate a spatiotemporal matrix from the ROI frames subset; iv) separate tissue signals from reverberation signals in the spatiotemporal matrix using an adaptive method; and v) generate an image of the subject with the reverberation signals suppressed by subtracting the separated reverberation signals from the ultrasound imaging data.
  • ROI region of interest
  • FIG. 1 is a block diagram of a non-limiting example ultrasound data acquisition.
  • FIG. 2 is a flowchart of non-limiting example steps is shown for a method 200 to adaptively suppress reverberation clutter signals in ultrasound imaging
  • FIG. 3A is an image depicting a non-limiting example region of interest of a B mode image of a tissue-mimicking phantom without added reverberation clutter signals.
  • FIG. 3B depicts non-limiting example reverberation clutter signals.
  • FIG. 3C depicts the non-limiting example of FIG. 3A mixed with the reverberation clutter signals of FIG. 3B.
  • FIG. 3D depicts the non-limiting example of FIG. 3A with the reverberation clutter signals suppressed in accordance with the present disclosure.
  • FIG. 4A is a non-limiting example B mode image of a tissue-mimicking phantom computed without any reverberation clutter signals.
  • FIG. 4B is a 2-D attenuation coefficient map fusion with the B mode image of FIG. 4A without reverberation signal suppression.
  • FIG. 4C is a 2-D attenuation coefficient map fusion with the B mode image of FIG. 4A with reverberation signal suppression.
  • FIG. 5A is a graph of a non-limiting example correlation between PDFF and estimated attenuation coefficient obtained from fifteen patients, without applying reverberation signal suppression.
  • FIG. 5B is a graph of the non-limiting example of FIG. 5A showing the correlation between PDFF and estimated attenuation coefficient with applying reverberation signal suppression.
  • FIG. 6 is a block diagram of a non-limiting example ultrasound system that can implement the methods described in accordance with the present disclosure.
  • a robust principal component analysis may be used to separate a static or low-dimension background signal from sparse, moving or high-dimension objects in the presence of outliers.
  • a tissue signal may be transformed to the wavelet domain, or another suitable sparse domain, to fulfill the sparsity condition.
  • the use of the RPCA combined with wavelet kernels may be used to suppress reverberation clutter signals to achieve robust ultrasound attenuation coefficient estimation.
  • the ultrasound data may include in-phase/quadrature phase (IQ) data, or another format of ultrasound data such as post-beamformed radio frequency (RF) data, envelop data or pre-beamformed channel data, and the like.
  • IQ in-phase/quadrature phase
  • RF radio frequency
  • envelop data envelop data or pre-beamformed channel data, and the like.
  • a total of t consecutive frames 102 of ultrasound in-phase/quadrature-phase (IQ) data are acquired, with each frame 102 including of MxN ultrasound in-phase quadrature data (M row and N column).
  • the region-of-interest 104 may be taken by selecting mxn of ultrasound IQ data for each frame 102.
  • the ultrasound IQ spatiotemporal data are reshaped into a two-dimensional (2D) spatiotemporal matrix 106 with a dimension of ( mnxt ), with each column representing one ultrasound frame.
  • 2D two-dimensional
  • each column representing one ultrasound frame.
  • the methods in accordance with the present disclosure can be applied to ultrasound data acquired in either fundamental or harmonic imaging mode.
  • the tissue signals may have large motion variability as compared with the reverberation signals across frames.
  • multiple ultrasound frames may be acquired with subjects breathing freely or breathing heavily so that the liver moves significantly during real-time in-vivo scanning.
  • the ultrasound probe may be held tightly upon the subject’s body surface so that the clutter signals do not change significantly during breathing.
  • the beating heart may present a moving tissue signal to facilitate separation from static clutter signals from the body wall. Since the moving tissue signals possess high dimensional signals and the static reverberation clutter signals possess low dimensional signals, the reverberation clutter signals can be separated from the tissue signals using adaptive methods.
  • RPCA may be used to estimate the reverberation clutter signals from the received signals.
  • the tissue signals can be estimated by subtracting the received signals from the estimated reverberation clutter signals.
  • Any appropriate separation method may be used.
  • a model-based method e.g. principal component analysis, singular value decomposition, non-parametric-based method, or blind source method (e.g. independent component analysis), and the like may be used for the separation method.
  • Ultrasound imaging data of a subject may be accessed or acquired at step 202.
  • imaging data are spatiotemporal data.
  • the imaging data may represent a time series of two-dimensional image frames or three-dimensional image volumes.
  • the following teaching uses 2D image frames as a non-limiting example.
  • the methods in accordance with the present disclosure can be applied to 2D image frames, 3D volume data, and the like.
  • a region of interest (ROI) may be generated from a select set of frames of the ultrasound imaging data at step 204.
  • a spatiotemporal matrix of the selected frames may be generated at step 206, and may be generated from entire frames, or from a portion of the frames that include the ROI.
  • a spatiotemporal matrix may be generated as described in FIG. 1 where the ROI may be taken by selecting mxn of the ultrasound IQ data for each frame (MxN) where the frames are compiled over time (t).
  • the ultrasound IQ spatiotemporal data are reshaped into a two- dimensional (2D) spatiotemporal matrix with a dimension of [mnxt], with each column representing one ultrasound frame.
  • a spatiotemporal matrix may be used to display the similarity of the ultrasound frames in an ensemble and may be computed using pixels from the ROI, such as a lesion area.
  • the ROI data-points may be transformed from 3- dimensional Cartesian coordinates to 2 -dimensional Casorati co-ordinates, where each row and column represents the spatial and temporal data-points, respectively.
  • the matrix can be quantitatively summarized by statistics (e.g., mean, median) to measure performance.
  • performance metrics can be provided on a range of 0-1, 0%-100%, or another suitable range.
  • the high-dimensional tissue signals and low-dimensional reverberation signals may be identified and separated by processing the spatiotemporal matrix at step 208 using methods in accordance with the present disclosure.
  • Reverberation clutter signals may then be suppressed in the region of interest at step 210.
  • An image of the subject with suppressed reverberation clutter signals may also be generated at step 212.
  • a signal model for the observed received spatiotemporal IQ data can be expressed as:
  • RPCA may be used to recover low-rank components and to reduce the impact of corrupted data.
  • An RPCA technique may be expressed as follows:
  • represent the nuclear norm
  • S and l 2 are the regularization parameters which affect the estimated L and S.
  • the method may be used in separating sparse dynamic data from static data, and may exploit the sparse property of signal S.
  • the tissue signals S may not be sparse in the spatiotemporal domain, thus, making it difficult to meet the sparsity conditions.
  • the sparsity of the tissue signal in the wavelet-domain may be used to address sparsity condition issues instead of the spatiotemporal domain.
  • Such an optimization problem can be expressed as
  • W H is the adjoint 2D wavelet transformation. Any appropriate wavelet kernel may be used, and any wavelet kernel can be used for the 2D wavelet transformation and adjoint 2D wavelet transformation.
  • an Alternating Direction Method of Multiplier may be used as a possible optimization method to solve eq. (3).
  • Any appropriate optimization algorithm maybe used, such as such as augmented Lagrange multiplier, fast alternating minimization, iteratively reweighted least squares, and the like can be used to solve eq. (3).
  • ADMM Alternating Direction Method of Multiplier
  • ADMM may perform the following four steps to minimize the cost function
  • Eqs. (6) and (7) are convex problems possessing closed-form solutions: singular value thresholding (SVT) and soft thresholding (ST), respectively. Additionally, eqs. (8) and (9) can be solved by differentiating with respect to L k and S k , summarized as follows:
  • E x and E 2 can be computed using a gradient decent method.
  • a non limiting example method is summarized as the Table I below:
  • the RPCA algorithm may involve repeated computations of the singular value decomposition (SVD) and thresholding of matrices during the singular value thresholding process.
  • This repeated computation of the SVD may be a bottleneck of computational complexity, but as one non-limiting example singular value thresholding may be used to alleviate this complexity by computing fewer singular values as those singular values that he above a specified threshold.
  • Speeding up the algorithms that involve thresholding of singular values may be accomplished by using a truncated SVD approach to compute only those singular values of interest.
  • the truncated SVD approach is one non-limiting example of the possible methods for the fast computation of SVD, and any appropriate computation of SVD processes may be used.
  • FIGS. 3A-D a non-limiting example selected ROI of a 1 st frame of B mode image of a calibrated tissue-mimicking phantom is shown.
  • Two hypoechoic cysts 302 were clearly visualized in the B mode image of FIG. 3A, which depicts the ROI of the 1 st frame of B image of the tissue-mimicking phantom without added reverberation clutter signals.
  • FIG. 3B shows the selected ROI of the 1 st frame of B mode image of the reverberation clutter signals.
  • FIG. 3A shows the selected ROI of the 1 st frame of B mode image of the reverberation clutter signals.
  • 3C shows the selected ROI of the 1 st frame of B mode image of the calibrated tissue-mimicking phantom where the tissue signals were mixed with the reverberation clutter signals, and two hypoechoic cysts’ signals were corrupted by the clutter signals. After applying reverberation clutter signal suppression, the two hypoechoic cysts can be better visualized than those of the mixed signals, as shown in FIG. 3D.
  • FIGS. 4A-4D a non-limiting example of a 2-D attenuation coefficient map fusion is shown with FIG. 4A depicting the B-mode image of a tissue- mimicking phantom (with calibrated attenuation coefficient of 0.95 dB/cm/MHz) computed without any reverberation clutter signals.
  • FIG. 4B and 4C show the 2-D attenuation coefficient map fusion with the B-mode image of a tissue-mimicking phantom without and with the reverberation signal suppression.
  • the estimated attenuation coefficient values without and with reverberation signal suppression were 0.78 and 0.88, respectively.
  • FIGS. 5A-B non-limiting examples of correlations between proton density fat fraction (PDFF) acquired with MRI and ACE obtained in a fundamental mode during free breathing are shown. Patients breathed freely during data acquisition so that the liver moved significantly to facilitate separation of liver signals from unwanted static reverberation clutter signals from the body wall using the methods in accordance with the present disclosure.
  • FIG. 5A shows the correlation between PDFF and estimated attenuation coefficient obtained from fifteen patients, without applying reverberation signal suppression.
  • FIG. 5B shows the correlation between PDFF and estimated attenuation coefficient with applying reverberation signal suppression on the same fifteen patients.
  • the Pearson’s correlation coefficients in FIG. 5A and FIG. 5B were 0.69 and 0.82, respectively.
  • RPCA may be used to mitigate the reverberation clutter artifacts in ultrasound attenuation coefficient estimation.
  • the methods in accordance with the present disclosure provide accurate and robust ultrasound attenuation coefficient estimation.
  • tissue moves while clutters are static.
  • the methods in accordance with the present disclosure may also be used in situations where the unwanted clutter signals move while the desired tissue signal are static: in such cases, tissue signal will be low dimensional (low rank) and clutter signal will be high dimensional (high rank). Therefore, clutters can still be isolated and subtracted from the received signal to obtain a cleaner tissue signal to achieve clutter suppression.
  • FIG. 6 illustrates an example of an ultrasound system 600 that can implement the methods described in the present disclosure.
  • the ultrasound system 600 includes a transducer array 602 that includes a plurality of separately driven transducer elements 604.
  • the transducer array 602 can include any suitable ultrasound transducer array, including linear arrays, curved arrays, phased arrays, and so on.
  • the transducer array 602 can include a ID transducer, a 1.5D transducer, a 1.75D transducer, a 2D transducer, a 3D transducer, and so on.
  • a given transducer element 604 When energized by a transmitter 606, a given transducer element 604 produces a burst of ultrasonic energy.
  • the ultrasonic energy reflected back to the transducer array 602 e.g., an echo
  • an electrical signal e.g., an echo signal
  • each transducer element 604 can be applied separately to a receiver 608 through a set of switches 610.
  • the transmitter 606, receiver 608, and switches 610 are operated under the control of a controller 612, which may include one or more processors.
  • the controller 612 can include a computer system.
  • the transmitter 606 can be programmed to transmit unfocused or focused ultrasound waves. In some configurations, the transmitter 606 can also be programmed to transmit diverged waves, spherical waves, cylindrical waves, plane waves, or combinations thereof. Furthermore, the transmitter 606 can be programmed to transmit spatially or temporally encoded pulses.
  • the receiver 608 can be programmed to implement a suitable detection sequence for the imaging task at hand.
  • the detection sequence can include one or more of line-by-line scanning, compounding plane wave imaging, synthetic aperture imaging, and compounding diverging beam imaging.
  • the transmitter 606 and the receiver 608 can be programmed to implement a high frame rate. For instance, a frame rate associated with an acquisition pulse repetition frequency ("PRF”) of at least 100 Hz can be implemented.
  • PRF acquisition pulse repetition frequency
  • the ultrasound system 600 can sample and store at least one hundred ensembles of echo signals in the temporal direction.
  • the controller 612 can be programmed to implement an imaging sequence using the techniques described in the present disclosure, or as otherwise known in the art. In some embodiments, the controller 612 receives user inputs defining various factors used in the implementation of the imaging sequence.
  • a scan can be performed by setting the switches 610 to their transmit position, thereby directing the transmitter 606 to be turned on momentarily to energize transducer elements 604 during a single transmission event.
  • the switches 610 can then be set to their receive position and the subsequent echo signals produced by the transducer elements 604 in response to one or more detected echoes are measured and applied to the receiver 608.
  • the separate echo signals from the transducer elements 604 can be combined in the receiver 608 to produce a single echo signal.
  • the echo signals are communicated to a processing unit 614, which may be implemented by a hardware processor and memory, to process echo signals or images generated from echo signals.
  • the processing unit 614 can suppress reverberation signal clutter noise/artifacts using the methods described in the present disclosure. Images produced from the echo signals by the processing unit 614 can be displayed on a display system 616.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Radiology & Medical Imaging (AREA)
  • Medical Informatics (AREA)
  • Veterinary Medicine (AREA)
  • Public Health (AREA)
  • General Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Pathology (AREA)
  • Animal Behavior & Ethology (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Surgery (AREA)
  • Molecular Biology (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • General Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Gynecology & Obstetrics (AREA)
  • Cardiology (AREA)
  • Ultra Sonic Daignosis Equipment (AREA)

Abstract

L'invention concerne des systèmes et des procédés pour la suppression adaptative de signaux de fouillis de réverbération dans une imagerie ultrasonore. Une analyse de composante principale robuste (RPCA) peut être utilisée pour séparer un signal d'arrière-plan statique ou de faible dimension d'objets épars, mobiles ou de grande dimension en présence de valeurs aberrantes. Un signal de tissu peut être transformé en domaine d'ondelettes pour satisfaire les conditions de rareté de la RPCA. L'utilisation de la RPCA combinée à des noyaux d'ondelettes peut être utilisée pour supprimer des signaux de fouillis de réverbération afin d'obtenir une estimation de coefficient d'atténuation ultrasonore robuste.
PCT/US2022/034182 2021-06-23 2022-06-20 Systèmes et procédés de suppression d'artéfacts de fouillis de réverbération dans une imagerie ultrasonore WO2022271601A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
EP22744000.5A EP4359816A1 (fr) 2021-06-23 2022-06-20 Systèmes et procédés de suppression d'artéfacts de fouillis de réverbération dans une imagerie ultrasonore

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US202163214002P 2021-06-23 2021-06-23
US63/214,002 2021-06-23

Publications (1)

Publication Number Publication Date
WO2022271601A1 true WO2022271601A1 (fr) 2022-12-29

Family

ID=82608330

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2022/034182 WO2022271601A1 (fr) 2021-06-23 2022-06-20 Systèmes et procédés de suppression d'artéfacts de fouillis de réverbération dans une imagerie ultrasonore

Country Status (2)

Country Link
EP (1) EP4359816A1 (fr)
WO (1) WO2022271601A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116068500A (zh) * 2023-03-16 2023-05-05 东南大学 一种新型多通道合成孔径雷达空时干扰抑制方法

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140185899A1 (en) * 2011-10-12 2014-07-03 Seno Medical Instruments, Inc. System and method for acquiring optoacoustic data and producing parametric maps using interframe persistent artifact removal
EP3641658A1 (fr) * 2017-06-23 2020-04-29 Mayo Foundation for Medical Education and Research Procédés d'estimation de coefficient d'atténuation indépendant du système ultrasonore

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140185899A1 (en) * 2011-10-12 2014-07-03 Seno Medical Instruments, Inc. System and method for acquiring optoacoustic data and producing parametric maps using interframe persistent artifact removal
EP3641658A1 (fr) * 2017-06-23 2020-04-29 Mayo Foundation for Medical Education and Research Procédés d'estimation de coefficient d'atténuation indépendant du système ultrasonore

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
GOVINAHALLISATHYANARAYANA SUSHANTH ET AL: "Closed-Loop Low-Rank Echocardiographic Artifact Removal", IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL, IEEE, USA, vol. 68, no. 3, 31 July 2020 (2020-07-31), pages 510 - 525, XP011840419, ISSN: 0885-3010, [retrieved on 20210224], DOI: 10.1109/TUFFC.2020.3013268 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116068500A (zh) * 2023-03-16 2023-05-05 东南大学 一种新型多通道合成孔径雷达空时干扰抑制方法

Also Published As

Publication number Publication date
EP4359816A1 (fr) 2024-05-01

Similar Documents

Publication Publication Date Title
US11589840B2 (en) Methods for super-resolution ultrasound imaging of microvessels
Szasz et al. Beamforming through regularized inverse problems in ultrasound medical imaging
Mauldin et al. The singular value filter: A general filter design strategy for PCA-based signal separation in medical ultrasound imaging
US10746859B2 (en) System and method for acoustic imaging with coherent compounding using intercostal spaces
Vignon et al. Capon beamforming in medical ultrasound imaging with focused beams
Loizou et al. Despeckle filtering for ultrasound imaging and video, volume I: Algorithms and software
CN104272134B (zh) 超声成像系统中的杂波抑制
US9451932B2 (en) Clutter suppression in ultrasonic imaging systems
JP6932192B2 (ja) 超音波画像クラッタをフィルタリングする方法及びシステム
US20130343627A1 (en) Suppression of reverberations and/or clutter in ultrasonic imaging systems
KR20150065158A (ko) 3차원 탄성 초음파 이미징에서의 움직임 보정
Nair et al. Robust short-lag spatial coherence imaging
CA2685776A1 (fr) Systemes d'affichage d'image de deformation
JP7405950B2 (ja) 微小脈管の高時空分解能超音波イメージングを行うための方法
US11087466B2 (en) Methods and system for compound ultrasound image generation
Simson et al. Deep learning beamforming for sub-sampled ultrasound data
Ozgun et al. Multidimensional clutter filtering of aperture domain data for improved blood flow sensitivity
EP4359816A1 (fr) Systèmes et procédés de suppression d'artéfacts de fouillis de réverbération dans une imagerie ultrasonore
Jalilian et al. Increasing frame rate of echocardiography based on a novel 2d spatio-temporal meshless interpolation
Stevens et al. Dehazing ultrasound using diffusion models
JP2021525619A (ja) 胎児体重推定を実施するための方法およびシステム
WO2018099867A1 (fr) Procédés et systèmes de filtrage de fouillis d'images ultrasonores
Bottenus et al. Resolution and speckle reduction in cardiac imaging
Ng Speckle noise reduction via homomorphic elliptical threshold rotations in the complex wavelet domain
Toffali et al. Improving the quality of monostatic synthetic-aperture ultrasound imaging through deep-learning-based beamforming

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22744000

Country of ref document: EP

Kind code of ref document: A1

WWE Wipo information: entry into national phase

Ref document number: 2022744000

Country of ref document: EP

NENP Non-entry into the national phase

Ref country code: DE

ENP Entry into the national phase

Ref document number: 2022744000

Country of ref document: EP

Effective date: 20240123