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Guillaumin JB, Djerroudi L, Aubry JF, Tardivon A, Dizeux A, Tanter M, Vincent-Salomon A, Berthon B. Biopathologic Characterization and Grade Assessment of Breast Cancer With 3-D Multiparametric Ultrasound Combining Shear Wave Elastography and Backscatter Tensor Imaging. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:474-483. [PMID: 38195266 DOI: 10.1016/j.ultrasmedbio.2023.12.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 11/17/2023] [Accepted: 12/03/2023] [Indexed: 01/11/2024]
Abstract
OBJECTIVE Despite recent improvements in medical imaging, the final diagnosis and biopathologic characterization of breast cancers currently still requires biopsies. Ultrasound is commonly used for clinical examination of breast masses. B-Mode and shear wave elastography (SWE) are already widely used to detect suspicious masses and differentiate benign lesions from cancers. But additional ultrasound modalities such as backscatter tensor imaging (BTI) could provide relevant biomarkers related to tissue organization. Here we describe a 3-D multiparametric ultrasound approach applied to breast carcinomas in the aims of (i) validating the ability of BTI to reveal the underlying organization of collagen fibers and (ii) assessing the complementarity of SWE and BTI to reveal biopathologic features of diagnostic interest. METHODS Three-dimensional SWE and BTI were performed ex vivo on 64 human breast carcinoma samples using a linear ultrasound probe moved by a set of motors. Here we describe a 3-D multiparametric representation of the breast masses and quantitative measurements combining B-mode, SWE and BTI. RESULTS Our results reveal for the first time that BTI can capture the orientation of the collagen fibers around tumors. BTI was found to be a relevant marker for assessing cancer stages, revealing a more tangent tissue orientation for in situ carcinomas than for invasive cancers. In invasive cases, the combination of BTI and SWE parameters allowed for classification of invasive tumors with respect to their grade with an accuracy of 95.7%. CONCLUSION Our results highlight the potential of 3-D multiparametric ultrasound imaging for biopathologic characterization of breast tumors.
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Affiliation(s)
- Jean-Baptiste Guillaumin
- Physics for Medicine Institute, ESPCI Paris, PSL Research University, Inserm U1273, CNRS UMR 8063, Paris, France
| | | | - Jean-François Aubry
- Physics for Medicine Institute, ESPCI Paris, PSL Research University, Inserm U1273, CNRS UMR 8063, Paris, France.
| | | | - Alexandre Dizeux
- Physics for Medicine Institute, ESPCI Paris, PSL Research University, Inserm U1273, CNRS UMR 8063, Paris, France
| | - Mickaël Tanter
- Physics for Medicine Institute, ESPCI Paris, PSL Research University, Inserm U1273, CNRS UMR 8063, Paris, France
| | | | - Béatrice Berthon
- Physics for Medicine Institute, ESPCI Paris, PSL Research University, Inserm U1273, CNRS UMR 8063, Paris, France
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Azam S, Montaha S, Raiaan MAK, Rafid AKMRH, Mukta SH, Jonkman M. An Automated Decision Support System to Analyze Malignancy Patterns of Breast Masses Employing Medically Relevant Features of Ultrasound Images. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:45-59. [PMID: 38343240 DOI: 10.1007/s10278-023-00925-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 09/22/2023] [Accepted: 10/23/2023] [Indexed: 03/02/2024]
Abstract
An automated computer-aided approach might aid radiologists in diagnosing breast cancer at a primary stage. This study proposes a novel decision support system to classify breast tumors into benign and malignant based on clinically important features, using ultrasound images. Nine handcrafted features, which align with the clinical markers used by radiologists, are extracted from the region of interest (ROI) of ultrasound images. To validate that these elected clinical markers have a significant impact on predicting the benign and malignant classes, ten machine learning (ML) models are experimented with resulting in test accuracies in the range of 96 to 99%. In addition, four feature selection techniques are explored where two features are eliminated according to the feature ranking score of each feature selection method. The Random Forest classifier is trained with the resultant four feature sets. Results indicate that even when eliminating only two features, the performance of the model is reduced for each feature selection technique. These experiments validate the efficiency and effectiveness of the clinically important features. To develop the decision support system, a probability density function (PDF) graph is generated for each feature in order to find a threshold range to distinguish benign and malignant tumors. Based on the threshold range of particular features, a decision support system is developed in such a way that if at least eight out of nine features are within the threshold range, the image will be denoted as true predicted. With this algorithm, a test accuracy of 99.38% and an F1 Score of 99.05% is achieved, which means that our decision support system outperforms all the previously trained ML models. Moreover, after calculating individual class-based test accuracies, for the benign class, a test accuracy of 99.31% has been attained where only three benign instances are misclassified out of 437 instances, and for the malignant class, a test accuracy of 99.52% has been attained where only one malignant instance is misclassified out of 210 instances. This system is robust, time-effective, and reliable as the radiologists' criteria are followed and may aid specialists in making a diagnosis.
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Affiliation(s)
- Sami Azam
- Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, 0909, Australia.
| | - Sidratul Montaha
- Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, 0909, Australia
| | | | | | | | - Mirjam Jonkman
- Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, 0909, Australia
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Sharma A, Oluyemi E, Myers K, Ambinder E, Bell MAL. Spatial Coherence Approaches to Distinguish Suspicious Mass Contents in Fundamental and Harmonic Breast Ultrasound Images. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2024; 71:70-84. [PMID: 37956000 PMCID: PMC10851341 DOI: 10.1109/tuffc.2023.3332207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
When compared to fundamental B-mode imaging, coherence-based beamforming, and harmonic imaging are independently known to reduce acoustic clutter, distinguish solid from fluid content in indeterminate breast masses, and thereby reduce unnecessary biopsies during a breast cancer diagnosis. However, a systematic investigation of independent and combined coherence beamforming and harmonic imaging approaches is necessary for the clinical deployment of the most optimal approach. Therefore, we compare the performance of fundamental and harmonic images created with short-lag spatial coherence (SLSC), M-weighted SLSC (M-SLSC), SLSC combined with robust principal component analysis with no M-weighting (r-SLSC), and r-SLSC with M-weighting (R-SLSC), relative to traditional fundamental and harmonic B-mode images, when distinguishing solid from fluid breast masses. Raw channel data acquired from 40 total breast masses (28 solid, 7 fluid, 5 mixed) were beamformed and analyzed. The contrast of fluid masses was better with fundamental rather than harmonic coherence imaging, due to the lower spatial coherence within the fluid masses in the fundamental coherence images. Relative to SLSC imaging, M-SLSC, r-SLSC, and R-SLSC imaging provided similar contrast across multiple masses (with the exception of clinically challenging complicated cysts) and minimized the range of generalized contrast-to-noise ratios (gCNRs) of fluid masses, yet required additional computational resources. Among the eight coherence imaging modes compared, fundamental SLSC imaging best identified fluid versus solid breast mass contents, outperforming fundamental and harmonic B-mode imaging. With fundamental SLSC images, the specificity and sensitivity to identify fluid masses using the reader-independent metrics of contrast difference, mean lag one coherence (LOC), and gCNR were 0.86 and 1, 1 and 0.89, and 1 and 1, respectively. Results demonstrate that fundamental SLSC imaging and gCNR (or LOC if no coherence image or background region of interest is introduced) have the greatest potential to impact clinical decisions and improve the diagnostic certainty of breast mass contents. These observations are additionally anticipated to extend to masses in other organs.
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Li G, Xiao L, Wang G, Liu Y, Liu L, Huang Q. Knowledge Tensor-Aided Breast Ultrasound Image Assistant Inference Framework. Healthcare (Basel) 2023; 11:2014. [PMID: 37510455 PMCID: PMC10379593 DOI: 10.3390/healthcare11142014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 06/27/2023] [Accepted: 07/11/2023] [Indexed: 07/30/2023] Open
Abstract
Breast cancer is one of the most prevalent cancers in women nowadays, and medical intervention at an early stage of cancer can significantly improve the prognosis of patients. Breast ultrasound (BUS) is a widely used tool for the early screening of breast cancer in primary care hospitals but it relies heavily on the ability and experience of physicians. Accordingly, we propose a knowledge tensor-based Breast Imaging Reporting and Data System (BI-RADS)-score-assisted generalized inference model, which uses the BI-RADS score of senior physicians as the gold standard to construct a knowledge tensor model to infer the benignity and malignancy of breast tumors and axes the diagnostic results against those of junior physicians to provide an aid for breast ultrasound diagnosis. The experimental results showed that the diagnostic AUC of the knowledge tensor constructed using the BI-RADS characteristics labeled by senior radiologists achieved 0.983 (95% confidential interval (CI) = 0.975-0.992) for benign and malignant breast cancer, while the diagnostic performance of the knowledge tensor constructed using the BI-RADS characteristics labeled by junior radiologists was only 0.849 (95% CI = 0.823-0.876). With the knowledge tensor fusion, the AUC is improved to 0.887 (95% CI = 0.864-0.909). Therefore, our proposed knowledge tensor can effectively help reduce the misclassification of BI-RADS characteristics by senior radiologists and, thus, improve the diagnostic performance of breast-ultrasound-assisted diagnosis.
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Affiliation(s)
- Guanghui Li
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China
- School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi'an 710072, China
| | - Lingli Xiao
- Department of Ultrasound, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China
| | - Guanying Wang
- Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Ying Liu
- Department of Ultrasound, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China
| | - Longzhong Liu
- Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Qinghua Huang
- School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi'an 710072, China
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Wiacek A, Oluyemi E, Myers K, Ambinder E, Bell MAL. Coherence Metrics for Reader-Independent Differentiation of Cystic From Solid Breast Masses in Ultrasound Images. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:256-268. [PMID: 36333154 PMCID: PMC9712258 DOI: 10.1016/j.ultrasmedbio.2022.08.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 08/22/2022] [Accepted: 08/28/2022] [Indexed: 06/16/2023]
Abstract
Traditional breast ultrasound imaging is a low-cost, real-time and portable method to assist with breast cancer screening and diagnosis, with particular benefits for patients with dense breast tissue. We previously demonstrated that incorporating coherence-based beamforming additionally improves the distinction of fluid-filled from solid breast masses, based on qualitative image interpretation by board-certified radiologists. However, variable sensitivity (range: 0.71-1.00 when detecting fluid-filled masses) was achieved by the individual radiologist readers. Therefore, we propose two objective coherence metrics, lag-one coherence (LOC) and coherence length (CL), to quantitatively determine the content of breast masses without requiring reader assessment. Data acquired from 31 breast masses were analyzed. Ideal separation (i.e., 1.00 sensitivity and specificity) was achieved between fluid-filled and solid breast masses based on the mean or median LOC value within each mass. When separated based on mean and median CL values, the sensitivity/specificity decreased to 1.00/0.95 and 0.92/0.89, respectively. The greatest sensitivity and specificity were achieved in dense, rather than non-dense, breast tissue. These results support the introduction of an objective, reader-independent method for automated diagnoses of cystic breast masses.
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Affiliation(s)
- Alycen Wiacek
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
| | - Eniola Oluyemi
- Department of Radiology and Radiological Science, Johns Hopkins Medicine, Baltimore, Maryland, USA
| | - Kelly Myers
- Department of Radiology and Radiological Science, Johns Hopkins Medicine, Baltimore, Maryland, USA
| | - Emily Ambinder
- Department of Radiology and Radiological Science, Johns Hopkins Medicine, Baltimore, Maryland, USA
| | - Muyinatu A Lediju Bell
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA; Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA
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Long J, Trahey G, Bottenus N. Spatial Coherence in Medical Ultrasound: A Review. ULTRASOUND IN MEDICINE & BIOLOGY 2022; 48:975-996. [PMID: 35282988 PMCID: PMC9067166 DOI: 10.1016/j.ultrasmedbio.2022.01.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 01/10/2022] [Accepted: 01/16/2022] [Indexed: 05/28/2023]
Abstract
Traditional pulse-echo ultrasound imaging heavily relies on the discernment of signals based on their relative magnitudes but is limited in its ability to mitigate sources of image degradation, the most prevalent of which is acoustic clutter. Advances in computing power and data storage have made it possible for echo data to be alternatively analyzed through the lens of spatial coherence, a measure of the similarity of these signals received across an array. Spatial coherence is not currently explicitly calculated on diagnostic ultrasound scanners but a large number of studies indicate that it can be employed to describe image quality, to adaptively select system parameters and to improve imaging and target detection. With the additional insights provided by spatial coherence, it is poised to play a significant role in the future of medical ultrasound. This review details the theory of spatial coherence in pulse-echo ultrasound and key advances made over the last few decades since its introduction in the 1980s.
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Affiliation(s)
- James Long
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA.
| | - Gregg Trahey
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Nick Bottenus
- Department of Mechanical Engineering, University of Colorado Boulder, Boulder, Colorado, USA
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Gubbi MR, Gonzalez EA, Bell MAL. Theoretical Framework to Predict Generalized Contrast-to-Noise Ratios of Photoacoustic Images With Applications to Computer Vision. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2022; 69:2098-2114. [PMID: 35446763 DOI: 10.1109/tuffc.2022.3169082] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The successful integration of computer vision, robotic actuation, and photoacoustic imaging to find and follow targets of interest during surgical and interventional procedures requires accurate photoacoustic target detectability. This detectability has traditionally been assessed with image quality metrics, such as contrast, contrast-to-noise ratio, and signal-to-noise ratio (SNR). However, predicting target tracking performance expectations when using these traditional metrics is difficult due to unbounded values and sensitivity to image manipulation techniques like thresholding. The generalized contrast-to-noise ratio (gCNR) is a recently introduced alternative target detectability metric, with previous work dedicated to empirical demonstrations of applicability to photoacoustic images. In this article, we present theoretical approaches to model and predict the gCNR of photoacoustic images with an associated theoretical framework to analyze relationships between imaging system parameters and computer vision task performance. Our theoretical gCNR predictions are validated with histogram-based gCNR measurements from simulated, experimental phantom, ex vivo, and in vivo datasets. The mean absolute errors between predicted and measured gCNR values ranged from 3.2 ×10-3 to 2.3 ×10-2 for each dataset, with channel SNRs ranging -40 to 40 dB and laser energies ranging 0.07 [Formula: see text] to 68 mJ. Relationships among gCNR, laser energy, target and background image parameters, target segmentation, and threshold levels were also investigated. Results provide a promising foundation to enable predictions of photoacoustic gCNR and visual servoing segmentation accuracy. The efficiency of precursory surgical and interventional tasks (e.g., energy selection for photoacoustic-guided surgeries) may also be improved with the proposed framework.
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Vayyeti A, Thittai AK. Optimally-weighted non-linear beamformer for conventional focused beam ultrasound imaging systems. Sci Rep 2021; 11:21622. [PMID: 34732736 PMCID: PMC8566575 DOI: 10.1038/s41598-021-00741-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 10/14/2021] [Indexed: 11/09/2022] Open
Abstract
A novel non-linear beamforming method, namely, filtered delay optimally-weighted multiply and sum (F-DowMAS) beamforming is reported for conventional focused beamforming (CFB) technique. The performance of F-DowMAS was compared against delay and sum (DAS), filtered delay multiply and sum (F-DMAS), filtered delay weight multiply and sum (F-DwMAS) and filter delay Euclidian weighted multiply and sum (F-DewMAS) methods. Notably, in the proposed method the optimal adaptive weights are computed for each imaging point to compensate for the effects due to spatial variations in beam pattern in CFB technique. F-DowMAS, F-DMAS, and DAS were compared in terms of the resulting image quality metrics, Lateral resolution (LR), axial resolution (AR), contrast ratio (CR) and contrast-to-noise ratio (CNR), estimated from experiments on a commercially available tissue-mimicking phantom. The results demonstrate that F-DowMAS improved the AR by 57.04% and 46.95%, LR by 58.21% and 53.40%, CR by 67.35% and 39.25%, and CNR by 44.04% and 30.57% compared to those obtained using DAS and F-DMAS, respectively. Thus, it can be concluded that the newly proposed F-DowMAS outperforms DAS and F-DMAS. As an aside, we also show that the optimal weighting strategy can be extended to benefit DAS.
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Affiliation(s)
- Anudeep Vayyeti
- Biomedical Ultrasound Laboratory, Department of Applied Mechanics, Indian Institute of Technology, Madras, Chennai, India
| | - Arun K Thittai
- Biomedical Ultrasound Laboratory, Department of Applied Mechanics, Indian Institute of Technology, Madras, Chennai, India.
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Three-Dimensional Sound Field Reconstruction and Sound Power Estimation by Stereo Vision and Beamforming Technology. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app11010092] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The size of the sound field reconstruction area has an important influence on the beamforming sound source localization method and determines the speed of reconstruction. To reduce the sound field reconstruction area, stereo vision technology is introduced to continuously obtain the three-dimensional surface of the target and reconstruct the sound field on it. The fusion method can quickly locate the three-dimensional position of the sound source, and the computational complexity of this method is mathematically analyzed. The sound power level can be estimated dynamically by the sound intensity scaling method based on beamforming and the depth information of the sound source. Experimental results in a hemi-anechoic chamber show that this method can quickly identify the three-dimensional position of the moving source. When the depth of the moving sound source changes, the estimated sound power is more stable than the sound pressure on the microphone.
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Wiacek A, Gonzalez E, Bell MAL. CohereNet: A Deep Learning Architecture for Ultrasound Spatial Correlation Estimation and Coherence-Based Beamforming. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2020; 67:2574-2583. [PMID: 32203018 PMCID: PMC8034551 DOI: 10.1109/tuffc.2020.2982848] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Deep fully connected networks are often considered "universal approximators" that are capable of learning any function. In this article, we utilize this particular property of deep neural networks (DNNs) to estimate normalized cross correlation as a function of spatial lag (i.e., spatial coherence functions) for applications in coherence-based beamforming, specifically short-lag spatial coherence (SLSC) beamforming. We detail the composition, assess the performance, and evaluate the computational efficiency of CohereNet, our custom fully connected DNN, which was trained to estimate the spatial coherence functions of in vivo breast data from 18 unique patients. CohereNet performance was evaluated on in vivo breast data from three additional patients who were not included during training, as well as data from in vivo liver and tissue mimicking phantoms scanned with a variety of ultrasound transducer array geometries and two different ultrasound systems. The mean correlation between the SLSC images computed on a central processing unit (CPU) and the corresponding DNN SLSC images created with CohereNet was 0.93 across the entire test set. The DNN SLSC approach was up to 3.4 times faster than the CPU SLSC approach, with similar computational speed, less variability in computational times, and improved image quality compared with a graphical processing unit (GPU)-based SLSC approach. These results are promising for the application of deep learning to estimate correlation functions derived from ultrasound data in multiple areas of ultrasound imaging and beamforming (e.g., speckle tracking, elastography, and blood flow estimation), possibly replacing GPU-based approaches in low-power, remote, and synchronization-dependent applications.
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