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Li H, Bhatt M, Qu Z, Zhang S, Hartel MC, Khademhosseini A, Cloutier G. Deep learning in ultrasound elastography imaging: A review. Med Phys 2022; 49:5993-6018. [PMID: 35842833 DOI: 10.1002/mp.15856] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 02/04/2022] [Accepted: 07/06/2022] [Indexed: 11/11/2022] Open
Abstract
It is known that changes in the mechanical properties of tissues are associated with the onset and progression of certain diseases. Ultrasound elastography is a technique to characterize tissue stiffness using ultrasound imaging either by measuring tissue strain using quasi-static elastography or natural organ pulsation elastography, or by tracing a propagated shear wave induced by a source or a natural vibration using dynamic elastography. In recent years, deep learning has begun to emerge in ultrasound elastography research. In this review, several common deep learning frameworks in the computer vision community, such as multilayer perceptron, convolutional neural network, and recurrent neural network are described. Then, recent advances in ultrasound elastography using such deep learning techniques are revisited in terms of algorithm development and clinical diagnosis. Finally, the current challenges and future developments of deep learning in ultrasound elastography are prospected. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Hongliang Li
- Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center, Montréal, Québec, Canada.,Institute of Biomedical Engineering, University of Montreal, Montréal, Québec, Canada
| | - Manish Bhatt
- Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center, Montréal, Québec, Canada
| | - Zhen Qu
- Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center, Montréal, Québec, Canada
| | - Shiming Zhang
- California Nanosystems Institute, University of California, Los Angeles, California, USA
| | - Martin C Hartel
- California Nanosystems Institute, University of California, Los Angeles, California, USA
| | - Ali Khademhosseini
- California Nanosystems Institute, University of California, Los Angeles, California, USA
| | - Guy Cloutier
- Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center, Montréal, Québec, Canada.,Institute of Biomedical Engineering, University of Montreal, Montréal, Québec, Canada.,Department of Radiology, Radio-Oncology and Nuclear Medicine, University of Montreal, Montréal, Québec, Canada
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Seyedpour SM, Nabati M, Lambers L, Nafisi S, Tautenhahn HM, Sack I, Reichenbach JR, Ricken T. Application of Magnetic Resonance Imaging in Liver Biomechanics: A Systematic Review. Front Physiol 2021; 12:733393. [PMID: 34630152 PMCID: PMC8493836 DOI: 10.3389/fphys.2021.733393] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 08/25/2021] [Indexed: 12/15/2022] Open
Abstract
MRI-based biomechanical studies can provide a deep understanding of the mechanisms governing liver function, its mechanical performance but also liver diseases. In addition, comprehensive modeling of the liver can help improve liver disease treatment. Furthermore, such studies demonstrate the beginning of an engineering-level approach to how the liver disease affects material properties and liver function. Aimed at researchers in the field of MRI-based liver simulation, research articles pertinent to MRI-based liver modeling were identified, reviewed, and summarized systematically. Various MRI applications for liver biomechanics are highlighted, and the limitations of different viscoelastic models used in magnetic resonance elastography are addressed. The clinical application of the simulations and the diseases studied are also discussed. Based on the developed questionnaire, the papers' quality was assessed, and of the 46 reviewed papers, 32 papers were determined to be of high-quality. Due to the lack of the suitable material models for different liver diseases studied by magnetic resonance elastography, researchers may consider the effect of liver diseases on constitutive models. In the future, research groups may incorporate various aspects of machine learning (ML) into constitutive models and MRI data extraction to further refine the study methodology. Moreover, researchers should strive for further reproducibility and rigorous model validation and verification.
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Affiliation(s)
- Seyed M. Seyedpour
- Institute of Mechanics, Structural Analysis and Dynamics, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Stuttgart, Germany
- Biomechanics Lab, Institute of Mechanics, Structural Analysis and Dynamics, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Stuttgart, Germany
| | - Mehdi Nabati
- Department of Mechanical Engineering, Faculty of Engineering, Boğaziçi University, Istanbul, Turkey
| | - Lena Lambers
- Institute of Mechanics, Structural Analysis and Dynamics, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Stuttgart, Germany
- Biomechanics Lab, Institute of Mechanics, Structural Analysis and Dynamics, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Stuttgart, Germany
| | - Sara Nafisi
- Faculty of Pharmacy, Istinye University, Istanbul, Turkey
| | - Hans-Michael Tautenhahn
- Department of General, Visceral and Vascular Surgery, Jena University Hospital, Jena, Germany
| | - Ingolf Sack
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Campus Charité Mitte, Berlin, Germany
| | - Jürgen R. Reichenbach
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital-Friedrich Schiller University Jena, Jena, Germany
- Center of Medical Optics and Photonics, Friedrich Schiller University, Jena, Germany
- Michael Stifel Center for Data-driven and Simulation Science Jena, Friedrich Schiller University, Jena, Germany
| | - Tim Ricken
- Institute of Mechanics, Structural Analysis and Dynamics, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Stuttgart, Germany
- Biomechanics Lab, Institute of Mechanics, Structural Analysis and Dynamics, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Stuttgart, Germany
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Manduca A, Bayly PJ, Ehman RL, Kolipaka A, Royston TJ, Sack I, Sinkus R, Van Beers BE. MR elastography: Principles, guidelines, and terminology. Magn Reson Med 2020; 85:2377-2390. [PMID: 33296103 DOI: 10.1002/mrm.28627] [Citation(s) in RCA: 99] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 10/20/2020] [Accepted: 11/09/2020] [Indexed: 12/13/2022]
Abstract
Magnetic resonance elastography (MRE) is a phase contrast-based MRI technique that can measure displacement due to propagating mechanical waves, from which material properties such as shear modulus can be calculated. Magnetic resonance elastography can be thought of as quantitative, noninvasive palpation. It is increasing in clinical importance, has become widespread in the diagnosis and staging of liver fibrosis, and additional clinical applications are being explored. However, publications have reported MRE results using many different parameters, acquisition techniques, processing methods, and varied nomenclature. The diversity of terminology can lead to confusion (particularly among clinicians) about the meaning of and interpretation of MRE results. This paper was written by the MRE Guidelines Committee, a group formalized at the first meeting of the ISMRM MRE Study Group, to clarify and move toward standardization of MRE nomenclature. The purpose of this paper is to (1) explain MRE terminology and concepts to those not familiar with them, (2) define "good practices" for practitioners of MRE, and (3) identify opportunities to standardize terminology, to avoid confusion.
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Affiliation(s)
- Armando Manduca
- Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
| | - Philip J Bayly
- Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Richard L Ehman
- Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
| | - Arunark Kolipaka
- Department of Radiology, Ohio State University, Columbus, Ohio, USA
| | - Thomas J Royston
- Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Ingolf Sack
- Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Ralph Sinkus
- Imaging Sciences & Biomedical Engineering, Kings College London, London, United Kingdom
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Tzschätzsch H, Kreft B, Braun J, Sack I. Transtemporal Investigation of Brain Parenchyma Elasticity Using 2-D Shear Wave Elastography: Trustworthy? ULTRASOUND IN MEDICINE & BIOLOGY 2019; 45:1344-1345. [PMID: 30770137 DOI: 10.1016/j.ultrasmedbio.2019.01.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 06/26/2018] [Accepted: 01/08/2019] [Indexed: 06/09/2023]
Affiliation(s)
- Heiko Tzschätzsch
- Department of Radiology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Bernhard Kreft
- Department of Radiology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Jürgen Braun
- Institute of Medical Informatics, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Ingolf Sack
- Department of Radiology, Charité-Universitätsmedizin Berlin, Berlin, Germany.
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