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Li X, Kim J, Yang M, Ok AH, Zbýň Š, Link TM, Majumdar S, Ma CB, Spindler KP, Winalski CS. Cartilage compositional MRI-a narrative review of technical development and clinical applications over the past three decades. Skeletal Radiol 2024; 53:1761-1781. [PMID: 38980364 PMCID: PMC11303573 DOI: 10.1007/s00256-024-04734-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 06/11/2024] [Accepted: 06/13/2024] [Indexed: 07/10/2024]
Abstract
Articular cartilage damage and degeneration are among hallmark manifestations of joint injuries and arthritis, classically osteoarthritis. Cartilage compositional MRI (Cart-C MRI), a quantitative technique, which aims to detect early-stage cartilage matrix changes that precede macroscopic alterations, began development in the 1990s. However, despite the significant advancements over the past three decades, Cart-C MRI remains predominantly a research tool, hindered by various technical and clinical hurdles. This paper will review the technical evolution of Cart-C MRI, delve into its clinical applications, and conclude by identifying the existing gaps and challenges that need to be addressed to enable even broader clinical application of Cart-C MRI.
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Affiliation(s)
- Xiaojuan Li
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, 9500 Euclid Avenue, ND20, Cleveland, OH, 44195, USA.
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
- Department of Diagnostic Radiology, Cleveland Clinic, Cleveland, OH, USA.
| | - Jeehun Kim
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, 9500 Euclid Avenue, ND20, Cleveland, OH, 44195, USA
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Mingrui Yang
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, 9500 Euclid Avenue, ND20, Cleveland, OH, 44195, USA
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Ahmet H Ok
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, 9500 Euclid Avenue, ND20, Cleveland, OH, 44195, USA
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Diagnostic Radiology, Cleveland Clinic, Cleveland, OH, USA
| | - Štefan Zbýň
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, 9500 Euclid Avenue, ND20, Cleveland, OH, 44195, USA
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Diagnostic Radiology, Cleveland Clinic, Cleveland, OH, USA
| | - Thomas M Link
- Department of Radiology and Biomedical Imaging, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Sharmilar Majumdar
- Department of Radiology and Biomedical Imaging, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - C Benjamin Ma
- Department of Orthopaedic Surgery, UCSF, San Francisco, CA, USA
| | - Kurt P Spindler
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, 9500 Euclid Avenue, ND20, Cleveland, OH, 44195, USA
- Department of Orthopaedic Surgery, Cleveland Clinic, Cleveland, OH, USA
| | - Carl S Winalski
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, 9500 Euclid Avenue, ND20, Cleveland, OH, 44195, USA
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Diagnostic Radiology, Cleveland Clinic, Cleveland, OH, USA
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Tong MW, Tolpadi AA, Bhattacharjee R, Han M, Majumdar S, Pedoia V. Synthetic Knee MRI T 1p Maps as an Avenue for Clinical Translation of Quantitative Osteoarthritis Biomarkers. Bioengineering (Basel) 2023; 11:17. [PMID: 38247894 PMCID: PMC10812962 DOI: 10.3390/bioengineering11010017] [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: 11/17/2023] [Revised: 12/15/2023] [Accepted: 12/21/2023] [Indexed: 01/23/2024] Open
Abstract
A 2D U-Net was trained to generate synthetic T1p maps from T2 maps for knee MRI to explore the feasibility of domain adaptation for enriching existing datasets and enabling rapid, reliable image reconstruction. The network was developed using 509 healthy contralateral and injured ipsilateral knee images from patients with ACL injuries and reconstruction surgeries acquired across three institutions. Network generalizability was evaluated on 343 knees acquired in a clinical setting and 46 knees from simultaneous bilateral acquisition in a research setting. The deep neural network synthesized high-fidelity reconstructions of T1p maps, preserving textures and local T1p elevation patterns in cartilage with a normalized mean square error of 2.4% and Pearson's correlation coefficient of 0.93. Analysis of reconstructed T1p maps within cartilage compartments revealed minimal bias (-0.10 ms), tight limits of agreement, and quantification error (5.7%) below the threshold for clinically significant change (6.42%) associated with osteoarthritis. In an out-of-distribution external test set, synthetic maps preserved T1p textures, but exhibited increased bias and wider limits of agreement. This study demonstrates the capability of image synthesis to reduce acquisition time, derive meaningful information from existing datasets, and suggest a pathway for standardizing T1p as a quantitative biomarker for osteoarthritis.
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Affiliation(s)
- Michelle W. Tong
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA (S.M.); (V.P.)
- Department of Bioengineering, University of California Berkeley, Berkeley, CA 94720, USA
| | - Aniket A. Tolpadi
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA (S.M.); (V.P.)
- Department of Bioengineering, University of California Berkeley, Berkeley, CA 94720, USA
| | - Rupsa Bhattacharjee
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA (S.M.); (V.P.)
| | - Misung Han
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA (S.M.); (V.P.)
| | - Sharmila Majumdar
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA (S.M.); (V.P.)
| | - Valentina Pedoia
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA (S.M.); (V.P.)
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Cigdem O, Deniz CM. Artificial intelligence in knee osteoarthritis: A comprehensive review for 2022. OSTEOARTHRITIS IMAGING 2023; 3:100161. [PMID: 38948116 PMCID: PMC11213283 DOI: 10.1016/j.ostima.2023.100161] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Objective The aim of this literature review is to yield a comprehensive and exhaustive overview of the existing evidence and up-to-date applications of artificial intelligence for knee osteoarthritis. Methods A literature review was performed by using PubMed, Google Scholar, and IEEE databases for articles published in peer-reviewed journals in 2022. The articles focusing on the use of artificial intelligence in diagnosis and prognosis of knee osteoarthritis and accelerating the image acquisition were selected. For each selected study, the code availability, considered number of patients and knees, imaging type, covariates, grading type of osteoarthritis, models, validation approaches, objectives, and results were reviewed. Results 395 articles were screened, and 35 of them were reviewed. Eight articles were based on diagnosis, six on prognosis prediction, three on classification, three on accelerated image acquisition, and 15 on segmentation of knee osteoarthritis. 57% of the articles used MRI, 26% radiography, 6% MRI together with radiography, 6% ultrasonography, and 6% only clinical data. 23% of the articles made the computer codes available for their study, and 26% used clinical data. External validation and nested cross-validation were used in 17% and 14% of articles, respectively. Conclusions The use of artificial intelligence provided a promising potential to enhance the detection and management of knee osteoarthritis. Translating the developed models into clinics is still in the early stages of development. The translation of artificial intelligence models is expected to be further examined in prospective studies to support clinicians in improving routine healthcare practice.
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Affiliation(s)
- Ozkan Cigdem
- Department of Radiology, New York University Grossman School of Medicine, 227 E 30th St, 7th Floor, New York, NY 10016, United States
| | - Cem M Deniz
- Department of Radiology, New York University Grossman School of Medicine, 227 E 30th St, 7th Floor, New York, NY 10016, United States
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Singh D, Monga A, de Moura HL, Zhang X, Zibetti MVW, Regatte RR. Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review. Bioengineering (Basel) 2023; 10:1012. [PMID: 37760114 PMCID: PMC10525988 DOI: 10.3390/bioengineering10091012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/22/2023] [Accepted: 08/24/2023] [Indexed: 09/29/2023] Open
Abstract
Magnetic Resonance Imaging (MRI) is an essential medical imaging modality that provides excellent soft-tissue contrast and high-resolution images of the human body, allowing us to understand detailed information on morphology, structural integrity, and physiologic processes. However, MRI exams usually require lengthy acquisition times. Methods such as parallel MRI and Compressive Sensing (CS) have significantly reduced the MRI acquisition time by acquiring less data through undersampling k-space. The state-of-the-art of fast MRI has recently been redefined by integrating Deep Learning (DL) models with these undersampled approaches. This Systematic Literature Review (SLR) comprehensively analyzes deep MRI reconstruction models, emphasizing the key elements of recently proposed methods and highlighting their strengths and weaknesses. This SLR involves searching and selecting relevant studies from various databases, including Web of Science and Scopus, followed by a rigorous screening and data extraction process using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. It focuses on various techniques, such as residual learning, image representation using encoders and decoders, data-consistency layers, unrolled networks, learned activations, attention modules, plug-and-play priors, diffusion models, and Bayesian methods. This SLR also discusses the use of loss functions and training with adversarial networks to enhance deep MRI reconstruction methods. Moreover, we explore various MRI reconstruction applications, including non-Cartesian reconstruction, super-resolution, dynamic MRI, joint learning of reconstruction with coil sensitivity and sampling, quantitative mapping, and MR fingerprinting. This paper also addresses research questions, provides insights for future directions, and emphasizes robust generalization and artifact handling. Therefore, this SLR serves as a valuable resource for advancing fast MRI, guiding research and development efforts of MRI reconstruction for better image quality and faster data acquisition.
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Affiliation(s)
- Dilbag Singh
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA; (A.M.); (H.L.d.M.); (X.Z.); (M.V.W.Z.)
| | | | | | | | | | - Ravinder R. Regatte
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA; (A.M.); (H.L.d.M.); (X.Z.); (M.V.W.Z.)
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Zibetti MVW, Menon RG, de Moura HL, Zhang X, Kijowski R, Regatte RR. Updates on Compositional MRI Mapping of the Cartilage: Emerging Techniques and Applications. J Magn Reson Imaging 2023; 58:44-60. [PMID: 37010113 PMCID: PMC10323700 DOI: 10.1002/jmri.28689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 03/06/2023] [Accepted: 03/06/2023] [Indexed: 04/04/2023] Open
Abstract
Osteoarthritis (OA) is a widely occurring degenerative joint disease that is severely debilitating and causes significant socioeconomic burdens to society. Magnetic resonance imaging (MRI) is the preferred imaging modality for the morphological evaluation of cartilage due to its excellent soft tissue contrast and high spatial resolution. However, its utilization typically involves subjective qualitative assessment of cartilage. Compositional MRI, which refers to the quantitative characterization of cartilage using a variety of MRI methods, can provide important information regarding underlying compositional and ultrastructural changes that occur during early OA. Cartilage compositional MRI could serve as early imaging biomarkers for the objective evaluation of cartilage and help drive diagnostics, disease characterization, and response to novel therapies. This review will summarize current and ongoing state-of-the-art cartilage compositional MRI techniques and highlight emerging methods for cartilage compositional MRI including MR fingerprinting, compressed sensing, multiexponential relaxometry, improved and robust radio-frequency pulse sequences, and deep learning-based acquisition, reconstruction, and segmentation. The review will also briefly discuss the current challenges and future directions for adopting these emerging cartilage compositional MRI techniques for use in clinical practice and translational OA research studies. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Marcelo V. W. Zibetti
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Rajiv G. Menon
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Hector L. de Moura
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Xiaoxia Zhang
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Richard Kijowski
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Ravinder R. Regatte
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
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Xie D, Yin C. Exploration of Chinese cultural communication mode based on the Internet of Things and mobile multimedia technology. PeerJ Comput Sci 2023; 9:e1330. [PMID: 37346562 PMCID: PMC10280474 DOI: 10.7717/peerj-cs.1330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 03/15/2023] [Indexed: 06/23/2023]
Abstract
Image retrieval technology has emerged as a popular research area of China's development of cultural digital image dissemination and creative creation with the growth of the Internet and the digital information age. This study uses the shadow image in Shaanxi culture as the research object, suggests a shadow image retrieval model based on CBAM-ResNet50, and implements it in the IoT system to achieve more effective deep-level cultural information retrieval. First, ResNet50 is paired with an attention mechanism to enhance the network's capacity to extract sophisticated semantic characteristics. The second step is configuring the IoT system's picture acquisition, processing, and output modules. The image processing module incorporates the CBAM-ResNet50 network to provide intelligent and effective shadow play picture retrieval. The experiment results show that shadow plays on GPU can retrieve images at a millisecond level. Both the first image and the first six photographs may be accurately retrieved, with a retrieval accuracy of 92.5 percent for the first image. This effectively communicates Chinese culture and makes it possible to retrieve detailed shadow-play images.
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Affiliation(s)
- Dan Xie
- General Department, Xi’an Traffic Engineering Institute, Xi’an, Shaanxi, China
- University of Technology MARA, Selangor, Shah Anam, Malaysia
| | - Chao Yin
- School of History and Culture, Shaanxi Normal University, Xi’an, Shaanxi, China
- Xi’an Tie Yi Middle School, Xi’an, Shaanxi, China
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