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Sakai T, Yoneyama M, Zhang S, Kitsukawa K, Yokota H, Ichikawa R, Aoki Y, Watanabe A, Sato Y, Yanagawa N, Murayama D, Ito H, Ochi S, Miyati T. Clinical evaluation of 3D high-resolution isotropic knee MRI using Multi-Interleaved X-prepared TSE with inTUitive RElaxometry (MIXTURE) for simultaneous morphology and T2 mapping. Eur J Radiol 2024; 177:111579. [PMID: 38897053 DOI: 10.1016/j.ejrad.2024.111579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 05/25/2024] [Accepted: 06/16/2024] [Indexed: 06/21/2024]
Abstract
PURPOSE Quantitative MRI techniques such as T2 mapping are useful in comprehensive evaluation of various pathologies of the knee joint yet require separate scans to conventional morphological measurements and long acquisition times. The recently introduced 3D MIXTURE (Multi-Interleaved X-prepared Turbo-Spin Echo with Intuitive Relaxometry) technique can obtain simultaneous morphologic and quantitative information of the knee joint. To compare MIXTURE with conventional methods and to identify differences in morphological and quantitative information. METHODS Phantom studies were conducted, and in vivo human scans were performed (20 patients) presented with knee arthralgia. MIXTURE is based on 3D TSE without and with T2 preparation modules in an interleaved manner for both morphology with PDW and fat suppressed T2W imaging as well as quantitative T2 mapping within one single scan. Image quality and lesion depiction were visually assessed and compared between MIXTURE and conventional 2D TSE by two experienced radiologists. Contrast-to-noise ratio was used to assess the adjacent tissue contrast in a quantitative way for both obtained PDW and fat suppressed T2W images. Quantitative T2 values were measured in phantom and from in vivo knee cartilage. RESULTS The overall diagnostic confidence and contrast-to-noise ratio were deemed comparable between MIXTURE and 2D TSE. While the chosen T2 preparation modules for MIXTURE rendered consistent T2 values comparing to the current standard, measured cartilage T2 values ranged from 26.1 to 50.7 ms, with significant difference between the lesion and normal areas (p < 0.05). CONCLUSIONS MIXTURE can help to provide high-resolution information for both anatomical and pathological assessment.
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Affiliation(s)
- Takayuki Sakai
- Department of Radiology, Eastern Chiba Medical Center, Chiba, Japan; Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Ishikawa, Japan; Faculty of Health Sciences, Tsukuba International University, Ibaraki, Japan.
| | | | | | - Kaoru Kitsukawa
- Department of Radiology, Chiba University Hospital Comprehensive Radiology Center, Chiba, Japan
| | - Hajime Yokota
- Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Rina Ichikawa
- Department of Radiology, Chiba University Hospital Comprehensive Radiology Center, Chiba, Japan
| | - Yasuchika Aoki
- Department of General Medical Science, Graduate School of Medicine, Chiba University, Chiba, Japan; Department of Orthopaedic Surgery, Eastern Chiba Medical Center, Chiba, Japan
| | - Atsuya Watanabe
- Department of General Medical Science, Graduate School of Medicine, Chiba University, Chiba, Japan; Department of Orthopaedic Surgery, Eastern Chiba Medical Center, Chiba, Japan
| | - Yusuke Sato
- Department of General Medical Science, Graduate School of Medicine, Chiba University, Chiba, Japan; Department of Orthopaedic Surgery, Eastern Chiba Medical Center, Chiba, Japan
| | - Noriyuki Yanagawa
- Faculty of Health Sciences, Tsukuba International University, Ibaraki, Japan
| | - Daichi Murayama
- Department of Radiology, Eastern Chiba Medical Center, Chiba, Japan
| | - Hajime Ito
- Department of Radiology, Eastern Chiba Medical Center, Chiba, Japan
| | - Shigehiro Ochi
- Department of Radiology, Eastern Chiba Medical Center, Chiba, Japan
| | - Tosiaki Miyati
- Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Ishikawa, Japan
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Lemainque T, Pridöhl N, Zhang S, Huppertz M, Post M, Yüksel C, Yoneyama M, Prescher A, Kuhl C, Truhn D, Nebelung S. Time-efficient combined morphologic and quantitative joint MRI: an in situ study of standardized knee cartilage defects in human cadaveric specimens. Eur Radiol Exp 2024; 8:66. [PMID: 38834751 DOI: 10.1186/s41747-024-00462-0] [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: 11/23/2023] [Accepted: 03/27/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND Quantitative techniques such as T2 and T1ρ mapping allow evaluating the cartilage and meniscus. We evaluated multi-interleaved X-prepared turbo-spin echo with intuitive relaxometry (MIXTURE) sequences with turbo spin-echo (TSE) contrast and additional parameter maps versus reference TSE sequences in an in situ model of human cartilage defects. METHODS Standardized cartilage defects of 8, 5, and 3 mm in diameter were created in the lateral femora of ten human cadaveric knee specimens (81 ± 10 years old; nine males, one female). MIXTURE sequences providing proton density-weighted fat-saturated images and T2 maps or T1-weighted images and T1ρ maps as well as the corresponding two- and three-dimensional TSE reference sequences were acquired before and after defect creation (3-T scanner; knee coil). Defect delineability, bone texture, and cartilage relaxation times were quantified. Appropriate parametric or non-parametric tests were used. RESULTS Overall, defect delineability and texture features were not significantly different between the MIXTURE and reference sequences (p ≤ 0.47). After defect creation, relaxation times significantly increased in the central femur (T2pre = 51 ± 4 ms [mean ± standard deviation] versus T2post = 56 ± 4 ms; p = 0.002) and all regions combined (T1ρpre = 40 ± 4 ms versus T1ρpost = 43 ± 4 ms; p = 0.004). CONCLUSIONS MIXTURE permitted time-efficient simultaneous morphologic and quantitative joint assessment based on clinical image contrasts. While providing T2 or T1ρ maps in clinically feasible scan time, morphologic image features, i.e., cartilage defects and bone texture, were comparable between MIXTURE and reference sequences. RELEVANCE STATEMENT Equally time-efficient and versatile, the MIXTURE sequence platform combines morphologic imaging using familiar contrasts, excellent image correspondence versus corresponding reference sequences and quantitative mapping information, thereby increasing the diagnostic value beyond mere morphology. KEY POINTS • Combined morphologic and quantitative MIXTURE sequences are based on three-dimensional TSE contrasts. • MIXTURE sequences were studied in an in situ human cartilage defect model. • Morphologic image features, i.e., defect delineabilty and bone texture, were investigated. • Morphologic image features were similar between MIXTURE and reference sequences. • MIXTURE allowed time-efficient simultaneous morphologic and quantitative knee joint assessment.
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Affiliation(s)
- Teresa Lemainque
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, Pauwelsstr. 30, Aachen, 52074, Germany.
| | - Nicola Pridöhl
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, Pauwelsstr. 30, Aachen, 52074, Germany
| | - Shuo Zhang
- Philips GmbH Market DACH, Hamburg, Germany
| | - Marc Huppertz
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, Pauwelsstr. 30, Aachen, 52074, Germany
| | - Manuel Post
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, Pauwelsstr. 30, Aachen, 52074, Germany
| | - Can Yüksel
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, Pauwelsstr. 30, Aachen, 52074, Germany
| | | | - Andreas Prescher
- Institute of Molecular and Cellular Anatomy, RWTH Aachen University, Aachen, 52074, Germany
| | - Christiane Kuhl
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, Pauwelsstr. 30, Aachen, 52074, Germany
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, Pauwelsstr. 30, Aachen, 52074, Germany
| | - Sven Nebelung
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, Pauwelsstr. 30, Aachen, 52074, Germany
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Lemainque T, Pridöhl N, Huppertz M, Post M, Yüksel C, Siepmann R, Radke KL, Zhang S, Yoneyama M, Prescher A, Kuhl C, Truhn D, Nebelung S. Two for One-Combined Morphologic and Quantitative Knee Joint MRI Using a Versatile Turbo Spin-Echo Platform. Diagnostics (Basel) 2024; 14:978. [PMID: 38786276 PMCID: PMC11120432 DOI: 10.3390/diagnostics14100978] [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: 03/14/2024] [Revised: 04/25/2024] [Accepted: 05/03/2024] [Indexed: 05/25/2024] Open
Abstract
Quantitative MRI techniques such as T2 and T1ρ mapping are beneficial in evaluating knee joint pathologies; however, long acquisition times limit their clinical adoption. MIXTURE (Multi-Interleaved X-prepared Turbo Spin-Echo with IntUitive RElaxometry) provides a versatile turbo spin-echo (TSE) platform for simultaneous morphologic and quantitative joint imaging. Two MIXTURE sequences were designed along clinical requirements: "MIX1", combining proton density (PD)-weighted fat-saturated (FS) images and T2 mapping (acquisition time: 4:59 min), and "MIX2", combining T1-weighted images and T1ρ mapping (6:38 min). MIXTURE sequences and their reference 2D and 3D TSE counterparts were acquired from ten human cadaveric knee joints at 3.0 T. Contrast, contrast-to-noise ratios, and coefficients of variation were comparatively evaluated using parametric tests. Clinical radiologists (n = 3) assessed diagnostic quality as a function of sequence and anatomic structure using five-point Likert scales and ordinal regression, with a significance level of α = 0.01. MIX1 and MIX2 had at least equal diagnostic quality compared to reference sequences of the same image weighting. Contrast, contrast-to-noise ratios, and coefficients of variation were largely similar for the PD-weighted FS and T1-weighted images. In clinically feasible scan times, MIXTURE sequences yield morphologic, TSE-based images of diagnostic quality and quantitative parameter maps with additional insights on soft tissue composition and ultrastructure.
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Affiliation(s)
- Teresa Lemainque
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany (C.Y.); (R.S.); (S.N.)
| | - Nicola Pridöhl
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany (C.Y.); (R.S.); (S.N.)
| | - Marc Huppertz
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany (C.Y.); (R.S.); (S.N.)
| | - Manuel Post
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany (C.Y.); (R.S.); (S.N.)
| | - Can Yüksel
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany (C.Y.); (R.S.); (S.N.)
| | - Robert Siepmann
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany (C.Y.); (R.S.); (S.N.)
| | - Karl Ludger Radke
- Department of Diagnostic and Interventional Radiology, University Hospital Düsseldorf, University Dusseldorf, 40225 Düsseldorf, Germany
| | - Shuo Zhang
- Philips GmbH Market DACH, 22335 Hamburg, Germany
- Philips Healthcare, 5684 PZ Best, The Netherlands
| | | | - Andreas Prescher
- Institute of Molecular and Cellular Anatomy, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany
| | - Christiane Kuhl
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany (C.Y.); (R.S.); (S.N.)
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany (C.Y.); (R.S.); (S.N.)
| | - Sven Nebelung
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany (C.Y.); (R.S.); (S.N.)
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Roemer FW, Jarraya M, Hayashi D, Crema MD, Haugen IK, Hunter DJ, Guermazi A. A perspective on the evolution of semi-quantitative MRI assessment of osteoarthritis: Past, present and future. Osteoarthritis Cartilage 2024; 32:460-472. [PMID: 38211810 DOI: 10.1016/j.joca.2024.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 12/15/2023] [Accepted: 01/04/2024] [Indexed: 01/13/2024]
Abstract
OBJECTIVE This perspective describes the evolution of semi-quantitative (SQ) magnetic resonance imaging (MRI) in characterizing structural tissue pathologies in osteoarthritis (OA) imaging research over the last 30 years. METHODS Authors selected representative articles from a PubMed search to illustrate key steps in SQ MRI development, validation, and application. Topics include main scoring systems, reading techniques, responsiveness, reliability, technical considerations, and potential impact of artificial intelligence (AI). RESULTS Based on original research published between 1993 and 2023, this article introduces available scoring systems, including but not limited to Whole-Organ Magnetic Resonance Imaging Score (WORMS) as the first system for whole-organ assessment of the knee and the now commonly used MRI Osteoarthritis Knee Score (MOAKS) instrument. Specific systems for distinct OA subtypes or applications have been developed as well as MRI scoring instruments for other joints such as the hip, the fingers or thumb base. SQ assessment has proven to be valid, reliable, and responsive, aiding OA investigators in understanding the natural history of the disease and helping to detect response to treatment. AI may aid phenotypic characterization in the future. SQ MRI assessment's role is increasing in eligibility and safety evaluation in knee OA clinical trials. CONCLUSIONS Evidence supports the validity, reliability, and responsiveness of SQ MRI assessment in understanding structural aspects of disease onset and progression. SQ scoring has helped explain associations between structural tissue damage and clinical manifestations, as well as disease progression. While AI may support human readers to more efficiently perform SQ assessment in the future, its current application in clinical trials still requires validation and regulatory approval.
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Affiliation(s)
- Frank W Roemer
- Universitätsklinikum Erlangen & Friedrich Alexander Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany; Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, USA.
| | - Mohamed Jarraya
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Daichi Hayashi
- Tufts Medical Center, Tufts University School of Medicine, Boston, MA, USA; Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Michel D Crema
- Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, USA; Institute of Sports Imaging, French National Institute of Sports (INSEP), Paris, France
| | - Ida K Haugen
- Center for Treatment of Rheumatic and Musculoskeletal Diseases (REMEDY), Diakonhjemmet Hospital, Oslo, Norway
| | - David J Hunter
- Department of Rheumatology, Royal North Shore Hospital and Sydney Musculoskeletal Health, Kolling Institute, University of Sydney, St. Leonards, NSW, Australia
| | - Ali Guermazi
- Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, USA; Boston VA Healthcare System, West Roxbury, MA, USA
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Yoon MA, Gold GE, Chaudhari AS. Accelerated Musculoskeletal Magnetic Resonance Imaging. J Magn Reson Imaging 2023. [PMID: 38156716 DOI: 10.1002/jmri.29205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 12/13/2023] [Accepted: 12/14/2023] [Indexed: 01/03/2024] Open
Abstract
With a substantial growth in the use of musculoskeletal MRI, there has been a growing need to improve MRI workflow, and faster imaging has been suggested as one of the solutions for a more efficient examination process. Consequently, there have been considerable advances in accelerated MRI scanning methods. This article aims to review the basic principles and applications of accelerated musculoskeletal MRI techniques including widely used conventional acceleration methods, more advanced deep learning-based techniques, and new approaches to reduce scan time. Specifically, conventional accelerated MRI techniques, including parallel imaging, compressed sensing, and simultaneous multislice imaging, and deep learning-based accelerated MRI techniques, including undersampled MR image reconstruction, super-resolution imaging, artifact correction, and generation of unacquired contrast images, are discussed. Finally, new approaches to reduce scan time, including synthetic MRI, novel sequences, and new coil setups and designs, are also reviewed. We believe that a deep understanding of these fast MRI techniques and proper use of combined acceleration methods will synergistically improve scan time and MRI workflow in daily practice. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Min A Yoon
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
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Walter SS, Fritz B, Kijowski R, Fritz J. 2D versus 3D MRI of osteoarthritis in clinical practice and research. Skeletal Radiol 2023; 52:2211-2224. [PMID: 36907953 DOI: 10.1007/s00256-023-04309-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/14/2023] [Accepted: 02/14/2023] [Indexed: 03/14/2023]
Abstract
Accurately detecting and characterizing articular cartilage defects is critical in assessing patients with osteoarthritis. While radiography is the first-line imaging modality, magnetic resonance imaging (MRI) is the most accurate for the noninvasive assessment of articular cartilage. Multiple semiquantitative grading systems for cartilage lesions in MRI were developed. The Outerbridge and modified Noyes grading systems are commonly used in clinical practice and for research. Other useful grading systems were developed for research, many of which are joint-specific. Both two-dimensional (2D) and three-dimensional (3D) pulse sequences are used to assess cartilage morphology and biochemical composition. MRI techniques for morphological assessment of articular cartilage can be categorized into 2D and 3D FSE/TSE spin-echo and gradient-recalled echo sequences. T2 mapping is most commonly used to qualitatively assess articular cartilage microstructural composition and integrity, extracellular matrix components, and water content. Quantitative techniques may be able to label articular cartilage alterations before morphological defects are visible. Accurate detection and characterization of shallow low-grade partial and small articular cartilage defects are the most challenging for any technique, but where high spatial resolution 3D MRI techniques perform best. This review article provides a practical overview of commonly used 2D and 3D MRI techniques for articular cartilage assessments in osteoarthritis.
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Affiliation(s)
- Sven S Walter
- Department of Radiology, Division of Musculoskeletal Radiology, NYU Grossman School of Medicine, 660 1St Ave, 3rd Floor, Rm 313, New York, NY, 10016, USA
- Department for Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, University Hospital Tuebingen, 72076, Tübingen, Germany
| | - Benjamin Fritz
- Department of Radiology, Balgrist University Hospital, Forchstrasse 340, CH-8008, Zurich, Switzerland
- Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Richard Kijowski
- New York University Grossman School of Medicine, New York University, New York, NY, 10016, USA
| | - Jan Fritz
- Department of Radiology, Division of Musculoskeletal Radiology, NYU Grossman School of Medicine, 660 1St Ave, 3rd Floor, Rm 313, New York, NY, 10016, USA.
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Lee J, Jung M, Park J, Kim S, Im Y, Lee N, Song HT, Lee YH. Highly accelerated knee magnetic resonance imaging using deep neural network (DNN)-based reconstruction: prospective, multi-reader, multi-vendor study. Sci Rep 2023; 13:17264. [PMID: 37828048 PMCID: PMC10570285 DOI: 10.1038/s41598-023-44248-7] [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: 03/12/2023] [Accepted: 10/05/2023] [Indexed: 10/14/2023] Open
Abstract
In this prospective, multi-reader, multi-vendor study, we evaluated the performance of a commercially available deep neural network (DNN)-based MR image reconstruction in enabling accelerated 2D fast spin-echo (FSE) knee imaging. Forty-five subjects were prospectively enrolled and randomly divided into three 3T MRIs. Conventional 2D FSE and accelerated 2D FSE sequences were acquired for each subject, and the accelerated FSE images were reconstructed and enhanced with DNN-based reconstruction software (FSE-DNN). Quantitative assessments and diagnostic performances were independently evaluated by three musculoskeletal radiologists. For statistical analyses, paired t-tests, and Pearson's correlation were used for image quality comparison and inter-reader agreements. Accelerated FSE-DNN reduced scan times by 41.0% on average. FSE-DNN showed better SNR and CNR (p < 0.001). Overall image quality of FSE-DNN was comparable (p > 0.05), and diagnostic performances of FSE-DNN showed comparable lesion detection. Two of cartilage lesions were under-graded or over-graded (n = 2) while there was no significant difference in other image sets (n = 43). Overall inter-reader agreement between FSE-conventional and FSE-DNN showed good agreement (R2 = 0.76; p < 0.001). In conclusion, DNN-based reconstruction can be applied to accelerated knee imaging in multi-vendor MRI scanners, with reduced scan time and comparable image quality. This study suggests the potential for DNN-accelerated knee MRI in clinical practice.
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Affiliation(s)
- Joohee Lee
- Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Min Jung
- Department of Orthopaedic Surgery, Yonsei University College of Medicine, Seoul, Korea
| | - Jiwoo Park
- Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Sungjun Kim
- Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Yunjin Im
- Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Nim Lee
- Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Ho-Taek Song
- Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Young Han Lee
- Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
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Barbieri M, Watkins LE, Mazzoli V, Desai AD, Rubin E, Schmidt A, Gold GE, Hargreaves BA, Chaudhari AS, Kogan F. [Formula: see text] Field inhomogeneity correction for qDESS [Formula: see text] mapping: application to rapid bilateral knee imaging. MAGMA (NEW YORK, N.Y.) 2023; 36:711-724. [PMID: 37142852 PMCID: PMC10524110 DOI: 10.1007/s10334-023-01094-y] [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] [Received: 12/23/2022] [Revised: 04/17/2023] [Accepted: 04/18/2023] [Indexed: 05/06/2023]
Abstract
PURPOSE [Formula: see text] mapping is a powerful tool for studying osteoarthritis (OA) changes and bilateral imaging may be useful in investigating the role of between-knee asymmetry in OA onset and progression. The quantitative double-echo in steady-state (qDESS) can provide fast simultaneous bilateral knee [Formula: see text] and high-resolution morphometry for cartilage and meniscus. The qDESS uses an analytical signal model to compute [Formula: see text] relaxometry maps, which require knowledge of the flip angle (FA). In the presence of [Formula: see text] inhomogeneities, inconsistencies between the nominal and actual FA can affect the accuracy of [Formula: see text] measurements. We propose a pixel-wise [Formula: see text] correction method for qDESS [Formula: see text] mapping exploiting an auxiliary [Formula: see text] map to compute the actual FA used in the model. METHODS The technique was validated in a phantom and in vivo with simultaneous bilateral knee imaging. [Formula: see text] measurements of femoral cartilage (FC) of both knees of six healthy participants were repeated longitudinally to investigate the association between [Formula: see text] variation and [Formula: see text]. RESULTS The results showed that applying the [Formula: see text] correction mitigated [Formula: see text] variations that were driven by [Formula: see text] inhomogeneities. Specifically, [Formula: see text] left-right symmetry increased following the [Formula: see text] correction ([Formula: see text] = 0.74 > [Formula: see text] = 0.69). Without the [Formula: see text] correction, [Formula: see text] values showed a linear dependence with [Formula: see text]. The linear coefficient decreased using the [Formula: see text] correction (from 24.3 ± 1.6 ms to 4.1 ± 1.8) and the correlation was not statistically significant after the application of the Bonferroni correction (p value > 0.01). CONCLUSION The study showed that [Formula: see text] correction could mitigate variations driven by the sensitivity of the qDESS [Formula: see text] mapping method to [Formula: see text], therefore, increasing the sensitivity to detect real biological changes. The proposed method may improve the robustness of bilateral qDESS [Formula: see text] mapping, allowing for an accurate and more efficient evaluation of OA pathways and pathophysiology through longitudinal and cross-sectional studies.
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Affiliation(s)
- Marco Barbieri
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Lauren E. Watkins
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | | | - Arjun D. Desai
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Elka Rubin
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Andrew Schmidt
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Garry Evan Gold
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Brian Andrew Hargreaves
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Akshay Sanjay Chaudhari
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Feliks Kogan
- Department of Radiology, Stanford University, Stanford, CA, USA
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Improving Data-Efficiency and Robustness of Medical Imaging Segmentation Using Inpainting-Based Self-Supervised Learning. Bioengineering (Basel) 2023; 10:bioengineering10020207. [PMID: 36829701 PMCID: PMC9951871 DOI: 10.3390/bioengineering10020207] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 01/16/2023] [Accepted: 01/23/2023] [Indexed: 02/09/2023] Open
Abstract
We systematically evaluate the training methodology and efficacy of two inpainting-based pretext tasks of context prediction and context restoration for medical image segmentation using self-supervised learning (SSL). Multiple versions of self-supervised U-Net models were trained to segment MRI and CT datasets, each using a different combination of design choices and pretext tasks to determine the effect of these design choices on segmentation performance. The optimal design choices were used to train SSL models that were then compared with baseline supervised models for computing clinically-relevant metrics in label-limited scenarios. We observed that SSL pretraining with context restoration using 32 × 32 patches and Poission-disc sampling, transferring only the pretrained encoder weights, and fine-tuning immediately with an initial learning rate of 1 × 10-3 provided the most benefit over supervised learning for MRI and CT tissue segmentation accuracy (p < 0.001). For both datasets and most label-limited scenarios, scaling the size of unlabeled pretraining data resulted in improved segmentation performance. SSL models pretrained with this amount of data outperformed baseline supervised models in the computation of clinically-relevant metrics, especially when the performance of supervised learning was low. Our results demonstrate that SSL pretraining using inpainting-based pretext tasks can help increase the robustness of models in label-limited scenarios and reduce worst-case errors that occur with supervised learning.
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Barbieri M, Chaudhari AS, Moran CJ, Gold GE, Hargreaves BA, Kogan F. A method for measuring B 0 field inhomogeneity using quantitative double-echo in steady-state. Magn Reson Med 2023; 89:577-593. [PMID: 36161727 PMCID: PMC9712261 DOI: 10.1002/mrm.29465] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 08/31/2022] [Accepted: 09/01/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE To develop and validate a method forB 0 $$ {B}_0 $$ mapping for knee imaging using the quantitative Double-Echo in Steady-State (qDESS) exploiting the phase difference (Δ θ $$ \Delta \theta $$ ) between the two echoes acquired. Contrary to a two-gradient-echo (2-GRE) method,Δ θ $$ \Delta \theta $$ depends only on the first echo time. METHODS Bloch simulations were applied to investigate robustness to noise of the proposed methodology and all imaging studies were validated with phantoms and in vivo simultaneous bilateral knee acquisitions. Two phantoms and five healthy subjects were scanned using qDESS, water saturation shift referencing (WASSR), and multi-GRE sequences.Δ B 0 $$ \Delta {B}_0 $$ maps were calculated with the qDESS and the 2-GRE methods and compared against those obtained with WASSR. The comparison was quantitatively assessed exploiting pixel-wise difference maps, Bland-Altman (BA) analysis, and Lin's concordance coefficient (ρ c $$ {\rho}_c $$ ). For in vivo subjects, the comparison was assessed in cartilage using average values in six subregions. RESULTS The proposed method for measuringΔ B 0 $$ \Delta {B}_0 $$ inhomogeneities from a qDESS acquisition providedΔ B 0 $$ \Delta {B}_0 $$ maps that were in good agreement with those obtained using WASSR.Δ B 0 $$ \Delta {B}_0 $$ ρ c $$ {\rho}_c $$ values were≥ $$ \ge $$ 0.98 and 0.90 in phantoms and in vivo, respectively. The agreement between qDESS and WASSR was comparable to that of a 2-GRE method. CONCLUSION The proposed method may allow B0 correction for qDESST 2 $$ {T}_2 $$ mapping using an inherently co-registeredΔ B 0 $$ \Delta {B}_0 $$ map without requiring an additional B0 measurement sequence. More generally, the method may help shorten knee imaging protocols that require an auxiliaryΔ B 0 $$ \Delta {B}_0 $$ map by exploiting a qDESS acquisition that also providesT 2 $$ {T}_2 $$ measurements and high-quality morphological imaging.
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Affiliation(s)
- Marco Barbieri
- Department of Radiology, Stanford University, Stanford, CA, U.S.A
| | - Akshay S. Chaudhari
- Department of Radiology, Stanford University, Stanford, CA, U.S.A
- Department of Biomedical Data Science, Stanford University, Stanford, CA, U.S.A
| | | | - Garry E. Gold
- Department of Radiology, Stanford University, Stanford, CA, U.S.A
- Department of Bioengineering, Stanford University, Stanford, CA, U.S.A
| | - Brian A. Hargreaves
- Department of Radiology, Stanford University, Stanford, CA, U.S.A
- Department of Bioengineering, Stanford University, Stanford, CA, U.S.A
- Department of Electrical Engineering, Stanford University, Stanford, CA, U.S.A
| | - Feliks Kogan
- Department of Radiology, Stanford University, Stanford, CA, U.S.A
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Oei EHG, van Zadelhoff TA, Eijgenraam SM, Klein S, Hirvasniemi J, van der Heijden RA. 3D MRI in Osteoarthritis. Semin Musculoskelet Radiol 2021; 25:468-479. [PMID: 34547812 DOI: 10.1055/s-0041-1730911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Osteoarthritis (OA) is among the top 10 burdensome diseases, with the knee the most affected joint. Magnetic resonance imaging (MRI) allows whole-knee assessment, making it ideally suited for imaging OA, considered a multitissue disease. Three-dimensional (3D) MRI enables the comprehensive assessment of OA, including quantitative morphometry of various joint tissues. Manual tissue segmentation on 3D MRI is challenging but may be overcome by advanced automated image analysis methods including artificial intelligence (AI). This review presents examples of the utility of 3D MRI for knee OA, focusing on the articular cartilage, bone, meniscus, synovium, and infrapatellar fat pad, and it highlights several applications of AI that facilitate segmentation, lesion detection, and disease classification.
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Affiliation(s)
- Edwin H G Oei
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Tijmen A van Zadelhoff
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Susanne M Eijgenraam
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Stefan Klein
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Jukka Hirvasniemi
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Rianne A van der Heijden
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
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12
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Sveinsson B, Chaudhari AS, Zhu B, Koonjoo N, Torriani M, Gold GE, Rosen MS. Synthesizing Quantitative T2 Maps in Right Lateral Knee Femoral Condyles from Multicontrast Anatomic Data with a Conditional Generative Adversarial Network. Radiol Artif Intell 2021; 3:e200122. [PMID: 34617020 PMCID: PMC8489449 DOI: 10.1148/ryai.2021200122] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 04/11/2021] [Accepted: 05/03/2021] [Indexed: 04/09/2023]
Abstract
PURPOSE To develop a proof-of-concept convolutional neural network (CNN) to synthesize T2 maps in right lateral femoral condyle articular cartilage from anatomic MR images by using a conditional generative adversarial network (cGAN). MATERIALS AND METHODS In this retrospective study, anatomic images (from turbo spin-echo and double-echo in steady-state scans) of the right knee of 4621 patients included in the 2004-2006 Osteoarthritis Initiative were used as input to a cGAN-based CNN, and a predicted CNN T2 was generated as output. These patients included men and women of all ethnicities, aged 45-79 years, with or at high risk for knee osteoarthritis incidence or progression who were recruited at four separate centers in the United States. These data were split into 3703 (80%) for training, 462 (10%) for validation, and 456 (10%) for testing. Linear regression analysis was performed between the multiecho spin-echo (MESE) and CNN T2 in the test dataset. A more detailed analysis was performed in 30 randomly selected patients by means of evaluation by two musculoskeletal radiologists and quantification of cartilage subregions. Radiologist assessments were compared by using two-sided t tests. RESULTS The readers were moderately accurate in distinguishing CNN T2 from MESE T2, with one reader having random-chance categorization. CNN T2 values were correlated to the MESE values in the subregions of 30 patients and in the bulk analysis of all patients, with best-fit line slopes between 0.55 and 0.83. CONCLUSION With use of a neural network-based cGAN approach, it is feasible to synthesize T2 maps in femoral cartilage from anatomic MRI sequences, giving good agreement with MESE scans.See also commentary by Yi and Fritz in this issue.Keywords: Cartilage Imaging, Knee, Experimental Investigations, Quantification, Vision, Application Domain, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms© RSNA, 2021.
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Affiliation(s)
- Bragi Sveinsson
- From the Athinoula A. Martinos Center for Biomedical Imaging,
Department of Radiology, Massachusetts General Hospital, Harvard Medical School,
149 13th St, Suite 2301, Boston, MA 02129 (B.S., B.Z., N.K., M.S.R.);
Division of Musculoskeletal Imaging and Intervention, Department of Radiology,
Massachusetts General Hospital, Harvard Medical School, Boston, Mass (M.T.);
Department of Radiology, Stanford University, Stanford, Calif (A.S.C., G.E.G.);
and Department of Physics, Harvard University, Cambridge, Mass (M.S.R.)
| | - Akshay S. Chaudhari
- From the Athinoula A. Martinos Center for Biomedical Imaging,
Department of Radiology, Massachusetts General Hospital, Harvard Medical School,
149 13th St, Suite 2301, Boston, MA 02129 (B.S., B.Z., N.K., M.S.R.);
Division of Musculoskeletal Imaging and Intervention, Department of Radiology,
Massachusetts General Hospital, Harvard Medical School, Boston, Mass (M.T.);
Department of Radiology, Stanford University, Stanford, Calif (A.S.C., G.E.G.);
and Department of Physics, Harvard University, Cambridge, Mass (M.S.R.)
| | - Bo Zhu
- From the Athinoula A. Martinos Center for Biomedical Imaging,
Department of Radiology, Massachusetts General Hospital, Harvard Medical School,
149 13th St, Suite 2301, Boston, MA 02129 (B.S., B.Z., N.K., M.S.R.);
Division of Musculoskeletal Imaging and Intervention, Department of Radiology,
Massachusetts General Hospital, Harvard Medical School, Boston, Mass (M.T.);
Department of Radiology, Stanford University, Stanford, Calif (A.S.C., G.E.G.);
and Department of Physics, Harvard University, Cambridge, Mass (M.S.R.)
| | - Neha Koonjoo
- From the Athinoula A. Martinos Center for Biomedical Imaging,
Department of Radiology, Massachusetts General Hospital, Harvard Medical School,
149 13th St, Suite 2301, Boston, MA 02129 (B.S., B.Z., N.K., M.S.R.);
Division of Musculoskeletal Imaging and Intervention, Department of Radiology,
Massachusetts General Hospital, Harvard Medical School, Boston, Mass (M.T.);
Department of Radiology, Stanford University, Stanford, Calif (A.S.C., G.E.G.);
and Department of Physics, Harvard University, Cambridge, Mass (M.S.R.)
| | - Martin Torriani
- From the Athinoula A. Martinos Center for Biomedical Imaging,
Department of Radiology, Massachusetts General Hospital, Harvard Medical School,
149 13th St, Suite 2301, Boston, MA 02129 (B.S., B.Z., N.K., M.S.R.);
Division of Musculoskeletal Imaging and Intervention, Department of Radiology,
Massachusetts General Hospital, Harvard Medical School, Boston, Mass (M.T.);
Department of Radiology, Stanford University, Stanford, Calif (A.S.C., G.E.G.);
and Department of Physics, Harvard University, Cambridge, Mass (M.S.R.)
| | - Garry E. Gold
- From the Athinoula A. Martinos Center for Biomedical Imaging,
Department of Radiology, Massachusetts General Hospital, Harvard Medical School,
149 13th St, Suite 2301, Boston, MA 02129 (B.S., B.Z., N.K., M.S.R.);
Division of Musculoskeletal Imaging and Intervention, Department of Radiology,
Massachusetts General Hospital, Harvard Medical School, Boston, Mass (M.T.);
Department of Radiology, Stanford University, Stanford, Calif (A.S.C., G.E.G.);
and Department of Physics, Harvard University, Cambridge, Mass (M.S.R.)
| | - Matthew S. Rosen
- From the Athinoula A. Martinos Center for Biomedical Imaging,
Department of Radiology, Massachusetts General Hospital, Harvard Medical School,
149 13th St, Suite 2301, Boston, MA 02129 (B.S., B.Z., N.K., M.S.R.);
Division of Musculoskeletal Imaging and Intervention, Department of Radiology,
Massachusetts General Hospital, Harvard Medical School, Boston, Mass (M.T.);
Department of Radiology, Stanford University, Stanford, Calif (A.S.C., G.E.G.);
and Department of Physics, Harvard University, Cambridge, Mass (M.S.R.)
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Chaudhari AS, Sandino CM, Cole EK, Larson DB, Gold GE, Vasanawala SS, Lungren MP, Hargreaves BA, Langlotz CP. Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices. J Magn Reson Imaging 2021; 54:357-371. [PMID: 32830874 PMCID: PMC8639049 DOI: 10.1002/jmri.27331] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 07/27/2020] [Accepted: 07/31/2020] [Indexed: 12/16/2022] Open
Abstract
Artificial intelligence algorithms based on principles of deep learning (DL) have made a large impact on the acquisition, reconstruction, and interpretation of MRI data. Despite the large number of retrospective studies using DL, there are fewer applications of DL in the clinic on a routine basis. To address this large translational gap, we review the recent publications to determine three major use cases that DL can have in MRI, namely, that of model-free image synthesis, model-based image reconstruction, and image or pixel-level classification. For each of these three areas, we provide a framework for important considerations that consist of appropriate model training paradigms, evaluation of model robustness, downstream clinical utility, opportunities for future advances, as well recommendations for best current practices. We draw inspiration for this framework from advances in computer vision in natural imaging as well as additional healthcare fields. We further emphasize the need for reproducibility of research studies through the sharing of datasets and software. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
| | - Christopher M Sandino
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Elizabeth K Cole
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - David B Larson
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | | | - Matthew P Lungren
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Brian A Hargreaves
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Biomedical Informatics, Stanford University, Stanford, California, USA
| | - Curtis P Langlotz
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Biomedical Informatics, Stanford University, Stanford, California, USA
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Imaging of Synovial Inflammation in Osteoarthritis, From the AJR Special Series on Inflammation. AJR Am J Roentgenol 2021; 218:405-417. [PMID: 34286595 DOI: 10.2214/ajr.21.26170] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Synovitis, inflammation of the synovial membrane, is a common manifestation in osteoarthritis (OA) and is recognized to play a role in the complex pathophysiology of OA. Increased recognition of the importance of synovitis in the OA disease process and potential as a target for treatment has increased the need for non-invasive detection and characterization of synovitis using medical imaging. Numerous imaging methods can assess synovitis involvement in OA with varying sensitivity and specificity as well as complexity. This article reviews the role of contrast-enhanced MRI, conventional MRI, novel unenhanced MRI, gray-scale ultrasound (US), and power Doppler US in the assessment of synovitis in patients with OA. The role of imaging in disease evaluation as well as challenges in conventional imaging methods are discussed. We also provide an overview into the potential utility of emerging techniques for imaging of early inflammation and molecular inflammatory markers of synovitis, including quantitative MRI, superb microvascular imaging, and PET. The potential development of therapeutic treatments targeting inflammatory features, particularly in early OA, would greatly increase the importance of these imaging methods for clinical decision making and evaluation of therapeutic efficacy.
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Zijlstra F, Seevinck PR. Multiple-echo steady-state (MESS): Extending DESS for joint T 2 mapping and chemical-shift corrected water-fat separation. Magn Reson Med 2021; 86:3156-3165. [PMID: 34270127 PMCID: PMC8596862 DOI: 10.1002/mrm.28921] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 06/21/2021] [Accepted: 06/21/2021] [Indexed: 12/21/2022]
Abstract
Purpose To extend the double echo steady‐state (DESS) sequence to enable chemical‐shift corrected water‐fat separation. Methods This study proposes multiple‐echo steady‐state (MESS), a sequence that modifies the readouts of the DESS sequence to acquire two echoes each with bipolar readout gradients with higher readout bandwidth. This enables water‐fat separation and eliminates the need for water‐selective excitation that is often used in combination with DESS, without increasing scan time. An iterative fitting approach was used to perform joint chemical‐shift corrected water‐fat separation and T2 estimation on all four MESS echoes simultaneously. MESS and water‐selective DESS images were acquired for five volunteers, and were compared qualitatively as well as quantitatively on cartilage T2 and thickness measurements. Signal‐to‐noise ratio (SNR) and T2 quantification were evaluated numerically using pseudo‐replications of the acquisition. Results The water‐fat separation provided by MESS was robust and with quality comparable to water‐selective DESS. MESS T2 estimation was similar to DESS, albeit with slightly higher variability. Noise analysis showed that SNR in MESS was comparable to DESS on average, but did exhibit local variations caused by uncertainty in the water‐fat separation. Conclusion In the same acquisition time as DESS, MESS provides water‐fat separation with comparable SNR in the reconstructed water and fat images. By providing additional image contrasts in addition to the water‐selective DESS images, MESS provides a promising alternative to DESS.
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Affiliation(s)
- Frank Zijlstra
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.,Department of Radiology and Nuclear Medicine, St Olav's University Hospital, Trondheim, Norway
| | - Peter R Seevinck
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.,MRIGuidance BV, Utrecht, The Netherlands
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16
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Chaudhari AS, Grissom MJ, Fang Z, Sveinsson B, Lee JH, Gold GE, Hargreaves BA, Stevens KJ. Diagnostic Accuracy of Quantitative Multicontrast 5-Minute Knee MRI Using Prospective Artificial Intelligence Image Quality Enhancement. AJR Am J Roentgenol 2021; 216:1614-1625. [PMID: 32755384 PMCID: PMC8862596 DOI: 10.2214/ajr.20.24172] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
BACKGROUND. Potential approaches for abbreviated knee MRI, including prospective acceleration with deep learning, have achieved limited clinical implementation. OBJECTIVE. The objective of this study was to evaluate the interreader agreement between conventional knee MRI and a 5-minute 3D quantitative double-echo steady-state (qDESS) sequence with automatic T2 mapping and deep learning super-resolutionaugmentation and to compare the diagnostic performance of the two methods regarding findings from arthroscopic surgery. METHODS. Fifty-one patients with knee pain underwent knee MRI that included an additional 3D qDESS sequence with automatic T2 mapping. Fourier interpolation was followed by prospective deep learning super resolution to enhance qDESS slice resolution twofold. A musculoskeletal radiologist and a radiology resident performed independent retrospective evaluations of articular cartilage, menisci, ligaments, bones, extensor mechanism, and synovium using conventional MRI. Following a 2-month washout period, readers reviewed qDESS images alone followed by qDESS with the automatic T2 maps. Interreader agreement between conventional MRI and qDESS was computed using percentage agreement and Cohen kappa. The sensitivity and specificity of conventional MRI, qDESS alone, and qDESS plus T2 mapping were compared with arthroscopic findings using exact McNemar tests. RESULTS. Conventional MRI and qDESS showed 92% agreement in evaluating all tissues. Kappa was 0.79 (95% CI, 0.76-0.81) across all imaging findings. In 43 patients who underwent arthroscopy, sensitivity and specificity were not significantly different (p = .23 to > .99) between conventional MRI (sensitivity, 58-93%; specificity, 27-87%) and qDESS alone (sensitivity, 54-90%; specificity, 23-91%) for cartilage, menisci, ligaments, and synovium. For grade 1 cartilage lesions, sensitivity and specificity were 33% and 56%, respectively, for conventional MRI; 23% and 53% for qDESS (p = .81); and 46% and 39% for qDESS with T2 mapping (p = .80). For grade 2A lesions, values were 27% and 53% for conventional MRI, 26% and 52% for qDESS (p = .02), and 58% and 40% for qDESS with T2 mapping (p < .001). CONCLUSION. The qDESS method prospectively augmented with deep learning showed strong interreader agreement with conventional knee MRI and near-equivalent diagnostic performance regarding arthroscopy. The ability of qDESS to automatically generate T2 maps increases sensitivity for cartilage abnormalities. CLINICAL IMPACT. Using prospective artificial intelligence to enhance qDESS image quality may facilitate an abbreviated knee MRI protocol while generating quantitative T2 maps.
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Affiliation(s)
- Akshay S Chaudhari
- Department of Radiology, Lucas Center for Imaging, Stanford University, 1201 Welch Rd, PS 055B, Stanford, CA 94305
| | | | | | - Bragi Sveinsson
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA
- Department of Radiology, Harvard Medical School, Boston, MA
| | - Jin Hyung Lee
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA
- Department of Bioengineering, Stanford University, Stanford, CA
- Department of Neurosurgery, Stanford University, Stanford, CA
- Department of Electrical Engineering, Stanford University, Stanford, CA
| | - Garry E Gold
- Department of Radiology, Lucas Center for Imaging, Stanford University, 1201 Welch Rd, PS 055B, Stanford, CA 94305
- Department of Bioengineering, Stanford University, Stanford, CA
- Department of Orthopaedic Surgery, Stanford University, Redwood City, CA
| | - Brian A Hargreaves
- Department of Radiology, Lucas Center for Imaging, Stanford University, 1201 Welch Rd, PS 055B, Stanford, CA 94305
- Department of Bioengineering, Stanford University, Stanford, CA
- Department of Electrical Engineering, Stanford University, Stanford, CA
| | - Kathryn J Stevens
- Department of Radiology, Lucas Center for Imaging, Stanford University, 1201 Welch Rd, PS 055B, Stanford, CA 94305
- Department of Orthopaedic Surgery, Stanford University, Redwood City, CA
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Non-contrast MRI of synovitis in the knee using quantitative DESS. Eur Radiol 2021; 31:9369-9379. [PMID: 33993332 DOI: 10.1007/s00330-021-08025-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 03/22/2021] [Accepted: 04/28/2021] [Indexed: 12/22/2022]
Abstract
OBJECTIVES To determine whether synovitis graded by radiologists using hybrid quantitative double-echo in steady-state (qDESS) images can be utilized as a non-contrast approach to assess synovitis in the knee, compared against the reference standard of contrast-enhanced MRI (CE-MRI). METHODS Twenty-two knees (11 subjects) with moderate to severe osteoarthritis (OA) were scanned using CE-MRI, qDESS with a high diffusion weighting (qDESSHigh), and qDESS with a low diffusion weighting (qDESSLow). Four radiologists graded the overall impression of synovitis, their diagnostic confidence, and regional grading of synovitis severity at four sites (suprapatellar pouch, intercondylar notch, and medial and lateral peripatellar recesses) in the knee using a 4-point scale. Agreement between CE-MRI and qDESS, inter-rater agreement, and intra-rater agreement were assessed using a linearly weighted Gwet's AC2. RESULTS Good agreement was seen between CE-MRI and both qDESSLow (AC2 = 0.74) and qDESSHigh (AC2 = 0.66) for the overall impression of synovitis, but both qDESS sequences tended to underestimate the severity of synovitis compared to CE-MRI. Good inter-rater agreement was seen for both qDESS sequences (AC2 = 0.74 for qDESSLow, AC2 = 0.64 for qDESSHigh), and good intra-rater agreement was seen for both sequences as well (qDESSLow AC2 = 0.78, qDESSHigh AC2 = 0.80). Diagnostic confidence was moderate to high for qDESSLow (mean = 2.36) and slightly less than moderate for qDESSHigh (mean = 1.86), compared to mostly high confidence for CE-MRI (mean = 2.73). CONCLUSIONS qDESS shows potential as an alternative MRI technique for assessing the severity of synovitis without the use of a gadolinium-based contrast agent. KEY POINTS The use of the quantitative double-echo in steady-state (qDESS) sequence for synovitis assessment does not require the use of a gadolinium-based contrast agent. Preliminary results found that low diffusion-weighted qDESS (qDESSLow) shows good agreement to contrast-enhanced MRI for characterization of the severity of synovitis, with a relative bias towards underestimation of severity. Preliminary results also found that qDESSLow shows good inter- and intra-rater agreement for the depiction of synovitis, particularly for readers experienced with the sequence.
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Del Grande F, Rashidi A, Luna R, Delcogliano M, Stern SE, Dalili D, Fritz J. Five-Minute Five-Sequence Knee MRI Using Combined Simultaneous Multislice and Parallel Imaging Acceleration: Comparison with 10-Minute Parallel Imaging Knee MRI. Radiology 2021; 299:635-646. [PMID: 33825510 DOI: 10.1148/radiol.2021203655] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Background Rapid knee MRI using combined simultaneous multislice (SMS) technique and parallel imaging (PI) acceleration can add value through reduced acquisition time but requires validation of clinical efficacy. Purpose To evaluate the performance of clinical fourfold SMS-PI-accelerated, 5-minute, five-sequence, multicontrast knee MRI protocols compared with standard twofold PI-accelerated, 10-minute knee MRI protocols. Materials and Methods Adults with painful knee conditions were prospectively enrolled from April 2018 to October 2019. Participants underwent fourfold SMS-PI-accelerated, 5-minute, turbo spin-echo (TSE) knee MRI and standard-of-care twofold PI-accelerated, 10-minute, TSE knee MRI at either 1.5 T or 3.0 T. Three radiologists independently evaluated the knee MRI studies for meniscal, tendinous, ligamentous, and osseocartilaginous injuries. Statistical analyses included k-based intermethod agreements and diagnostic performance testing. P < .05 was considered indicative of a statistically significant difference. Results A total of 252 adults were evaluated (mean age ± standard deviation, 47 years ± 17; 134 men). Among the participants, 104 (mean age, 42 years ± 18; 57 women) were in the 1.5-T arm and 148 (mean age, 46 years ± 17; 87 men) were in the 3.0-T arm. Twenty-nine participants (mean age, 38 years ± 12; 15 men) in the 1.5-T arm and 42 (mean age, 41 years ± 16; 24 men) in the 3.0-T arm underwent arthroscopy a mean of 45 days ± 31 and 45 days ± 22 after MRI, respectively. Intermethod agreements were good at 1.5 T (κ >0.71 [95% CI: 0.56, 0.83]) and very good at 3.0 T (κ >0.85 [95% CI: 0.69, 0.96]). The diagnostic performances of corresponding 5-minute and 10-minute MRI protocols were similar for 1.5 T, with areas under the receiver operating characteristic curve (AUCs) greater than 0.78 (95% CI: 0.71, 0.84) (P > .32), and 3.0 T, with AUCs greater than 0.83 (95% CI: 0.78, 0.88) (P > .32). Conclusion Comparisons of 5-minute five-sequence simultaneous multislice- and parallel imaging (PI)-accelerated and 10-minute five-sequence PI-accelerated turbo spin-echo MRI of the knee suggest similar performances at 1.5 and 3.0 T. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Subhas in this issue.
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Affiliation(s)
- Filippo Del Grande
- From the Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (F.D.G., A.R., R.L., D.D.); Department of Radiology, Ospedale Regionale di Lugano, Lugano, Switzerland (F.D.G.), Department of Orthopedic Surgery, Ospedale Regionale di Lugano, Lugano, Ticino, Switzerland (M.D.); Centre for Data Analytics, Bond University, Gold Coast, Australia (S.E.S.); Nuffield Orthopedic Center, Oxford University Hospitals NHS Foundation Trust, Oxford, England (D.D.); and Department of Radiology, Grossman School of Medicine, New York University, 660 1st Ave, 3rd Floor, Room 313, New York, NY 10016 (J.F.)
| | - Ali Rashidi
- From the Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (F.D.G., A.R., R.L., D.D.); Department of Radiology, Ospedale Regionale di Lugano, Lugano, Switzerland (F.D.G.), Department of Orthopedic Surgery, Ospedale Regionale di Lugano, Lugano, Ticino, Switzerland (M.D.); Centre for Data Analytics, Bond University, Gold Coast, Australia (S.E.S.); Nuffield Orthopedic Center, Oxford University Hospitals NHS Foundation Trust, Oxford, England (D.D.); and Department of Radiology, Grossman School of Medicine, New York University, 660 1st Ave, 3rd Floor, Room 313, New York, NY 10016 (J.F.)
| | - Rodrigo Luna
- From the Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (F.D.G., A.R., R.L., D.D.); Department of Radiology, Ospedale Regionale di Lugano, Lugano, Switzerland (F.D.G.), Department of Orthopedic Surgery, Ospedale Regionale di Lugano, Lugano, Ticino, Switzerland (M.D.); Centre for Data Analytics, Bond University, Gold Coast, Australia (S.E.S.); Nuffield Orthopedic Center, Oxford University Hospitals NHS Foundation Trust, Oxford, England (D.D.); and Department of Radiology, Grossman School of Medicine, New York University, 660 1st Ave, 3rd Floor, Room 313, New York, NY 10016 (J.F.)
| | - Marco Delcogliano
- From the Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (F.D.G., A.R., R.L., D.D.); Department of Radiology, Ospedale Regionale di Lugano, Lugano, Switzerland (F.D.G.), Department of Orthopedic Surgery, Ospedale Regionale di Lugano, Lugano, Ticino, Switzerland (M.D.); Centre for Data Analytics, Bond University, Gold Coast, Australia (S.E.S.); Nuffield Orthopedic Center, Oxford University Hospitals NHS Foundation Trust, Oxford, England (D.D.); and Department of Radiology, Grossman School of Medicine, New York University, 660 1st Ave, 3rd Floor, Room 313, New York, NY 10016 (J.F.)
| | - Steven E Stern
- From the Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (F.D.G., A.R., R.L., D.D.); Department of Radiology, Ospedale Regionale di Lugano, Lugano, Switzerland (F.D.G.), Department of Orthopedic Surgery, Ospedale Regionale di Lugano, Lugano, Ticino, Switzerland (M.D.); Centre for Data Analytics, Bond University, Gold Coast, Australia (S.E.S.); Nuffield Orthopedic Center, Oxford University Hospitals NHS Foundation Trust, Oxford, England (D.D.); and Department of Radiology, Grossman School of Medicine, New York University, 660 1st Ave, 3rd Floor, Room 313, New York, NY 10016 (J.F.)
| | - Danoob Dalili
- From the Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (F.D.G., A.R., R.L., D.D.); Department of Radiology, Ospedale Regionale di Lugano, Lugano, Switzerland (F.D.G.), Department of Orthopedic Surgery, Ospedale Regionale di Lugano, Lugano, Ticino, Switzerland (M.D.); Centre for Data Analytics, Bond University, Gold Coast, Australia (S.E.S.); Nuffield Orthopedic Center, Oxford University Hospitals NHS Foundation Trust, Oxford, England (D.D.); and Department of Radiology, Grossman School of Medicine, New York University, 660 1st Ave, 3rd Floor, Room 313, New York, NY 10016 (J.F.)
| | - Jan Fritz
- From the Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (F.D.G., A.R., R.L., D.D.); Department of Radiology, Ospedale Regionale di Lugano, Lugano, Switzerland (F.D.G.), Department of Orthopedic Surgery, Ospedale Regionale di Lugano, Lugano, Ticino, Switzerland (M.D.); Centre for Data Analytics, Bond University, Gold Coast, Australia (S.E.S.); Nuffield Orthopedic Center, Oxford University Hospitals NHS Foundation Trust, Oxford, England (D.D.); and Department of Radiology, Grossman School of Medicine, New York University, 660 1st Ave, 3rd Floor, Room 313, New York, NY 10016 (J.F.)
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19
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Rapid Musculoskeletal MRI in 2021: Clinical Application of Advanced Accelerated Techniques. AJR Am J Roentgenol 2021; 216:718-733. [DOI: 10.2214/ajr.20.22902] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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20
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Abstract
Articular cartilage of the knee can be evaluated with high accuracy by magnetic resonance imaging (MRI) in preoperative patients with knee pain, but image quality and reporting are variable. This article discusses the normal MRI appearance of articular cartilage as well as the common MRI abnormalities of knee cartilage that may be considered for operative treatment. This article focuses on a practical approach to preoperative MRI of knee articular cartilage using routine MRI techniques. Current and future directions of knee MRI related to articular cartilage are also discussed.
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Affiliation(s)
| | - Akshay Chaudhari
- Department of Radiology, Stanford University, Stanford, California
| | - Robert D. Boutin
- Department of Radiology, Musculoskeletal Imaging, Stanford University School of Medicine, Stanford, California
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21
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Chaudhari AS, Kogan F, Pedoia V, Majumdar S, Gold GE, Hargreaves BA. Rapid Knee MRI Acquisition and Analysis Techniques for Imaging Osteoarthritis. J Magn Reson Imaging 2020; 52:1321-1339. [PMID: 31755191 PMCID: PMC7925938 DOI: 10.1002/jmri.26991] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 10/22/2019] [Accepted: 10/22/2019] [Indexed: 12/16/2022] Open
Abstract
Osteoarthritis (OA) of the knee is a major source of disability that has no known treatment or cure. Morphological and compositional MRI is commonly used for assessing the bone and soft tissues in the knee to enhance the understanding of OA pathophysiology. However, it is challenging to extend these imaging methods and their subsequent analysis techniques to study large population cohorts due to slow and inefficient imaging acquisition and postprocessing tools. This can create a bottleneck in assessing early OA changes and evaluating the responses of novel therapeutics. The purpose of this review article is to highlight recent developments in tools for enhancing the efficiency of knee MRI methods useful to study OA. Advances in efficient MRI data acquisition and reconstruction tools for morphological and compositional imaging, efficient automated image analysis tools, and hardware improvements to further drive efficient imaging are discussed in this review. For each topic, we discuss the current challenges as well as potential future opportunities to alleviate these challenges. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 3.
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Affiliation(s)
| | - Feliks Kogan
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Valentina Pedoia
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
- Center of Digital Health Innovation (CDHI), University of California San Francisco, San Francisco, California, USA
| | - Sharmila Majumdar
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
- Center of Digital Health Innovation (CDHI), University of California San Francisco, San Francisco, California, USA
| | - Garry E. Gold
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Brian A. Hargreaves
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
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22
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Fürst D, Wirth W, Chaudhari A, Eckstein F. Layer-specific analysis of femorotibial cartilage t2 relaxation time based on registration of segmented double echo steady state (dess) to multi-echo-spin-echo (mese) images. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2020; 33:819-828. [PMID: 32458188 DOI: 10.1007/s10334-020-00852-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 04/06/2020] [Accepted: 05/12/2020] [Indexed: 12/18/2022]
Abstract
OBJECTIVE To develop and validate a 3D registration approach by which double echo steady state (DESS) MR images with cartilage thickness segmentations are used to extract the cartilage transverse relaxation time (T2) from multi-echo-spin-echo (MESE) MR images, without direct segmentations for MESE. MATERIALS AND METHODS Manual DESS segmentations of 89 healthy reference knees (healthy) and 60 knees with early radiographic osteoarthritis (early ROA) from the Osteoarthritis Initiative were registered to corresponding MESE images that had independent direct T2 segmentations. For validation purposes, (a) regression analysis of deep and superficial cartilage T2 was performed and (b) between-group differences between healthy vs. early ROA knees were compared for registered vs. direct MESE analysis. RESULTS Moderate to high correlations were observed for the deep (r = 0.80) and the superficial T2 (r = 0.81), with statistically significant between-group differences (ROA vs. healthy) of + 1.4 ms (p = 0.002) vs. + 1.3 ms (p < 0.001) for registered vs. direct T2 segmentation in the deep, and + 1.3 ms (p = 0.002) vs. + 2.3 ms (p < 0.001) in the superficial layer. DISCUSSION This registration approach enables extracting cartilage T2 from MESE scans using DESS (cartilage thickness) segmentations, avoiding the need for direct MESE T2 segmentations.
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Affiliation(s)
- David Fürst
- Ludwig Boltzmann Institute for Arthritis and Rehabilitation, Paracelsus Medical University, Salzburg, Austria.
- Department of Imaging & Functional Musculoskeletal Research, Institute of Anatomy, Cell Biology, Paracelsus Medical University Salzburg & Nuremberg, Strubergasse 21, 5020, Salzburg, Austria.
- Chondrometrics GmbH, Ainring, Germany.
| | - Wolfang Wirth
- Ludwig Boltzmann Institute for Arthritis and Rehabilitation, Paracelsus Medical University, Salzburg, Austria
- Department of Imaging & Functional Musculoskeletal Research, Institute of Anatomy, Cell Biology, Paracelsus Medical University Salzburg & Nuremberg, Strubergasse 21, 5020, Salzburg, Austria
- Chondrometrics GmbH, Ainring, Germany
| | | | - Felix Eckstein
- Ludwig Boltzmann Institute for Arthritis and Rehabilitation, Paracelsus Medical University, Salzburg, Austria
- Department of Imaging & Functional Musculoskeletal Research, Institute of Anatomy, Cell Biology, Paracelsus Medical University Salzburg & Nuremberg, Strubergasse 21, 5020, Salzburg, Austria
- Chondrometrics GmbH, Ainring, Germany
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23
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Said O, Schock J, Krämer N, Thüring J, Hitpass L, Schad P, Kuhl C, Abrar D, Truhn D, Nebelung S. An MRI-compatible varus-valgus loading device for whole-knee joint functionality assessment based on compartmental compression: a proof-of-concept study. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2020; 33:839-854. [PMID: 32314105 PMCID: PMC8302563 DOI: 10.1007/s10334-020-00844-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 03/27/2020] [Accepted: 04/06/2020] [Indexed: 02/06/2023]
Abstract
OBJECTIVE Beyond static assessment, functional techniques are increasingly applied in magnetic resonance imaging (MRI) studies. Stress MRI techniques bring together MRI and mechanical loading to study knee joint and tissue functionality, yet prototypical axial compressive loading devices are bulky and complex to operate. This study aimed to design and validate an MRI-compatible pressure-controlled varus-valgus loading device that applies loading along the joint line. METHODS Following the device's thorough validation, we demonstrated proof of concept by subjecting a structurally intact human cadaveric knee joint to serial imaging in unloaded and loaded configurations, i.e. to varus and valgus loading at 7.5 kPa (= 73.5 N), 15 kPa (= 147.1 N), and 22.5 kPa (= 220.6 N). Following clinical standard (PDw fs) and high-resolution 3D water-selective cartilage (WATSc) sequences, we performed manual segmentations and computations of morphometric cartilage measures. We used CT and radiography (to quantify joint space widths) and histology and biomechanics (to assess tissue quality) as references. RESULTS We found (sub)regional decreases in cartilage volume, thickness, and mean joint space widths reflective of areal pressurization of the medial and lateral femorotibial compartments. DISCUSSION Once substantiated by larger sample sizes, varus-valgus loading may provide a powerful alternative stress MRI technique.
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Affiliation(s)
- Oliver Said
- Department of Diagnostic and Interventional Radiology, Aachen University Hospital, Aachen, Germany
| | - Justus Schock
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Hospital Düsseldorf, University Dusseldorf, Moorenstraße 5, 40225, Düsseldorf, Germany
- Institute of Computer Vision and Imaging, RWTH University Aachen, Aachen, Germany
| | - Nils Krämer
- Department of Diagnostic and Interventional Radiology, Aachen University Hospital, Aachen, Germany
| | - Johannes Thüring
- Department of Diagnostic and Interventional Radiology, Aachen University Hospital, Aachen, Germany
| | - Lea Hitpass
- Department of Diagnostic and Interventional Radiology, Aachen University Hospital, Aachen, Germany
| | - Philipp Schad
- Department of Diagnostic and Interventional Radiology, Aachen University Hospital, Aachen, Germany
| | - Christiane Kuhl
- Department of Diagnostic and Interventional Radiology, Aachen University Hospital, Aachen, Germany
| | - Daniel Abrar
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Hospital Düsseldorf, University Dusseldorf, Moorenstraße 5, 40225, Düsseldorf, Germany
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, Aachen University Hospital, Aachen, Germany
- Institute of Computer Vision and Imaging, RWTH University Aachen, Aachen, Germany
| | - Sven Nebelung
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Hospital Düsseldorf, University Dusseldorf, Moorenstraße 5, 40225, Düsseldorf, Germany.
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24
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Eijgenraam SM, Chaudhari AS, Reijman M, Bierma-Zeinstra SMA, Hargreaves BA, Runhaar J, Heijboer FWJ, Gold GE, Oei EHG. Time-saving opportunities in knee osteoarthritis: T 2 mapping and structural imaging of the knee using a single 5-min MRI scan. Eur Radiol 2020; 30:2231-2240. [PMID: 31844957 PMCID: PMC7062657 DOI: 10.1007/s00330-019-06542-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 10/09/2019] [Accepted: 10/23/2019] [Indexed: 12/22/2022]
Abstract
OBJECTIVES To assess the discriminative power of a 5-min quantitative double-echo steady-state (qDESS) sequence for simultaneous T2 measurements of cartilage and meniscus, and structural knee osteoarthritis (OA) assessment, in a clinical OA population, using radiographic knee OA as reference standard. METHODS Fifty-three subjects were included and divided over three groups based on radiographic and clinical knee OA: 20 subjects with no OA (Kellgren-Lawrence grade (KLG) 0), 18 with mild OA (KLG2), and 15 with moderate OA (KLG3). All patients underwent a 5-min qDESS scan. We measured T2 relaxation times in four cartilage and four meniscus regions of interest (ROIs) and performed structural OA evaluation with the MRI Osteoarthritis Knee Score (MOAKS) using qDESS with multiplanar reformatting. Between-group differences in T2 values and MOAKS were calculated using ANOVA. Correlations of the reference standard (i.e., radiographic knee OA) with T2 and MOAKS were assessed with correlation analyses for ordinal variables. RESULTS In cartilage, mean T2 values were 36.1 ± SD 4.3, 40.6 ± 5.9, and 47.1 ± 4.3 ms for no, mild, and moderate OA, respectively (p < 0.001). In menisci, mean T2 values were 15 ± 3.6, 17.5 ± 3.8, and 20.6 ± 4.7 ms for no, mild, and moderate OA, respectively (p < 0.001). Statistically significant correlations were found between radiographic OA and T2 and between radiographic OA and MOAKS in all ROIs (p < 0.05). CONCLUSION Quantitative T2 and structural assessment of cartilage and meniscus, using a single 5-min qDESS scan, can distinguish between different grades of radiographic OA, demonstrating the potential of qDESS as an efficient tool for OA imaging. KEY POINTS • Quantitative T2values of cartilage and meniscus as well as structural assessment of the knee with a single 5-min quantitative double-echo steady-state (qDESS) scan can distinguish between different grades of knee osteoarthritis (OA). • Quantitative and structural qDESS-based measurements correlate significantly with the reference standard, radiographic degree of OA, for all cartilage and meniscus regions. • By providing quantitative measurements and diagnostic image quality in one rapid MRI scan, qDESS has great potential for application in large-scale clinical trials in knee OA.
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Affiliation(s)
- Susanne M Eijgenraam
- Deptartment of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Dr. Molewaterplein 40, Room Nd-547, 3015, GD, Rotterdam, The Netherlands
- Deptartment of Orthopedic Surgery, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | | | - Max Reijman
- Deptartment of Orthopedic Surgery, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Sita M A Bierma-Zeinstra
- Deptartment of Orthopedic Surgery, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
- Deptartment of General Practice, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Brian A Hargreaves
- Deptartment of Radiology, Stanford University, Stanford, CA, USA
- Deptartment of Electrical Engineering, Stanford University, Stanford, CA, USA
- Deptartment of Bioengineering, Stanford University, Stanford, CA, USA
| | - Jos Runhaar
- Deptartment of General Practice, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Frank W J Heijboer
- Deptartment of Orthopedic Surgery, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Garry E Gold
- Deptartment of Radiology, Stanford University, Stanford, CA, USA
- Deptartment of Bioengineering, Stanford University, Stanford, CA, USA
- Deptartment of Orthopedic Surgery, Stanford University, Stanford, CA, USA
| | - Edwin H G Oei
- Deptartment of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Dr. Molewaterplein 40, Room Nd-547, 3015, GD, Rotterdam, The Netherlands.
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25
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Chaudhari AS, Stevens KJ, Wood JP, Chakraborty AK, Gibbons EK, Fang Z, Desai AD, Lee JH, Gold GE, Hargreaves BA. Utility of deep learning super-resolution in the context of osteoarthritis MRI biomarkers. J Magn Reson Imaging 2020; 51:768-779. [PMID: 31313397 PMCID: PMC6962563 DOI: 10.1002/jmri.26872] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 07/02/2019] [Accepted: 07/03/2019] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Super-resolution is an emerging method for enhancing MRI resolution; however, its impact on image quality is still unknown. PURPOSE To evaluate MRI super-resolution using quantitative and qualitative metrics of cartilage morphometry, osteophyte detection, and global image blurring. STUDY TYPE Retrospective. POPULATION In all, 176 MRI studies of subjects at varying stages of osteoarthritis. FIELD STRENGTH/SEQUENCE Original-resolution 3D double-echo steady-state (DESS) and DESS with 3× thicker slices retrospectively enhanced using super-resolution and tricubic interpolation (TCI) at 3T. ASSESSMENT A quantitative comparison of femoral cartilage morphometry was performed for the original-resolution DESS, the super-resolution, and the TCI scans in 17 subjects. A reader study by three musculoskeletal radiologists assessed cartilage image quality, overall image sharpness, and osteophytes incidence in all three sets of scans. A referenceless blurring metric evaluated blurring in all three image dimensions for the three sets of scans. STATISTICAL TESTS Mann-Whitney U-tests compared Dice coefficients (DC) of segmentation accuracy for the DESS, super-resolution, and TCI images, along with the image quality readings and blurring metrics. Sensitivity, specificity, and diagnostic odds ratio (DOR) with 95% confidence intervals compared osteophyte detection for the super-resolution and TCI images, with the original-resolution as a reference. RESULTS DC for the original-resolution (90.2 ± 1.7%) and super-resolution (89.6 ± 2.0%) were significantly higher (P < 0.001) than TCI (86.3 ± 5.6%). Segmentation overlap of super-resolution with the original-resolution (DC = 97.6 ± 0.7%) was significantly higher (P < 0.0001) than TCI overlap (DC = 95.0 ± 1.1%). Cartilage image quality for sharpness and contrast levels, and the through-plane quantitative blur factor for super-resolution images, was significantly (P < 0.001) better than TCI. Super-resolution osteophyte detection sensitivity of 80% (76-82%), specificity of 93% (92-94%), and DOR of 32 (22-46) was significantly higher (P < 0.001) than TCI sensitivity of 73% (69-76%), specificity of 90% (89-91%), and DOR of 17 (13-22). DATA CONCLUSION Super-resolution appears to consistently outperform naïve interpolation and may improve image quality without biasing quantitative biomarkers. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:768-779.
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Affiliation(s)
| | - Kathryn J Stevens
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
| | - Jeff P Wood
- Austin Radiological Association, Austin, Texas, USA
| | | | - Eric K Gibbons
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, USA
| | | | - Arjun D Desai
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Jin Hyung Lee
- Department of Neurology & Neurological Sciences, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
- Department of Neurosurgery, Stanford University, Stanford, California, USA
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Brian A Hargreaves
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
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