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Eckstein F, Walter-Rittel TC, Chaudhari AS, Brisson NM, Maleitzke T, Duda GN, Wisser A, Wirth W, Winkler T. The design of a sample rapid magnetic resonance imaging (MRI) acquisition protocol supporting assessment of multiple articular tissues and pathologies in knee osteoarthritis. OSTEOARTHRITIS AND CARTILAGE OPEN 2024; 6:100505. [PMID: 39183946 PMCID: PMC11342198 DOI: 10.1016/j.ocarto.2024.100505] [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: 02/29/2024] [Accepted: 07/21/2024] [Indexed: 08/27/2024] Open
Abstract
Objective This expert opinion paper proposes a design for a state-of-the-art magnetic resonance image (MRI) acquisition protocol for knee osteoarthritis clinical trials in early and advanced disease. Semi-quantitative and quantitative imaging endpoints are supported, partly amendable to automated analysis. Several (peri-) articular tissues and pathologies are covered, including synovitis. Method A PubMed literature search was conducted, with focus on the past 5 years. Further, osteoarthritis imaging experts provided input. Specific MRI sequences, orientations, spatial resolutions and parameter settings were identified to align with study goals. We strived for implementation on standard clinical scanner hardware, with a net acquisition time ≤30 min. Results Short- and long-term longitudinal MRIs should be obtained at ≥1.5T, if possible without hardware changes during the study. We suggest a series of gradient- and spin-echo-sequences, supporting MOAKS, quantitative analysis of cartilage morphology and T2, and non-contrast-enhanced depiction of synovitis. These sequences should be properly aligned and positioned using localizer images. One of the sequences may be repeated in each participant (re-test), optimally at baseline and follow-up, to estimate within-study precision. All images should be checked for quality and protocol-adherence as soon as possible after acquisition. Alternative approaches are suggested that expand on the structural endpoints presented. Conclusions We aim to bridge the gap between technical MRI acquisition guides and the wealth of imaging literature, proposing a balance between image acquisition efficiency (time), safety, and technical/methodological diversity. This approach may entertain scientific innovation on tissue structure and composition assessment in clinical trials on disease modification of knee osteoarthritis.
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Affiliation(s)
- Felix Eckstein
- Research Program for Musculoskeletal Imaging, Center for Anatomy & Cell Biology, Paracelsus Medical University, Salzburg, Austria
- Ludwig Boltzmann Institute for Arthritis and Rehabilitation (LBIAR), Paracelsus Medical University, Salzburg, Austria
- Chondrometrics GmbH, Freilassing, Germany
| | - Thula Cannon Walter-Rittel
- Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Berlin, Germany
| | | | - Nicholas M. Brisson
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Julius Wolff Institute, Berlin, Germany
- Berlin Movement Diagnostics (BeMoveD), Center for Musculoskeletal Surgery, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Tazio Maleitzke
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Julius Wolff Institute, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Musculoskeletal Surgery, Berlin, Germany
- Trauma Orthopaedic Research Copenhagen Hvidovre (TORCH), Department of Orthopaedic Surgery, Copenhagen University Hospital - Amager and Hvidovre, Hvidovre, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Georg N. Duda
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Julius Wolff Institute, Berlin, Germany
- Berlin Movement Diagnostics (BeMoveD), Center for Musculoskeletal Surgery, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin Institute of Health Center for Regenerative Therapies, Berlin, Germany
| | - Anna Wisser
- Research Program for Musculoskeletal Imaging, Center for Anatomy & Cell Biology, Paracelsus Medical University, Salzburg, Austria
- Ludwig Boltzmann Institute for Arthritis and Rehabilitation (LBIAR), Paracelsus Medical University, Salzburg, Austria
- Chondrometrics GmbH, Freilassing, Germany
| | - Wolfgang Wirth
- Research Program for Musculoskeletal Imaging, Center for Anatomy & Cell Biology, Paracelsus Medical University, Salzburg, Austria
- Ludwig Boltzmann Institute for Arthritis and Rehabilitation (LBIAR), Paracelsus Medical University, Salzburg, Austria
- Chondrometrics GmbH, Freilassing, Germany
| | - Tobias Winkler
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Julius Wolff Institute, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Musculoskeletal Surgery, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin Institute of Health Center for Regenerative Therapies, Berlin, Germany
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Gatti AA, Blankemeier L, Van Veen D, Hargreaves B, Delp SL, Gold GE, Kogan F, Chaudhari AS. ShapeMed-Knee: A Dataset and Neural Shape Model Benchmark for Modeling 3D Femurs. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.06.24306965. [PMID: 38766040 PMCID: PMC11100941 DOI: 10.1101/2024.05.06.24306965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Analyzing anatomic shapes of tissues and organs is pivotal for accurate disease diagnostics and clinical decision-making. One prominent disease that depends on anatomic shape analysis is osteoarthritis, which affects 30 million Americans. To advance osteoarthritis diagnostics and prognostics, we introduce ShapeMed-Knee, a 3D shape dataset with 9,376 high-resolution, medical-imaging-based 3D shapes of both femur bone and cartilage. Besides data, ShapeMed-Knee includes two benchmarks for assessing reconstruction accuracy and five clinical prediction tasks that assess the utility of learned shape representations. Leveraging ShapeMed-Knee, we develop and evaluate a novel hybrid explicit-implicit neural shape model which achieves up to 40% better reconstruction accuracy than a statistical shape model and implicit neural shape model. Our hybrid models achieve state-of-the-art performance for preserving cartilage biomarkers; they're also the first models to successfully predict localized structural features of osteoarthritis, outperforming shape models and convolutional neural networks applied to raw magnetic resonance images and segmentations. The ShapeMed-Knee dataset provides medical evaluations to reconstruct multiple anatomic surfaces and embed meaningful disease-specific information. ShapeMed-Knee reduces barriers to applying 3D modeling in medicine, and our benchmarks highlight that advancements in 3D modeling can enhance the diagnosis and risk stratification for complex diseases. The dataset, code, and benchmarks will be made freely accessible.
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Affiliation(s)
- Anthony A Gatti
- Department of Radiology at Stanford University, Stanford, CA, 94305, USA
| | - Louis Blankemeier
- Department of Electrical Engineering at Stanford University, Stanford, CA, 94305, USA
| | - Dave Van Veen
- Department of Electrical Engineering at Stanford University, Stanford, CA, 94305, USA
| | - Brian Hargreaves
- Department of Radiology at Stanford University, Stanford, CA, 94305, USA
| | - Scott L Delp
- Department of Bioengineering at Stanford University, Stanford, CA, 94305, USA
| | - Garry E Gold
- Department of Radiology at Stanford University, Stanford, CA, 94305, USA
| | - Feliks Kogan
- Department of Radiology at Stanford University, Stanford, CA, 94305, USA
| | - Akshay S Chaudhari
- Department of Radiology at Stanford University, Stanford, CA, 94305, USA
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Roemer FW, Wirth W, Demehri S, Kijowski R, Jarraya M, Hayashi D, Eckstein F, Guermazi A. Imaging Biomarkers of Osteoarthritis. Semin Musculoskelet Radiol 2024; 28:14-25. [PMID: 38330967 DOI: 10.1055/s-0043-1776432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
Currently no disease-modifying osteoarthritis drug has been approved for the treatment of osteoarthritis (OA) that can reverse, hold, or slow the progression of structural damage of OA-affected joints. The reasons for failure are manifold and include the heterogeneity of structural disease of the OA joint at trial inclusion, and the sensitivity of biomarkers used to measure a potential treatment effect.This article discusses the role and potential of different imaging biomarkers in OA research. We review the current role of radiography, as well as advances in quantitative three-dimensional morphological cartilage assessment and semiquantitative whole-organ assessment of OA. Although magnetic resonance imaging has evolved as the leading imaging method in OA research, recent developments in computed tomography are also discussed briefly. Finally, we address the experience from the Foundation for the National Institutes of Health Biomarker Consortium biomarker qualification study and the future role of artificial intelligence.
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Affiliation(s)
- Frank W Roemer
- Department of Radiology, Chobanian & Avedisian Boston University School of Medicine, Boston, Massachusetts
- Department of Radiology, Universitätsklinikum Erlangen & Friedrich Alexander Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany
| | - Wolfgang Wirth
- Center of Anatomy, and Ludwig Boltzmann Institute for Arthritis and Rehabilitation (LBIAR), Paracelsus Medical University, Salzburg, Austria
- Chondrometrics, GmbH, Freilassing, Germany
| | - Shadpour Demehri
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Richard Kijowski
- Department of Radiology, New York University Grossmann School of Medicine, New York, New York
| | - Mohamed Jarraya
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Daichi Hayashi
- Department of Radiology, Tufts Medical Center, Tufts University School of Medicine, Boston, Massachusetts
- Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts
| | - Felix Eckstein
- Center of Anatomy, and Ludwig Boltzmann Institute for Arthritis and Rehabilitation (LBIAR), Paracelsus Medical University, Salzburg, Austria
- Chondrometrics, GmbH, Freilassing, Germany
| | - Ali Guermazi
- Department of Radiology, Chobanian & Avedisian Boston University School of Medicine, Boston, Massachusetts
- Department of Radiology, Boston VA Healthcare System, West Roxbury, Massachusetts
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