1
|
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.
Collapse
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
| |
Collapse
|
2
|
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] [MESH Headings] [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.
Collapse
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
| |
Collapse
|
3
|
Eckstein F, Brisson NM, Maschek S, Wisser A, Berenbaum F, Duda GN, Wirth W. Clinical validation of fully automated laminar knee cartilage transverse relaxation time (T2) analysis in anterior cruciate ligament (ACL)-injured knees- on behalf of the osteoarthritis (OA)-Bio consortium. Quant Imaging Med Surg 2024; 14:4319-4332. [PMID: 39022226 PMCID: PMC11250285 DOI: 10.21037/qims-24-194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 05/06/2024] [Indexed: 07/20/2024]
Abstract
Background Magnetic resonance imaging (MRI) cartilage transverse relaxation time (T2) reflects cartilage composition, mechanical properties, and early osteoarthritis (OA). T2 analysis requires cartilage segmentation. In this study, we clinically validate fully automated T2 analysis at 1.5 Tesla (T) in anterior cruciate ligament (ACL)-injured and healthy knees. Methods We studied 71 participants: 20 ACL-injured patients with, and 22 without dynamic knee instability, 13 with surgical reconstruction, and 16 healthy controls. Sagittal multi-echo-spin-echo (MESE) MRIs were acquired at baseline and 1-year follow-up. Femorotibial cartilage was segmented manually; a convolutional neural network (CNN) algorithm was trained on MRI data from the same scanner. Results Dice similarity coefficients (DSCs) of automated versus manual segmentation in the 71 participants were 0.83 (femora) and 0.89 (tibiae). Deep femorotibial T2 was similar between automated (45.7±2.6 ms) and manual (45.7±2.7 ms) segmentation (P=0.828), whereas superficial layer T2 was slightly overestimated by automated analysis (53.2±2.2 vs. 52.1±2.1 ms for manual; P<0.001). T2 correlations were r=0.91-0.99 for deep and r=0.86-0.97 for superficial layers across regions. The only statistically significant T2 increase over 1 year was observed in the deep layer of the lateral femur [standardized response mean (SRM) =0.58 for automated vs. 0.52 for manual analysis; P<0.001]. There was no relevant difference in baseline/longitudinal T2 values/changes between the ACL-injured groups and healthy participants, with either segmentation method. Conclusions This clinical validation study suggests that automated cartilage T2 analysis from MESE at 1.5T is technically feasible and accurate. More efficient 3D sequences and longer observation intervals may be required to detect the impact of ACL injury induced joint instability on cartilage composition (T2).
Collapse
Affiliation(s)
- Felix Eckstein
- Chondrometrics GmbH, Freilassing, Germany
- Research Program for Musculoskeletal Imaging, Center for Anatomy and Cell Biology & Ludwig Boltzmann Institute for Arthritis and Rehabilitation (LBIAR), Paracelsus Medical University, Salzburg, Austria
| | - Nicholas M. Brisson
- Julius Wolff Institute, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
- Berlin Movement Diagnostics (BeMoveD), Center for Musculoskeletal Surgery, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | | | - Anna Wisser
- Chondrometrics GmbH, Freilassing, Germany
- Research Program for Musculoskeletal Imaging, Center for Anatomy and Cell Biology & Ludwig Boltzmann Institute for Arthritis and Rehabilitation (LBIAR), Paracelsus Medical University, Salzburg, Austria
| | - Francis Berenbaum
- Moving Biotech, Lille, France
- Department of Rheumatology, Sorbonne University, INSERM, AP-HP, Saint-Antoine Hospital, Paris, France
| | - Georg N. Duda
- Julius Wolff Institute, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
- Berlin Movement Diagnostics (BeMoveD), Center for Musculoskeletal Surgery, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Wolfgang Wirth
- Chondrometrics GmbH, Freilassing, Germany
- Research Program for Musculoskeletal Imaging, Center for Anatomy and Cell Biology & Ludwig Boltzmann Institute for Arthritis and Rehabilitation (LBIAR), Paracelsus Medical University, Salzburg, Austria
| |
Collapse
|
4
|
Herger S, Wirth W, Eckstein F, Nüesch C, Egloff C, Mündermann A. Anterior cruciate ligament injury and age affect knee cartilage T2 but not thickness. Osteoarthritis Cartilage 2024:S1063-4584(24)01269-X. [PMID: 38950877 DOI: 10.1016/j.joca.2024.06.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 06/21/2024] [Accepted: 06/26/2024] [Indexed: 07/03/2024]
Abstract
OBJECTIVE To investigate the effect of unilateral anterior cruciate ligament (ACL) injury on cartilage thickness and composition, specifically laminar transverse relaxation time (T2) by magnetic resonance imaging (MRI), in younger and older participants and to compare within-person side differences in these parameters between ACL-injured and healthy controls. DESIGN Quantitative double-echo steady-state 3 Tesla MRI-sequences were acquired in both knees of 85 participants in four groups: 20-30 years: healthy, HEA20-30, n = 24; ACL-injured, ACL20-30, n = 23; 40-60 years: healthy, HEA40-60, n = 24; ACL-injured, ACL40-60, n = 14 (ACL injury 2-10 years prior to study inclusion). Weight-bearing femorotibial cartilages were manually segmented; cartilage T2 and thickness were computed using custom software. Mean and side differences in subregional cartilage thickness, superficial and deep cartilage T2 were compared within and between groups using non-parametric statistics. RESULTS Cartilage thickness did not differ within or between groups. Only the side difference in medial femorotibial cartilage thickness was greater in ACL20-30 than in HEA20-30. Deep zone T2 was longer in the ACL-injured than in the contralateral uninjured knees and than in healthy controls, especially in the lateral compartment. Most ACL-injured participants had side differences in femorotibial deep zone T2 above the threshold derived from controls. CONCLUSION In the ACL-injured knee, early compositional differences in femorotibial cartilage (T2) appear to occur in the deep zone and precede cartilage thickness loss. These results suggest that monitoring laminar T2 after ACL injury may be useful in diagnosing and monitoring early articular cartilage changes.
Collapse
Affiliation(s)
- Simon Herger
- Department of Orthopedics and Traumatology, University Hospital Basel, Basel Switzerland; Department of Spine Surgery, University Hospital Basel, Basel, Switzerland; Department of Biomedical Engineering, University of Basel, Basel, Switzerland; Department of Clinical Research, University of Basel, Basel, Switzerland.
| | - Wolfgang Wirth
- Research Program for Musculoskeletal Imaging, Center of Anatomy and Cell Biology, and Ludwig Boltzmann Institute for Arthritis and Rehabilitation (LBIAR), Paracelsus Medical University, Salzburg, Austria; Chondrometrics GmbH, Freilassing, Germany.
| | - Felix Eckstein
- Research Program for Musculoskeletal Imaging, Center of Anatomy and Cell Biology, and Ludwig Boltzmann Institute for Arthritis and Rehabilitation (LBIAR), Paracelsus Medical University, Salzburg, Austria; Chondrometrics GmbH, Freilassing, Germany.
| | - Corina Nüesch
- Department of Orthopedics and Traumatology, University Hospital Basel, Basel Switzerland; Department of Spine Surgery, University Hospital Basel, Basel, Switzerland; Department of Biomedical Engineering, University of Basel, Basel, Switzerland; Department of Clinical Research, University of Basel, Basel, Switzerland.
| | - Christian Egloff
- Department of Orthopedics and Traumatology, University Hospital Basel, Basel Switzerland.
| | - Annegret Mündermann
- Department of Orthopedics and Traumatology, University Hospital Basel, Basel Switzerland; Department of Spine Surgery, University Hospital Basel, Basel, Switzerland; Department of Biomedical Engineering, University of Basel, Basel, Switzerland; Department of Clinical Research, University of Basel, Basel, Switzerland.
| |
Collapse
|
5
|
Casula V, Kajabi AW. Quantitative MRI methods for the assessment of structure, composition, and function of musculoskeletal tissues in basic research and preclinical applications. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01174-7. [PMID: 38904746 DOI: 10.1007/s10334-024-01174-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 05/04/2024] [Accepted: 05/30/2024] [Indexed: 06/22/2024]
Abstract
Osteoarthritis (OA) is a disabling chronic disease involving the gradual degradation of joint structures causing pain and dysfunction. Magnetic resonance imaging (MRI) has been widely used as a non-invasive tool for assessing OA-related changes. While anatomical MRI is limited to the morphological assessment of the joint structures, quantitative MRI (qMRI) allows for the measurement of biophysical properties of the tissues at the molecular level. Quantitative MRI techniques have been employed to characterize tissues' structural integrity, biochemical content, and mechanical properties. Their applications extend to studying degenerative alterations, early OA detection, and evaluating therapeutic intervention. This article is a review of qMRI techniques for musculoskeletal tissue evaluation, with a particular emphasis on articular cartilage. The goal is to describe the underlying mechanism and primary limitations of the qMRI parameters, their association with the tissue physiological properties and their potential in detecting tissue degeneration leading to the development of OA with a primary focus on basic and preclinical research studies. Additionally, the review highlights some clinical applications of qMRI, discussing the role of texture-based radiomics and machine learning in advancing OA research.
Collapse
Affiliation(s)
- Victor Casula
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland.
| | - Abdul Wahed Kajabi
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
- Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| |
Collapse
|
6
|
Bugeja JM, Pagnozzi AM. Editorial for "Reproducibility of Quantitative Double-Echo Steady-State T 2 Mapping of Knee Cartilage". J Magn Reson Imaging 2024. [PMID: 38733372 DOI: 10.1002/jmri.29448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Accepted: 04/02/2024] [Indexed: 05/13/2024] Open
Affiliation(s)
- Jessica M Bugeja
- The Australian e-Health Research Centre, Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Queensland, Australia
| | - Alex M Pagnozzi
- The Australian e-Health Research Centre, Health and Biosecurity, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Queensland, Australia
| |
Collapse
|
7
|
Williams AA, Asay JL, Asare D, Desai AD, Gold GE, Hargreaves BA, Chaudhari AS, Chu CR. Reproducibility of Quantitative Double-Echo Steady-State T 2 Mapping of Knee Cartilage. J Magn Reson Imaging 2024. [PMID: 38703134 DOI: 10.1002/jmri.29431] [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: 02/09/2024] [Revised: 04/18/2024] [Accepted: 04/19/2024] [Indexed: 05/06/2024] Open
Abstract
BACKGROUND Cartilage T2 can detect joints at risk of developing osteoarthritis. The quantitative double-echo steady state (qDESS) sequence is attractive for knee cartilage T2 mapping because of its acquisition time of under 5 minutes. Understanding the reproducibility errors associated with qDESS T2 is essential to profiling the technical performance of this biomarker. PURPOSE To examine the combined acquisition and segmentation reproducibility of knee cartilage qDESS T2 using two different regional analysis schemes: 1) manual segmentation of subregions loaded during common activities and 2) automatic subregional segmentation. STUDY TYPE Prospective. SUBJECTS 11 uninjured participants (age: 28 ± 3 years; 8 (73%) female). FIELD STRENGTH/SEQUENCE 3-T, qDESS. ASSESSMENT Test-retest T2 maps were acquired twice on the same day and with a 1-week interval between scans. For each acquisition, average cartilage T2 was calculated in four manually segmented regions encompassing tibiofemoral contact areas during common activities and 12 automatically segmented regions from the deep-learning open-source framework for musculoskeletal MRI analysis (DOSMA) encompassing medial and lateral anterior, central, and posterior tibiofemoral regions. Test-retest T2 values from matching regions were used to evaluate reproducibility. STATISTICAL TESTS Coefficients of variation (%CV), root-mean-square-average-CV (%RMSA-CV), and intraclass correlation coefficients (ICCs) assessed test-retest T2 reproducibility. The median of test-retest standard deviations was used for T2 precision. Bland-Altman (BA) analyses examined test-retest biases. The smallest detectable difference (SDD) was defined as the BA limit of agreement of largest magnitude. Significance was accepted for P < 0.05. RESULTS All cartilage regions across both segmentation schemes demonstrated intraday and interday qDESS T2 CVs and RMSA-CVs of ≤5%. T2 ICC values >0.75 were observed in the majority of regions but were more variable in interday tibial comparisons. Test-retest T2 precision was <1.3 msec. The T2 SDD was 3.8 msec. DATA CONCLUSION Excellent CV and RMSA-CV reproducibility may suggest that qDESS T2 increases or decreases >5% (3.8 msec) could represent changes to cartilage composition. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY Stage 2.
Collapse
Affiliation(s)
- Ashley A Williams
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
- VA Palo Alto Health Care System, Palo Alto, California, USA
| | - Jessica L Asay
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Daniella Asare
- VA Palo Alto Health Care System, Palo Alto, California, USA
| | - Arjun D Desai
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Garry E Gold
- Department of Radiology, 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 Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Akshay S Chaudhari
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | - Constance R Chu
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
- VA Palo Alto Health Care System, Palo Alto, California, USA
| |
Collapse
|
8
|
Eckstein F, Wluka AE, Wirth W, Cicuttini F. 30 Years of MRI-based cartilage & bone morphometry in knee osteoarthritis: From correlation to clinical trials. Osteoarthritis Cartilage 2024; 32:439-451. [PMID: 38331162 DOI: 10.1016/j.joca.2024.02.002] [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: 09/26/2023] [Revised: 12/20/2023] [Accepted: 02/01/2024] [Indexed: 02/10/2024]
Abstract
OBJECTIVE The first publication on morphometric analysis of articular cartilage using magnetic resonance imaging (MRI) in 1994 set the scene for a game change in osteoarthritis (OA) research. The current review highlights milestones in cartilage and bone morphometry, summarizing the rapid progress made in imaging, its application to understanding joint (patho-)physiology, and its use in interventional clinical trials. METHODS Based on a Pubmed search of articles from 1994 to 2023, the authors subjectively selected representative work illustrating important steps in the development or application of magnetic resonance-based cartilage and bone morphometry, with a focus on studies in humans, and on the knee. Research on OA-pathophysiology is addressed only briefly, given length constraints. Compositional and semi-quantitative assessment are not covered here. RESULTS The selected articles are presented in historical order as well as by content. We review progress in the technical aspects of image acquisition, segmentation and analysis, advances in understanding tissue growth, physiology, function, and adaptation, and a selection of clinical trials examining the efficacy of interventions on knee cartilage and bone. A perspective is provided of how lessons learned may be applied to future research and clinical management. CONCLUSIONS Over the past 30 years, MRI-based morphometry of cartilage and bone has contributed to a paradigm shift in understanding articular tissue physiology and OA pathophysiology, and to the development of new treatment strategies. It is likely that these technologies will continue to play a key role in the development and (accelerated) approval of therapy, potentially targeted to different OA phenotypes.
Collapse
Affiliation(s)
- Felix Eckstein
- Department of Imaging & Functional Musculoskeletal Research, Center of Anatomy and Cell Biology, Paracelsus Medical University (PMU), Salzburg, Austria; Ludwig Boltzmann Institute for Arthritis and Rehabilitation (LBIAR), Paracelsus Medical University, Salzburg, Austria; Chondrometrics GmbH, Freilassing, Bavaria, Germany.
| | - Anita E Wluka
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Wolfgang Wirth
- Department of Imaging & Functional Musculoskeletal Research, Center of Anatomy and Cell Biology, Paracelsus Medical University (PMU), Salzburg, Austria; Ludwig Boltzmann Institute for Arthritis and Rehabilitation (LBIAR), Paracelsus Medical University, Salzburg, Austria; Chondrometrics GmbH, Freilassing, Bavaria, Germany
| | - Flavia Cicuttini
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| |
Collapse
|
9
|
Kadalie E, Trotier AJ, Corbin N, Miraux S, Ribot EJ. Rapid whole brain 3D T 2 mapping respiratory-resolved Double-Echo Steady State (DESS) sequence with improved repeatability. Magn Reson Med 2024; 91:221-236. [PMID: 37794821 DOI: 10.1002/mrm.29847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 08/11/2023] [Accepted: 08/11/2023] [Indexed: 10/06/2023]
Abstract
PURPOSE To propose a quantitative 3D double-echo steady-state (DESS) sequence that offers rapid and repeatable T2 mapping of the human brain using different encoding schemes that account for respiratory B0 variation. METHODS A retrospective self-gating module was firstly implemented into the standard DESS sequence in order to suppress the respiratory artifact via data binning. A compressed-sensing trajectory (CS-DESS) was then optimized to accelerate the acquisition. Finally, a spiral Cartesian encoding (SPICCS-DESS) was incorporated to further disrupt the coherent respiratory artifact. These different versions were compared to a standard DESS sequence (fully DESS) by assessing the T2 distribution and repeatability in different brain regions of eight volunteers at 3 T. RESULTS The respiratory artifact correction was determined to be optimal when the data was binned into seven respiratory phases. Compared to the fully DESS, T2 distribution was improved for the CS-DESS and SPICCS-DESS with interquartile ranges reduced significantly by a factor ranging from 2 to 12 in the caudate, putamen, and thalamus regions. In the gray and white matter areas, average absolute test-retest T2 differences across all volunteers were respectively 3.5 ± 2% and 3.1 ± 2.1% for the SPICCS-DESS, 4.6 ± 4.6% and 4.9 ± 5.1% for the CS-DESS, and 15% ± 13% and 7.3 ± 5.6% for the fully DESS. The SPICCS-DESS sequence's acquisition time could be reduced by half (<4 min) while maintaining its efficient T2 mapping. CONCLUSION The respiratory-resolved SPICCS-DESS sequence offers rapid, robust, and repeatable 3D T2 mapping of the human brain, which can be especially effective for longitudinal monitoring of cerebral pathologies.
Collapse
Affiliation(s)
- Emile Kadalie
- Univ. Bordeaux, CNRS, Centre de Résonance Magnétique des Systèmes Biologiques (CRMSB), UMR 5536, F-33000, Bordeaux, France
| | - Aurélien J Trotier
- Univ. Bordeaux, CNRS, Centre de Résonance Magnétique des Systèmes Biologiques (CRMSB), UMR 5536, F-33000, Bordeaux, France
| | - Nadège Corbin
- Univ. Bordeaux, CNRS, Centre de Résonance Magnétique des Systèmes Biologiques (CRMSB), UMR 5536, F-33000, Bordeaux, France
| | - Sylvain Miraux
- Univ. Bordeaux, CNRS, Centre de Résonance Magnétique des Systèmes Biologiques (CRMSB), UMR 5536, F-33000, Bordeaux, France
| | - Emeline J Ribot
- Univ. Bordeaux, CNRS, Centre de Résonance Magnétique des Systèmes Biologiques (CRMSB), UMR 5536, F-33000, Bordeaux, France
| |
Collapse
|
10
|
Mickevicius NJ. Magnetic resonance coherence pathway unraveling. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2024; 358:107613. [PMID: 38134509 DOI: 10.1016/j.jmr.2023.107613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 12/08/2023] [Accepted: 12/15/2023] [Indexed: 12/24/2023]
Abstract
Efficiently acquiring multi-contrast magnetic resonance imaging data is crucial for patient comfort and clinical throughput. Developing scan acceleration methods tailored for specific applications drastically improves the value of an MRI examination. Here, we propose a novel method to control the aliasing of simultaneously acquired images of multiple spin echo coherence pathways with the goal of producing high quality multi-contrast images from a single acquisition. Modulating the radiofrequency phase of several pulses applied in brief succession also uniquely modulates the phase of spin echo coherence pathways. A method, termed magnetic resonance coherence pathway unraveling (MR-CPU), to control the aliasing of simultaneously acquired coherence pathway images is developed here along with parallel imaging-based reconstruction methods to separate them. MR-CPU was validated in phantom experiments and tested in vivo. High levels of correlation between reference pathway images and MR-CPU-derived coherence pathway images were found from the phantom experiments. Minimal artifacts arising from the separation of the overlapped coherence pathway images were observed in vivo. MR-CPU provides a novel mechanism through which to acquire and separate multiple overlapped coherence pathway images, thus adding to the diagnostic potential of an MRI exam without the penalty of additional scan time.
Collapse
|
11
|
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.
Collapse
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
| | | |
Collapse
|
12
|
Williams AA, Koltsov JCB, Brett A, He J, Chu CR. Using 3D MRI Bone Shape to Predict Pre-Osteoarthritis of the Knee 2 Years After Anterior Cruciate Ligament Reconstruction. Am J Sports Med 2023; 51:3677-3686. [PMID: 37936374 DOI: 10.1177/03635465231207615] [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] [Indexed: 11/09/2023]
Abstract
BACKGROUND Anterior cruciate ligament (ACL) injury increases risks for osteoarthritis (OA), a poorly modifiable and disabling condition. Joint changes of potentially reversible pre-OA have been described just 2 years after ACL reconstruction (ACLR) when early bone shape changes have also been reported. PURPOSE This study evaluates relationships between interlimb differences in tibiofemoral bone shape derived from statistical shape modeling (SSM) of magnetic resonance imaging (MRI) and participant factors on patient-reported outcomes 2 years after unilateral ACLR. STUDY DESIGN Cross-sectional study; Level of evidence, 3. METHODS SSM-derived tibiofemoral bone shape and subchondral bone area were assessed from bilateral knee MRI scans of 72 participants with unilateral ACLR (mean age, 34 ± 11 years; 32 women) and compared with a reference cohort of 398 older individuals without OA (mean age, 50 ± 3 years; 213 women). Multivariable logistic regression models examined relationships between participant and surgical factors with interlimb differences in bone shapes or subchondral bone areas. Relationships between patient-reported outcomes and the interlimb differences in bone shape and subchondral area were examined using similar models. RESULTS Bone shape scores and subchondral bone areas were greater (more OA-like) in ACLR knees than uninjured contralateral knees in every bone metric tested (P≤ .001). Interlimb differences in femur shape scores of participants with ACLR were 65% greater (P < .001) than those of the significantly older reference cohort. Taller height, medial meniscal tears, and decreasing age were associated with larger interlimb differences in shape scores and subchondral areas (P < .05). Bone-patellar tendon-bone (BPTB) autograft recipients demonstrated greater interlimb subchondral area differences compared with allograft recipients (P < .05). Interlimb differences for hamstring autograft recipients did not differ from those with BPTB or allograft. Greater interlimb differences in medial femur subchondral areas were associated with worse patient-reported Knee injury and Osteoarthritis Outcome Score Symptoms (R = 0.27; P = .040). CONCLUSION Even in the absence of radiographic OA, just 2 years after unilateral ACLR patients showed greater bone shape scores and subchondral areas consistent with pre-OA in their ACLR knees. Furthermore, greater medial femur bone areas were weakly associated with worse symptoms. Patients who are younger, are taller, have meniscal tears, or have BPTB grafts may be at increased risk for bony asymmetries 2 years after ACLR.
Collapse
Affiliation(s)
- Ashley A Williams
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
- Department of Surgery, Veterans Affairs Palo Alto Health Care System, Palo Alto, California, USA
| | - Jayme C B Koltsov
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
| | | | - Jade He
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
- Department of Surgery, Veterans Affairs Palo Alto Health Care System, Palo Alto, California, USA
| | - Constance R Chu
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
- Department of Surgery, Veterans Affairs Palo Alto Health Care System, Palo Alto, California, USA
| |
Collapse
|
13
|
Wirth W, Ladel C, Maschek S, Wisser A, Eckstein F, Roemer F. Quantitative measurement of cartilage morphology in osteoarthritis: current knowledge and future directions. Skeletal Radiol 2023; 52:2107-2122. [PMID: 36380243 PMCID: PMC10509082 DOI: 10.1007/s00256-022-04228-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 10/29/2022] [Accepted: 10/31/2022] [Indexed: 11/16/2022]
Abstract
Quantitative measures of cartilage morphology ("cartilage morphometry") extracted from high resolution 3D magnetic resonance imaging (MRI) sequences have been shown to be sensitive to osteoarthritis (OA)-related change and also to treatment interventions. Cartilage morphometry is therefore nowadays widely used as outcome measure for observational studies and randomized interventional clinical trials. The objective of this narrative review is to summarize the current status of cartilage morphometry in OA research, to provide insights into aspects relevant for the design of future studies and clinical trials, and to give an outlook on future developments. It covers the aspects related to the acquisition of MRIs suitable for cartilage morphometry, the analysis techniques needed for deriving quantitative measures from the MRIs, the quality assurance required for providing reliable cartilage measures, and the appropriate participant recruitment criteria for the enrichment of study cohorts with knees likely to show structural progression. Finally, it provides an overview over recent clinical trials that relied on cartilage morphometry as a structural outcome measure for evaluating the efficacy of disease-modifying OA drugs (DMOAD).
Collapse
Affiliation(s)
- Wolfgang Wirth
- Department of Imaging & Functional Musculoskeletal Research, Institute of Anatomy & Cell Biology, Paracelsus Medical University Salzburg & Nuremberg, Strubergasse 21, 5020 Salzburg, Austria
- Ludwig Boltzmann Inst. for Arthritis and Rehabilitation, Paracelsus Medical University Salzburg & Nuremberg, Salzburg, Austria
- Chondrometrics GmbH, Freilassing, Germany
| | | | - Susanne Maschek
- Department of Imaging & Functional Musculoskeletal Research, Institute of Anatomy & Cell Biology, Paracelsus Medical University Salzburg & Nuremberg, Strubergasse 21, 5020 Salzburg, Austria
- Chondrometrics GmbH, Freilassing, Germany
| | - Anna Wisser
- Department of Imaging & Functional Musculoskeletal Research, Institute of Anatomy & Cell Biology, Paracelsus Medical University Salzburg & Nuremberg, Strubergasse 21, 5020 Salzburg, Austria
- Ludwig Boltzmann Inst. for Arthritis and Rehabilitation, Paracelsus Medical University Salzburg & Nuremberg, Salzburg, Austria
- Chondrometrics GmbH, Freilassing, Germany
| | - Felix Eckstein
- Department of Imaging & Functional Musculoskeletal Research, Institute of Anatomy & Cell Biology, Paracelsus Medical University Salzburg & Nuremberg, Strubergasse 21, 5020 Salzburg, Austria
- Ludwig Boltzmann Inst. for Arthritis and Rehabilitation, Paracelsus Medical University Salzburg & Nuremberg, Salzburg, Austria
- Chondrometrics GmbH, Freilassing, Germany
| | - Frank Roemer
- Quantitative Imaging Center, Department of Radiology, Boston University School of Medicine, Boston, MA USA
- Department of Radiology, Universitätsklinikum Erlangen and Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
| |
Collapse
|
14
|
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.
Collapse
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
| |
Collapse
|
15
|
Schmidt AM, Desai AD, Watkins LE, Crowder HA, Black MS, Mazzoli V, Rubin EB, Lu Q, MacKay JW, Boutin RD, Kogan F, Gold GE, Hargreaves BA, Chaudhari AS. Generalizability of Deep Learning Segmentation Algorithms for Automated Assessment of Cartilage Morphology and MRI Relaxometry. J Magn Reson Imaging 2023; 57:1029-1039. [PMID: 35852498 PMCID: PMC9849481 DOI: 10.1002/jmri.28365] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 07/06/2022] [Accepted: 07/07/2022] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Deep learning (DL)-based automatic segmentation models can expedite manual segmentation yet require resource-intensive fine-tuning before deployment on new datasets. The generalizability of DL methods to new datasets without fine-tuning is not well characterized. PURPOSE Evaluate the generalizability of DL-based models by deploying pretrained models on independent datasets varying by MR scanner, acquisition parameters, and subject population. STUDY TYPE Retrospective based on prospectively acquired data. POPULATION Overall test dataset: 59 subjects (26 females); Study 1: 5 healthy subjects (zero females), Study 2: 8 healthy subjects (eight females), Study 3: 10 subjects with osteoarthritis (eight females), Study 4: 36 subjects with various knee pathology (10 females). FIELD STRENGTH/SEQUENCE A 3-T, quantitative double-echo steady state (qDESS). ASSESSMENT Four annotators manually segmented knee cartilage. Each reader segmented one of four qDESS datasets in the test dataset. Two DL models, one trained on qDESS data and another on Osteoarthritis Initiative (OAI)-DESS data, were assessed. Manual and automatic segmentations were compared by quantifying variations in segmentation accuracy, volume, and T2 relaxation times for superficial and deep cartilage. STATISTICAL TESTS Dice similarity coefficient (DSC) for segmentation accuracy. Lin's concordance correlation coefficient (CCC), Wilcoxon rank-sum tests, root-mean-squared error-coefficient-of-variation to quantify manual vs. automatic T2 and volume variations. Bland-Altman plots for manual vs. automatic T2 agreement. A P value < 0.05 was considered statistically significant. RESULTS DSCs for the qDESS-trained model, 0.79-0.93, were higher than those for the OAI-DESS-trained model, 0.59-0.79. T2 and volume CCCs for the qDESS-trained model, 0.75-0.98 and 0.47-0.95, were higher than respective CCCs for the OAI-DESS-trained model, 0.35-0.90 and 0.13-0.84. Bland-Altman 95% limits of agreement for superficial and deep cartilage T2 were lower for the qDESS-trained model, ±2.4 msec and ±4.0 msec, than the OAI-DESS-trained model, ±4.4 msec and ±5.2 msec. DATA CONCLUSION The qDESS-trained model may generalize well to independent qDESS datasets regardless of MR scanner, acquisition parameters, and subject population. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 1.
Collapse
Affiliation(s)
- Andrew M Schmidt
- Department of Radiology, Stanford University, Palo Alto, California, USA
| | - Arjun D Desai
- Department of Radiology, Stanford University, Palo Alto, California, USA
- Electrical Engineering, Stanford University, Palo Alto, California, USA
| | - Lauren E Watkins
- Department of Radiology, Stanford University, Palo Alto, California, USA
- Bioengineering, Stanford University, Palo Alto, California, USA
| | - Hollis A Crowder
- Mechanical Engineering, Stanford University, Palo Alto, California, USA
| | - Marianne S Black
- Department of Radiology, Stanford University, Palo Alto, California, USA
- Mechanical Engineering, Stanford University, Palo Alto, California, USA
| | - Valentina Mazzoli
- Department of Radiology, Stanford University, Palo Alto, California, USA
| | - Elka B Rubin
- Department of Radiology, Stanford University, Palo Alto, California, USA
| | - Quin Lu
- Philips Healthcare North America, Gainesville, Florida, USA
| | - James W MacKay
- Department of Radiology, University of Cambridge, Cambridge, UK
- Norwich Medical School, University of East Anglia, Norwich, UK
| | - Robert D Boutin
- Department of Radiology, Stanford University, Palo Alto, California, USA
| | - Feliks Kogan
- Department of Radiology, Stanford University, Palo Alto, California, USA
| | - Garry E Gold
- Department of Radiology, Stanford University, Palo Alto, California, USA
- Bioengineering, Stanford University, Palo Alto, California, USA
| | - Brian A Hargreaves
- Department of Radiology, Stanford University, Palo Alto, California, USA
- Electrical Engineering, Stanford University, Palo Alto, California, USA
- Bioengineering, Stanford University, Palo Alto, California, USA
| | - Akshay S Chaudhari
- Department of Radiology, Stanford University, Palo Alto, California, USA
- Biomedical Data Science, Stanford University, Palo Alto, California, USA
| |
Collapse
|
16
|
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.
Collapse
|
17
|
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.
Collapse
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
| |
Collapse
|
18
|
Automated Protocoling for MRI Exams-Challenges and Solutions. J Digit Imaging 2022; 35:1293-1302. [PMID: 36042118 PMCID: PMC9582071 DOI: 10.1007/s10278-022-00610-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 12/13/2021] [Accepted: 01/12/2022] [Indexed: 12/04/2022] Open
Abstract
Automated protocoling for MRI examinations is an amendable target for workflow automation with artificial intelligence. However, there are still challenges to overcome for a successful and robust approach. These challenges are outlined and analyzed in this work. Through a literature review, we analyzed limitations of currently published approaches for automated protocoling. Then, we assessed these limitations quantitatively based on data from a private radiology practice. For this, we assessed the information content provided by the clinical indication by computing the overlap coefficients for the sets of ICD-10-coded admitting diagnoses of different MRI protocols. Additionally, we assessed the heterogeneity of protocol trees from three different MRI scanners based on the overlap coefficient, on MRI protocol and sequence level. Additionally, we applied sequence name standardization to demonstrate its effect on the heterogeneity assessment, i.e., the overlap coefficient, of different protocol trees. The overlap coefficient for the set of ICD-10-coded admitting diagnoses for different protocols ranges from 0.14 to 0.56 for brain/head MRI exams and 0.04 to 0.57 for spine exams. The overlap coefficient across the set of sequences used at two different scanners increases when applying sequence name standardization (from 0.81/0.86 to 0.93). Automated protocoling for MRI examinations has the potential to reduce the workload for radiologists. However, an automated protocoling approach cannot be solely based on admitting diagnosis as it does not provide sufficient information. Moreover, sequence name standardization increases the overlap coefficient across the set of sequences used at different scanners and therefore facilitates transfer learning.
Collapse
|
19
|
Herger S, Vach W, Nüesch C, Liphardt AM, Egloff C, Mündermann A. Dose-response relationship of in vivo ambulatory load and mechanosensitive cartilage biomarkers—The role of age, tissue health and inflammation: A study protocol. PLoS One 2022; 17:e0272694. [PMID: 35984848 PMCID: PMC9390933 DOI: 10.1371/journal.pone.0272694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 07/19/2022] [Indexed: 11/18/2022] Open
Abstract
Objective To describe a study protocol for investigating the in vivo dose-response relationship between ambulatory load magnitude and mechanosensitive blood markers of articular cartilage, the influence of age, cartilage tissue health and presence of inflammation on this relationship, and its ability to predict changes in articular cartilage quality and morphology within 2 years. Design Prospective experimental multimodal (clinical, biomechanical, biological) data collection under walking stress and three different load conditions varied in a randomized crossover design. Experimental protocol At baseline, equal numbers of healthy and anterior cruciate ligament injured participants aged 20–30 or 40–60 years will be assessed clinically and complete questionnaires regarding their knee health. Biomechanical parameters (joint kinetics, joint kinematics, and surface electromyography) will be recorded while performing different tasks including overground and treadmill walking, single leg balance and hopping tasks. Magnetic resonance images (MRI) of both of knees will be obtained. On separate stress test days, participants will perform a 30-minute walking stress with either reduced (80% body weight (BW)), normal (100%BW) or increased (120%BW) load. Serum blood samples will be taken immediately before, immediately after, 30, 120 and 210 minutes after the walking stress. Concentration of articular cartilage blood biomarkers will be assessed using enzyme linked immunosorbent assays. At 24-month follow-up, participants will be again assessed clinically, undergo an MRI, complete questionnaires, and have a blood sample taken. Conclusion The study design provides a standardized set up that allows to better understand the influence of ambulatory load on articular cartilage biomarkers and thereby extend current knowledge on in vivo cartilage metabolism and mechanosensitivity. Further, this study will help to elucidate the prognostic value of the load-induced cartilage biomarker response for early articular cartilage degeneration. Trial registration The protocol was approved by the regional ethics committee and has been registered at clinicaltrials.gov (NCT04128566).
Collapse
Affiliation(s)
- Simon Herger
- Department of Orthopaedics and Traumatology, University Hospital Basel, Basel, Switzerland
- Department of Spine Surgery, University Hospital Basel, Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland
- Department of Clinical Research, University of Basel, Basel, Switzerland
- * E-mail:
| | - Werner Vach
- Basel Academy for Quality and Research in Medicine, Basel, Switzerland
| | - Corina Nüesch
- Department of Orthopaedics and Traumatology, University Hospital Basel, Basel, Switzerland
- Department of Spine Surgery, University Hospital Basel, Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland
- Department of Clinical Research, University of Basel, Basel, Switzerland
| | - Anna-Maria Liphardt
- Department of Internal Medicine 3 –Rheumatology and Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsklinikum Erlangen, Erlangen, Germany
| | - Christian Egloff
- Department of Orthopaedics and Traumatology, University Hospital Basel, Basel, Switzerland
- Department of Clinical Research, University of Basel, Basel, Switzerland
| | - Annegret Mündermann
- Department of Orthopaedics and Traumatology, University Hospital Basel, Basel, Switzerland
- Department of Spine Surgery, University Hospital Basel, Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland
- Department of Clinical Research, University of Basel, Basel, Switzerland
| |
Collapse
|
20
|
Thomas KA, Krzemiński D, Kidziński Ł, Paul R, Rubin EB, Halilaj E, Black MS, Chaudhari A, Gold GE, Delp SL. Open Source Software for Automatic Subregional Assessment of Knee Cartilage Degradation Using Quantitative T2 Relaxometry and Deep Learning. Cartilage 2021; 13:747S-756S. [PMID: 34496667 PMCID: PMC8808775 DOI: 10.1177/19476035211042406] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
OBJECTIVE We evaluated a fully automated femoral cartilage segmentation model for measuring T2 relaxation values and longitudinal changes using multi-echo spin-echo (MESE) magnetic resonance imaging (MRI). We open sourced this model and developed a web app available at https://kl.stanford.edu into which users can drag and drop images to segment them automatically. DESIGN We trained a neural network to segment femoral cartilage from MESE MRIs. Cartilage was divided into 12 subregions along medial-lateral, superficial-deep, and anterior-central-posterior boundaries. Subregional T2 values and four-year changes were calculated using a radiologist's segmentations (Reader 1) and the model's segmentations. These were compared using 28 held-out images. A subset of 14 images were also evaluated by a second expert (Reader 2) for comparison. RESULTS Model segmentations agreed with Reader 1 segmentations with a Dice score of 0.85 ± 0.03. The model's estimated T2 values for individual subregions agreed with those of Reader 1 with an average Spearman correlation of 0.89 and average mean absolute error (MAE) of 1.34 ms. The model's estimated four-year change in T2 for individual subregions agreed with Reader 1 with an average correlation of 0.80 and average MAE of 1.72 ms. The model agreed with Reader 1 at least as closely as Reader 2 agreed with Reader 1 in terms of Dice score (0.85 vs. 0.75) and subregional T2 values. CONCLUSIONS Assessments of cartilage health using our fully automated segmentation model agreed with those of an expert as closely as experts agreed with one another. This has the potential to accelerate osteoarthritis research.
Collapse
Affiliation(s)
- Kevin A. Thomas
- Department of Biomedical Data Science,
Stanford University, Stanford, CA, USA
| | - Dominik Krzemiński
- Cardiff University Brain Research
Imaging Centre, Cardiff University, Cardiff, Wales, UK
| | - Łukasz Kidziński
- Department of Bioengineering, Stanford
University, Stanford, CA, USA
| | - Rohan Paul
- Department of Biomedical Data Science,
Stanford University, Stanford, CA, USA
| | - Elka B. Rubin
- Department of Radiology, Stanford
University, Stanford, CA, USA
| | - Eni Halilaj
- Department of Mechanical Engineering,
Carnegie Mellon University, Pittsburgh, PA, USA
| | | | - Akshay Chaudhari
- Department of Biomedical Data Science,
Stanford University, Stanford, CA, USA
- Department of Radiology, Stanford
University, Stanford, CA, USA
| | - Garry E. Gold
- Department of Bioengineering, Stanford
University, Stanford, CA, USA
- Department of Radiology, Stanford
University, Stanford, CA, USA
- Department of Orthopaedic Surgery,
Stanford University, Stanford, CA, USA
| | - Scott L. Delp
- Department of Bioengineering, Stanford
University, Stanford, CA, USA
- Department of Orthopaedic Surgery,
Stanford University, Stanford, CA, USA
- Department of Mechanical Engineering,
Stanford University, Stanford, CA, USA
| |
Collapse
|
21
|
Abstract
Osteoarthritis, characterized by the breakdown of articular cartilage and other joint structures, is one of the most prevalent and disabling chronic diseases in the United States. Magnetic resonance imaging is a commonly used imaging modality to evaluate patients with joint pain. Both two-dimensional fast spin-echo sequences (2D-FSE) and three-dimensional (3D) sequences are used in clinical practice to evaluate articular cartilage. The 3D sequences have many advantages compared with 2D-FSE sequences, such as their high in-plane spatial resolution, thin continuous slices that reduce the effects of partial volume averaging, and ability to create multiplanar reformat images following a single acquisition. This article reviews the different 3D imaging techniques available for evaluating cartilage morphology, illustrates the strengths and weaknesses of 3D approaches compared with 2D-FSE approaches for cartilage imaging, and summarizes the diagnostic performance of 2D-FSE and 3D sequences for detecting cartilage lesions within the knee and hip joints.
Collapse
Affiliation(s)
- Richard Kijowski
- Department of Radiology, New York University Grossman School of Medicine, New York, New York
| |
Collapse
|
22
|
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.
Collapse
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.)
| |
Collapse
|
23
|
Konopka JA, Finlay AK, Eckstein F, Dragoo JL. Effects of unloader bracing on clinical outcomes and articular cartilage regeneration following microfracture of isolated chondral defects: a randomized trial. Knee Surg Sports Traumatol Arthrosc 2021; 29:2889-2898. [PMID: 33025056 DOI: 10.1007/s00167-020-06228-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 08/10/2020] [Indexed: 01/18/2023]
Abstract
PURPOSE To determine whether the use of an unloading brace can increase the thickness of cartilage regenerate after microfracture surgery. METHODS This is a randomized (1:1) controlled clinical trial. Twenty-four patients who underwent microfracture between 2012 and 2015 were identified and were randomly assigned to an unloading brace group or a no-brace group. All patients were kept non-weight bearing for the first eight weeks after surgery and then patients in the intervention group began using an unloading brace for an average of 63.9 (SD = 41.6) days to protect clot stability by exerting a varus or valgus force on the knee to decrease the force on the knee's lateral or medial compartment, respectively. Quality of the cartilage repair was assessed with knee magnetic resonance imaging to determine repair tissue thickness (primary outcome), repair tissue volume, and T2 relaxation times at 12 and 24 months after surgery. Clinical outcomes were evaluated with KOOS, Tegner, SF12, and Lysholm questionnaires at six, 12 and 24 months after surgery. RESULTS Three patients were lost to follow-up, resulting in 21 patients ultimately analyzed. The unloading brace repair tissue was greater than the no-brace group in volume (26.8 ± 23.7 mm3 vs - 8.4 ± 22.7 mm3, p = 0.005) and thickness (0.2 ± 0.2 mm versus - 0.4 ± 0.3 mm, p = 0.001) at 12 months and in cartilage thickness in the unloading brace group at 24 months (0.4 ± 0.4 mm versus - 0.1 ± 0.3 mm, p = 0.029). There was a positive correlation between wearing the brace longer and improved 6-month KOOS symptom scores (r = 0.82, p = 0.013), 6-month KOOS QOL scores (r = 0.80, p = 0.017), 6-month Tegner scores (r = 0.94, p = 0.002), and Tegner score changes from baseline to 6 months (r = 0.80, p = 0.032). CONCLUSION This study found a significant mid-term increase in cartilage repair tissue thickness following unloading bracing in patients recovering from microfracture for isolated chondral defects. LEVEL OF EVIDENCE II.
Collapse
Affiliation(s)
- Jaclyn A Konopka
- Department of Orthopaedic Surgery, Stanford University Medical Center, 450 Broadway, MC 6342, Redwood City, CA, 94063, USA
| | - Andrea K Finlay
- Department of Orthopaedic Surgery, Stanford University Medical Center, 450 Broadway, MC 6342, Redwood City, CA, 94063, USA
| | - Felix Eckstein
- Institute of Anatomy, Paracelsus Medical University, Salzburg, Austria
- Chondrometrics GmbH, Ainring, Germany
| | - Jason L Dragoo
- Department of Orthopaedic Surgery, Stanford University Medical Center, 450 Broadway, MC 6342, Redwood City, CA, 94063, USA.
| |
Collapse
|
24
|
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: 40] [Impact Index Per Article: 13.3] [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.
Collapse
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
| |
Collapse
|
25
|
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.
Collapse
|
26
|
MR-contrast-aware image-to-image translations with generative adversarial networks. Int J Comput Assist Radiol Surg 2021; 16:2069-2078. [PMID: 34148167 PMCID: PMC8616894 DOI: 10.1007/s11548-021-02433-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 06/11/2021] [Indexed: 11/06/2022]
Abstract
Purpose A magnetic resonance imaging (MRI) exam typically consists of several sequences that yield different image contrasts. Each sequence is parameterized through multiple acquisition parameters that influence image contrast, signal-to-noise ratio, acquisition time, and/or resolution. Depending on the clinical indication, different contrasts are required by the radiologist to make a diagnosis. As MR sequence acquisition is time consuming and acquired images may be corrupted due to motion, a method to synthesize MR images with adjustable contrast properties is required. Methods Therefore, we trained an image-to-image generative adversarial network conditioned on the MR acquisition parameters repetition time and echo time. Our approach is motivated by style transfer networks, whereas the “style” for an image is explicitly given in our case, as it is determined by the MR acquisition parameters our network is conditioned on. Results This enables us to synthesize MR images with adjustable image contrast. We evaluated our approach on the fastMRI dataset, a large set of publicly available MR knee images, and show that our method outperforms a benchmark pix2pix approach in the translation of non-fat-saturated MR images to fat-saturated images. Our approach yields a peak signal-to-noise ratio and structural similarity of 24.48 and 0.66, surpassing the pix2pix benchmark model significantly. Conclusion Our model is the first that enables fine-tuned contrast synthesis, which can be used to synthesize missing MR-contrasts or as a data augmentation technique for AI training in MRI. It can also be used as basis for other image-to-image translation tasks within medical imaging, e.g., to enhance intermodality translation (MRI → CT) or 7 T image synthesis from 3 T MR images.
Collapse
|
27
|
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.
Collapse
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
| |
Collapse
|
28
|
Watkins L, MacKay J, Haddock B, Mazzoli V, Uhlrich S, Gold G, Kogan F. Assessment of quantitative [ 18F]Sodium fluoride PET measures of knee subchondral bone perfusion and mineralization in osteoarthritic and healthy subjects. Osteoarthritis Cartilage 2021; 29:849-858. [PMID: 33639259 PMCID: PMC8159876 DOI: 10.1016/j.joca.2021.02.563] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 01/06/2021] [Accepted: 02/01/2021] [Indexed: 02/02/2023]
Abstract
OBJECTIVE Molecular information derived from dynamic [18F]sodium fluoride ([18F]NaF) PET imaging holds promise as a quantitative marker of bone metabolism. The objective of this work was to evaluate physiological mechanisms of [18F]NaF uptake in subchondral bone of individuals with and without knee osteoarthritis (OA). METHODS Eleven healthy volunteers and twenty OA subjects were included. Both knees of all subjects were scanned simultaneously using a 3T hybrid PET/MRI system. MRI MOAKS assessment was performed to score the presence and size of osteophytes, bone marrow lesions, and cartilage lesions. Subchondral bone kinetic parameters of bone perfusion (K1), tracer extraction fraction, and total tracer uptake into bone (Ki) were evaluated using the Hawkins 3-compartment model. Measures were compared between structurally normal-appearing bone regions and those with structural findings. RESULTS Mean and maximum SUV and kinetic parameters Ki, K1, and extraction fraction were significantly different between Healthy subjects and subjects with OA. Between-group differences in metabolic parameters were observed both in regions where the OA group had degenerative changes as well as in regions that appeared structurally normal. CONCLUSIONS Results suggest that bone metabolism is altered in OA subjects, including bone regions with and without structural findings, compared to healthy subjects. Kinetic parameters of [18F]NaF uptake in subchondral bone show potential to quantitatively evaluate the role of bone physiology in OA initiation and progression. Objective measures of bone metabolism from [18F]NaF PET imaging can complement assessments of structural abnormalities observed on MRI.
Collapse
Affiliation(s)
- L Watkins
- Department of Bioengineering, Stanford University, Stanford CA, USA; Department of Radiology, Stanford University, Stanford CA, USA.
| | - J MacKay
- Norwich Medical School, University of East Anglia, Norwich, United Kingdom; Department of Radiology, University of Cambridge, Cambridge, United Kingdom
| | | | - V Mazzoli
- Department of Radiology, Stanford University, Stanford CA, USA
| | - S Uhlrich
- Department of Mechanical Engineering, Stanford University, Stanford CA, USA
| | - G Gold
- Department of Bioengineering, Stanford University, Stanford CA, USA; Department of Radiology, Stanford University, Stanford CA, USA
| | - F Kogan
- Department of Radiology, Stanford University, Stanford CA, USA
| |
Collapse
|
29
|
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.
Collapse
|
30
|
de Vries BA, Breda SJ, Sveinsson B, McWalter EJ, Meuffels DE, Krestin GP, Hargreaves BA, Gold GE, Oei EHG. Detection of knee synovitis using non-contrast-enhanced qDESS compared with contrast-enhanced MRI. Arthritis Res Ther 2021; 23:55. [PMID: 33581741 PMCID: PMC7881494 DOI: 10.1186/s13075-021-02436-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 02/02/2021] [Indexed: 01/15/2023] Open
Abstract
Background To assess diagnostic accuracy of quantitative double-echo in steady-state (qDESS) MRI for detecting synovitis in knee osteoarthritis (OA). Methods Patients with different degrees of radiographic knee OA were included prospectively. All underwent MRI with both qDESS and contrast-enhanced T1-weighted magnetic resonance imaging (CE-MRI). A linear combination of the two qDESS images can be used to create an image that displays contrast between synovium and the synovial fluid. Synovitis on both qDESS and CE-MRI was assessed semi-quantitatively, using a whole-knee synovitis sum score, indicating no/equivocal, mild, moderate, and severe synovitis. The correlation between sum scores of qDESS and CE-MRI (reference standard) was determined using Spearman’s rank correlation coefficient and intraclass correlation coefficient for absolute agreement. Receiver operating characteristic analysis was performed to assess the diagnostic performance of qDESS for detecting different degrees of synovitis, with CE-MRI as reference standard. Results In the 31 patients included, very strong correlation was found between synovitis sum scores on qDESS and CE-MRI (ρ = 0.96, p < 0.001), with high absolute agreement (0.84 (95%CI 0.14–0.95)). Mean sum score (SD) values on qDESS 5.16 (3.75) were lower than on CE-MRI 7.13 (4.66), indicating systematically underestimated synovitis severity on qDESS. For detecting mild synovitis or higher, high sensitivity and specificity were found for qDESS (1.00 (95%CI 0.80–1.00) and 0.909 (0.571–1.00), respectively). For detecting moderate synovitis or higher, sensitivity and specificity were good (0.727 (95%CI 0.393–0.927) and 1.00 (0.800–1.00), respectively). Conclusion qDESS MRI is able to, however with an underestimation, detect synovitis in patients with knee OA.
Collapse
Affiliation(s)
- Bas A de Vries
- Department of Radiology & Nuclear Medicine, Erasmus MC, Doctor Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Stephan J Breda
- Department of Radiology & Nuclear Medicine, Erasmus MC, Doctor Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands.,Department of Orthopedic Surgery, Erasmus MC, Rotterdam, The Netherlands
| | - Bragi Sveinsson
- Department of Radiology, Massachusetts General Hospital, Boston, USA.,Harvard Medical School, Boston, USA
| | - Emily J McWalter
- Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, Canada
| | - Duncan E Meuffels
- Department of Orthopedic Surgery, Erasmus MC, Rotterdam, The Netherlands
| | - Gabriel P Krestin
- Department of Radiology & Nuclear Medicine, Erasmus MC, Doctor Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | | | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, USA
| | - Edwin H G Oei
- Department of Radiology & Nuclear Medicine, Erasmus MC, Doctor Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands.
| |
Collapse
|
31
|
Abstract
PURPOSE OF REVIEW Osteoarthritis is a major source of disability, pain and socioeconomic cost worldwide. The epidemiology of the disorder is multifactorial including genetic, biological and biomechanical components, some of them detectable by MRI. This review provides the most recent update on MRI biomarkers which can provide functional information of the joint structures for diagnosis, prognosis and treatment response monitoring in osteoarthritis trials. RECENT FINDINGS Compositional or functional MRI can provide clinicians with valuable information on glycosaminoglycan content (chemical exchange saturation transfer, sodium MRI, T1ρ) and collagen organization (T2, T2, apparent diffusion coefficient, magnetization transfer) in joint structures. Other parameters may also provide useful information, such as volumetric measurements of joint structures or advanced image data postprocessing and analysis. Automated tools seem to have a great potential to be included in these efforts providing standardization and acceleration of the image data analysis process. SUMMARY Functional or compositional MRI has great potential to provide noninvasive imaging biomarkers for osteoarthritis. Osteoarthritis as a whole joint condition needs to be diagnosed in early stages to facilitate selection of patients into clinical trials and/or to measure treatment effectiveness. Advanced evaluation including machine learning, neural networks and multidimensional data analysis allow for wall-to-wall understanding of parameter interactions and their role in clinical evaluation of osteoarthritis.
Collapse
|
32
|
High-resolution in vivo MR imaging of intraspinal cervical nerve rootlets at 3 and 7 Tesla. Eur Radiol 2021; 31:4625-4633. [PMID: 33409779 DOI: 10.1007/s00330-020-07557-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 11/09/2020] [Accepted: 11/20/2020] [Indexed: 10/22/2022]
Abstract
OBJECTIVES No routine imaging technology allows reliable visualization of nerve rootlets inside the spinal canal with positive contrast. The stronger MR signal at 7 T, with optimized protocols, may offer a solution. The purpose was to evaluate the potential of 3D Dual-Echo Steady-State (DESS) MR imaging of the cervical spine at 3 and 7 T in assessing the micro-anatomy of the nerve rootlets. MATERIALS/METHODS This prospective study was approved by the local ethics committee. Twenty-one patients, clinically referred to cervical-spine MRI, underwent additional MR exams at 3 T and 7 T, each of which consisted of a single 3D-DESS series with equal acquisition times. Artifacts, visualization quality, and number of identified rootlets (C2 to C8) were rated by two musculoskeletal radiologists. Results were compared by Wilcoxon tests. Interobserver reliability was assessed using weighted κ statistics and intraclass correlation coefficient (ICC). RESULTS Intraspinal rootlets could successfully be visualized at both field strengths. Rating differences for artifacts and quality of rootlet depiction were not significant for the two field strengths. The mean number of identified rootlets was larger for 7-T than for 3-T MR for every assessed nerve; however, this difference was not statistically significant using the Bonferroni correction (p values ranging from 0.002 to 0.53). Interobserver agreement was substantial to almost perfect (weighted κ values of 0.69 and 0.82). The ICC for the number of identified rootlets was 0.80. CONCLUSION Non-invasive 3D-DESS MR-imaging at 3 and 7 T has the potential to provide precise assessments of the micro-anatomy of intraspinal cervical nerve roots. KEY POINTS • Cervical rootlets can be successfully visualized with positive contrast using 3D-DESS MR-imaging. • 3D-DESS MR-imaging at 3 and 7 T provides precise assessments of the micro-anatomy of cervical nerves. • The mean number of identified cervical rootlets using 3D-DESS was larger for 7 T than for 3 T MR; however, this difference was not statistically significant.
Collapse
|
33
|
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.
Collapse
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
| |
Collapse
|
34
|
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.
Collapse
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
| |
Collapse
|
35
|
Abstract
Deep learning methods have shown promising results for accelerating quantitative musculoskeletal (MSK) magnetic resonance imaging (MRI) for T2 and T1ρ relaxometry. These methods have been shown to improve musculoskeletal tissue segmentation on parametric maps, allowing efficient and accurate T2 and T1ρ relaxometry analysis for monitoring and predicting MSK diseases. Deep learning methods have shown promising results for disease detection on quantitative MRI with diagnostic performance superior to conventional machine-learning methods for identifying knee osteoarthritis.
Collapse
Affiliation(s)
- Fang Liu
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| |
Collapse
|
36
|
Roemer FW, Demehri S, Omoumi P, Link TM, Kijowski R, Saarakkala S, Crema MD, Guermazi A. State of the Art: Imaging of Osteoarthritis—Revisited 2020. Radiology 2020; 296:5-21. [DOI: 10.1148/radiol.2020192498] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
|
37
|
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.
Collapse
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
| |
Collapse
|
38
|
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.
Collapse
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.
| |
Collapse
|
39
|
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: 42] [Impact Index Per Article: 10.5] [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.
Collapse
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
| |
Collapse
|
40
|
Kijowski R, Demehri S, Roemer F, Guermazi A. Osteoarthritis year in review 2019: imaging. Osteoarthritis Cartilage 2020; 28:285-295. [PMID: 31877380 DOI: 10.1016/j.joca.2019.11.009] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 10/17/2019] [Accepted: 11/15/2019] [Indexed: 02/02/2023]
Abstract
OBJECTIVE To provide a narrative review of original articles on osteoarthritis (OA) imaging published between April 1, 2018 and March 30, 2019. METHODS All original research articles on OA imaging published in English between April 1, 2018 and March 30, 2019 were identified using a PubMed database search. The search terms of "Osteoarthritis" or "OA" were combined with the search terms "Radiography", "X-Rays", "Magnetic Resonance Imaging", "MRI", "Ultrasound", "US", "Computed Tomography", "Dual Energy X-Ray Absorptiometry", "DXA", "DEXA", "CT", "Nuclear Medicine", "Scintigraphy", "Single-Photon Emission Computed Tomography", "SPECT", "Positron Emission Tomography", "PET", "PET-CT", or "PET-MRI". Articles were reviewed to determine relevance based upon the following criteria: 1) study involved human subjects with OA or risk factors for OA and 2) study involved imaging to evaluate OA disease status or OA treatment response. Relevant articles were ranked according to scientific merit, with the best publications selected for inclusion in the narrative report. RESULTS The PubMed search revealed a total of 1257 articles, of which 256 (20.4%) were considered relevant to OA imaging. Two-hundred twenty-six (87.1%) articles involved the knee joint, while 195 (76.2%) articles involved the use of magnetic resonance imaging (MRI). The proportion of published studies involving the use of MRI was higher than previous years. An increasing number of articles were also published on imaging of subjects with joint injury and on deep learning application in OA imaging. CONCLUSION MRI and other imaging modalities continue to play an important role in research studies designed to better understand the pathogenesis, progression, and treatment of OA.
Collapse
Affiliation(s)
- R Kijowski
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA.
| | - S Demehri
- Department of Radiology, Johns Hopkins University, Baltimore, MD, USA.
| | - F Roemer
- Department of Radiology, Boston University, Boston, MA, USA; Department of Radiology, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Universitätsklinikum Erlangen, Erlangen, Germany.
| | - A Guermazi
- Department of Radiology, Boston University, Boston, MA, USA.
| |
Collapse
|
41
|
Chaudhari AS, Stevens KJ, Sveinsson B, Wood JP, Beaulieu CF, Oei EH, Rosenberg JK, Kogan F, Alley MT, Gold GE, Hargreaves BA. Combined 5-minute double-echo in steady-state with separated echoes and 2-minute proton-density-weighted 2D FSE sequence for comprehensive whole-joint knee MRI assessment. J Magn Reson Imaging 2019; 49:e183-e194. [PMID: 30582251 PMCID: PMC7850298 DOI: 10.1002/jmri.26582] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Accepted: 11/01/2018] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Clinical knee MRI protocols require upwards of 15 minutes of scan time. PURPOSE/HYPOTHESIS To compare the imaging appearance of knee abnormalities depicted with a 5-minute 3D double-echo in steady-state (DESS) sequence with separate echo images, with that of a routine clinical knee MRI protocol. A secondary goal was to compare the imaging appearance of knee abnormalities depicted with 5-minute DESS paired with a 2-minute coronal proton-density fat-saturated (PDFS) sequence. STUDY TYPE Prospective. SUBJECTS Thirty-six consecutive patients (19 male) referred for a routine knee MRI. FIELD STRENGTH/SEQUENCES DESS and PDFS at 3T. ASSESSMENT Five musculoskeletal radiologists evaluated all images for the presence of internal knee derangement using DESS, DESS+PDFS, and the conventional imaging protocol, and their associated diagnostic confidence of the reading. STATISTICAL TESTS Differences in positive and negative percent agreement (PPA and NPA, respectively) and 95% confidence intervals (CIs) for DESS and DESS+PDFS compared with the conventional protocol were calculated and tested using exact McNemar tests. The percentage of observations where DESS or DESS+PDFS had equivalent confidence ratings to DESS+Conv were tested with exact symmetry tests. Interreader agreement was calculated using Krippendorff's alpha. RESULTS DESS had a PPA of 90% (88-92% CI) and NPA of 99% (99-99% CI). DESS+PDFS had increased PPA of 99% (95-99% CI) and NPA of 100% (99-100% CI) compared with DESS (both P < 0.001). DESS had equivalent diagnostic confidence to DESS+Conv in 94% of findings, whereas DESS+PDFS had equivalent diagnostic confidence in 99% of findings (both P < 0.001). All readers had moderate concordance for all three protocols (Krippendorff's alpha 47-48%). DATA CONCLUSION Both 1) 5-minute 3D-DESS with separated echoes and 2) 5-minute 3D-DESS paired with a 2-minute coronal PDFS sequence depicted knee abnormalities similarly to a routine clinical knee MRI protocol, which may be a promising technique for abbreviated knee MRI. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.
Collapse
Affiliation(s)
- Akshay S. Chaudhari
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Kathryn J. Stevens
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
| | - Bragi Sveinsson
- Department of Radiology, Stanford University, Stanford, California, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Jeff P. Wood
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Christopher F. Beaulieu
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
| | - Edwin H.G. Oei
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | | | - Feliks Kogan
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Marcus T. Alley
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Garry E. Gold
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, 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
| |
Collapse
|
42
|
Tamir JI, Taviani V, Alley MT, Perkins B, Hart L, Obrien K, Wishah F, Sandberg JK, Anderson MJ, Turek JS, Willke TL, Lustig M, Vasanawala SS. Targeted rapid knee MRI exam using T 2 shuffling. J Magn Reson Imaging 2019; 49:e195-e204. [PMID: 30637847 PMCID: PMC6551292 DOI: 10.1002/jmri.26600] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Revised: 11/18/2018] [Accepted: 11/19/2018] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND MRI is commonly used to evaluate pediatric musculoskeletal pathologies, but same-day/near-term scheduling and short exams remain challenges. PURPOSE To investigate the feasibility of a targeted rapid pediatric knee MRI exam, with the goal of reducing cost and enabling same-day MRI access. STUDY TYPE A cost effectiveness study done prospectively. SUBJECTS Forty-seven pediatric patients. FIELD STRENGTH/SEQUENCE 3T. The 10-minute protocol was based on T2 Shuffling, a four-dimensional acquisition and reconstruction of images with variable T2 contrast, and a T1 2D fast spin-echo (FSE) sequence. A distributed, compressed sensing-based reconstruction was implemented on a four-node high-performance compute cluster and integrated into the clinical workflow. ASSESSMENT In an Institutional Review Board-approved study with informed consent/assent, we implemented a targeted pediatric knee MRI exam for assessing pediatric knee pain. Pediatric patients were subselected for the exam based on insurance plan and clinical indication. Over a 2-year period, 47 subjects were recruited for the study and 49 MRIs were ordered. Date and time information was recorded for MRI referral, registration, and completion. Image quality was assessed from 0 (nondiagnostic) to 5 (outstanding) by two readers, and consensus was subsequently reached. STATISTICAL TESTS A Wilcoxon rank-sum test assessed the null hypothesis that the targeted exam times compared with conventional knee exam times were unchanged. RESULTS Of the 49 cases, 20 were completed on the same day as exam referral. Median time from registration to exam completion was 18.7 minutes. Median reconstruction time for T2 Shuffling was reduced from 18.9 minutes to 95 seconds using the distributed implementation. Technical fees charged for the targeted exam were one-third that of the routine clinical knee exam. No subject had to return for additional imaging. DATA CONCLUSION The targeted knee MRI exam is feasible and reduces the imaging time, cost, and barrier to same-day MRI access for pediatric patients. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 6 J. Magn. Reson. Imaging 2019.
Collapse
Affiliation(s)
- Jonathan I. Tamir
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA
| | - Valentina Taviani
- Global Applied Science Laboratory, GE Healthcare, Menlo Park, California, USA
| | - Marcus T. Alley
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Becki Perkins
- Department of Radiology, Lucile Packard Children’s Hospital, Stanford, California, USA
| | - Lori Hart
- Department of Radiology, Lucile Packard Children’s Hospital, Stanford, California, USA
| | - Kendall Obrien
- Department of Radiology, Lucile Packard Children’s Hospital, Stanford, California, USA
| | - Fidaa Wishah
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Jesse K Sandberg
- Department of Radiology, Stanford University, Stanford, California, USA
| | | | - Javier S. Turek
- Brain-Inspired Computing Lab, Intel Labs, Hillsboro, Oregon, USA
| | | | - Michael Lustig
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA
| | | |
Collapse
|
43
|
Chaudhari AS, Fang Z, Kogan F, Wood J, Stevens KJ, Gibbons EK, Lee JH, Gold GE, Hargreaves BA. Super-resolution musculoskeletal MRI using deep learning. Magn Reson Med 2018; 80:2139-2154. [PMID: 29582464 PMCID: PMC6107420 DOI: 10.1002/mrm.27178] [Citation(s) in RCA: 196] [Impact Index Per Article: 32.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2017] [Revised: 02/14/2018] [Accepted: 02/22/2018] [Indexed: 12/22/2022]
Abstract
PURPOSE To develop a super-resolution technique using convolutional neural networks for generating thin-slice knee MR images from thicker input slices, and compare this method with alternative through-plane interpolation methods. METHODS We implemented a 3D convolutional neural network entitled DeepResolve to learn residual-based transformations between high-resolution thin-slice images and lower-resolution thick-slice images at the same center locations. DeepResolve was trained using 124 double echo in steady-state (DESS) data sets with 0.7-mm slice thickness and tested on 17 patients. Ground-truth images were compared with DeepResolve, clinically used tricubic interpolation, and Fourier interpolation methods, along with state-of-the-art single-image sparse-coding super-resolution. Comparisons were performed using structural similarity, peak SNR, and RMS error image quality metrics for a multitude of thin-slice downsampling factors. Two musculoskeletal radiologists ranked the 3 data sets and reviewed the diagnostic quality of the DeepResolve, tricubic interpolation, and ground-truth images for sharpness, contrast, artifacts, SNR, and overall diagnostic quality. Mann-Whitney U tests evaluated differences among the quantitative image metrics, reader scores, and rankings. Cohen's Kappa (κ) evaluated interreader reliability. RESULTS DeepResolve had significantly better structural similarity, peak SNR, and RMS error than tricubic interpolation, Fourier interpolation, and sparse-coding super-resolution for all downsampling factors (p < .05, except 4 × and 8 × sparse-coding super-resolution downsampling factors). In the reader study, DeepResolve significantly outperformed (p < .01) tricubic interpolation in all image quality categories and overall image ranking. Both readers had substantial scoring agreement (κ = 0.73). CONCLUSION DeepResolve was capable of resolving high-resolution thin-slice knee MRI from lower-resolution thicker slices, achieving superior quantitative and qualitative diagnostic performance to both conventionally used and state-of-the-art methods.
Collapse
Affiliation(s)
- Akshay S. Chaudhari
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | | | - Feliks Kogan
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Jeff Wood
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Kathryn J Stevens
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
| | - Eric K. Gibbons
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Jin Hyung Lee
- Department of Bioengineering, Stanford University, Stanford, California, USA
- LVIS Corporation, Palo Alto, California, USA
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Garry E. Gold
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, 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
| |
Collapse
|
44
|
van Beek EJR, Kuhl C, Anzai Y, Desmond P, Ehman RL, Gong Q, Gold G, Gulani V, Hall-Craggs M, Leiner T, Lim CCT, Pipe JG, Reeder S, Reinhold C, Smits M, Sodickson DK, Tempany C, Vargas HA, Wang M. Value of MRI in medicine: More than just another test? J Magn Reson Imaging 2018; 49:e14-e25. [PMID: 30145852 DOI: 10.1002/jmri.26211] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Accepted: 05/16/2018] [Indexed: 02/06/2023] Open
Abstract
There is increasing scrutiny from healthcare organizations towards the utility and associated costs of imaging. MRI has traditionally been used as a high-end modality, and although shown extremely important for many types of clinical scenarios, it has been suggested as too expensive by some. This editorial will try and explain how value should be addressed and gives some insights and practical examples of how value of MRI can be increased. It requires a global effort to increase accessibility, value for money, and impact on patient management. We hope this editorial sheds some light and gives some indications of where the field may wish to address some of its research to proactively demonstrate the value of MRI. Level of Evidence: 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019;49:e14-e25.
Collapse
Affiliation(s)
| | - Christiane Kuhl
- Department of Diagnostic and Interventional Radiology, University of Aachen, Aachen, Germany
| | - Yoshimi Anzai
- Department of Radiology, University of Utah, Salt Lake City, Utah, USA
| | - Patricia Desmond
- Royal Melbourne Hospital, University of Melbourne, Melbourne, Australia
| | - Richard L Ehman
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Garry Gold
- Department of Radiology, Engineering and Orthopaedic Surgery, Stanford University, Stanford, California, USA
| | - Vikas Gulani
- Departments of Radiology, Urology and Biomedical Imaging, Case Western Reserve University, University Hospitals of Cleveland, Cleveland, Ohio, USA
| | - Margaret Hall-Craggs
- Department of Medical Imaging and Radiology, University College Hospital NHS Trust, London, UK
| | - Tim Leiner
- Department of Radiology and Nuclear Medicine, University Medical Centre, Utrecht, The Netherlands
| | - C C Tschoyoson Lim
- Department of Neuroradiology, National Neuroscience Institute and Duke NUS Medical School, Singapore, Singapore
| | - James G Pipe
- Department of Imaging Research, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Scott Reeder
- Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine and Emergency Medicine, University of Madison, Madison, Wisconsin, USA
| | - Caroline Reinhold
- Department of Radiology, McGill University Health Center, Montreal, Canada
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Daniel K Sodickson
- Department of Radiology, New York University Langone Health, New York, New York, USA
| | - Clare Tempany
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - H Alberto Vargas
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Meiyun Wang
- Department of Radiology, Henan Provincial People's Hospital, Zhengzhou, Henan, China
| |
Collapse
|
45
|
Fritz J, Ahlawat S, Fritz B, Thawait GK, Stern SE, Raithel E, Klyce W, Lee RJ. 10‐Min 3D Turbo Spin Echo MRI of the Knee in Children: Arthroscopy‐Validated Accuracy for the Diagnosis of Internal Derangement. J Magn Reson Imaging 2018; 49:e139-e151. [DOI: 10.1002/jmri.26241] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2018] [Accepted: 06/11/2018] [Indexed: 12/14/2022] Open
Affiliation(s)
- Jan Fritz
- Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of Medicine Baltimore Maryland USA
| | - Shivani Ahlawat
- Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of Medicine Baltimore Maryland USA
| | - Benjamin Fritz
- RadiologyBalgrist University Hospital Zurich Switzerland
- Faculty of MedicineUniversity of Zurich Zurich Switzerland
| | - Gaurav K. Thawait
- Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of Medicine Baltimore Maryland USA
| | - Steven E. Stern
- Bond Business SchoolBond University Gold Coast QLD Australia
| | | | - Walter Klyce
- Department of Orthopaedic SurgeryJohns Hopkins University School of Medicine Baltimore Maryland USA
| | - Rushyuan J. Lee
- Department of Orthopaedic SurgeryJohns Hopkins University School of Medicine Baltimore Maryland USA
| |
Collapse
|
46
|
Chaudhari A, Fang Z, Hyung Lee J, Gold G, Hargreaves B. Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging. MACHINE LEARNING FOR MEDICAL IMAGE RECONSTRUCTION 2018. [DOI: 10.1007/978-3-030-00129-2_1] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
|