Semin Musculoskelet Radiol 2021; 25(03): 468-479
DOI: 10.1055/s-0041-1730911
Review Article

3D MRI in Osteoarthritis

Edwin H.G. Oei
1   Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
,
Tijmen A. van Zadelhoff
1   Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
,
Susanne M. Eijgenraam
1   Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
,
Stefan Klein
1   Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
,
Jukka Hirvasniemi
1   Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
,
Rianne A. van der Heijden
1   Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
› Author Affiliations

Abstract

Osteoarthritis (OA) is among the top 10 burdensome diseases, with the knee the most affected joint. Magnetic resonance imaging (MRI) allows whole-knee assessment, making it ideally suited for imaging OA, considered a multitissue disease. Three-dimensional (3D) MRI enables the comprehensive assessment of OA, including quantitative morphometry of various joint tissues. Manual tissue segmentation on 3D MRI is challenging but may be overcome by advanced automated image analysis methods including artificial intelligence (AI). This review presents examples of the utility of 3D MRI for knee OA, focusing on the articular cartilage, bone, meniscus, synovium, and infrapatellar fat pad, and it highlights several applications of AI that facilitate segmentation, lesion detection, and disease classification.



Publication History

Article published online:
21 September 2021

© 2021. Thieme. All rights reserved.

Thieme Medical Publishers, Inc.
333 Seventh Avenue, 18th Floor, New York, NY 10001, USA

 
  • References

  • 1 Hunter DJ, Bierma-Zeinstra S. Osteoarthritis. Lancet 2019; 393 (10182): 1745-1759
  • 2 Deveza LA, Loeser RF. Is osteoarthritis one disease or a collection of many?. Rheumatology (Oxford) 2018; 57 (Suppl. 04) iv34-iv42
  • 3 Berenbaum F. Osteoarthritis as an inflammatory disease (osteoarthritis is not osteoarthrosis!). Osteoarthritis Cartilage 2013; 21 (01) 16-21
  • 4 Oei EH, van Tiel J, Robinson WH, Gold GE. Quantitative radiologic imaging techniques for articular cartilage composition: toward early diagnosis and development of disease-modifying therapeutics for osteoarthritis. Arthritis Care Res (Hoboken) 2014; 66 (08) 1129-1141
  • 5 Hunter DJ, Guermazi A, Lo GH. et al. Evolution of semi-quantitative whole joint assessment of knee OA: MOAKS (MRI Osteoarthritis Knee Score). Osteoarthritis Cartilage 2011; 19 (08) 990-1002
  • 6 Pedoia V, Norman B, Mehany SN, Bucknor MD, Link TM, Majumdar S. 3D convolutional neural networks for detection and severity staging of meniscus and PFJ cartilage morphological degenerative changes in osteoarthritis and anterior cruciate ligament subjects. J Magn Reson Imaging 2019; 49 (02) 400-410
  • 7 Bonaretti S, Gold GE, Beaupre GS. pyKNEEr: An image analysis workflow for open and reproducible research on femoral knee cartilage. PLoS One 2020; 15 (01) e0226501
  • 8 Desai AD, Caliva F, Iriondo C. et al. The international workshop on osteoarthritis imaging knee MRI segmentation challenge: a multi-institute evaluation and analysis framework on a standardized dataset. Radiol Artif Intell 2021; 3 (03) e200078
  • 9 Pan F, Tian J, Mattap SM, Cicuttini F, Jones G. Association between metabolic syndrome and knee structural change on MRI. Rheumatology (Oxford) 2020; 59 (01) 185-193
  • 10 Haj-Mirzaian A, Guermazi A, Hafezi-Nejad N. et al. Superolateral Hoffa's fat pad (SHFP) oedema and patellar cartilage volume loss: quantitative analysis using longitudinal data from the Foundation for the National Institute of Health (FNIH) Osteoarthritis Biomarkers Consortium. Eur Radiol 2018; 28 (10) 4134-4145
  • 11 Cai G, Aitken D, Laslett LL. et al. Effect of intravenous zoledronic acid on tibiofemoral cartilage volume among patients with knee osteoarthritis with bone marrow lesions: a randomized clinical trial. JAMA 2020; 323 (15) 1456-1466
  • 12 McAlindon TE, LaValley MP, Harvey WF. et al. Effect of intra-articular triamcinolone vs saline on knee cartilage volume and pain in patients with knee osteoarthritis: a randomized clinical trial. JAMA 2017; 317 (19) 1967-1975
  • 13 Runhaar J, Dam EB, Oei EHG, Bierma-Zeinstra SMA. Medial cartilage surface integrity as a surrogate measure for incident radiographic knee osteoarthritis following weight changes. Cartilage 2019; December 12 (Epub ahead of print)
  • 14 Colotti R, Omoumi P, Bonanno G, Ledoux JB, van Heeswijk RB. Isotropic three-dimensional T2 mapping of knee cartilage: development and validation. J Magn Reson Imaging 2018; 47 (02) 362-371
  • 15 Eijgenraam SM, Chaudhari AS, Reijman M. et al. Time-saving opportunities in knee osteoarthritis: T2 mapping and structural imaging of the knee using a single 5-min MRI scan. Eur Radiol 2020; 30 (04) 2231-2240
  • 16 Weinans H, Siebelt M, Agricola R, Botter SM, Piscaer TM, Waarsing JH. Pathophysiology of peri-articular bone changes in osteoarthritis. Bone 2012; 51 (02) 190-196
  • 17 Roemer FW, Kwoh CK, Hayashi D, Felson DT, Guermazi A. The role of radiography and MRI for eligibility assessment in DMOAD trials of knee OA. Nat Rev Rheumatol 2018; 14 (06) 372-380
  • 18 Guermazi A, Niu J, Hayashi D. et al. Prevalence of abnormalities in knees detected by MRI in adults without knee osteoarthritis: population based observational study (Framingham Osteoarthritis Study). BMJ 2012; 345: e5339
  • 19 Bowes MA, Kacena K, Alabas OA. et al. Machine-learning, MRI bone shape and important clinical outcomes in osteoarthritis: data from the Osteoarthritis Initiative. Ann Rheum Dis 2020; 80 (04) 502-508
  • 20 MacKay JW, Kapoor G, Driban JB. et al. Association of subchondral bone texture on magnetic resonance imaging with radiographic knee osteoarthritis progression: data from the Osteoarthritis Initiative Bone Ancillary Study. Eur Radiol 2018; 28 (11) 4687-4695
  • 21 Ambellan F, Tack A, Ehlke M, Zachow S. Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: data from the Osteoarthritis Initiative. Med Image Anal 2019; 52: 109-118
  • 22 Kessler DA, MacKay JW, Crowe VA. et al. The optimisation of deep neural networks for segmenting multiple knee joint tissues from MRIs. Comput Med Imaging Graph 2020; 86: 101793
  • 23 Morales Martinez A, Caliva F, Flament I. et al. Learning osteoarthritis imaging biomarkers from bone surface spherical encoding. Magn Reson Med 2020; 84 (04) 2190-2203
  • 24 Deniz CM, Xiang S, Hallyburton RS. et al. Segmentation of the proximal femur from MR images using deep convolutional neural networks. Sci Rep 2018; 8 (01) 16485
  • 25 Cantarelli Rodrigues T, Deniz CM, Alaia EF. et al. Three-dimensional MRI bone models of the glenohumeral joint using deep learning: evaluation of normal anatomy and glenoid bone loss. Radiol Artif Intell 2020; 2 (05) e190116
  • 26 Bowes MA, Vincent GR, Wolstenholme CB, Conaghan PG. A novel method for bone area measurement provides new insights into osteoarthritis and its progression. Ann Rheum Dis 2015; 74 (03) 519-525
  • 27 Neogi T, Bowes MA, Niu J. et al. Magnetic resonance imaging-based three-dimensional bone shape of the knee predicts onset of knee osteoarthritis: data from the osteoarthritis initiative. Arthritis Rheum 2013; 65 (08) 2048-2058
  • 28 Dube B, Bowes MA, Hensor EMA, Barr A, Kingsbury SR, Conaghan PG. The relationship between two different measures of osteoarthritis bone pathology, bone marrow lesions and 3D bone shape: data from the Osteoarthritis Initiative. Osteoarthritis Cartilage 2018; 26 (10) 1333-1337
  • 29 Inamdar G, Pedoia V, Rossi-Devries J. et al. MR study of longitudinal variations in proximal femur 3D morphological shape and associations with cartilage health in hip osteoarthritis. J Orthop Res 2019; 37 (01) 161-170
  • 30 Chaudhari AS, Stevens KJ, Sveinsson B. et al. 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 (07) e183-e194
  • 31 Crema MD, Nogueira-Barbosa MH, Roemer FW. et al. Three-dimensional turbo spin-echo magnetic resonance imaging (MRI) and semiquantitative assessment of knee osteoarthritis: comparison with two-dimensional routine MRI. Osteoarthritis Cartilage 2013; 21 (03) 428-433
  • 32 Roemer FW, Frobell R, Hunter DJ. et al. MRI-detected subchondral bone marrow signal alterations of the knee joint: terminology, imaging appearance, relevance and radiological differential diagnosis. Osteoarthritis Cartilage 2009; 17 (09) 1115-1131
  • 33 Yusuf E, Kortekaas MC, Watt I, Huizinga TWJ, Kloppenburg M. Do knee abnormalities visualised on MRI explain knee pain in knee osteoarthritis? A systematic review. Ann Rheum Dis 2011; 70 (01) 60-67
  • 34 Barr AJ, Campbell TM, Hopkinson D, Kingsbury SR, Bowes MA, Conaghan PG. A systematic review of the relationship between subchondral bone features, pain and structural pathology in peripheral joint osteoarthritis. Arthritis Res Ther 2015; 17 (01) 228
  • 35 Ratzlaff C, Guermazi A, Collins J. et al. A rapid, novel method of volumetric assessment of MRI-detected subchondral bone marrow lesions in knee osteoarthritis. Osteoarthritis Cartilage 2013; 21 (06) 806-814
  • 36 Astuto B, Flament I, Namiri NK. et al. Automatic deep learning assisted detection and grading of abnormalities in knee mri studies. Radiol Artif Intell 2021; 3 (03) e200165
  • 37 Fithian DC, Kelly MA, Mow VC. Material properties and structure-function relationships in the menisci. Clin Orthop Relat Res 1990; (252) 19-31
  • 38 Englund M, Roemer FW, Hayashi D, Crema MD, Guermazi A. Meniscus pathology, osteoarthritis and the treatment controversy. Nat Rev Rheumatol 2012; 8 (07) 412-419
  • 39 Xu D, van der Voet J, Hansson NM. et al. Association between meniscal volume and development of knee osteoarthritis. Rheumatology (Oxford) 2021; 60 (03) 1392-1399
  • 40 Rizk B, Brat H, Zille P. et al. Meniscal lesion detection and characterization in adult knee MRI: a deep learning model approach with external validation. Phys Med 2021; 83: 64-71
  • 41 Clockaerts S, Bastiaansen-Jenniskens YM, Runhaar J. et al. The infrapatellar fat pad should be considered as an active osteoarthritic joint tissue: a narrative review. Osteoarthritis Cartilage 2010; 18 (07) 876-882
  • 42 Felson DT. The sources of pain in knee osteoarthritis. Curr Opin Rheumatol 2005; 17 (05) 624-628
  • 43 Macchi V, Stocco E, Stecco C. et al. The infrapatellar fat pad and the synovial membrane: an anatomo-functional unit. J Anat 2018; 233 (02) 146-154
  • 44 Greif DN, Kouroupis D, Murdock CJ. et al. Infrapatellar fat pad/synovium complex in early-stage knee osteoarthritis: potential new target and source of therapeutic mesenchymal stem/stromal cells. Front Bioeng Biotechnol 2020; 8: 860
  • 45 Shakoor D, Demehri S, Roemer FW, Loeuille D, Felson DT, Guermazi A. Are contrast-enhanced and non-contrast MRI findings reflecting synovial inflammation in knee osteoarthritis: a meta-analysis of observational studies. Osteoarthritis Cartilage 2020; 28 (02) 126-136
  • 46 Davis JE, Ward RJ, MacKay JW. et al. Effusion-synovitis and infrapatellar fat pad signal intensity alteration differentiate accelerated knee osteoarthritis. Rheumatology (Oxford) 2019; 58 (03) 418-426
  • 47 de Vries BA, Breda SJ, Sveinsson B. et al. Detection of knee synovitis using non-contrast-enhanced qDESS compared with contrast-enhanced MRI. Arthritis Res Ther 2021; 23 (01) 55
  • 48 Riis RG, Gudbergsen H, Simonsen O. et al. The association between histological, macroscopic and magnetic resonance imaging assessed synovitis in end-stage knee osteoarthritis: a cross-sectional study. Osteoarthritis Cartilage 2017; 25 (02) 272-280
  • 49 Tofts PS, Brix G, Buckley DL. et al. Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging 1999; 10 (03) 223-232
  • 50 de Vries BA, van der Heijden RA, Poot DHJ. et al. Quantitative DCE-MRI demonstrates increased blood perfusion in Hoffa's fat pad signal abnormalities in knee osteoarthritis, but not in patellofemoral pain. Eur Radiol 2020; 30 (06) 3401-3408
  • 51 MacKay JW, Nezhad FS, Rifai T. et al. Dynamic contrast-enhanced MRI of synovitis in knee osteoarthritis: repeatability, discrimination and sensitivity to change in a prospective experimental study. Eur Radiol 2021
  • 52 Daugaard CL, Henriksen M, Riis RGC. et al. The impact of a significant weight loss on inflammation assessed on DCE-MRI and static MRI in knee osteoarthritis: a prospective cohort study. Osteoarthritis Cartilage 2020; 28 (06) 766-773
  • 53 Perry TA, Gait A, O'Neill TW. et al. Measurement of synovial tissue volume in knee osteoarthritis using a semiautomated MRI-based quantitative approach. Magn Reson Med 2019; 81 (05) 3056-3064
  • 54 O'Neill TW, Parkes MJ, Maricar N. et al. Synovial tissue volume: a treatment target in knee osteoarthritis (OA). Ann Rheum Dis 2016; 75 (01) 84-90
  • 55 Gait AD, Hodgson R, Parkes MJ. et al. Synovial volume vs synovial measurements from dynamic contrast enhanced MRI as measures of response in osteoarthritis. Osteoarthritis Cartilage 2016; 24 (08) 1392-1398
  • 56 van der Heijden RA, de Vries BA, Poot DHJ. et al. Quantitative volume and dynamic contrast-enhanced MRI derived perfusion of the infrapatellar fat pad in patellofemoral pain. Quant Imaging Med Surg 2021; 11 (01) 133-142
  • 57 Chang GH, Felson DT, Qiu S, Guermazi A, Capellini TD, Kolachalama VB. Assessment of knee pain from MR imaging using a convolutional Siamese network. Eur Radiol 2020; 30 (06) 3538-3548
  • 58 Tolpadi AA, Lee JJ, Pedoia V, Majumdar S. Deep learning predicts total knee replacement from magnetic resonance images. Sci Rep 2020; 10 (01) 6371