- Participants understand the application of machine learning and deep learning in AI applied in imaging modalities. The integration of AI in the daily routine will be a prerequisite for the further usability of digitized modalities.
8.3.1 - MRI News - Mocart 2.0
Since the first introduction of the MOCART score, cartilage repair techniques and MR imaging have undergone significant change. Novel, scaffold-based surgical treatment options render the assessment of subchondral bone more important. At the same time, other aspects, such as the assessment of adhesions, which were formerly frequent complications with periosteal flaps, became less relevant due to the introduction of second-generation ACI. Furthermore, the continuous development of new MR sequences and improved MR hardware - in particular, MR scanners that operate at high field-strengths (3 Tesla) and better coil configuration (phased array designs) have improved routine clinical MR examinations and MR protocols over the last decade. These developments will be addressed with this update to the MOCART score.
Furthermore, we recognized that the linguistically defined categories of the original MOCART score may be interpreted in different ways by different readers, thus introducing variability and decreasing interrater reliability.
Hence, the aim of this study was to develop an incremental update to the original MOCART score for the assessment of cartilage repair of the knee joint, which would account for the above-mentioned advancements and address issues identified in the clinical routine. Intra- and inter-rater reliability should be evaluated by expert readers.
Materials and Methods: The degree of defect filling is now assessed in 25% increments, with hypertrophic filling of up to 150% receiving the same scoring as complete repair. Integration now assesses only the integration to neighboring cartilage, and the severity of surface damage is assessed in reference to cartilage repair length rather than depth. The signal intensity of the repair tissue is scored as minor abnormal or severely abnormal on a proton density-weighted TSE sequence only and differentiates between hyperintene and hypointense signal alterations. The assessment of the variables “subchondral lamina”, “adhesions” and “synovitis” was removed and the points were reallocated to the newly introduced variable “bony defect or bony overgrowth”. The variable “subchondral bone” was renamed to “subchondral changes” and assesses minor and severe edema-like marrow signal, as well as subchondral cysts or osteonecrosis-like signal. Overall, a MOCART score ranging from 0 to 100 points may be reached. Four independent readers (two expert readers and two radiology residents with limited experience) assessed the 3 Tesla MRI examinations of 24 patients, who had undergone cartilage repair of a femoral cartilage defect using the new MOCART 2.0 knee score. One of the expert readers and both inexperienced readers performed two readings, separated by a four-week interval. For the inexperienced readers, the first reading was based on the evaluation sheet only. For the second reading, a newly introduced atlas was used as an additional reference. Interrater reliability was assessed using intraclass correlation coefficients (ICCs). ICCs were interpreted according to the criteria of Landis and Koch.
Results: The overall intra-rater (ICC = 0.88, p < 0.001) as well as the inter-rater (ICC = 0.84, p < 0.001) reliability of the expert readers was almost perfect. Based on the evaluation sheet of the MOCART 2.0 knee score, the overall inter-rater reliability of the inexperienced readers compared to expert reader 1 was moderate (ICC = 0.45, p < 0.01), ranging from poor (structure: ICC=0.15, p = 0.09) to substantial (“bony defect or overgrowth”: ICC = 0.65, p < 0.001) for different variables. With the additional use of the atlas, the overall inter-rater reliability of the inexperienced readers was substantial (ICC = 0.63, p < 0.001), ranging from moderate (signal: ICC = 0.42, p < 0.01) to substantial (integration ICC = 0.73, p < 0.001).
Conclusions: The MOCART 2.0 knee score was updated to account for important changes in the past decade and demonstrates almost-perfect inter- and intra-rater reliability in expert readers. In inexperienced readers use of the atlas may improve inter-rater reliability, and thus, increase the comparability of results across studies.
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2. Trattnig S, Winalski CS, Marlovits S, Jurvelin JS, Welsch GH, Potter HG. Magnetic Resonance Imaging of Cartilage Repair: A Review. Cartilage. 2011 Jan;2(1):5-26.
3. Trattnig S, Domayer S, Welsch GW, Mosher T, Eckstein F. MR imaging of cartilage and its repair in the knee--a review. Eur Radiol. 2009 Jul;19(7):1582-94.
4. Marlovits S, Singer P, Zeller P, Mandl I, Haller J, Trattnig S. Magnetic resonance observation of cartilage repair tissue (MOCART) for the evaluation of autologous chondrocyte transplantation: determination of interobserver variability and correlation to clinical outcome after 2 years. Eur J Radiol. 2006 Jan;57(1):16-23.
5. Marlovits S, Striessnig G, Resinger CT, Aldrian SM, Vecsei V, Imhof H, et al. Definition of pertinent parameters for the evaluation of articular cartilage repair tissue with high-resolution magnetic resonance imaging. Eur J Radiol. 2004 Dec;52(3):310-9. Epub 2004/11/17.
6. Trattnig S, Ba-Ssalamah A, Pinker K, Plank C, Vecsei V, Marlovits S. Matrix-based autologous chondrocyte implantation for cartilage repair: noninvasive monitoring by high-resolution magnetic resonance imaging. Magn Reson Imaging. 2005 Sep;23(7):779-87. Epub 2005/10/11.
7. Aldrian S, Zak L, Wondrasch B, Albrecht C, Stelzeneder B, Binder H, et al. Clinical and radiological long-term outcomes after matrix-induced autologous chondrocyte transplantation: a prospective follow-up at a minimum of 10 years. Am J Sports Med. 2014 Nov;42(11):2680-8. Epub 2014/09/11.
8. Anderson DE, Williams RJ, 3rd, DeBerardino TM, Taylor DC, Ma CB, Kane MS, et al. Magnetic Resonance Imaging Characterization and Clinical Outcomes After NeoCart Surgical Therapy as a Primary Reparative Treatment for Knee Cartilage Injuries. Am J Sports Med. 2017 Jan 01:363546516677255.
9. Filardo G, Kon E, Di Martino A, Busacca M, Altadonna G, Marcacci M. Treatment of knee osteochondritis dissecans with a cell-free biomimetic osteochondral scaffold: clinical and imaging evaluation at 2-year follow-up. Am J Sports Med. 2013 Aug;41(8):1786-93. Epub 2013/06/14.
10. Niemeyer P, Laute V, John T, Becher C, Diehl P, Kolombe T, et al. The Effect of Cell Dose on the Early Magnetic Resonance Morphological Outcomes of Autologous Cell Implantation for Articular Cartilage Defects in the Knee: A Randomized Clinical Trial. Am J Sports Med. 2016 Aug;44(8):2005-14. Epub 2016/05/22.
11. Christensen BB, Foldager CB, Jensen J, Jensen NC, Lind M. Poor osteochondral repair by a biomimetic collagen scaffold: 1- to 3-year clinical and radiological follow-up. Knee Surg Sports Traumatol Arthrosc. 2015 Feb 18. Epub 2015/02/19.
12. Moradi B, Schonit E, Nierhoff C, Hagmann S, Oberle D, Gotterbarm T, et al. First-generation autologous chondrocyte implantation in patients with cartilage defects of the knee: 7 to 14 years' clinical and magnetic resonance imaging follow-up evaluation. Arthroscopy. 2012 Dec;28(12):1851-61.
13. Welsch GH, Mamisch TC, Zak L, Mauerer A, Apprich S, Stelzeneder D, et al. Morphological and biochemical T2 evaluation of cartilage repair tissue based on a hybrid double echo at steady state (DESS-T2d) approach. J Magn Reson Imaging. 2011 Oct;34(4):895-903.
14. Roemer FW, Guermazi A, Trattnig S, Apprich S, Marlovits S, Niu J, et al. Whole joint MRI assessment of surgical cartilage repair of the knee: cartilage repair osteoarthritis knee score (CROAKS). Osteoarthritis Cartilage. 2014 Jun;22(6):779-99.
15. Roemer FW, Crema MD, Trattnig S, Guermazi A. Advances in imaging of osteoarthritis and cartilage. Radiology. 2011 Aug;260(2):332-54. Epub 2011/07/23.
16. Springer E, Bohndorf K, Juras V, Szomolanyi P, Zbyn S, Schreiner MM, et al. Comparison of Routine Knee Magnetic Resonance Imaging at 3 T and 7 T. Invest Radiol. 2017 Jan;52(1):42-54.
17. Kreuz PC, Steinwachs M, Erggelet C, Krause SJ, Ossendorf C, Maier D, et al. Classification of graft hypertrophy after autologous chondrocyte implantation of full-thickness chondral defects in the knee. Osteoarthritis Cartilage. 2007 Dec;15(12):1339-47. Epub 2007/07/17.
18. Siebold R, Suezer F, Schmitt B, Trattnig S, Essig M. Good clinical and MRI outcome after arthroscopic autologous chondrocyte implantation for cartilage repair in the knee. Knee Surg Sports Traumatol Arthrosc. 2018 Mar;26(3):831-9. Epub 2017/03/05.
19. Trattnig S, Ohel K, Mlynarik V, Juras V, Zbyn S, Korner A. Morphological and compositional monitoring of a new cell-free cartilage repair hydrogel technology - GelrinC by MR using semi-quantitative MOCART scoring and quantitative T2 index and new zonal T2 index calculation. Osteoarthritis Cartilage. 2015 Jul 14. Epub 2015/07/19.
20. Mlynarik V, Szomolanyi P, Toffanin R, Vittur F, Trattnig S. Transverse relaxation mechanisms in articular cartilage. J Magn Reson. 2004 Aug;169(2):300-7. Epub 2004/07/21.
21. Trattnig S, Ohel K, Mlynarik V, Juras V, Zbyn S, Korner A. Morphological and compositional monitoring of a new cell-free cartilage repair hydrogel technology - GelrinC by MR using semi-quantitative MOCART scoring and quantitative T2 index and new zonal T2 index calculation. Osteoarthritis Cartilage. 2015 Dec;23(12):2224-32.
22. Welsch GH, Zak L, Mamisch TC, Resinger C, Marlovits S, Trattnig S. Three-dimensional magnetic resonance observation of cartilage repair tissue (MOCART) score assessed with an isotropic three-dimensional true fast imaging with steady-state precession sequence at 3.0 Tesla. Invest Radiol. 2009 Sep;44(9):603-12. Epub 2009/08/21.
23. Welsch GH, Zak L, Mamisch TC, Paul D, Lauer L, Mauerer A, et al. Advanced morphological 3D magnetic resonance observation of cartilage repair tissue (MOCART) scoring using a new isotropic 3D proton-density, turbo spin echo sequence with variable flip angle distribution (PD-SPACE) compared to an isotropic 3D steady-state free precession sequence (True-FISP) and standard 2D sequences. J Magn Reson Imaging. 2011 Jan;33(1):180-8. Epub 2010/12/25.
8.3.2 - AI in OA Diagnosis
Introduction Radiographic classification of osteoarthritis (OA) in the knee has typically been performed using semi-quantitative grading schemes 1, the most widely used of which being the Kellgren-Lawrence (KL) scale 2 which was recognized by the World Health Organization in 1961 as the standard for clinical studies of OA. The KL grading scheme requires the assessment of presence and severity degree of several individual radiographic features (IRFs), including osteophytes, sclerosis, and joint space narrowing. These assessments are them summarized into a 5 point scale, reflecting the severity of OA. However, the KL grading scheme has come under criticism for assuming a unique progression mode of OA 3 and for depending on subjective assessments 4,5, exacerbated by the vague verbal definitions of individual radiographic features at each stage 6. In order to deal with these issues, the Osteoarthritis Research Society International (OARSI) proposed a classification system for each of the IRFs supported by a reference atlas, in which canonical examples of the classification of each of the IRFs are depicted 7.
In a first study (Part A) Joint Space Width has been the gold standard to assess loss of cartilage in knee OA. Here we describe a novel quantitative measure of joint space width: standardized JSW (stdJSW). We assess the performance of this quantitative metric for joint space width (JSW) at tracking Joint Space Narrowing OARSI grade (JSN) changes and provide reference values for different joint space narrowing OARSI grades and their annual change.
One of the main purposes of a systematic OA grading scheme, such as the KL and the OARSI systems, is to standardize diagnostic and assessments of OA. However, several studies report that the KL grading scheme, as well as the accessory assessments, suffer from subjectivity and low inter-observer reliability 8,9. This leads to differences in assessments of the prevalence of the disease 4 and variability of diagnoses of the same patient. This is especially problematic for the early stages of the disease: severe forms of OA are easily recognized in radiographs but its early stages are less consensual 10. In part this stems from the high degree of subjectivity of the assessments11, even with the guidance of the OARSI atlas. This problem has consequences at several levels: In the clinical practice, it can lead to misdiagnosis, leading to unnecessary examination procedures or omitted treatment, and psychological stress to the patient12. In the context of clinical trials, the variability of assessments can decrease the power to detect statistical effects of the efficacy of treatments13 and complicate the estimation of prevalence and incidence rates 14.
One potential, albeit not practical, solution for the problem of variability of diagnosis would be to have the same radiograph reviewed by several physicians and to have a procedure to determine consensus, as it is done when establishing the gold-standard readings in many clinical studies. This is clearly not a practical solution for clinical practice. However, one way to approach such a problem could be make use of a computer assisted detection system to standardize the readings of the relevant features. Artificial intelligence, and especially deep learning, has proven remarkably efficient at recognizing complex visual patterns. When applied to medical imaging, these systems can provide guidance and recommendations for radiographic assessments to the reader in a robust fashion. These artificial intelligence systems can be trained on the assessments of several clinicians (or the consensus readings after several physicians have reviewed the case) and so incorporate the experience of several clinicians and could potentially simulate a consensus procedure. Here we take this latter approach.
In Part B we make use of a computer assisted detection system (KOALA, IB Lab GmbH) that was trained in a large dataset of radiographs graded for KL and JSN, Sclerosis and Osteophyte OARSI grades through a consensus procedure. KOALA makes use of deep learning networks to provide fully automated KL and OARSI grades in the form of a report. Here, we assess how the use of this computer assisted detection system affects physicians’ performance in terms of inter-observer variability at assessing KL grade and IRFs, as well as their accuracy performance at detecting several clinically relevant conditions.
Methods Part A: We collected 18.934 individual knee images from the OAI study, from the follow-up visits up to month 48 (baseline plus 4 follow-up exams). Absolute JSW and JSN readings were collected from the OAI study. Standardized JSW was calculated for each knee as well as 12-month JSN grade changes. For each JSN grade and 12-month grade change, the distribution of JSW loss was calculated for stdJSW and absolute JSW measurements retrieved from the OAI study. Area under the curve of the ROC curves was calculated for the performance of both absolute and stdJSW at discriminating between different JSN grades. Standardized response mean (SRM) was used to compare the responsiveness of the two measures to change in JSN grade.
Part B: A set of 124 unilateral knee radiographs from the OAI study were analyzed by a computerized method with regard to Kellgren-Lawrence grade, as well as Joint Space Narrowing, Osteophytes and Sclerosis OARSI grades. Physicians scored all images, with respect to osteophytes, sclerosis, joint space narrowing OARSI grades and KL grade, in two modalities: through a plain radiograph (unaided) and a radiograph presented together with the report from the computer assisted detection system (aided). Intra-Class Correlation between the physicians was calculated for both modalities. Furthermore, physicians’ performance was compared to the grading of the OAIstudy , and accuracy, sensitivity and specificity were calculated in both modalities for each of the scored features.
Results Part A: The areas under the ROC curve for stdJSW at discriminating between successive JSN grades were AUCstdJSW= 0.87, 0.95, and 0.96, for JSN>0, JSN>1 and JSN>2, respectively, whereas these were AUCfJSW= 0.79, 0.90, 0.98 for absolute JSW. We find that standardized JSW is significantly more responsive than absolute JSW, as measured by the SRM.
Part B: Agreement rates for KL grade, sclerosis, and osteophyte OARSI grades, were statistically increased in the aided vs the unaided modality. Readings for Joint Space Narrowing OARSI grade did not show a statistically difference between the two modalities. Readers’ accuracy and specificity for KL grade > 0, KL>1, sclerosis OARSI grade > 0, and osteophyte OARSI grade > 0 was significantly increased in the aided modality. Reader sensitivity was high in both modalities.
Conclusions Our results (Part A) show that stdJSW outperforms absolute JSW at discriminating and tracking changes in JSN. Furthermore, our results show that this effect is in part because stdJSW cancels the variation in JSWs that comes from variation in height. In conclusion, our study suggests that the use of a computer assisted detection system, such as KOALA, improves both agreement rate and accuracy when assessing radiographic features relevant for the diagnosis of knee osteoarthritis. These improvements in physician performance and reliability come without trade-offs in terms of accuracy. These results argue for the use of this type of software as a way to improve the standard of care when diagnosing knee osteoarthritis.
1. Braun HJ, Gold GE. Diagnosis of osteoarthritis: imaging. Bone. 2012;51(2):278-288. doi:10.1016/j.bone.2011.11.019
2. Kellgren JH, Lawrence JS. Radiological assessment of osteo-arthrosis. Ann Rheum Dis. 1957;16(4):494-502. http://www.ncbi.nlm.nih.gov/pubmed/13498604.
3. Kohn MD, Sassoon AA, Fernando ND. Classifications in Brief: Kellgren-Lawrence Classification of Osteoarthritis. Clin Orthop Relat Res. 2016;474(8):1886-1893. doi:10.1007/s11999-016-4732-4
4. Culvenor AG, Engen CN, Øiestad BE, Engebretsen L, Risberg MA. Defining the presence of radiographic knee osteoarthritis: a comparison between the Kellgren and Lawrence system and OARSI atlas criteria. Knee Surgery, Sport Traumatol Arthrosc. 2015;23(12):3532-3539. doi:10.1007/s00167-014-3205-0
5. Wright RW, MARS Group TM, Wright RW, et al. Osteoarthritis Classification Scales: Interobserver Reliability and Arthroscopic Correlation. J Bone Joint Surg Am. 2014;96(14):1145-1151. doi:10.2106/JBJS.M.00929
6. Schiphof D, Boers M, Bierma-Zeinstra SMA. Differences in descriptions of Kellgren and Lawrence grades of knee osteoarthritis. Ann Rheum Dis. 2008;67(7):1034-1036. doi:10.1136/ard.2007.079020
7. Altman RD, Gold GE. Atlas of individual radiographic features in osteoarthritis, revised. Osteoarthr Cartil. 2007;15 Suppl A:A1-56. doi:10.1016/j.joca.2006.11.009
8. Gunther KP, Sun Y. Reliability of radiographic assessment in hip and knee osteoarthritis. Osteoarthr Cartil. 1999;7(2):239-246. doi:10.1053/joca.1998.0152 [doi]
9. Damen J, Schiphof D, Wolde S Ten, Cats HA, Bierma-Zeinstra SMA, Oei EHG. Inter-observer reliability for radiographic assessment of early osteoarthritis features: the CHECK (cohort hip and cohort knee) study. Osteoarthr Cartil. 2014;22(7):969-974. doi:10.1016/j.joca.2014.05.007
10. Hart DJ, Spector TD. Kellgren & Lawrence grade 1 osteophytes in the knee--doubtful or definite? Osteoarthr Cartil. 2003;11(2):149-150. doi:10.1053/JOCA.2002.0853
11. Gossec L, Jordan JM, Mazzuca SA, et al. Comparative evaluation of three semi-quantitative radiographic grading techniques for knee osteoarthritis in terms of validity and reproducibility in 1759 X-rays: report of the OARSI-OMERACT task force. Osteoarthr Cartil. 2008;16(7):742-748. doi:10.1016/j.joca.2008.02.021
12. Balint G, Szebenyi B, Bálint G, et al. Diagnosis of osteoarthritis - Guidelines and current pitfalls. Drugs. 1996;52(SUPPL. 3):1-13.
13. Sadler ME, Yamamoto RT, Khurana L, Dallabrida SM. The impact of rater training on clinical outcomes assessment data: a literature review. Int J Clin Trials. 2017;4(3):101. doi:10.18203/2349-3259.ijct20173133
14. Marshall DA, Vanderby S, Barnabe C, et al. Estimating the Burden of Osteoarthritis to Plan for the Future. Arthritis Care Res. 2015;67(10):1379-1386. doi:10.1002/acr.22612
8.3.3 - New Modalities in Joint Imaging
New Modalities in Joint Imaging
Largely due to the application of MRI to large clinical studies, it is now accepted consensus to perceive degenerative joint disease as a multi-tissue disorder eventually leading to joint failure. Whole joint MRI assessment has contributed much to this change in perception. In addition, the heterogeneous nature of the clinical and structural manifestations of degenerative joint disorders have led to recent suggestions that there may be several phenotypes or subpopulations that are characterized by distinct clinical manifestations of disease, by certain laboratory parameters, biochemical markers, and/or imaging findings. Three main structural phenotypes have been proposed, i.e. meniscus/cartilage, subchondral bone and inflammation. These may progress differently and may represent specific tissue targets for future therapeutic approaches. Recently, a scoring instrument, has been presented that enables phenotypic characterization based on simplified MRI assessment that will be introduced. Combined with accelerated image acquisition, MRI may now be used in screening endeavors for osteoarthritis clinical trials.
Quantitative measurement of cartilage morphology exploits the three-dimensional nature of MRI data to assess morphologic tissue parameters as continuous variables based on segmentation approaches. By removing the link between magnitude and location of change, location-independent analyses such as the “ordered values”-approach or thinning/thickening scores circumvent the challenge of selecting a particular joint compartment or subregion as an outcome measure of progression a priori. Quantitative measurements of cartilage volume and thickness change have been used as outcomes in interventional trials.
Compositional MRI can detect changes in cartilage composition and ultra-structure during the earliest stages of cartilage degeneration prior to the onset of morphologic cartilage loss. A wide variety of compositional MRI methods have been used to evaluate patients with joint disorders including T2 mapping, sodium imaging, T2* mapping, gagCEST imaging, T1-rho mapping, diffusion-weighted imaging, ultra-short echo-time imaging, and delayed gadolinium enhanced MRI of cartilage (dGEMRIC) that will be dicussed. In addition, recent developments in MRI hardware including evolution and potential clinical application of ultrahigh field MRI at 7 T and modern low-field MRI systems will be introduced.
Ultrasound provides real-time, multi-planar imaging at relatively low cost. It offers reliable assessment of several inflammatory and structural abnormalities, without contrast administration or exposure to radiation. Ultrasound is able to visualize the patellofemoral joint including the anterior femoral surface and Hoffa’s fat pad, and the medial and lateral joint line including osteophytes and the body of the meniscus. Ultrasonography has proven to be a useful adjuvant to routine clinical assessment to aid management of disease, rather than a stand-alone diagnostic test
While CT has higher spatial resolution and superior multi-planar capabilities than MRI, it has less versatile tissue contrast. CT arthrography can improve tissue contrast through use of intra-articular iodinated contrast agents. CT arthrography is considered the in-vivo reference standard for measuring cartilage thickness and has high diagnostic performance for detecting cartilage defects and meniscus tears. Furthermore, CT arthrography can provide in-vivo assessment of the proteoglycan content of cartilage. Dual-energy CT (DECT) can distinguish between structures with similar densities but different elemental compositions based upon attenuation differences. DECT can improve characterization of crystal deposition diseases. DECT can also create bone subtraction images that can identify attenuation changes within bone marrow due to post-traumatic and degenerative bone marrow edema lesions. Newly developed extremity cone beam CT provides high-resolution imaging of the knee and ankle in the weight-bearing position with low radiation doses. Weight-bearing CT (WBCT) can assess structural features of knee joint degeneration and measure tibiofemoral joint space width with high scan-rescan reliability. WBCT may have a role in assessing relevance of articular structures that show change under weight-bearing conditions. In addition to WBCT, four-dimensional CT has been developed, which is not only able to detect patellofemoral maltracking, which is an important risk factor for patellofemoral osteoarthritis but can also provide an accurate and reliable visual assessment of patellar tracking.
Molecular imaging techniques have been used to evaluate joints including single-photon emission computed tomography (SPECT) to assess bone turnover, and positron emission tomography (PET) imaging, which utilizes 18F-fluorodeoxyglucose (FDG) to assess metabolic activity and 18F-SodiumFluoride (NaF) to assess bone turnover. Both SPECT and PET are typically combined with CT or MRI to provide improved anatomic localization of radiopharmaceutical uptake and to correlate findings of metabolic activity and bone turn-over with morphologic and compositional findings of joint degeneration. 18F-FDG uptake on PET imaging is correlated with the severity of synovitis in subjects with knee osteoarthritis. Furthermore, 18F-NaF uptake but not 18F-FDG uptake is seen in bone marrow edema lesions, osteophytes, and subchondral sclerosis.
In summary, conventional radiography remains the initial and most widely used imaging technique for evaluation of a patient with joint pain or a known or suspected diagnosis of osteoarthritis. In research and clinical trials, it is still an important tool for stratifying patients into different categories for inclusion criteria and eligibility. MRI plays a crucial role in multi-tissue joint assessment and in guiding future therapies due to its ability to image the joint as a whole organ and to directly and three-dimensionally assess cartilage morphology and composition. Ultrasound plays an important role in the diagnosis and follow-up of inflammatory joint pathologies in clinical practice. The clinical role of CT, scintigraphy, and PET in the diagnosis and follow-up of OA is still limited but is rapidly advancing. A comparative summary of the strengths and limitations of currently available imaging methods for the evaluation joint disorders is shown below.
Roemer FW, Demehri S, Omoumi P, Link TM, Kijowski R, Saarakkala S, et al. State of the Art: Imaging of Osteoarthritis-Revisited 2020. Radiology. 2020;296(1):5-21.
Guermazi A, Alizai H, Crema MD, Trattnig S, Regatte RR, Roemer FW. Compositional MRI techniques for evaluation of cartilage degeneration in osteoarthritis. Osteoarthritis Cartilage. 2015;23(10):1639-53.
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(6):372-80.
Roemer FW, Collins J, Kwoh CK, Hannon MJ, Neogi T, Felson DT, et al. MRI-based screening for structural definition of eligibility in clinical DMOAD trials: Rapid OsteoArthritis MRI Eligibility Score (ROAMES). Osteoarthritis Cartilage. 2020;28(1):71-81.
Eckstein F, Ateshian G, Burgkart R, Burstein D, Cicuttini F, Dardzinski B, et al. Proposal for a nomenclature for magnetic resonance imaging based measures of articular cartilage in osteoarthritis. Osteoarthritis Cartilage. 2006;14(10):974-83.
Wirth W, Nevitt M, Hellio Le Graverand MP, Benichou O, Dreher D, Davies RY, et al. Sensitivity to change of cartilage morphometry using coronal FLASH, sagittal DESS, and coronal MPR DESS protocols--comparative data from the Osteoarthritis Initiative (OAI). Osteoarthritis Cartilage. 2010;18(4):547-54.
Eckstein F, Buck R, Wirth W. Location-independent analysis of structural progression of osteoarthritis-Taking it all apart, and putting the puzzle back together makes the difference. Semin Arthritis Rheum. 2017;46(4):404-10.
Hochberg MC, Guermazi A, Guehring H, Aydemir A, Wax S, Fleuranceau-Morel P, et al. Effect of Intra-Articular Sprifermin vs Placebo on Femorotibial Joint Cartilage Thickness in Patients With Osteoarthritis: The FORWARD Randomized Clinical Trial. JAMA. 2019;322(14):1360-70.
Liebl H, Joseph G, Nevitt MC, Singh N, Heilmeier U, Subburaj K, et al. Early T2 changes predict onset of radiographic knee osteoarthritis: data from the osteoarthritis initiative. Ann Rheum Dis. 2015;74(7):1353-9.
Keen HI, Wakefield RJ, Conaghan PG. A systematic review of ultrasonography in osteoarthritis. Ann Rheum Dis. 2009;68(5):611-9
Vande Berg BC, Lecouvet FE, Poilvache P, Jamart J, Materne R, Lengele B, et al. Assessment of knee cartilage in cadavers with dual-detector spiral CT arthrography and MR imaging. Radiology. 2002;222(2):430-6.
Omoumi P, Babel H, Jolles BM, Favre J. Relationships between cartilage thickness and subchondral bone mineral density in non-osteoarthritic and severely osteoarthritic knees: In vivo concomitant 3D analysis using CT arthrography. Osteoarthritis Cartilage. 2019;27(4):621-9.
Segal NA, Bergin J, Kern A, Findlay C, Anderson DD. Test-retest reliability of tibiofemoral joint space width measurements made using a low-dose standing CT scanner. Skeletal Radiol. 2017;46(2):217-22.
Kogan F, Fan AP, McWalter EJ, Oei EHG, Quon A, Gold GE. PET/MRI of metabolic activity in osteoarthritis: A feasibility study. J Magn Reson Imaging. 2017;45(6):1736-45.
Savic D, Pedoia V, Seo Y, Yang J, Bucknor M, Franc BL, et al. Imaging Bone-Cartilage Interactions in Osteoarthritis Using [(18)F]-NaF PET-MRI. Mol Imaging. 2016;15:1-12.
8.3.4 - What Do We See in Imaging after Different Cartilage Procedures?
What do we see in Imaging after different Cartilage Procedures?
What do we see in Imaging after different Cartilage Procedures?
Ali Guermazi, MD, PhD
Boston University School of Medicine, Boston/MA, USA
Since 1994, when the first autologous chondrocyte transplantation surgery was described by Brittberg and colleagues, knee cartilage repair surgery has evolved rapidly thanks to many factors including improvement of pre-surgical assessment, imaging techniques, increased availability of matrix products including both fresh and frozen allografts, and focused research on the clinical outcomes. Cartilage repair surgery is aimed to alleviate patient symptoms, to promote cartilage healing, and to prevent or delay the onset of osteoarthritis. There are still a variety of barriers (including cost, regulatory, insurance, and logistical issues) between new cartilage repair products/techniques and their routine clinical applications. However, over the recent years there have been significant advances in our scientific knowledge in regards to cartilage repair techniques and imaging methods for evaluating post-operative repair status. Different MRI techniques are available to assess post-operative cartilage. Conventional, morphological MRI sequences include 2-dimensional (2D) and 3-dimensional (3D) fast spin echo (FSE) sequences provide excellent signal to noise ratio, contrast between tissues, and faster acquisition times. 2D-FSE is the core imaging technique and is part of the cartilage imaging protocol recommended by International Cartilage Repair Society. Isotropic, or near isotropic, 3D sequences can produce higher spatial resolution and high quality reformatted images in any plane, and are thus advantageous over 2D-FSE for shorter image acquisition time. Gradient echo type sequences such as SPGR, DESS are excellent for cartilage segmentation and quantification of cartilage volume and thickness due to the good image contrast between cartilage and subchondral bone, but not ideal for focal cartilage defect evaluation. There are semiquantitative MRI scoring tools for assessment of post-operative cartilage after repair surgery. One is called MRI Observation of Cartilage Repair Tissue (MOCART) and its usefulness in randomized controlled clinical trials of autologous cartilage tissue implants has been demonstrated. MOCART is comprehensive for assessment of the repair site itself; however, assessment of the other structures of the joint is paramount to assess longitudinal outcomes and development of osteoarthritis. The cartilage repair OA knee score (CROAKS) combines features of MOCART and the MRI osteoarthritis knee score (MOAKS), which is an established semi-quantitative scoring system for whole organ assessment of the knee, to provide a comprehensive, reproducible tool for longitudinal postoperative assessment after surgical cartilage. Compositional MRI acquisitions provide a way to detect biochemical and microstructural changes in the cartilage extracellular matrix even before gross morphological changes occur. Although not in routine clinical use, these techniques have been used extensively in cartilage research. Compositional MRI can supplement morphologic imaging, by potentially defining the biomechanical quality of cartilage repair tissue. Available surgical cartilage repair techniques include microfracture/marrow stimulation, osteochondral autograft/allograft (OATS) transplantation, particulate cartilage allograft, autologous chondrocyte implantation (ACI), open reduction and internal fixation of a large osteochondral lesion, and femoral condyle transplantation. All cartilage repair techniques have the same primary goal; to decrease pain symptoms, improve mobility and function, and to prevent the progression of osteoarthritis. These cartilage repair surgery techniques have shown to improve functional outcomes, however, there is urgent need to define outcomes clinically and by MRI measurements including local assessment and in regard to long-term osteoarthritis development/progression. In clinical practice, the MRI assessment of repair tissue relies heavily on morphologic imaging. Compositional MRI provides the opportunity to measure the biochemical and microstructural time-dependent processes of maturation occurring within the cartilage repair tissue. Compositional MRI techniques hold great promise for the clinical determination of surgical success, although such techniques are still limited for research use. Before they can become routinely used in clinical practice, however, compositional MRI techniques needs to be standardized and validated for post-operative cartilage repair tissue evaluation and made time efficient. The combination of MRI–based morphologic and compositional imaging plays a key role in post-operative assessment of cartilage repair tissue and its integration to native tissues.