A fully automated system for quantification of knee bone marrow lesions using MRI and the osteoarthritis initiative cohort

Abstract

Background/Objective: Bone marrow lesions (BMLs) have been associated with pain and cartilage degeneration in patients with knee osteoarthritis; their specific detection and quantification is therefore of primary importance. This study aimed at developing a fully automated quantitative BML assessment technology for human knee osteoarthritis using magnetic resonance images (MRI) and two sequences, a T1/T2*-weighted gradient echo (DESS) and a water-sensitive intermediate-weighted turbo spin echo (IW-TSE).

Methods: The automated BML quantification first characterizes the bone and cartilage domains in the DESS sequence using our already published automated technology, then proceeds to the BML quantification which was developed as a four-stage process: selection of structured bright areas corresponding to BMLs, geometric filtering of unrelated structures, segmentation of the BML, and quantification of BML proportion within bone regions. For the IW-TSE sequence, the first step consists of the transfer of the bone and cartilage objects from the DESS to the IW-TSE images, followed by the BML detection and quantification as for the DESS. Validation was performed on 154 OA patients from a subset of the Osteoarthritis Initiative (OAI) cohort (public data sets) in which BML manual segmentation intra- and inter-reader reliability was done for each sequence (DESS and IW-TSE) using the intraclass correlation (ICC). BML comparison between the newly developed automated method with a manual segmentation was performed with ICC for the proportion of BML and Dice similarity coefficient (DSC) for BML localization and geometric extent. Finally, comparisons between the DESS and the IW-TSE sequences were performed for BML incidence and proportion.

Results: Excellent to very good correlations were obtained for both MRI sequences for intra- and inter-reader reliability of the manual BML segmentation. Comparison between the developed automated method and the manual BML segment-
ation showed excellent to very good correlations in the global knee and regions (ICC=0.99 to 0.68 for DESS and 0.99 to 0.77 for IW-TSE sequences) as well as very good to good similarity for the BML geometrical agreement (DESS, 0.60 to 0.41; IW-TSE, 0.59 to 0.41). Data revealed greater BML incidence at the sites of high articular constraints: lateral femoropatellar and medial tibiofemoral articulation. Average BML proportion revealed a scaling factor of about 4.5-fold more for the IW-TSE compared to the DESS.

Conclusions: The newly developed fully automated MRI based BML assessment technology not only detects the absence/
presence of these pathological signals in the osteoarthritic human knee, but also provides accurate quantitative assessment of BMLs in the global knee and knee regions. Such automated system will enable large scale studies to be conducted within shorter durations, as well as increase stability of the reading.