DADP: Dynamic abnormality detection and progression for longitudinal knee magnetic resonance images from the Osteoarthritis Initiative

• Published in: Medical image analysis Vol. 77; p. 102343
• Main Authors: Huang, Chao, Xu, Zhenlin, Shen, Zhengyang, Luo, Tianyou, Li, Tengfei, Nissman, Daniel, Nelson, Amanda, Golightly, Yvonne, Niethammer, Marc, Zhu, Hongtu
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.04.2022; Elsevier BV
• Subjects: Abnormal region detection; Arthritis; Biomedical materials; Cartilage; Cartilage diseases; Cartilage, Articular - diagnostic imaging; Degeneration; Disease Progression; Dynamic conditional random field model; Dynamic functional mixed effect model; Heterogeneity; Humans; Joint diseases; Joints (anatomy); Knee; Knee Joint - diagnostic imaging; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Osteoarthritis; Osteoarthritis, Knee - diagnostic imaging; Radiography; Resonance; Statistical methods; Statistics; Subgroups; Thickness; Abnormal region detection; 41A10; 65D05; 65D17; Dynamic functional mixed effect model; Dynamic conditional random field model; Osteoarthritis; 41A05
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2021.102343

•We propose a DADP framework for longitudinal knee MR image analysis.•A DL pipeline is proposed to extract 2D cartilage thickness maps from 3D images.•A DFMEM is proposed for dynamic abnormality detection and progression.•Statistical inferences are introduced for both association and subgroup analysis.•Statistical disease mapping is conducted at both population and subgroup level. [Display omitted] Osteoarthritis (OA) is the most common disabling joint disease. Magnetic resonance (MR) imaging has been commonly used to assess knee joint degeneration due to its distinct advantage in detecting morphologic cartilage changes. Although several statistical methods over conventional radiography have been developed to perform quantitative cartilage analyses, little work has been done capturing the development and progression of cartilage lesions (or abnormal regions) and how they naturally progress. There are two major challenges, including (i) the lack of building spatial-temporal correspondences and correlations in cartilage thickness and (ii) the spatio-temporal heterogeneity in abnormal regions. The goal of this work is to propose a dynamic abnormality detection and progression (DADP) framework for quantitative cartilage analysis, while addressing the two challenges. First, spatial correspondences are established on flattened 2D cartilage thickness maps extracted from 3D knee MR images both across time within each subject and across all subjects. Second, a dynamic functional mixed effects model (DFMEM) is proposed to quantify abnormality progression across time points and subjects, while accounting for the spatio-temporal heterogeneity. We systematically evaluate our DADP using simulations and real data from the Osteoarthritis Initiative (OAI). Our results show that DADP not only effectively detects subject-specific dynamic abnormal regions, but also provides population-level statistical disease mapping and subgroup analysis.