Principal component analysis-T voxel based relaxometry of the articular cartilage: A comparison of biochemical patterns in osteoarthritis and anterior cruciate ligament subjects

Valentina Pedoia, Colin Russell, Allison Randolph, Xiaojuan Li, Sharmila Majumdar, Matthew F. Koff, Steven R. Goldring, Mary Goldring, Jo A. Hannafin, Robert G. Marx, Danyal H. Nawabi, Miguel Otero, Hollis Potter, Scott A. Rodeo, Parina Shah, Russell F. Warren, Kimberly K. Amrami, Joel P. Felmlee, Matthew A. Frick, Aaron J. KrychMichael J. Stuart, Steven L. Williams, Keiko Amano, Maxwell Cheong, Martin Kretzschmar, Drew A. Lansdown, Alan Li, Thomas M. Link, C. Benjamin Ma, Narihiro Okazaki, Dragana Savic, Benedikt Schwaiger, Favian Su, Cory Wyatt, John A. Hardin

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Background: Quantitative MR, including T mapping, has been extensively used to probe early biochemical changes in knee articular cartilage of subjects with osteoarthritis (OA) and others at risk for cartilage degeneration, such as those with anterior cruciate ligament (ACL) injury and reconstruction. However, limited studies have been performed aimed to assess the spatial location and patterns of T. In this study we used a novel voxel-based relaxometry (VBR) technique coupled with principal component analysis (PCA) to extract relevant features so as to describe regional patterns and to investigate their similarities and differences in T maps in subjects with OA and subjects six months after ACL reconstruction (ACLR). Methods: T quantitative MRI images were collected for 180 subjects from two separate cohorts. The OA cohort included 93 osteoarthritic patients and 25 age-matched controls. The ACLR-6M cohort included 52 patients with unilateral ACL tears who were imaged 6 months after ACL reconstruction, and 10 age-matched controls. Non-rigid registration on a single template and local Z-score conversion were adopted for T spatial and intensity normalization of all the images in the dataset. PCA was used as a data dimensionality reduction to obtain a description of all subjects in a 10-dimensional feature space. Logistic linear regression was used to identify distinctive features of OA and ACL subjects Results: Global prolongation of the Z-score was observed in both OA and ACL subjects compared to controls [higher values in 1st principal component (PC1); P=0.01]. In addition, relaxation time differences between superficial and deep cartilage layers of the lateral tibia and trochlea were observed to be significant distinctive features between OA and ACL subjects. OA subjects demonstrated similar values between the two cartilage layers [higher value in 2nd principal component (PC2); P=0.008], while ACL reconstructed subjects showed T prolongation specifically in the cartilage superficial layer (lower values in PC2; P<0.0001). T elevation located outside of the weight-bearing area, located in the posterior and anterior aspects of the lateral femoral compartment, was also observed to be a key feature in distinguishing OA subjects from controls [higher value in 6th principal component (PC6); P=0.007]. Conclusions: This study is the first example of T local/regional pattern analysis and data-driven feature extraction in knees with cartilage degeneration. Our results revealed similarities and differences between OA and ACL relaxation patterns that could be potentially useful to better understand the pathogenesis of post-traumatic cartilage degeneration and the identification of imaging biomarkers for the early stratification of subjects at risk for developing post-traumatic OA.

Original languageEnglish (US)
Pages (from-to)623-633
Number of pages11
JournalQuantitative Imaging in Medicine and Surgery
Volume6
Issue number6
DOIs
StatePublished - 2016

Keywords

  • Anterior cruciate ligament (ACL)
  • Osteoarthritis (OA)
  • Principal component analysis (PCA)
  • T
  • Voxel based relaxometry

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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