| Literature DB >> 31755191 |
Akshay S Chaudhari1, Feliks Kogan1, Valentina Pedoia2,3, Sharmila Majumdar2,3, Garry E Gold1,4,5, Brian A Hargreaves1,5,6.
Abstract
Osteoarthritis (OA) of the knee is a major source of disability that has no known treatment or cure. Morphological and compositional MRI is commonly used for assessing the bone and soft tissues in the knee to enhance the understanding of OA pathophysiology. However, it is challenging to extend these imaging methods and their subsequent analysis techniques to study large population cohorts due to slow and inefficient imaging acquisition and postprocessing tools. This can create a bottleneck in assessing early OA changes and evaluating the responses of novel therapeutics. The purpose of this review article is to highlight recent developments in tools for enhancing the efficiency of knee MRI methods useful to study OA. Advances in efficient MRI data acquisition and reconstruction tools for morphological and compositional imaging, efficient automated image analysis tools, and hardware improvements to further drive efficient imaging are discussed in this review. For each topic, we discuss the current challenges as well as potential future opportunities to alleviate these challenges. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 3.Entities:
Keywords: compositional imaging; deep learning; morphological imaging; quantitative MRI; rapid MRI; segmentation
Mesh:
Year: 2019 PMID: 31755191 PMCID: PMC7925938 DOI: 10.1002/jmri.26991
Source DB: PubMed Journal: J Magn Reson Imaging ISSN: 1053-1807 Impact factor: 4.813