Deep Learning-Based Meniscus Tear Detection From Accelerated MRI

Abstract

Magnetic Resonance Imaging (MRI) is an exceptional diagnostic tool known for its ability to provide superior soft tissue contrast. Despite its proven efficacy, traditional MRI methods have some inherent limitations, including relatively longer scan times and the requirement for specialized expertise in data analysis. These factors can, at times, create challenges in the widespread adoption of MRI in certain research and clinical scenarios. Deep learning (DL) methods for MRI reconstruction and analysis offer a promising solution to address this issue. While these DL techniques have been validated using standard image quality metrics, they fall short in assessing clinically relevant details. This hinders their clinical reliability and practical application. Although radiologists have previously compared the diagnostic equivalence of accelerated DL-reconstructed images to conventional ones for evaluating knee internal derangement, it remains uncertain whether DL detectors can accurately identify clinically important details when reconstruction models are used. Furthermore, the artifacts or hallucinations generated by DL reconstructions in knee MRI have not been examined in a clinical setting. Thus, this study aims to determine the performance of DL detectors on retrospectively accelerated DL-reconstructed knee MRI, comparing them to conventional imaging and expert evaluations for detecting meniscal tears. Our investigation yields three significant contributions. First, an in-depth analysis of DL reconstruction highlights the presence of hallucinations in the femur, tibia, and false positive artifacts, indicating that the overall reconstruction quality does not directly affect pathological features. Second, the results of DL detectors demonstrate that their performance aligns well with image quality assessment metrics and expert scores. This finding validates the reliability of the detection outcomes. Finally, we propose an integrated (i.e. reconstruction + detection) process for meniscal tears on fastMRI+ data and achieved state-of-the-art results with average precision scores of 0.69 and 0.67 at 4− and 8-fold accelerations, respectively.INDEX TERMS MRI reconstruction, meniscal tear detection, and deep learning.