  This study investigates the relationship between deep learning (DL) image
reconstruction quality and anomaly detection performance, and evaluates the
efficacy of an artificial intelligence (AI) assistant in enhancing
radiologists' interpretation of meniscal anomalies on reconstructed images. A
retrospective study was conducted using an in-house reconstruction and anomaly
detection pipeline to assess knee MR images from 896 patients. The original and
14 sets of DL-reconstructed images were evaluated using standard reconstruction
and object detection metrics, alongside newly developed box-based
reconstruction metrics. Two clinical radiologists reviewed a subset of 50
patients' images, both original and AI-assisted reconstructed, with subsequent
assessment of their accuracy and performance characteristics. Results indicated
that the structural similarity index (SSIM) showed a weaker correlation with
anomaly detection metrics (mAP, r=0.64, p=0.01; F1 score, r=0.38, p=0.18),
while box-based SSIM had a stronger association with detection performance
(mAP, r=0.81, p<0.01; F1 score, r=0.65, p=0.01). Minor SSIM fluctuations did
not affect detection outcomes, but significant changes reduced performance.
Radiologists' AI-assisted evaluations demonstrated improved accuracy (86.0%
without assistance vs. 88.3% with assistance, p<0.05) and interrater agreement
(Cohen's kappa, 0.39 without assistance vs. 0.57 with assistance). An
additional review led to the incorporation of 17 more lesions into the dataset.
The proposed anomaly detection method shows promise in evaluating
reconstruction algorithms for automated tasks and aiding radiologists in
interpreting DL-reconstructed MR images.
