A Deep Learning System for Synthetic Knee Magnetic Resonance Imaging Is Artificial Intelligence-Based Fat-Suppressed Imaging Feasible?

Objectives The aim of this study was to determine the feasibility and performance of a deep learning system used to create synthetic artificial intelligence-based fat-suppressed magnetic resonance imaging (AFSMRI) scans of the knee. Materials and Methods This single-center study was approved by the institutional review board. Artificial intelligence-based FS MRI scans were created from non-FS images using a deep learning system with a modified convolutional neural network-based U-Net that used a training set of 25,920 images and validation set of 16,416 images. Three musculoskeletal radiologists reviewed 88 knee MR studies in 2 sessions, the original (proton density [PD] + FSPD) and the synthetic (PD + AFSMRI). Readers recorded AFSMRI quality (diagnostic/nondiagnostic) and the presence or absence of meniscal, ligament, and tendon tears; cartilage defects; and bone marrow abnormalities. Contrast-to-noise rate measurements were made among subcutaneous fat, fluid, bone marrow, cartilage, and muscle. The original MRI sequences were used as the reference standard to determine the diagnostic performance of AFSMRI (combined with the original PD sequence). This is a fully balanced study design, where all readers read all images the same number of times, which allowed the determination of the interchangeability of the original and synthetic protocols. Descriptive statistics, intermethod agreement, interobserver concordance, and interchangeability tests were applied. A P value less than 0.01 was considered statistically significant for the likelihood ratio testing, and P value less than 0.05 for all other statistical analyses. Results Artificial intelligence-based FS MRI quality was rated as diagnostic (98.9% [87/88] to 100% [88/88], all readers). Diagnostic performance (sensitivity/specificity) of the synthetic protocol was high, for tears of the menisci (91% [71/78], 86% [84/98]), cruciate ligaments (92% [12/13], 98% [160/163]), collateral ligaments (80% [16/20], 100% [156/156]), and tendons (90% [9/10], 100% [166/166]). For cartilage defects and bone marrow abnormalities, the synthetic protocol offered an overall sensitivity/specificity of 77% (170/221)/93% (287/307) and 76% (95/125)/90% (443/491), respectively. Intermethod agreement ranged from moderate to substantial for almost all evaluated structures (menisci, cruciate ligaments, collateral ligaments, and bone marrow abnormalities). No significant difference was observed between methods for all structural abnormalities by all readers (P > 0.05), except for cartilage assessment. Interobserver agreement ranged from moderate to substantial for almost all evaluated structures. Original and synthetic protocols were interchangeable for the diagnosis of all evaluated structures. There was no significant difference for the common exact match proportions for all combinations (P > 0.01). The conspicuity of all tissues assessed through contrast-to-noise rate was higher on AFSMRI than on original FSPD images (P < 0.05). Conclusions Artificial intelligence-based FS MRI (3D AFSMRI) is feasible and offers a method for fast imaging, with similar detection rates for structural abnormalities of the knee, compared with original 3D MR sequences.

Automated summary

This study demonstrated the feasibility and high quality of using a deep learning system to create synthetic AFSMRI scans, with high diagnostic performance for structural abnormalities of the knee. The synthetic protocol showed similar detection rates compared to original MR sequences.