Evaluation of a deep learning-based reconstruction method for denoising and image enhancement of shoulder MRI in patients with shoulder pain

ObjectivesTo evaluate the diagnostic performance of an automated reconstruction algorithm combining MR imaging acquired using compressed SENSE (CS) with deep learning (DL) in order to reconstruct denoised high-quality images from undersampled MR images in patients with shoulder pain.MethodsProspectively, thirty-eight patients (14 women, mean age 40.0 +/- 15.2 years) with shoulder pain underwent morphological MRI using a pseudo-random, density-weighted k-space scheme with an acceleration factor of 2.5 using CS only. An automated DL-based algorithm (CS DL) was used to create reconstructions of the same k-space data as used for CS reconstructions. Images were analyzed by two radiologists and assessed for pathologies, image quality, and visibility of anatomical landmarks using a 4-point Likert scale.ResultsOverall agreement for the detection of pathologies between the CS DL reconstructions and CS images was substantial to almost perfect (kappa 0.95 (95% confidence interval 0.82-1.00)). Image quality and the visibility of the rotator cuff, articular cartilage, and axillary recess were overall rated significantly higher for CS DL images compared to CS (p < 0.03). Contrast-to-noise ratios were significantly higher for cartilage/fluid (CS DL 198 +/- 24.3, CS 130 +/- 32.2, p = 0.02) and ligament/fluid (CS DL 184 +/- 17.3, CS 141 +/- 23.5, p = 0.03) and SNR values were significantly higher for ligaments and muscle of the CS DL reconstructions (p < 0.04).ConclusionEvaluation of shoulder pathologies was feasible using a DL-based algorithm for MRI reconstruction and denoising. In clinical routine, CS DL may be beneficial in particular for reducing image noise and may be useful for the detection and better discrimination of discrete pathologies.Summary statementAssessment of shoulder pathologies was feasible with improved image quality as well as higher SNR using a compressed sensing deep learning-based framework for image reconstructions and denoising.