MR imaging for shoulder diseases: Effect of compressed sensing and deep learning reconstruction on examination time and imaging quality compared with that of parallel imaging

Purpose: To compare capabilities of compressed sensing (CS) with and without deep learning reconstruction (DLR) with those of conventional parallel imaging (PI) with and without DLR for improving examination time and image quality of shoulder MRI for patients with various shoulder diseases.

Methods and materials: Thirty consecutive patients with suspected shoulder diseases underwent MRI at a 3 T MR system using PI and CS. All MR data was reconstructed with and without DLR. For quantitative image quality evaluation, ROI measurements were used to determine signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). For qualitative image quality assessment, two radiologists evaluated overall image quality, artifacts and diagnostic confidence level using a 5-point scoring system, and consensus of the two readers determined each final value. Tukey's HSD test was used to compare examination times to establish the capability of the two techniques for reducing examination time. All indexes for all methods were then compared by means of Tukey's HSD test or Wilcoxon's signed rank test.

Results: CS with and without DLR showed significantly shorter examination times than PI with and without DLR (p < 0.05). SNR and CNR of CS or PI with DLR were significantly higher than of those without DLR (p < 0.05). Use of DLR significantly improved overall image quality and artifact incidence of CS and PI (p < 0.05).

Conclusion: Examination time with CS is shorter than with PI without deterioration of image quality of shoulder MRI. Moreover, DLR is useful for both CS and PI for improvement of image quality on shoulder MRI.