Reduction in Acquisition Time and Improvement in Image Quality in T2-Weighted MR Imaging of Musculoskeletal Tumors of the Extremities Using a Novel Deep Learning-Based Reconstruction Technique in a Turbo Spin Echo (TSE) Sequence

Abstract

Background: The aim of this study was to assess the technical feasibility and the impact on image quality and acquisition time of a deep learning-accelerated fat-saturated T2-weighted turbo spin echo sequence in musculoskeletal imaging of the extremities. Methods: Twenty-three patients who underwent MRI of the extremities were prospectively included. Standard T2w turbo inversion recovery magnitude (TIRMStd) imaging was compared to a deep learning-accelerated T2w TSE (TSEDL) sequence. Image analysis of 23 patients with a mean age of 60 years (range 30–86) was performed regarding image quality, noise, sharpness, contrast, artifacts, lesion detectability and diagnostic confidence. Pathological findings were documented measuring the maximum diameter. Results: The analysis showed a significant improvement for the T2 TSEDL with regard to image quality, noise, contrast, sharpness, lesion detectability, and diagnostic confidence, as compared to T2 TIRMStd (each p < 0.001). There were no differences in the number of detected lesions. The time of acquisition (TA) could be reduced by 52–59%. Interrater agreement was almost perfect (κ = 0.886). Conclusion: Accelerated T2 TSEDL was technically feasible and superior to conventionally applied T2 TIRMStd. Concurrently, TA could be reduced by 52–59%. Therefore, deep learning-accelerated MR imaging is a promising and applicable method in musculoskeletal imaging.

Speeding Up and Improving Image Quality in Glioblastoma MRI Protocol by Deep Learning Image Reconstruction

“…These results are consistent with the literature, which showed a scan-time reduction for neuroradiological and non-neuroradiological applications [27][28][29][30][31][32]. Obtaining a consistently high image quality during the daily care of elderly, ill, and uncooperative patients can be a complex task.…”

Detection and Quantitative Assessment of Arthroscopically Proven Long Biceps Tendon Pathologies Using T2 Mapping

“…The combination of these sequences with quantitative T2 sequences might improve the detection of beginning histopathological changes even further than each of these techniques alone. The recently reported reduction in acquisition time using deep-learning-based reconstruction techniques in T2-weighted MR imaging may further enhance this advantage by allowing for the acquisition of additional information without negatively impacting the clinical workflow [41].…”

Editor’s Review of Key Research Papers Published in Tomography during the Last Year

“…DL could also be used to correctly classify breast lesions detected on an X-ray mammography to reduce the false positive recall rate and improve the efficacy of breast cancer screening [2]. Park et al [3] showed that radiomics features of ductal carcinoma in situ on breast MR imaging may predict ipsilateral tumoral recurrence, while DL-accelerated MR imaging may improve image quality in MR images of musculoskeletal tumors [4]. AI could also be implemented in imaging reconstruction and automatic diagnoses to reduce radiologist workloads.…”