Purpose: The purpose of this study was to test the technical feasibility and the impact on the image quality of a deep learning-based super-resolution reconstruction algorithm in 1.5 T abdominopelvic MR imaging. Methods: 44 patients who underwent abdominopelvic MRI were retrospectively included, of which 4 had to be subsequently excluded. After the acquisition of the conventional volume interpolated breath-hold examination (VIBE_Std), images underwent postprocessing, using a deep learning-based iterative denoising super-resolution reconstruction algorithm for partial Fourier acquisitions (VIBE_SR). Image analysis of 40 patients with a mean age of 56 years (range 18–84 years) was performed qualitatively by two radiologists independently using a Likert scale ranging from 1 to 5, where 5 was considered the best rating. Results: Image analysis showed an improvement of image quality, noise, sharpness of the organs and lymph nodes, and sharpness of the intestine for pre- and postcontrast images in VIBE_SR compared to VIBE_Std (each p < 0.001). Lesion detectability was better for VIBE_SR (p < 0.001), while there were no differences concerning the number of lesions. Average acquisition time was 16 s (±1) for the upper abdomen and 15 s (±1) for the pelvis for VIBE_Std, and 15 s (±1) for the upper abdomen and 14 s (±1) for the pelvis for VIBE_SR. Conclusion: This study demonstrated the technical feasibility of a deep learning-based super-resolution algorithm including partial Fourier technique in abdominopelvic MR images and illustrated a significant improvement of image quality, noise, and sharpness while reducing TA.

Objectives: The study aimed to investigate the diagnostic performance of simulated ultra-low-dose CT (ULD-CT) for torsion measurement of the lower limb. Methods: Thirty retrospectively identified patients were included (32.3 ± 14.2 years; 14 women, 16 men). ULD-CT simulations were generated at dose levels of 100%, 10%, 5%, and 1% using two reconstruction methods: standard filtered back projection (FBP) and iterative reconstruction (ADMIRE). Two readers measured the lower limb torsion in all data sets. The readers also captured image noise in standardized anatomical landmarks. All data sets were evaluated regarding subjective diagnostic confidence (DC; 5-point Likert scale). Effective radiation dose of the original data sets and the simulated ULD-CT was compared. Results: There was no significant difference of measured lower limb torsion in any simulated dose level compared to the original data sets in both readers. Dose length product (DLP) of the original examinations was 402.1 ± 4.3 mGy cm, which resulted in an effective radiation dose of 4.00 ± 2.12 mSv. Calculated effective radiation dose in ULD-CT at 1% of the original dose was 0.04 mSv. Image noise increased significantly with dose reduction (p < 0.0001) and was dependent on the reconstructional method (p < 0.0001) with less noise using ADMIRE compared to FBP. Both readers rated DC at doses 100%, 10%, and 5% with 5.0/5: there were no ratings worse than 3/5 at 1% dose level. Conclusions: The results suggest that radiation dose reduction down to 1% of original CT dose levels may be achieved in CT torsion measurements of the lower limb without compromising diagnostic accuracy. Key Points: • Modern CT delivers exceptional high image quality in musculoskeletal imaging, especially for evaluation of osseous structures. • Usually, this high image quality is accompanied by significant radiation exposure to the patient and may not always be required for the intended purpose, e.g., pure delineation of cortical bone of the lower limb. • This study shows the tremendous prospects of radiation dose reduction without compromising diagnostic confidence in CT torsion measurement of the lower limb.

Purpose: Not diagnosed or mistreated scapholunate ligament (SL) tears represent a frequent cause of degenerative wrist arthritis. A newly developed deep learning (DL)-based automated assessment of the SL distance on radiographs may support clinicians in initial image interpretation. Materials and Methods: A pre-trained DL algorithm was specifically fine-tuned on static and dynamic dorsopalmar wrist radiography (training data set n = 201) for the automated assessment of the SL distance. Afterwards the DL algorithm was evaluated (evaluation data set n = 364 patients with n = 1604 radiographs) and correlated with results of an experienced human reader and with arthroscopic findings. Results: The evaluation data set comprised arthroscopically diagnosed SL insufficiency according to Geissler’s stages 0–4 (56.5%, 2.5%, 5.5%, 7.5%, 28.0%). Diagnostic accuracy of the DL algorithm on dorsopalmar radiography regarding SL integrity was close to that of the human reader (e.g. differentiation of Geissler’s stages ≤ 2 versus > 2 with a sensitivity of 74% and a specificity of 78% compared to 77% and 80%) with a correlation coefficient of 0.81 (P < 0.01). Conclusion: A DL algorithm like this might become a valuable tool supporting clinicians’ initial decision making on radiography regarding SL integrity and consequential triage for further patient management.

This study analyzed the radiation exposure of a new ultra-low dose (ULD) protocol compared to a high-quality (HQ) protocol for CT-torsion measurement of the lower limb. The analyzed patients (n = 60) were examined in the period March to October 2019. In total, 30 consecutive patients were examined with the HQ and 30 consecutive patients with the new ULD protocol comprising automatic tube voltage selection, automatic exposure control, and iterative image reconstruction algorithms. Radiation dose parameters as well as the contrast-to-noise ratio (CNR) and diagnostic confidence (DC; rated by two radiologists) were analyzed and potential predictor variables, such as body mass index and body volume, were assessed. The new ULD protocol resulted in significantly lower radiation dose parameters, with a reduction of the median total dose equivalent to 0.17 mSv in the ULD protocol compared to 4.37 mSv in the HQ protocol (p < 0.001). Both groups showed no significant differences in regard to other parameters (p = 0.344–0.923). CNR was 12.2% lower using the new ULD protocol (p = 0.033). DC was rated best by both readers in every HQ CT and in every ULD CT. The new ULD protocol for CT-torsion measurement of the lower limb resulted in a 96% decrease of radiation exposure down to the level of a single pelvic radiograph while maintaining good image quality.

Purpose: Magnetic resonance imaging (MRI) scan time remains a limited and valuable resource. This study evaluates the diagnostic performance of a deep learning (DL)-based accelerated TSE study protocol compared to a standard TSE study protocol in ankle MRI. Material and methods: Between October 2020 and July 2021 forty-seven patients were enrolled in this study for an intraindividual comparison of a standard TSE study protocol and a DL TSE study protocol either on a 1.5 T or a 3 T scanner. Two radiologists evaluated the examinations regarding structural pathologies and image quality categories (5-point-Likert-scale; 1 = “non diagnostic”, 5 = “excellent”). Results: Both readers showed almost perfect/perfect agreement of DL TSE with standard TSE in all analyzed structural pathologies (0.81–1.00) with a median “good” or “excellent” rating (4–5/5) in all image quality categories in both 1.5 T and 3 T MRI. The reduction of total acquisition time of DL TSE compared to standard TSE was 49% in 1.5 T and 48% in 3 T MRI to a total acquisition time of 5 min 41 s and 5 min 46 s. Conclusion: In ankle MRI the new DL-based accelerated TSE study protocol delivers high agreement with standard TSE and high image quality, while reducing the acquisition time by 48%.