Objectives: To evaluate the feasibility of combining compressed sense (CS) with a newly developed deep learning-based algorithm (CS-AI) using convolutional neural networks to accelerate 2D MRI of the knee. Methods: In this prospective study, 20 healthy volunteers were scanned with a 3T MRI scanner. All subjects received a fat-saturated sagittal 2D proton density reference sequence without acceleration and four additional acquisitions with different acceleration levels: 2, 3, 4 and 6. All sequences were reconstructed with the conventional CS and a new CS-AI algorithm. Two independent, blinded readers rated all images by seven criteria (overall image impression, visible artifacts, delineation of anterior ligament, posterior ligament, menisci, cartilage, and bone) using a 5-point Likert scale. Signal- and contrast-to-noise ratios were calculated. Subjective ratings and quantitative metrics were compared between CS and CS-AI with similar acceleration levels and between all CS/CS-AI images and the non-accelerated reference sequence. Friedman and Dunn´s multiple comparison tests were used for subjective, ANOVA and the Tukey Kramer test for quantitative metrics. Results: Conventional CS images at the lowest acceleration level (CS2) were already rated significantly lower than reference for 6/7 criteria. CS-AI images maintained similar image quality to the reference up to CS-AI three for all criteria, which would allow for a reduction in scan time of 64% with unchanged image quality compared to the unaccelerated sequence. SNR and CNR were significantly higher for all CS-AI reconstructions compared to CS (all p < 0.05). Conclusions AI-based image reconstruction showed higher image quality than CS for 2D knee imaging. Its implementation in the clinical routine yields the potential for faster MRI acquisition but needs further validation in non-healthy study subjects. Advances in knowledge Combining compressed SENSE with a newly developed deep learning-based algorithm using convolutional neural networks allows a 64% reduction in scan time for 2D imaging of the knee. Implementation of the new deep learning-based algorithm in clinical routine in near future should enable better image quality/resolution with constant scan time, or reduced acquisition times while maintaining diagnostic quality.

Objective The purpose of the study was to investigate the potential added value of 18F-FDG-PET/MRI (functional information derived from PET) over standard diagnostic liver MRI (excellent soft tissue characterization) in diagnosing and staging suspected primary hepatobiliary malignancies including extrahepatic cholangiocarcinoma (ECC), intrahepatic cholangiocellular carcinoma (ICC) and gallbladder cancer (GBCA). Methods Twenty consecutive patients with suspected hepatobiliary malignancy were included in this retrospective study. All patients underwent combined whole-body (WB) 18F-FDG-PET/MRI including contrast-enhanced MRI of the liver, contrast-enhanced WB-MRI and WB 18F-FDG-PET. Two experienced readers staged hepatobiliary disease using TNM criteria: first based on MRI alone and then based on combined 18F-FDG-PET/MRI. Subsequently, the impact of FDG-PET/MRI on clinical management compared to MRI alone was recorded. Histopathologic proof served as the reference standard. Results Hepatobiliary neoplasms were present in 16/20 patients (ECC n = 3, ICC n = 8, GBCA n = 5), two patients revealed benign disease, two were excluded. TNM staging with 18F-FDG-PET/MRI was identical to MRI alone in 11/18 (61.1 %) patients and correctly changed the stage in 4/18 (22.2 %), resulting in a change in management for 2/4 patients (11.1 %). 18F-FDG-PET/MRI was false-positive in 3/18 cases (16.7 %). Both MRI and 18F-FDG-PET/MRI were falsely positive in 1 case without malignancy. Conclusions A small incremental benefit of 18F-FDG-PET/MRI over standard MRI of the liver was observed. However, in some cases 18F-FDG-PET/MRI may lead to false-positive findings. Overall there is seemingly limited role of 18F-FDG-PET/MRI in patients with suspected hepatobiliary malignancy.

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