Background: Ventricular volumetry using a short-axis stack of two-dimensional (D) cine balanced steady-state free precession (bSSFP) sequences is crucial in any cardiac magnetic resonance imaging (MRI) examination. This task becomes particularly challenging in children due to multiple breath-holds. Objective: To assess the diagnostic performance of accelerated 3-RR cine MRI sequences using deep learning reconstruction compared with standard 2-D cine bSSFP sequences. Material and methods: Twenty-nine consecutive patients (mean age 11 ± 5, median 12, range 1–17 years) undergoing cardiac MRI were scanned with a conventional segmented 2-D cine and a deep learning accelerated cine (three heartbeats) acquisition on a 1.5-tesla scanner. Short-axis volumetrics were performed (semi-)automatically in both datasets retrospectively by two experienced readers who visually assessed image quality employing a 4-point grading scale. Scan times and image quality were compared using the Wilcoxon rank-sum test. Volumetrics were assessed with linear regression and Bland–Altman analyses, and measurement agreement with intraclass correlation coefficient (ICC). Results: Mean acquisition time was significantly reduced with the 3-RR deep learning cine compared to the standard cine sequence (45.5 ± 13.8 s vs. 218.3 ± 44.8 s; P < 0.001). No significant differences in biventricular volumetrics were found. Left ventricular (LV) mass was increased in the deep learning cine compared with the standard cine sequence (71.4 ± 33.1 g vs. 69.9 ± 32.5 g; P < 0.05). All volumetric measurements had an excellent agreement with ICC > 0.9 except for ejection fraction (EF) (LVEF 0.81, RVEF 0.73). The image quality of deep learning cine images was decreased for end-diastolic and end-systolic contours, papillary muscles, and valve depiction (2.9 ± 0.5 vs. 3.5 ± 0.4; P < 0.05). Conclusion: Deep learning cine volumetrics did not differ significantly from standard cine results except for LV mass, which was slightly overestimated with deep learning cine. Deep learning cine sequences result in a significant reduction in scan time with only slightly lower image quality. Graphical

Background: Radial k-space sampling is widely employed in paediatric magnetic resonance imaging (MRI) to mitigate motion and aliasing artefacts. Artificial intelligence (AI)-based image reconstruction has been developed to enhance image quality and accelerate acquisition time. Objective: To assess image quality of deep learning (DL)-based denoising image reconstruction of body MRI in children. Materials and methods: Children who underwent thoraco-abdominal MRI employing radial k-space filling technique (PROPELLER) with conventional and DL-based image reconstruction between April 2022 and January 2023 were eligible for this retrospective study. Only cases with previous MRI including comparable PROPELLER sequences with conventional image reconstruction were selected. Image quality was compared between DL-reconstructed axial T1-weighted and T2-weighted images and conventionally reconstructed images from the same PROPELLER acquisition. Quantitative image quality was assessed by signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the liver and spleen. Qualitative image quality was evaluated by three observers using a 4-point Likert scale and included presence of noise, motion artefact, depiction of peripheral lung vessels and subsegmental bronchi at the lung bases, sharpness of abdominal organ borders, and visibility of liver and spleen vessels. Image quality was compared with the Wilcoxon signed-rank test. Scan time length was compared to prior MRI obtained with conventional image reconstruction. Results: In 21 children (median age 7 years, range 1.5 years to 15.8 years) included, the SNR and CNR of the liver and spleen on T1-weighted and T2-weighted images were significantly higher with DL-reconstruction (P<0.001) than with conventional reconstruction. The DL-reconstructed images showed higher overall image quality, with improved delineation of the peripheral vessels and the subsegmental bronchi in the lung bases, sharper abdominal organ margins and increased visibility of the peripheral vessels in the liver and spleen. Not respiratory-gated DL-reconstructed T1-weighted images demonstrated more pronounced respiratory motion artefacts in comparison to conventional reconstruction (P=0.015), while there was no difference for the respiratory-gated T2-weighted images. The median scan time per slice was reduced from 6.3 s (interquartile range, 4.2 – 7.0 s) to 4.8 s (interquartile range, 4.4 – 4.9 s) for the T1-weighted images and from 5.6 s (interquartile range, 5.4 – 5.9 s) to 4.2 s (interquartile range, 3.9 – 4.8 s) for the T2-weighted images. Conclusion: DL-based denoising image reconstruction of paediatric body MRI sequences employing radial k-space sampling allowed for improved overall image quality at shorter scan times. Respiratory motion artefacts were more pronounced on ungated T1-weighted images. Graphical

Background/Objectives: This study assesses the outcomes of integrating deep learning-augmented zero echo time (ZTE DL) MRI sequences into standard MRI protocols for assessment of the hands and feet. Methods: In this single-center, retrospective study, a standard MRI protocol of hands and feet at 1.5 T was compared with the same protocol with an added ZTE DL sequence. Pathological changes in the bone, including subcortical sclerosis, osteophytes, joint space narrowing and fractures, were rated as present or absent. Indeterminate intraosseous lesions (erosions/ganglia) and indeterminate extraosseous lesions (ossicles/soft tissue calcifications) were additionally assessed on a semi-quantitative 4-point Likert scale. Diagnostic confidence was rated as low, moderate, or high. Standard MRI vs. ZTE-DL-augmented MRI comparisons were evaluated using a mixed-effects model with reader as a random effect, and X-ray vs. ZTE-DL-augmented MRI comparisons using a paired Wilcoxon signed-rank test. Interreader agreement (two readers) was assessed using Kappa statistics. Results: The cohort encompassed 59 datasets (feet = 22, hands = 37) of 40 patients with an average age of 51.79 (SD ± 11.84) years (58% women (n = 34), 42% men (n = 25)). Additional ZTE DL sequences resulted in similar findings, but with higher diagnostic confidence for assessment of bone changes when compared to conventional MR sequences (p-values < 0.05) and, overall, when compared to radiographs in a subgroup (p < 0.05 for five of six pathologic bone changes). Interreader agreement of diagnostic confidence was moderate to substantial (kappa 0.59–0.71). Conclusions: Addition of ZTE DL sequences to standard MRI protocols of hands and feet at 1.5 T demonstrated similar findings in the assessment of pathological bone changes, but with higher diagnostic confidence compared to conventional MR sequences and radiographs. However, further validation against a reference standard is required to determine diagnostic accuracy.

Deep learning (DL)-based reconstruction algorithms are increasingly integrated into clinical practice, offering the potential for shorter imaging times and improved image quality. This study is the first to evaluate whether DL-reconstructed magnetic resonance imaging (MRI) images affect ventilation and perfusion parameters derived from phase-resolved functional lung (PREFUL) MRI. This prospective study included 24 healthy volunteers (12 male, mean age: 28.9 ± 12.8 years) with normal lung function confirmed by spirometry. Free-breathing lung MRI was performed at 1.5T using 2D fast spoiled gradient echo sequences. Raw imaging data were reconstructed using both conventional non-DL- and DL-based methods. Image quality was assessed via signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). PREFUL MRI was used to derive regional ventilation (Rvent), flow-volume loop correlation metric (FVL-CM), perfusion (Q), ventilation and perfusion defect percentages (VDPs and QDPs), and ventilation-perfusion (V/Q) match. Parameters were compared using paired statistical tests and Bland-Altman analysis. DL-based reconstruction significantly improved image quality (SNR: 41.7 ± 19.5 vs 18.0 ± 6.5; contrast-to-noise: 179.9 ± 84.4 vs 75.3 ± 25.7; both P<.001). No significant differences were found between DL and non-DL reconstructions for any PREFUL-derived parameters. Bland-Altman analysis showed good agreement and minimal bias across all functional metrics and strong to near-perfect correlation was found (r=0.8998 to 0.9998, P<.001). DL-based image reconstruction enhances image quality in lung MRI without altering ventilation or perfusion quantification from PREFUL analysis. In healthy individuals with normal PFTs, this approach can be safely integrated into functional lung MRI workflows. These results highlight the preservation of image characteristics with DL-based reconstruction and the robustness of the PREFUL method.