Hybrid positron emission tomography/magnetic resonance imaging (PET/MR) opens new possibilities in multimodal multiparametric (m2p) image analyses. But even the simultaneous acquisition of positron emission tomography (PET) and magnetic resonance imaging (MRI) does not guarantee perfect voxel-by-voxel co-registration due to organs and distortions, especially in diffusion-weighted imaging (DWI), which would be, however, crucial to derive biologically meaningful information. Thus, our aim was to optimize fusion and voxel-wise analyses of DWI and standardized uptake values (SUVs) using a novel software for m2p analyses. Using research software, we evaluated the precision of image co-registration and voxel-wise analyses including the rigid and elastic 3D registration of DWI and [18F]-Fluorodeoxyglucose (FDG)-PET from an integrated PET/MR system. We analyzed DWI distortions with a volume-preserving constraint in three different 3D-printed phantom models. A total of 12 PET/MR-DWI clinical datasets (bronchial carcinoma patients) were referenced to the T1 weighted-DIXON sequence. Back mapping of scatterplots and voxel-wise registration was performed and compared to the non-optimized datasets. Fusion was rated using a 5-point Likert scale. Using the 3D-elastic co-registration algorithm, geometric shapes were restored in phantom measurements; the measured ADC values did not change significantly (F = 1.12, p = 0.34). Reader assessment showed a significant improvement in fusion precision for DWI and morphological landmarks in the 3D-registered datasets (4.3 ± 0.2 vs. 4.6 ± 0.2, p = 0.009). Most pronounced differences were noted for the chest wall (p = 0.006), tumor (p = 0.007), and skin contour (p = 0.014). Co-registration increased the number of plausible ADC and SUV combinations by 25%. The volume-preserving elastic 3D registration of DWI significantly improved the precision of fusion with anatomical sequences in phantom and clinical datasets. The research software allowed for a voxel-wise analysis and visualization of [18F]FDG-PET/MR data as a “combined diffusivity–metabolic index” (cDMI). The clinical value of the optimized PET/MR biomarker can thus be tested in future PET/MR studies.

Objectives: To describe typical MRI findings 1 year after reverse total shoulder arthroplasty (rTSA) implantation. Materials and methods: Metal artifact reduction (MARS) MRI including CSSEMAC techniques was prospectively acquired in N = 25 consecutive patients (18/25 female; 61–80 years) 1 year after rTSA. MRI findings of bone and soft tissue and artifact reduction were assessed semi-quantitatively. Clinical Constant-Murley scores (CMS) were obtained, including its subscores pain, activities-of-daily-living (ADL), range-of-motion (ROM), and strength. Preoperative MRI was available in 13/25 subjects. Statistical analyses included descriptive statistics, Spearman correlations, nonparametric tests, and multivariable regression models. Results: MARS MRI of rTSA showed overall good image quality. Subacromial edema (88%), mild effusion (52%), and synovitis (44%) were frequent postoperative findings. Synovitis was associated with more pain ( B = −2.044, 95% CI [−3.617, −0.470], p = 0.039) and lower strength ( B = −4.497, 95% CI [−7.101, −1.893], p = 0.008). Minor bone marrow edema (BME) at the shaft was found in all subjects (100%). BME in Gruen zone 4 (88%) and 6 (80%) was most frequent. A higher number of Gruen zones with BME correlated significantly with lower ADL ( R = −0.532, p = 0.024). There was a significant increase in fatty infiltration between pre- and postoperative images for the midacromial deltoid muscle (1 (IQR, 1–1) versus 2 (IQR, 1–2), p = 0.008; postoperatively present 100%). It was associated with an increase in deltoid length (chi-square 4.35, p = 0.037), but not with inferior clinical scores. However, fatty infiltration of the more anterior deltoid muscle was associated with lower ADL ( B = −3.064, p = 0.008). Conclusion: Subacromial edema, minor shaft BME, and fatty infiltration of the midacromial deltoid muscle were typical, asymptomatic MRI findings 1 year after rTSA.

Objective: To evaluate the impact of accelerated, deep learning-based reconstructed T1-weighted VIBE Dixon images on fat-signal fraction (FSF) quantification compared with standard protocols. Methods: In this prospective single-center study, patients undergoing clinically indicated abdominal MRI underwent 3 T1-weighted VIBE acquisitions on a 1.5 T scanner: a standard sequence and 2 accelerated sequences (“fast” and “ultra-fast”). The accelerated scans employed higher CAIPIRINHA parallel imaging factors, partial Fourier sampling, and deep learning-based image reconstruction. Subsequently, whole-liver FSF was determined using a validated automated liver segmentation tool for in-phase and opposed-phase reconstructions. The quality of segmentation was assessed visually and by comparing liver volumes. Statistical analyses included calculation of mean absolute error and Spearman’s correlation for FSF agreement. Results: Between March 2025 and May 2025, 60 patients (mean age, 63.7 ± 13.9 y; 55% females) were enrolled. Acquisition times were 15 seconds for the standard sequence and 10 and 6 seconds for fast and ultra-fast sequences, respectively. The whole liver segmentations from the fast and ultra-fast sequences showed high correlations (ρ > 0.975, both P < 0.001) with minimal mean absolute error of 1.1% and 1.5% from the standard sequence. The liver fat quantification showed high concordance across protocols, too: median FSF was 2.3% (standard), 2.6% (fast), and 2.4% (ultra-fast), with a mean absolute error 0.92, P < 0.001). Conclusions: Liver fat quantification using highly accelerated, deep learning-enhanced MRI sequences enables reliable assessment of liver fat content with a significant reduction in scan time in low fat-fraction ranges.

Objectives: Vessel wall magnetic resonance imaging is important in the diagnosis of intracranial vascular diseases. Until now, adequate clinical implementation has required the use of 3 T scanners, which limits wider availability. This study aims to evaluate the interchangeability of a 3D turbo spin echo sequence with variable flip angle MRI sequence (T1 3D Sampling Perfection with Application optimized Contrast using different flip angle Evolution, SPACE) with deep learning image reconstruction for a 1.5 T scanner, with an established sequence (T1 3D SPACE) for a 3 T scanner. In addition, image quality parameters and diagnostic confidence are to be compared. Materials and Methods: Patients were prospectively examined using a T1-weighted turbo spin echo with variable flip angle at 3 T and a similar newly developed sequence with deep learning-based image reconstruction at 1.5 T. The images were read by 3 experienced radiologists regarding the presence of pathologic enhancement of the vessel wall for the interchangeability analysis. Image quality and diagnostic confidence were evaluated using 5-point Likert scales. Interreader and intrareader reliability were calculated for all parameters. Results: A total of 50 participants (42.6 ± 21.2 y, 26 males) were included. Regarding the presence of pathologic vessel wall enhancement, there was a high interreader agreement [Fleiss kappa = 0.72 (95% CI: 0.62; 0.81) for 1.5T, 0.78 (0.69; 0.88) for 3 T]. The sequences at 1.5 T and 3 T were interchangeable (IEI = 0.008). Image quality at 1.5 T was diagnostic in all cases, but rated superior at 3 T ( P < 0.001). Diagnostic confidence was also slightly but significantly higher at 3 T ( P < 0.001). Conclusions: The utilization of deep learning reconstruction enables vessel wall imaging in 1.5 T MRI. This new MRI sequence was interchangeable with established sequences at 3 T for the detection of pathologic vessel wall enhancement. This development could significantly improve the availability of vessel wall imaging in the clinical routine.

Myocardial and aortic stiffness are increasingly recognized as clinically relevant biomarkers for cardiovascular disease, yet their noninvasive quantification remains challenging. Magnetic resonance elastography (MRE) has emerged as a promising technique for the spatial mapping of tissue biomechanics by visualizing and analyzing propagating shear waves. This narrative review summarizes the current state of cardiovascular MRE, spanning technical developments in wave generation (acoustic, electromagnetic, gravitational, and transducer-free approaches), pulse sequence design (echo-planar imaging, gradient-recalled echo, spiral, and free-breathing three-dimensional acquisitions), and inversion algorithms (local frequency estimation, direct inversion, finite element methods, and multifrequency elastography). We present evidence from phantom validation, animal models (myocardial infarction, hypertension, right ventricular hypertrophy), and human studies encompassing cardiac amyloidosis, hypertrophic cardiomyopathy, diastolic dysfunction, and abdominal aortic aneurysm. Additionally, we discuss current challenges, including waveguide effects, standardization needs, and clinical translation barriers, and highlight emerging solutions through artificial intelligence, multifrequency methods, and transducer-free cardiac MRE.