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Background and Purpose: We aimed to test whether synthetic T1-weighted imaging derived from a post-contrast Quantitative Transient-state Imaging (QTI) acquisition enabled revealing pathological contrast enhancement in intracranial lesions., Methods: The analysis included 141 patients who underwent a 3 Tesla-MRI brain exam with intravenous contrast media administration, with the post-contrast acquisition protocol comprising a three-dimensional fast spoiled gradient echo (FSPGR) sequence and a QTI acquisition. Synthetic T1-weighted images were generated from QTI-derived quantitative maps of relaxation times and proton density. Two neuroradiologists assessed synthetic and conventional post-contrast T1-weighted images for the presence and pattern of pathological contrast enhancement in intracranial lesions. Enhancement volumes were quantitatively compared., Results: Using conventional imaging as a reference, synthetic T1-weighted imaging was 93% sensitive in revealing the presence of contrast enhancing lesions. The agreement for the presence/absence of contrast enhancement was almost perfect both between readers (k = 1 for both conventional and synthetic imaging) and between sequences (k = 0.98 for both readers). In 91% of lesions, synthetic T1-weighted imaging showed the same pattern of contrast enhancement visible in conventional imaging. Differences in enhancement pattern in the remaining lesions can be due to the lower spatial resolution and the longer acquisition delay from contrast media administration of QTI compared to FSPGR. Overall, enhancement volumes appeared larger in synthetic imaging., Conclusions: QTI-derived post-contrast synthetic T1-weighted imaging captures pathological contrast enhancement in most intracranial enhancing lesions. Further comparative studies employing quantitative imaging with higher spatial resolution is needed to support our data and explore possible future applications in clinical trials., (© 2024 The Authors. Journal of Neuroimaging published by Wiley Periodicals LLC on behalf of American Society of Neuroimaging.)

Voluntary and involuntary patient motion is a major problem for data quality in clinical routine of Magnetic Resonance Imaging (MRI). It has been thoroughly investigated and, yet it still remains unresolved. In quantitative MRI, motion artifacts impair the entire temporal evolution of the magnetization and cause errors in parameter estimation. Here, we present a novel strategy based on residual learning for retrospective motion correction in fast 3D whole-brain multiparametric MRI. We propose a 3D multiscale convolutional neural network (CNN) that learns the non-linear relationship between the motion-affected quantitative parameter maps and the residual error to their motion-free reference. For supervised model training, despite limited data availability, we propose a physics-informed simulation to generate self-contained paired datasets from a priori motion-free data. We evaluate motion-correction performance of the proposed method for the example of 3D Quantitative Transient-state Imaging at 1.5T and 3T. We show the robustness of the motion correction for various motion regimes and demonstrate the generalization capabilities of the residual CNN in terms of real-motion in vivo data of healthy volunteers and clinical patient cases, including pediatric and adult patients with large brain lesions. Our study demonstrates that the proposed motion correction outperforms current state of the art, reliably providing a high, clinically relevant image quality for mild to pronounced patient movements. This has important implications in clinical setups where large amounts of motion affected data must be discarded as they are rendered diagnostically unusable., Competing Interests: Declaration of Competing Interest Carolin M. Pirkl and Marion I. Menzel are employees at GE Healthcare, Munich, Germany. Matteo Cencini and Michela Tosetti receive research funding from GE Healthcare. All other authors declare that they do not have any financial or non-financial conflict of interests., (Copyright © 2022. Published by Elsevier B.V.)

Magnetic resonance fingerprinting (MRF) is highly promising as a quantitative MRI technique due to its accuracy, robustness, and efficiency. Previous studies have found high repeatability and reproducibility of 2D MRF acquisitions in the brain. Here, we have extended our investigations to 3D MRF acquisitions covering the whole brain using spiral projection k-space trajectories. Our travelling head study acquired test/retest data from the brains of 12 healthy volunteers and 8 MRI systems (3 systems at 3 T and 5 at 1.5 T, all from a single vendor), using a study design not requiring all subjects to be scanned at all sites. The pulse sequence and reconstruction algorithm were the same for all acquisitions. After registration of the MRF-derived PD T 1 and T 2 maps to an anatomical atlas, coefficients of variation (CVs) were computed to assess test/retest repeatability and inter-site reproducibility in each voxel, while a General Linear Model (GLM) was used to determine the voxel-wise variability between all confounders, which included test/retest, subject, field strength and site. Our analysis demonstrated a high repeatability (CVs 0.7-1.3% for T1, 2.0-7.8% for T 2 , 1.4-2.5% for normalized PD) and reproducibility (CVs of 2.0-5.8% for T 1 , 7.4-10.2% for T 2 , 5.2-9.2% for normalized PD) in gray and white matter. Both repeatability and reproducibility improved when compared to similar experiments using 2D acquisitions. Three-dimensional MRF obtains highly repeatable and reproducible estimations of T 1 and T 2 , supporting the translation of MRF-based fast quantitative imaging into clinical applications., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)