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Although free‐water diffusion reconstruction for diffusion‐weighted imaging (DWI) data can be applied to both single‐shell and multishell data, recent finding in synthetic data suggests that the free‐water indices from single‐shell acquisition should be interpreted with care, as they are heavily influenced by initialization parameters and cannot discriminate between free‐water and mean diffusivity modifications. However, whether using a longer multishell acquisition protocol significantly improve reconstruction for real human MRI data is still an open question. In this study, we compare canonical diffusion tensor imaging (DTI), single‐shell and multishell free‐water imaging (FW) indices derived from a short, clinical compatible diffusion protocol (b = 500 s/mm2, b = 1,000 s/mm2, 32 directions each) on their power to predict brain age. Age was chosen as it is well‐known to be related to widespread modification of the white matter and because brain‐age estimation has recently been found to be relevant to several neurodegenerative diseases. We used a previously developed and validated data‐driven whole‐brain machine learning pipeline to directly compare the precision of brain‐age estimates in a sample of 89 healthy males between 20 and 85 years old. We found that multishell FW outperform DTI indices in estimating brain age and that multishell FW, even when using low (500 ms2) b‐values secondary shell, outperform single‐shell FW. Single‐shell FW led to lower brain‐age estimation accuracy even of canonical DTI indices, suggesting that single‐shell FW indices should be used with caution. For all considered reconstruction algorithms, the most discriminant indices were those measuring free diffusion of water in the white matter. Multishell but not single‐shell free‐water diffusion imaging predicts brain age better than canonical diffusion imaging. Multishell free‐water in isolation performs similarly to more complex models including FA, MD, RD and AD.

Background and purpose Routine quality control procedures are still required for sCT based on artificial intelligence (AI) to verify the performance of the generators. The aim of this study was to evaluate three generators based on AI or bulk density (BD) assignment for the patient‐specific quality assurance (PSQA) of another AI‐based generator in clinical routine. A patient positioning study based on 2D/2D kV‐image comparing the performances of four sCT generators was also performed. Materials and methods On the four generators available commercially at our institution, one was chosen as the clinical one, and the three others were used for PSQA. Several dose metrics were calculated like the mean error, dose‐volume histogram metrics, and 1%/1 mm gamma analysis. A comparison against CT was considered as a reference. Translations and rotations found during patient positioning based on sCT were compared to those based on CT. Results Some of the metrics calculated against CT revealed patients outside the tolerances chosen (1% for point metrics; 90% for gamma pass rate). None of the generators was able to identify these outliers for all metrics studied. Performing a PSQA with other sCT generators introduced several false positives and false negatives. None of the generators was able to clearly identify, for all metrics studied, a true sCT failure caused by a metal implant. The smallest positioning deviations were found for the BD assignment sCT, the largest for the only AI generator not based on a T1 Dixon MR sequence. Conclusions PSQA of a sCT generator with another sCT generator should be performed with great care. Patient positioning is an important aspect to consider when evaluating a sCT generator. The results of this study should help medical physicists willing to set up a MR‐only workflow for the brain based on a 2D/2D kV‐image patient positioning.