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Purpose: Arterial Spin Labeling (ASL) is widely used in clinical research as a contrast-free MRI method for assessment of cerebral blood flow (CBF). While the recommended guideline for ASL acquisition is generally adopted to standardize quantification of CBF, ASL analysis still produces wide variability in CBF estimates, limiting research and clinical interpretation of ASL results. This study explored the extent of variability in ASL CBF quantification through the ISMRM OSIPI ASL MRI Challenge. The goal of the challenge was to minimize sources of variability in ASL analysis by establishing best practice in ASL data processing to make ASL analysis more reproducible and clinically meaningful. Methods: Eight international teams analyzed the challenge data consisting of a high-resolution T1- weighted anatomical image and ten pseudo-continuous ASL (PCASL) datasets. The datasets were simulated using an ASL digital reference object to produce ground-truth CBF values in normal and pathological states. The accuracy of CBF quantification from each team’s analysis was compared to ground-truth values across all voxels and within pre-defined brain regions. Reproducibility of CBF estimates across analysis pipelines was assessed using intra-class correlation coefficient (ICC), the limits of agreement (LOA) and the replicability of generating similar CBF estimates from the image processing approaches as documented. Results: The absolute errors in CBF estimates compared to the ground-truth synthetic data ranged from 18.36 to 48.12 ml/100g/min. Realistic motion incorporated in three of the ten synthetic data produced the largest absolute CBF error, largest variability between teams, and the least agreement (ICC and LOA) with ground truth results. Fifty percent (4/8) of the teams’ methods were replicated, and one method produced three times larger CBF errors (46.59 ml/100g/min) compared to submitted results. Conclusions: The apparent variability in CBF measurements, influenced by diff

For the segmentation of magnetic resonance brain images into anatomical regions, numerous fully automated methods have been proposed and compared to reference segmentations obtained manually. However, systematic differences might exist between the resulting segmentations, depending on the segmentation method and underlying brain atlas. This potentially results in sensitivity differences to disease and can further complicate the comparison of individual patients to normative data. In this study, we aim to answer two research questions: 1) to what extent are methods interchangeable, as long as the same method is being used for computing normative volume distributions and patient-specific volumes? and 2) can different methods be used for computing normative volume distributions and assessing patient-specific volumes? To answer these questions, we compared volumes of six brain regions calculated by five state-of-the-art segmentation methods: Erasmus MC (EMC), FreeSurfer (FS), geodesic information flows (GIF), multi-atlas label propagation with expectation–maximization (MALP-EM), and model-based brain segmentation (MBS). We applied the methods on 988 non-demented (ND) subjects and computed the correlation (PCC-v) and absolute agreement (ICC-v) on the volumes. For most regions, the PCC-v was good ((Formula presented.)), indicating that volume differences between methods in ND subjects are mainly due to systematic differences. The ICC-v was generally lower, especially for the smaller regions, indicating that it is essential that the same method is used to generate normative and patient data. To evaluate the impact on single-subject analysis, we also applied the methods to 42 patients with Alzheimer’s disease (AD). In the case where the normative distributions and the patient-specific volumes were calculated by the same method, the patient’s distance to the normative distribution was assessed with the z-score. We determined the diagnostic value of this z-score, which sho, ImPhys/Medical Imaging, ImPhys/Computational Imaging

For the segmentation of magnetic resonance brain images into anatomical regions, numerous fully automated methods have been proposed and compared to reference segmentations obtained manually. However, systematic differences might exist between the resulting segmentations, depending on the segmentation method and underlying brain atlas. This potentially results in sensitivity differences to disease and can further complicate the comparison of individual patients to normative data. In this study, we aim to answer two research questions: 1) to what extent are methods interchangeable, as long as the same method is being used for computing normative volume distributions and patient-specific volumes? and 2) can different methods be used for computing normative volume distributions and assessing patient-specific volumes? To answer these questions, we compared volumes of six brain regions calculated by five state-of-the-art segmentation methods: Erasmus MC (EMC), FreeSurfer (FS), geodesic information flows (GIF), multi-atlas label propagation with expectation–maximization (MALP-EM), and model-based brain segmentation (MBS). We applied the methods on 988 non-demented (ND) subjects and computed the correlation (PCC-v) and absolute agreement (ICC-v) on the volumes. For most regions, the PCC-v was good ((Formula presented.)), indicating that volume differences between methods in ND subjects are mainly due to systematic differences. The ICC-v was generally lower, especially for the smaller regions, indicating that it is essential that the same method is used to generate normative and patient data. To evaluate the impact on single-subject analysis, we also applied the methods to 42 patients with Alzheimer’s disease (AD). In the case where the normative distributions and the patient-specific volumes were calculated by the same method, the patient’s distance to the normative distribution was assessed with the z-score. We determined the diagnostic value of this z-score, which sho

Both normal aging and neurodegenerative diseases such as Alzheimer's disease cause morphological changes of the brain. To better distinguish between normal and abnormal cases, it is necessary to model changes in brain morphology owing to normal aging. To this end, we developed a method for analyzing and visualizing these changes for the entire brain morphology distribution in the general aging population. The method is applied to 1000 subjects from a large population imaging study in the elderly, from which 900 were used to train the model and 100 were used for testing. The results of the 100 test subjects show that the model generalizes to subjects outside the model population. Smooth percentile curves showing the brain morphology changes as a function of age and spatiotemporal atlases derived from the model population are publicly available via an interactive web application at agingbrain.bigr.nl., ImPhys/Quantitative Imaging