Advanced MRI Techniques and Applications

MRI2MRI transforms between brain MRI contrasts using a deep convolutional network, improving spatial registration across different imaging modalities.

The paper develops a defacing algorithm for MRI data to anonymize faces while preserving data integrity for subsequent processing.

The paper compares global and local image statistics between brain MRI images and natural scenes, finding significant differences that may affect human visual processing and machine analysis of MRI.

The paper introduces MRIQC Web-API, a resource for training automatic quality classifiers by crowdsourcing image quality metrics and expert ratings from MRI scans.

This paper shows that the magnitude PSF does not accurately represent the effects of k-space sampling and signal decay on spatial resolution in fMRI and proposes improved approximations.

This study tests IVIM as an alternative to ASL for brain perfusion imaging but finds IVIM does not measure perfusion parameters and instead detects cerebrospinal fluid signals.

This study shows low-level CO exposure reduces BOLD FMRI signals during specific tasks, suggesting CO may confound neuroimaging results.

The paper quantifies differences in spatial and temporal motion patterns between adults and infants during MRI, finding distinct motion modes and distribution characteristics.

This paper introduces a Bayesian approach to population receptive field mapping in fMRI data to quantify uncertainty in receptive field properties.