Automated vessel segmentation using cross-correlation and pooled covariance matrix analysis

Towards the automatic computational assessment of enlarged perivascular spaces on brain magnetic resonance images: a systematic review

Enlarged perivascular spaces (EPVS), visible in brain MRI, are an important marker of small vessel disease and neuroinflammation. We systematically evaluated the literature up to June 2012 on possible methods for their computational assessment and analyzed confounds with lacunes and small white matter hyperintensities. We found six studies that assessed/identified EPVS computationally by seven different methods, and four studies that described techniques to automatically segment similar structures and are potentially suitable for EPVS segmentation. T2-weighted MRI was the only sequence that identified all EPVS, but FLAIR and T1-weighted images were useful in their differentiation. Inconsistency within the literature regarding their diameter and terminology, and overlap in shape, intensity, location, and size with lacunes, conspires against their differentiation and the accuracy and reproducibility of any computational segmentation technique. The most promising approach will need to combine various MR sequences and consider all these features for accurate EPVS determination.

Peripheral vessel scout imaging based on continuously moving table acquisition of projection data

A fast and spatially seamless peripheral vessel scout is desirable for subsequent planning of magnetic resonance (MR) angiography. We implemented a continuously moving table sequence providing projection data with time-of-flight contrast of the entire lower extremities in less than 2 minutes. Variation of arterial signal during the cardiac cycle and autocorrelation were exploited to enhance vessel-to-background contrast. Subjective image analysis revealed excellent vessel depiction, indicating that the proposed scout allows for seamless expedited visualization of major arterial structures.