Automated detection of periventricular veins on 7 T brain MRI

Cerebral small vessel disease is common in elderly persons and a leading cause of cognitive decline, dementia, and acute stroke. With the introduction of ultra-high field strength 7.0T MRI, it is possible to visualize small vessels in the brain. In this work, a proof-of-principle study is conducted to assess the feasibility of automatically detecting periventricular veins. Periventricular veins are organized in a fan-pattern and drain venous blood from the brain towards the caudate vein of Schlesinger, which is situated along the lateral ventricles. Just outside this vein, a region-of- interest (ROI) through which all periventricular veins must cross is defined. Within this ROI, a combination of the vesselness filter, tubular tracking, and hysteresis thresholding is applied to locate periventricular veins. All detected locations were evaluated by an expert human observer. The results showed a positive predictive value of 88% and a sensitivity of 95% for detecting periventricular veins. The proposed method shows good results in detecting periventricular veins in the brain on 7.0T MR images. Compared to previous works, that only use a 1D or 2D ROI and limited image processing, our work presents a more comprehensive definition of the ROI, advanced image processing techniques to detect periventricular veins, and a quantitative analysis of the performance. The results of this proof-of-principle study are promising and will be used to assess periventricular veins on 7.0T brain MRI.