Purpose: To monitor changes in the number of cerebral microbleeds (CMBs) in a longitudinal study of healthy controls (HC) and mild-cognitively impaired (MCI) patients using susceptibility weighted imaging (SWI).Materials and Methods: SWI was used to image 28 HC and 75 MCI patients annually at 1.5 Tesla over a 4-year period. Magnitude and phase data were used to visualize CMBs for the first and last scans of 103 subjects.Results: Preliminary analysis revealed that none of the 28 HC had more than three CMBs. In the 75 MCI patients, five subjects had more than three CMBs in both first and last scans, while one subject had more than three bleeds only in the last scan. In five of these six MCI patients, the number of CMBs increased over time and all six went on to develop progressive cognitive impairment (PCI). Of the 130 total CMBs seen in the last scans of the six MCI cases, most were less than 4 mm in diameter.Conclusion: SWI can reveal small CMBs on the order of 1 mm in diameter and this technique can be used to follow their development longitudinally. Monitoring CMBs may be a means by which to evaluate patients for the presence of microvascular disease that leads to PCI.

Cerebral microbleeds (CMB) are increasingly being recognized as an important biomarker for neurovascular diseases. So far all attempts to count and quantify them have relied on manual methods that are time consuming and can be inconsistent. A technique is presented that semi-automatically identifies CMBs in susceptibility weighted images (SWI). This will both reduce the processing time and increase the consistency over manual methods. This technique relies on a statistical thresholding algorithm to identify hypointensities within the image. A support vector machine (SVM) supervised learning classifier is then used to separate true CMB from other marked hypointensities. The classifier relies on identifying features such as shape and signal intensity to identify true CMBs. The results from the automated section are then subject to manual review to remove false positives. This technique is able to achieve a sensitivity of 81.7% compared to the gold standard of manual review and consensus by multiple reviewers. In subjects with many CMBs this presents a faster alternative to current manual techniques at the cost of some lost sensitivity.

BACKGROUND AND PURPOSE The mesencephalon is involved in a number of human neurodegenerative disorders and has been typically imaged with T1-, T2- and T2*-weighted methods. Our aim was to collect high-contrast susceptibility-weighted imaging (SWI) data to differentiate among and within the basic mesencephalic structures: namely, the red nucleus, substantia nigra, and crus cerebri. MATERIALS AND METHODS High-resolution SWI, 3D T1-weighted, and T2-weighted data were collected to study contrast in the mesencephalon at 1.5T and 4T. Contrast between structures was calculated for SWI high-pass (HP)-filtered-phase, T1 gradient-echo, and spin-echo T2-weighted data. RESULTS SWI HP-filtered-phase data revealed similar contrast for the red nucleus and substantia nigra when compared with T2-weighted imaging. However, SWI was able to show structures within the red nucleus, substantia nigra, and medial geniculate body that were invisible on T2-weighted imaging. T1-weighted imaging, on the other hand, did not reveal measurable contrast for any of the structures of interest. SWI HP-filtered-phase data at 4T agreed well with india ink – stained cadaver brain studies, which appear to correlate with capillary density. CONCLUSIONS With SWI, it is possible to create better anatomic images of the mesencephalon, with improved contrast compared with conventional T1- or T2-weighted sequences.