Automatic and reproducible positioning of phase-contrast MRI for the quantification of global cerebral blood flow.

Phase-Contrast MRI (PC-MRI) is a noninvasive technique to measure blood flow. In particular, global but highly quantitative cerebral blood flow (CBF) measurement using PC-MRI complements several other CBF mapping methods such as arterial spin labeling and dynamic susceptibility contrast MRI by providing a calibration factor. The ability to estimate blood supply in physiological units also lays a foundation for assessment of brain metabolic rate. However, a major obstacle before wider applications of this method is that the slice positioning of the scan, ideally placed perpendicular to the feeding arteries, requires considerable expertise and can present a burden to the operator. In the present work, we proposed that the majority of PC-MRI scans can be positioned using an automatic algorithm, leaving only a small fraction of arteries requiring manual positioning. We implemented and evaluated an algorithm for this purpose based on feature extraction of a survey angiogram, which is of minimal operator dependence. In a comparative test-retest study with 7 subjects, the blood flow measurement using this algorithm showed an inter-session coefficient of variation (CoV) of 4.07 ± 3.03%. The Bland-Altman method showed that the automatic method differs from the manual method by between -8% and 11%, for 95% of the CBF measurements. This is comparable to the variance in CBF measurement using manually-positioned PC MRI alone. In a further application of this algorithm to 157 consecutive subjects from typical clinical cohorts, the algorithm provided successful positioning in 89.7% of the arteries. In 79.6% of the subjects, all four arteries could be planned using the algorithm. Chi-square tests of independence showed that the success rate was not dependent on the age or gender, but the patients showed a trend of lower success rate (p = 0.14) compared to healthy controls. In conclusion, this automatic positioning algorithm could improve the application of PC-MRI in CBF quantification

Network topology changes in chronic mild traumatic brain injury (mTBI)

Background: In mild traumatic brain injury (mTBI), diffuse axonal injury results in disruption of functional networks in the brain and is thought to be a major contributor to cognitive dysfunction even years after trauma. Objective: Few studies have assessed longitudinal changes in network topology in chronic mTBI. We utilized a graph theoretical approach to investigate alterations in global network topology based on resting-state functional connectivity in veterans with chronic mTBI. Methods: 50 veterans with chronic mTBI (mean of 20.7 yrs. from trauma) and 40 age-matched controls underwent two functional magnetic resonance imaging scans 18 months apart. Graph theory analysis was used to quantify network topology measures (density, clustering coefficient, global efficiency, and modularity). Hierarchical linear mixed models were used to examine longitudinal change in network topology. Results: With all network measures, we found a significant group × time interaction. At baseline, brain networks of individuals with mTBI were less clustered (p = 0.03) and more modular (p = 0.02) than those of HC. Over time, the mTBI networks became more densely connected (p = 0.002), with increased clustering (p = 0.001) and reduced modularity (p < 0.001). Network topology did not change across time in HC. Conclusion: These findings demonstrate that brain networks of individuals with mTBI remain plastic decades after injury and undergo significant changes in network topology even at the later phase of the disease

Detection of left and right VAs, left and right ICAs using the middle slice of the thresholded image volume.

<p>The cross sections from the same object are represented by the same color.</p

Subject information in groups and the p values of the Chi-square tests of independence.

<p>Subject information in groups and the p values of the Chi-square tests of independence.</p

Determination of the scan positioning of the left VA.

<p>Top left panel shows the left VA derivative template, bottom left panel shows the left VA derivative of a subject. Red stars show the turning points and red triangles show the position of the scan plane. Right panel shows the positioning in 3D. The blue curve shows the left VA. The green points show the turning points. The red point and the yellow and green lines show the scan plane.</p

An ideal case of scan planning.

<p>The ICA scans were placed at the level of foramen magnum, perpendicular to the trajectory of the vessels. The VA scans were placed in the middle of the two turns at the level of cervical vertebra and , perpendicular to the trajectory of the vessels.</p

Illustration of the automatic PC-MRI scan positioning shown on 2D MIP images of the 3D axial TOF angiogram for 4 subjects.

<p>The trace of the VAs are shown in red lines and optimal PC-MRI scanning positions of the four major brain feeding arteries are shown in yellow lines, with artery centers shown as red stars.</p

Identification of the brain feeding arteries from the angiogram.

<p>Top left two panels show the maximum intensity projection (MIP) of the arteries in two planes. Top right panel shows the 3D segmentation results of the carotid arteries and vertebral arteries.</p