Cerebral infarction associated with moyamoya disease: histogram-based quantitative analysis of diffusion tensor imaging -- a preliminary study.
ABSTRACT Moyamoya disease (MMD) is a rare disorder of unknown etiology in which terminal portions of the internal carotid arteries become steno-occlusive, with fine collateral "moyamoya vessels" formed secondarily, resulting in serial ischemic strokes throughout its clinical course. Whole-brain histogram (WBH) of diffusion tensor imaging (WBH-DTI) is an analytical tool whose feasibility has been ascertained in various pathologies. To elucidate whether WBH-DTI could detect any difference between ischemic MMD and normal controls, we examined 27 consecutive MMD patients without hemorrhage and 48 normal controls in this prospective study using a 3.0-T magnetic resonance scanner. WBHs of fractional anisotropy (FA) (WBH-FA) and mean diffusivity (MD) (WBH-MD) were compared among three groups: Group 1, MMD patients with infarct (n=15); Group 2, MMD patients without infarct (n=12); and Group 3, normal controls (n=48). Group 1 showed significantly higher peak height and significantly lower mean value on WBH-FA, as well as significantly lower peak height and significantly higher mean value on WBH-MD, compared with Groups 2 and 3. No significant difference was seen in parameters at either WBH-FA or WBH-MD between Groups 2 and 3. These results might reflect the pathological severity of each group, and WBH-DTI could feasibly detect differences between ischemic MMD with infarction and MMD without infarction and normal controls.
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ABSTRACT: Current clinical studies involve multidimensional high-resolution images containing an overwhelming amount of structural and functional information. The analysis of such a wealth of information is becoming increasingly difficult yet necessary in order to improve diagnosis, treatment and healthcare. Voxel-wise analysis is a class of modern methods of image processing in the medical field with increased popularity. It has replaced manual region of interest (ROI) analysis and has provided tools to make statistical inferences at voxel level. The introduction of voxel-based analysis software in all modern commercial scanners allows clinical use of these techniques. This review will explain the main principles, advantages and disadvantages behind these methods of image analysis.Pediatric Radiology 05/2010; 40(12):1857-67. · 1.57 Impact Factor
- Clinical neurology and neurosurgery 02/2012; 114(7):1042-5. · 1.30 Impact Factor
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ABSTRACT: Prospective motion correction methods using an optical system, diffusion-weighted prospective acquisition correction, or a free induction decay navigator have recently been applied to correct for motion in diffusion tensor imaging. These methods have some limitations and drawbacks. This article describes a novel technique using a three-dimensional-echo planar imaging navigator, of which the contrast is independent of the b-value, to perform prospective motion correction in diffusion weighted images, without having to reacquire volumes during which motion occurred, unless motion exceeded some preset thresholds. Water phantom and human brain data were acquired using the standard and navigated diffusion sequences, and the mean and whole brain histogram of the fractional anisotropy and mean diffusivity were analyzed. Our results show that adding the navigator does not influence the diffusion sequence. With head motion, the whole brain histogram-fractional anisotropy shows a shift toward lower anisotropy with a significant decrease in both the mean fractional anisotropy and the fractional anisotropy histogram peak location (P < 0.01), whereas the whole brain histogram-mean diffusivity shows a shift toward higher diffusivity with a significant increase in the mean diffusivity (P < 0.01), even after retrospective motion correction. These changes in the mean and the shape of the histograms are recovered substantially in the prospective motion corrected data acquired using the navigated sequence. Magn Reson Med, 2012. © 2012 Wiley Periodicals, Inc.Magnetic Resonance in Medicine 01/2012; 68(4):1097-108. · 3.27 Impact Factor