Magnetic Resonance Angiography in the Diagnosis of Cerebral Arteriovenous Malformation and Dural Arteriovenous Fistulas: Comparison of Time-Resolved Magnetic Resonance Angiography and Three Dimensional Time-of-Flight Magnetic Resonance Angiography.

BACKGROUND: Traditional digital subtraction angiography (DSA) is currently the gold standard diagnostic method for the diagnosis and evaluation of cerebral arteriovenous malformation (AVM) and dural arteriovenous fistulas (dAVF). OBJECTIVES: The aim of this study was to analyze different less invasive magnetic resonance angiography (MRA) images, time-resolved MRA (TR-MRA) and three-dimensional time-of-flight MRA (3D TOF MRA) to identify their diagnostic accuracy and to determine which approach is most similar to DSA. PATIENTS AND METHODS: A total of 41 patients with AVM and dAVF at their initial evaluation or follow-up after treatment were recruited in this study. We applied time-resolved angiography using keyhole (4D-TRAK) MRA to perform TR-MRA and 3D TOF MRA examinations simultaneously followed by DSA, which was considered as a standard reference. Two experienced neuroradiologists reviewed the images to compare the diagnostic accuracy, arterial feeder and venous drainage between these two MRA images. Inter-observer agreement for different MRA images was assessed by Kappa coefficient and the differences of diagnostic accuracy between MRA images were evaluated by the Wilcoxon rank sum test. RESULTS: Almost all vascular lesions (92.68%) were correctly diagnosed using 4D-TRAK MRA. However, 3D TOF MRA only diagnosed 26 patients (63.41%) accurately. There were statistically significant differences regarding lesion diagnostic accuracy (P = 0.008) and venous drainage identification (P < 0.0001) between 4D-TRAK MRA and 3D TOF MRA. The results indicate that 4D-TRAK MRA is superior to 3D TOF MRA in the assessment of lesions. CONCLUSION: Compared with 3D TOF MRA, 4D-TRAK MRA proved to be a more reliable screening modality and follow-up method for the diagnosis of cerebral AVM and dAVF.

Correction of left ventricular wall thickening from short-axis cine MRI for basal-descent through-plane motion.

PURPOSE: To solve the problem of the basal descent movement in quantification of the regional left ventricular (LV) myocardial wall thickness (WTh) and wall thickening (%WT) in short-axis (SA) cine MRI for effectively assessing the regional wall motion of LV myocardium. MATERIALS AND METHODS: LV long-axis tagged MRI and SA cine MRI were performed to calculate the longitudinal translation and circumferential WTh of LV myocardium in eight normal volunteers. The new SA end-systolic thickness (EST) data were reconstructed from the original EST data, based on the quantified longitudinal translation of LV myocardium. RESULTS: The mean %WT of six segments in the basal section after correction was significantly different from that before correction in both intra- and inter-operator experiments. The polar map also showed the significant improvement of the variability of regional %WT and lack of quantification of %WT in the most basal SA slices after correction. CONCLUSION: The proposed technique demonstrated an important advantage to calculate the %WT in the most basal SA myocardial tissue, which was considered difficult to be achieved using cine MRI.

Quantitative analysis in clinical applications of brain MRI using independent component analysis coupled with support vector machine.

PURPOSE: To effectively perform quantification of brain normal tissues and pathologies simultaneously, independent component analysis (ICA) coupled with support vector machine (SVM) is investigated and evaluated for effective volumetric measurements of normal and lesion tissues using multispectral MR images. MATERIALS AND METHODS: Synthetic and real MR data of normal brain and white matter lesion (WML) data were used to evaluate the accuracy and reproducibility of gray matter (GM), white matter (WM), and WML volume measurements by using the proposed ICA+SVM method to analyze three sets of MR images, T1-weighted, T2-weighted, and proton density/fluid-attenuated inversion recovery images. RESULTS: The Tanimoto indexes of GM/WM classification in the normal synthetic data calculated by the ICA+SVM method were 0.82/0.89 for data with 0% noise level. As for clinical MR data experiments, the ICA+SVM method clearly extracted the normal tissues and white matter hyperintensity lesions from the MR images, with low intra- and inter-operator coefficient of variations. CONCLUSION: The experiments conducted provide evidence that the ICA+SVM method has shown promise and potential in applications to classification of normal and pathological tissues in brain MRI.