کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
10712778 1025257 2011 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Automated vessel segmentation using cross-correlation and pooled covariance matrix analysis
موضوعات مرتبط
مهندسی و علوم پایه فیزیک و نجوم فیزیک ماده چگال
پیش نمایش صفحه اول مقاله
Automated vessel segmentation using cross-correlation and pooled covariance matrix analysis
چکیده انگلیسی
Time-resolved contrast-enhanced magnetic resonance angiography (CE-MRA) provides contrast dynamics in the vasculature and allows vessel segmentation based on temporal correlation analysis. Here we present an automated vessel segmentation algorithm including automated generation of regions of interest (ROIs), cross-correlation and pooled sample covariance matrix analysis. The dynamic images are divided into multiple equal-sized regions. In each region, ROIs for artery, vein and background are generated using an iterative thresholding algorithm based on the contrast arrival time map and contrast enhancement map. Region-specific multi-feature cross-correlation analysis and pooled covariance matrix analysis are performed to calculate the Mahalanobis distances (MDs), which are used to automatically separate arteries from veins. This segmentation algorithm is applied to a dual-phase dynamic imaging acquisition scheme where low-resolution time-resolved images are acquired during the dynamic phase followed by high-frequency data acquisition at the steady-state phase. The segmented low-resolution arterial and venous images are then combined with the high-frequency data in k-space and inverse Fourier transformed to form the final segmented arterial and venous images. Results from volunteer and patient studies demonstrate the advantages of this automated vessel segmentation and dual phase data acquisition technique.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Magnetic Resonance Imaging - Volume 29, Issue 3, April 2011, Pages 391-400
نویسندگان
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