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A new approach for volumetric assessment of left ventricular function with MDCT
Author(s) -
Shuji Yamamoto,
Seiki Hamada,
Michael I. Miyamoto,
Jun Masumoto,
K Komizu,
L Linuma,
M Moriyana
Publication year - 2006
Publication title -
biomedical imaging and intervention journal
Language(s) - English
Resource type - Journals
ISSN - 1823-5530
DOI - 10.2349/biij.2.3.e50
Subject(s) - volume rendering , ventricle , segmentation , computer science , ejection fraction , cardiac cycle , artificial intelligence , volume (thermodynamics) , computer vision , image segmentation , cardiac imaging , image processing , region of interest , cardiac ventricle , medicine , rendering (computer graphics) , radiology , cardiology , image (mathematics) , heart failure , physics , quantum mechanics
Cardiovascular CT is considered the diagnostic standard for establishing the presence of a functional and dynamic imaging system. It is difficult, however, to estimate the ventricular motion and volumes that are processed using hundreds and thousands of CT images, in a few moments. The main concept and design of our work are two fold — the development of effective semi-automatic tools for measuring the sequential left ventricular volumes from the hundreds or thousands of cardiac trans-axial images, and providing a simple interface with an interactive diagnostic tool for the volumetry of left ventricle and valuable cardiac 4D visualisation. We converted ten and more sequential volume data sets of the heart acquired from retrospective ECG-gating helical scan into 3D images by volume rendering. These sequential 3D images could be displayed as a movie (4D cardiac image) file. Furthermore, we developed a method for semi-automatic calculation of ejection fraction (EF) and cardiac cycle (%)-volume (ml) curve for estimation of the motion and the volume of the left ventricle. This method involved the use an interactive selection tool in the region of interest (ROI). All 3D processing methods, such as, cutting objects, segmentation, and image fusion were based on mask processing data. We now describe the software developed for cardiac 4D imaging and the estimation of ventricular volume.

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