To produce a black body angiographic image representation of a subject (42), an imaging scanner (10) acquires imaging data that includes balck blood vascular contrast. A reconstruction processor (38) reconstructs a gray scale image representation (100) from the imaging data. A post-acquisition processor (46) transforms (130) the image representation (100) into a pre-processed image representation (132). The processor (46) assigns (134) each image element into one of a plurality of classes including a black class corresponding to imaged vascular structures, bone, and air, and a gray class corresponding to other non-vascular tissues. The processor (46) averages (320) intensities of the image elements of the gray class to obtain a mean gray intensity value (322), and replaces (328) the values of image elements comprising non-vascular structures of the black class with the mean gray intensity value.

Black blood angiography method and apparatus

Pour produire une representation en image angiographique a sang noir d'un sujet (42), on utilise un scanner d'imagerie (10) qui procede a l'acquisition de donnees d'image contenant un contraste vasculaire de sang noir. Un processeur de construction (38) reconstruit une representation en image dans l'echelle des gris (100) a partir de ces donnees d'image. Un processeur de post-acquisition (46) transforme (130) cette representation en image (100) en une representation en image pretraitee (132). Ce processeur attribue (134) chaque element d'image dans une classe parmi plusieurs classes comportant une classe de noir correspondant aux structures vasculaires, aux structures osseuse et a l'air representes, et une classe de gris correspondant aux autres tissus non vasculaires. Le processeur (46) calcule la moyenne (320) des intensites de ces elements d'image de la classe de gris, pour obtenir une valeur d'intensite de gris moyenne (322), et il remplace (328) les valeurs des elements d'image comprenant les structures non vasculaires de la classe de noir par la valeur d'intensite de gris moyenne.

Procede et appareil d'angiographie a sang noir

Cardiac NMR imaging experiments yield massive amounts of data, thus eliminating manual processing as a practical analysis approach. We describe a novel, fast, and reliable statistical segmentation algorithm for automatically determining left and right ventricular volume. The algorithm first locates that portion of the image containing the ventricles, analyzes the histogram via unsupervised parametric clustering to determine suitable intensity ranges for blood and tissue, and then applies a region growing to determine ventricular area. A similar procedure is followed for each of the numerous anatomic and temporal slices obtained in the experiment, with past results used to guide segmentation of the remaining slices.

http://ieeexplore.ieee.org/iel4/1015/7498/00683859.pdf

A. Apicella

Papers

Segmentation Of Ventricles In Short Axis NMR Black Blood Images