Abstract: A lung segmentation processor (40) is configured to classify magnetic resonance (MR) images based on noise characteristics. The MR segmenatation processor generates a lung region of interest (ROI) and detailed structure segmentation of the lung from the ROI. The MR segmentation processor performs an iterative normalization and region definition approach that captures the entire lung and the soft tissues within the lung accurately. Accuracy of the segmentation relies on artifact classification coming inherently from MR images. The MR segmentation processor (40) correlates segmented lung internal tissue pixels with the lung density to determine the attenuation coefficients based on the correlation. Lung densities are computed using MR data obtained from imaging sequences that minimize echo and acquisition times. The densities differentiate healthy tissues and lesions, which an attenuation map processor (36) uses to create localized attenuation maps for the lung.

Abstract: An image data processor (106) includes a structural image data processor (114) that employs a multi-structure atlas to segment a region of interest from structural image data that includes tissue of interest and that segments the tissue of interests from the region of interest. The image data processor further includes functional image data processor (116) that identifies the tissue of interest in functional image data based on the segmented tissue of interest. An image data processor includes a multi-structure atlas generator (104) that generates a multi-structure atlas. The multi-structure atlas physically maps structure to tissue of interest such that locate the structure in structural image data based on the multi-structure atlas localizes the tissue of interest to the region of interest.

Abstract: An apparatus comprises: an imaging system (10) configured to acquire emission data from a cyclically varying element; a monitoring instrument (20, 22) configured to measure the cyclical varying of the cyclically varying element; and an electronic device (40) configured to locate an image feature corresponding to the cyclically varying element in the acquired emission data based on correlation of time variation of the emission data with the cyclical varying of the cyclically varying element measured by the monitoring instrument. The located image feature may be verified by: thresholding a projection image generated from the emission data to generate a mask image; identifying in the mask image one of (i) a hollow circular feature, (ii) a hollow oval feature, (iii) a circular cavity feature, and (iv) an oval cavity feature; and verifying the located image feature based on whether the identifying operation is successful.

Abstract: A method for cardiac imaging for determining a myocardial region of interest (ROI) is disclosed. The method includes acquiring functional imaging data of a subject, where the functional imaging data includes at least the myocardium. A ROI encompassing at most the myocardium from the acquired functional imaging data, and diagnostic parameters relating to the myocardium are estimated and quantified based on the determined ROI. In one embodiment, the ROI is determined from a projection image representation utilizing histogram based thresholding and ray casting based localization to determine the extents of the ROI. In another embodiment, the ROI is determined from a volumetric image representation utilizing clustering Manhattan distance based cleaning to determine cardiac angles used for reorienting the left ventricle.

Abstract: A cardiac imaging method includes acquiring a projection image representation which includes a myocardium (S100). The myocardium is segmented and a mask is generated (S102). The mask is optimized (S104). A blood pool is determined from the optimized mask (S106) and the mask is skeletonized based on a clusterfication of the myocardial slices (S108). The center of mass is determined (S110) from the blood pool and the skeletonized mask. Myocardial parameters are determined (S112) from the skeletonized mask.

Abstract: A system (100) for processing a medical image, the system comprising an input (110) for receiving the medical image; a processor (120) for obtaining an image characteristic of the medical image; a categorizer (130) for obtaining a category of the medical image in dependence on the image characteristic; and an algorithm selector (140) for configuring a segmentation means (150) by selecting a segmentation algorithm amongst a plurality of segmentation algorithms in dependence on the category, for enabling the segmentation (150) means to segment the medical image with the segmentation algorithm for obtaining a region of interest.

Abstract: An apparatus comprises: an imaging system (10) configured to acquire emission data from a cyclically varying element; a monitoring instrument (20, 22) configured to measure the cyclical varying of the cyclically varying element; and an electronic device (40) configured to locate an image feature corresponding to the cyclically varying element in the acquired emission data based on correlation of time variation of the emission data with the cyclical varying of the cyclically varying element measured by the monitoring instrument.

Abstract: A method for cardiac imaging for determining a myocardial region of interest (ROI) is disclosed. The method includes acquiring functional imaging data of a subject, where the functional imaging data includes at least the myocardium. A ROI encompassing at most the myocardium from the acquired functional imaging data, and diagnostic parameters relating to the myocardium are estimated and quantified based on the determined ROI. In one embodiment, the ROI is determined from a projection image representation utilizing histogram based thresholding and ray casting based localization to determine the extents of the ROI. In another embodiment, the ROI is determined from a volumetric image representation utilizing clustering Manhattan distance based cleaning to determine cardiac angles used for reorienting the left ventricle.

Abstract: A method is provided for generating a metavolume for reconstructing an image. The method provides for acquiring a plurality of low-resolution images of an imaged volume, the plurality of low-resolution images obtained from one or more imaging planes and merging the plurality of low-resolution images using a statistical operation to generate a metavolume. Systems and computer programs that afford functionality of the type defined by this method may be provided by the present technique.

Abstract: An apparatus, system and method to determine a coordinate system of a heart includes an imager and a computer. The computer is programmed to acquire a first set of initialization imaging data from an anatomical region of a free-breathing subject. A portion of the first set of initialization imaging data includes organ data, which includes cardiac data. The computer is further programmed to determine a location of a central region of a left ventricle of a heart, where the location is based on the organ data and a priori information. The computer is also programmed to determine a short axis of the left ventricle based on the determined location, acquire a first set of post-initialization imaging data from the free-breathing subject from an imaging plane orientation based on the determination of the short axis, and reconstruct at least one image from the first set of post-initialization imaging data.

Abstract: A method is provided for generating a metavolume for reconstructing an image. The method provides for acquiring a plurality of low-resolution images of an imaged volume, the plurality of low-resolution images obtained from one or more imaging planes and merging the plurality of low-resolution images using a statistical operation to generate a metavolume. Systems and computer programs that afford functionality of the type defined by this method may be provided by the present technique.

Abstract: An apparatus, system and method to determine a coordinate system of a heart includes an imager and a computer. The computer is programmed to acquire a first set of initialization imaging data from an anatomical region of a free-breathing subject. A portion of the first set of initialization imaging data includes organ data, which includes cardiac data. The computer is further programmed to determine a location of a central region of a left ventricle of a heart, where the location is based on the organ data and a priori information. The computer is also programmed to determine a short axis of the left ventricle based on the determined location, acquire a first set of post-initialization imaging data from the free-breathing subject from an imaging plane orientation based on the determination of the short axis, and reconstruct at least one image from the first set of post-initialization imaging data.