DENSITY GUIDED ATTENUATION MAP GENERATION IN PET/MR SYSTEMS

摘要

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.

主权项

1. A lung module system, comprising:
a volume normalization module configured to normalize MR image data that expands intensity values of near zero intensity pixels and compresses intensity values of higher intensity pixels; a thresholding module configured to generate and apply a threshold for the MR data to differentiate and separate lung pixels from non-lung pixels in a binary volume in three-dimensions; a lung region of interest (ROI) module generating a lung ROI from the thresholded MR data; a cropping module configured to crop the initial volume of MR data according to the lung ROI; and a segmentation module segmenting the MR data to differentiate a lung comprising at least two of lung pixels, lesion pixels, or air pixels.