Abstract:
According to some embodiments, emission projection data and second source scan data are received. A prior map and a prior weight map are generated from second source scan data. A penalty function calculates voxel-wise differences between the prior map and a given image, transforms the voxel-wise differences and calculates a weighted sum of the transformed differences, using weights based on the prior weight map. Joint reconstruction of an emission image and an attenuation map proceeds iteratively and uses the penalty function.
Abstract:
The present discussion relates to the use of deep learning techniques to accelerate iterative reconstruction of images, such as CT, PET, and MR images. The present approach utilizes deep learning techniques so as to provide a better initialization to one or more steps of the numerical iterative reconstruction algorithm by learning a trajectory of convergence from estimates at different convergence status so that it can reach the maximum or minimum of a cost function faster.
Abstract:
Aspects of the invention relate to generating an emission activity image as well as an emission attenuation map using an iterative updation based on both the raw emission projection data and the raw radiography projection data, and an optimization function. The outputs include an optimized emission activity image, and at least one of an optimized emission attenuation map or an optimized radiography image. In some aspects an attenuated corrected emission activity image is obtained using the optimized emission activity image, and the optimized emission attenuation map.