Modeling and Acceleration of Maximum A Posteriori Reconstruction from Large CT Datasets

Friday, November 18, 2011 - 9:45am - 10:45am
Keller 3-180
Jean-Baptiste Thibault (GE Healthcare)
Recent increases in detector coverage and trigger frequencies have opened up new clinical applications in modern Computed Tomography, but have also led to an explosion in the volume of CT raw datasets. This represents a particular challenge for accurate tomographic image reconstruction, particularly when using a model-based iterative framework based on Maximum A Posteriori estimation. Inclusion of detector physics, tube response, noise statistics, and image modeling involves certain complexity that drives up reconstruction time. However, recent results have started to demonstrate the significant potential of model-based iterative reconstruction for ultra-low-dose imaging aimed at improving patient safety, as well as high quality results in other targeted applications such as low contrast complex abdomen imaging and high-resolution medullar and cortical bone. This poses a particular challenge to come up with fast convergent algorithms that do not trade-off significant quality for speed, and are amenable to modern parallel computing hardware.

This talk will present the modeling framework for high quality model-based tomographic reconstruction and its advantages relative to alternative iterative approaches designed primarily with concern about reconstruction speed. In the proposed approach, speed and quality can be thought of as relatively orthogonal design elements, to the extent that convergence is reasonably achieved. First, the formulation of the optimization problem fully defines the target quality level as a function of the number and accuracy of the models designed to explicitly explain x-ray attenuation measurements based on realistic modeling of scanner behavior and non-idealities. Second, a globally convergent optimization algorithm chosen among a variety of potential alternatives is optimized to realize the performance targets for fast convergence, efficient implementation, and massive parallelization for practical applications. The development and continuous amelioration of such tools and models for tomographic reconstruction promise the establishment of a new platform for iterative reconstruction in modern CT that may someday replace standard analytical methods for routine high-quality low-dose imaging.