BAYESIAN IMAGE RECONSTRUCTION FROM REDUCED K-SPACE DATA,

Northern California Institute/Res/Edu, San Francisco CA

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Abstract

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The overall goal of this project is to develop and evaluate a new Bayesian reconstruction method for low-resolution MRI modalities that reduce artifacts and effectively increase resolution relative to standard Discrete Fourier Transform approaches. The new reconstruction method fully utilizes k-space data to reduce artifacts and the increase in resolution is achieved by incorporating high-resolution information from segmented structural MRI scans acquired in the same scanning session. The focus of the work within the Resource Research application will be to directly apply, extend and validate the Bayesian reconstruction methodology. Perfusion-Weighted MR Imaging (PWI) is the particular modality chosen for application. The Specific Aims of the project are to 1) To apply the Bayesian low-resolution reconstruction algorithm to PWI;2) To validate the Bayesian algorithm relative to standard DFT reconstruction;3) To study the robustness of the Bayesian reconstruction method to miss-registration and segmentation error;and 4) To apply the Bayesian reconstruction methodology to a full clinical study. The Administrative Supplement project aims to AS1) extend methods to 3D GRASE and AS2) evaluate and validate K-Bayes for 3D GRASE.