MODEL-BASED ESTIMATION WITH BOUNDARY SIDE INFORMATION OR BOUNDARY REGULARIZATION

We have previously developed a model-based strategy for joint estimati on of myocardial perfusion and boundaries using ECT (Emission Computed Tomography). We have also reported difficulties with boundary estimat ion in low contrast and low count rate situations. In this paper, we p ropose using boundary side information (obtainable from high resolutio n MRI and CT images) or boundary regularization to improve both perfus ion and boundary estimation in these situations. To fuse boundary side information into the emission measurements, we formulate a joint log- likelihood function to include auxiliary boundary measurements as well as ECT projection measurements. In addition, we introduce registratio n parameters to align auxiliary boundary measurements with ECT measure ments and jointly estimate these parameters with other parameters of i nterest from the composite measurements. In simulated PET 0-15 water m yocardial perfusion studies using a simplified model, we show that the joint estimation improves perfusion estimation performance and gives boundary alignment accuracy of <0.5 mm even at 0.2 million counts. We implement boundary regularization through formulating a penalized log- likelihood function. We also demonstrate in simulations that simultane ous regularization of the epicardial boundary and myocardial thickness gives comparable perfusion estimation accuracy with the use of bounda ry side information.