Testing SPECT Motion Correction Algorithms

Sklyar, Andrey V

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Testing SPECT Motion Correction Algorithms

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Frequently, testing of Single Photon Emission Computed Tomography (SPECT) motion correction algorithms is done either by using simplistic deformations that do not accurately simulate true patient motion or by applying the algorithms directly to data acquired from a real patient, where the true internal motion is unknown. In this work, we describe a way to combine these two approaches by using imaging data acquired from real volunteers to simulate the data that the motion correction algorithms would normally observe. The goal is to provide an assessment framework which can both: simulate realistic SPECT acquisitions that incorporate realistic body deformations and provide a ground truth volume to compare against. Every part of the motion correction algorithm needs to be exercised: from parameter estimation of the motion model, to the final reconstruction results. In order to build the ground truth anthropomorphic numerical phantoms, we acquire high resolution MRI scans and motion observation data of a volunteer in multiple different configurations. We then extract the organ boundaries using thresholding, active contours, and morphology. Phantoms of radioactivity uptake and density inside the body can be generated from these boundaries to be used to simulate SPECT acquisitions. We present results on extraction of the ribs, lungs, heart, spine, and the rest of the soft tissue in the thorax using our segmentation approach. In general, extracting the lungs, heart, and ribs in images that do not contain the spine works well, but the spine could be better extracted using other methods that we discuss. We also go in depth into the software development component of this work, describing the C++ coding framework we used and the High Level Interactive GUI Language (HLING). HLING solved a lot of problems but introduced a fair bit of its own. We include a set of requirements to provide a foundation for the next attempt at developing a declarative and minimally restrictive methodology for writing interactive image processing applications in C++ based on lessons learned during the development of HLING.

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