Myocardial perfusion imaging (MPI) with single-photon emission computed
tomography (SPECT) is a widely used and cost-effective diagnostic tool for
coronary artery disease. However, the lengthy scanning time in this imaging
procedure can cause patient discomfort, motion artifacts, and potentially
inaccurate diagnoses due to misalignment between the SPECT scans and the
CT-scans which are acquired for attenuation compensation. Reducing projection
angles is a potential way to shorten scanning time, but this can adversely
impact the quality of the reconstructed images. To address this issue, we
propose a detection-task-specific deep-learning method for sparse-view MPI
SPECT images. This method integrates an observer loss term that penalizes the
loss of anthropomorphic channel features with the goal of improving performance
in perfusion defect-detection task. We observed that, on the task of detecting
myocardial perfusion defects, the proposed method yielded an area under the
receiver operating characteristic (ROC) curve (AUC) significantly larger than
the sparse-view protocol. Further, the proposed method was observed to be able
to restore the structure of the left ventricle wall, demonstrating ability to
overcome sparse-sampling artifacts. Our preliminary results motivate further
evaluations of the method.

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