SPECT Angle Interpolation Based on Deep Learning Methodologies

TL;DR

This paper introduces a deep learning-based method for SPECT angle interpolation that enhances projection data quality, improves reconstruction accuracy, and effectively denoises sinograms, demonstrated on both phantom and real-world data.

Contribution

The paper presents a novel deep learning approach that simultaneously interpolates and denoises SPECT projections, significantly improving reconstruction quality over traditional methods.

Findings

Quadruples the number of projections effectively.

Reduces noise in sinograms while interpolating.

Enhances accuracy of SPECT image reconstruction.

Abstract

A novel method for SPECT angle interpolation based on deep learning methodologies is presented. Projection data from software phantoms were used to train the proposed model. For evaluation of the efficacy of the method, phantoms based on Shepp Logan, with various noise levels added were used, and the resulting interpolated sinograms are reconstructed using Ordered Subset Expectation Maximization (OSEM) and compared to the reconstructions of the original sinograms. The proposed method can quadruple the projections, and denoise the original sinogram, in the same process. As the results show, the proposed model significantly improves the reconstruction accuracy. Finally, to demonstrate the efficacy and capability of the proposed method results from real-world DAT-SPECT sinograms are presented.