A geometric based preprocessing for weighted ray transforms with applications in SPECT

TL;DR

This paper explores a geometric preprocessing method for weighted ray transforms in 3D, improving noise reduction in SPECT image reconstructions by reducing data complexity before applying standard algorithms.

Contribution

It introduces a geometric reduction technique transforming 3D weighted ray data into 2D plane data, enhancing noise robustness in SPECT imaging reconstructions.

Findings

Significant noise reduction in SPECT reconstructions.

Effective data simplification via geometric reduction.

Improved image quality demonstrated in numerical tests.

Abstract

In this work we investigate numerically the reconstruction approach proposed in Goncharov, Novikov, 2016, for weighted ray transforms (weighted Radon transforms along oriented straight lines) in 3D. In particular, the approach is based on a geometric reduction of the data modeled by weighted ray transforms to new data modeled by weighted Radon transforms along two-dimensional planes in 3D. Such reduction could be seen as a preprocessing procedure which could be further completed by any preferred reconstruction algorithm. In a series of numerical tests on modelized and real SPECT (single photon emission computed tomography) data we demonstrate that such procedure can significantly reduce the impact of noise on reconstructions.