Noise analysis, error estimates, and Gamma Radiation Measurement for limited detector computerized tomography application

TL;DR

This paper analyzes noise sources and error estimates in limited detector CT, proposing calibration methods and demonstrating that gamma radiation measurement with normal distribution minimizes noise in inverse reconstruction.

Contribution

It introduces a novel analysis combining CLT and KT-1 theorem for gamma radiation measurement, improving calibration for reduced noise in CT reconstruction.

Findings

Normal distribution data inflicts least noise in inverse recovery

Mutual conformity between CLT and KT-1 demonstrated for gamma radiation measurement

Calibration methods optimized for minimal error and noise in CT reconstruction

Abstract

Computed Tomography is one of the efficient and vital modalities of non-destructive techniques (NDT). Various factors influence the CT reconstruction result, including limited projection data, detector electronics optimization, background noise, detection noise, discretized nature of projection data, and many more. Radiation hardening and other aging factors that affect the operational settings may require recalibration of electronics parameters. Two well-known exercises are utilized with the motivation to improve reliability and accuracy in inverse recovery. The first exercise brute-forces an optimal candidate from the set of calibration methods for minimum error in inverse recovery. The second exercise, Kanpur Theorem-1 (KT-1) examines if optimal calibration sets electronics to impart minimum noise. The mutual conformity between statistics-derived CLT and Riemann integral transform-based KT-1 is shown first time using gamma radiation measurement. The analysis shows that measurement data with normal distribution inflicts the least noise in inverse recovery.