Systematic Review on Learning-based Spectral CT

Overview

Journal ArXiv

Date 2023 Jul 18

PMID 37461421

Authors

Alexandre Bousse

Venkata Sai Sundar Kandarpa

Simon Rit

Alessandro Perelli

Mengzhou Li

Guobao Wang

Jian Zhou

Ge Wang

Affiliations

Soon will be listed here.

Abstract

Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: dual-energy computed tomography (DECT) and photon-counting computed tomography (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT.

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