Denoising pediatric cardiac photon-counting CT data with sparse coding and data-adaptive, self-supervised deep learning.

ABSTRACT:

Background

The judicious use of CT in pediatric cardiac applications is warranted because young patients face the need for repeated imaging and increased lifetime cancer risk after ionizing radiation exposure. The quality of pediatric cardiac CT scans is variable because of limited protocols optimizations for pediatric patients, the common presence of metallic implants following treatment, and disparities in denoising algorithm performance between adult and pediatric scans. Two recent technological developments promise to improve the average quality of pediatric CT scans at fixed or reduced dose: clinical photon-counting CT (PCCT) and deep learning (DL) algorithms for CT image denoising. Given advancements to accommodate variable image quality, these technologies will deliver impro

SUBMITTER: Clark DP 

PROVIDER: S-EPMC12416440 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature