A CNN-based denoising method trained with images acquired with electron density phantoms for thin-sliced coronary artery calcium scans.

ABSTRACT:

Purpose

This work proposed a convolutional neural network (CNN)-based method trained with images acquired with electron density phantoms to reduce quantum noise for coronary artery calcium (CAC) scans reconstructed with slice thickness less than 3 mm.

Methods

A DenseNet model was used to estimate quantum noise for CAC scans reconstructed with slice thickness of 0.5, 1.0 and 1.5 mm. Training data was acquired using electron density phantoms in three different sizes. The label images of the CNN model were real noise maps, while the input images of the CNN model were pseudo noise maps. Image denoising was conducted by subtracting the CNN output images from thin-sliced CAC scans. The efficacy of the proposed method was verified through both phantom study and patient study.

Re

SUBMITTER: Yang CC 

PROVIDER: S-EPMC10930013 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature