Sixty-four-fold data reduction of chest radiographs using a super-resolution convolutional neural network.

ABSTRACT:

Objectives

To develop and validate a super-resolution (SR) algorithm generating clinically feasible chest radiographs from 64-fold reduced data.

Methods

An SR convolutional neural network was trained to produce original-resolution images (output) from 64-fold reduced images (input) using 128 × 128 patches (n = 127 030). For validation, 112 radiographs-including those with pneumothorax (n = 17), nodules (n = 20), consolidations (n = 18), and ground-glass opacity (GGO; n = 16)-were collected. Three image sets were prepared: the original images and those reconstructed using SR and conventional linear interpolation (LI) using 64-fold reduced data. The mean-squared error (MSE) was calculated to measure similarity between the reconstructed and original images, and image noise was

SUBMITTER: Nam JG 

PROVIDER: S-EPMC11027241 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature