Work

A Compressed Sensing Reconstruction Method Based on Untrained Convolutional Neural Network for Medical Imaging

Year: 2025

Type: article

Abstract: Medical imaging holds significant importance in disease diagnosis and surgical preparation, necessitating higher standards for the storage and transmission of medical images. We propose a method for compressed sensing reconstruction of medical imaging based on untrained convolutional neural networks. This method does not require any training data, offering vast potential in the field of medical imaging. We employ the generator model of Deep Convolutional Generative Adversarial Networks (DCGAN) as the structure for the untrained convolutional neural network. We utilized the Musculoskeletal Radiographs (MURA) and Open Access Series of Imaging Studies (OASIS) datasets to demonstrate that our method significantly improved the reconstruction effect in medical imaging, regardless of whether the sampling rate was high or low.

Source: Journal of Mechanics in Medicine and Biology

DOI: 10.1142/s0219519425400044

Authors: LU WANG, LONG CHENG, JINSONG SHEN, YI GU

Institution: Jiangnan University

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funders: Natural Science Foundation of Jiangsu Province, Fundamental Research Funds for the Central Universities