Advanced Image Domain Adaptation and Multi-Angle Reconstruction in Medical Imaging Using Deep Neural Models
Year: 2025
Type: article
Abstract: This research presents a Conditional Generative Adversarial Network (CGAN)-based method designed to create various imaging perspectives from one 2D medical image for the substitute creation of 3D imaging outputs which avoid extra scanning requirements.The model produces 90° , 180° and 270° rotated views from axial slices based on the 167 highresolution 3D T1-weighted MRI scans of healthy subjects found in the Calgary-Campinas Public Dataset.Using deep convolutional layers and the Adam optimizer with 0.001 learning rate the CGAN architecture reaches its optimal condition.The training process was done through 1057 batches each time the model completed one iteration.The model demonstrates its effectiveness through evaluation metrics which produce PSNR results up to 35.6dB together with SSIM results up to 0.8 and MSE values that indicate superior reconstruction quality.The presented technique presents a safer and more economical solution to traditional 3D imaging techniques which minimizes radiation exposure in patients while avoiding strong magnetic fields.The model shows a potential to enhance diagnosis abilities by condensing it into use particularly in diagnosis institutions where only a few facilities have access to the use of modern imaging apparatus.
Source: Traitement du signal
DOI: 10.18280/ts.420541
Authors: Mayas Aljibawi, Swathi Nadipineni, Hemant Amhia, Vijay Dhote, Sundarappan Balamuralitharan, Ellappan Mohan, Saurabh Singh, Vandana Roy
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4415720123