PadGAN: An End-to-End dMRI Data Augmentation Method for Macaque Brain
Source abstract
Abstract
Currently, an increasing number of macaque brain MRI datasets are being made publicly accessible. Unlike human, publicly accessible macaque brain datasets suffer from data quality in diffusion magnetic resonance imaging (dMRI) data. Typically, dMRI data require a minimum ratio of 1:10 between low b-value (b < 10) volumes and high b-value (b > 300) volumes. However, the currently accessible macaque datasets do not meet this ratio. Due to site differences in macaque brain images, traditional human brain image-to-image translation models struggle to perform well on macaque brain images. Our work introduces a novel end-to-end primary-auxiliary dual generative adversarial network (PadGAN) for generating low b-value images. The auxiliary generator in the PadGAN is responsible for extracting the latent space features from peak information maps and transmitting them to the primary generator, enabling the primary generator to generate images with rich details. Experimental results demonstrate that PadGAN outperforms existing methods both qualitatively and quantitatively (mean SSIM increased by 0.1139). Diffusion probabilistic tractography using dMRI data augmented by our method yields superior results.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neuroimaging Techniques and Applications · Generative Adversarial Networks and Image Synthesis · Fetal and Pediatric Neurological Disorders
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
College of Computer and Information Engineering, Shanxi Technology and Business University, Taiyuan 030031, China
Funding
Shanxi Science and Technology Department · Natural Science Foundation of Shanxi · National Natural Science Foundation of China
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