Keywords: Other AI/ML, Brain, Controllable Synthesis
Motivation: Clinical scan time constraints limit the number of modalities and weighting degrees of images acquired. Generating high-quality images with controlled weighting from limited data can significantly enhance diagnostic accuracy.
Goal(s): To develop a generalizable CW-GAN for synthesizing MRI images with controllable contrast and weighting degrees, while improving cross-dataset generalization.
Approach: CW-GAN learns target modality features through adversarial training, using special masks as priori information and data enhancement for improved generalization.
Results: CW-GAN produces high-quality MR images with adjustable weighting, outperforming CycleGAN and pGAN in both image quality and controllability.
Impact: CW-GAN provides a powerful tool for MRI image synthesis with controlled weighting, outperforming existing methods in generalization and controllability, offering valuable potential for clinical applications and advancing MRI deep learning-based tasks.
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