Magnetic Resonance Imaging (MRI) is a crucial tool in clinical
diagnostics, with T1-weighted (T1) and T2-weighted (T2). Acquiring
high-quality T2-weighted MRI, especially for infant brains, presents
challenges due to lengthy acquisition times, motion artifacts, and
scanner variability. This study introduces the Adaptive Dual Domain
U-Net, a novel 3D U-Net architecture enhanced with dynamic channel
alignment for synthesizing T2-weighted MRI from T1-weighted inputs.
The proposed model addresses domain variability, integrates
explainability tools using Captum, and employs patch-based training
for efficient memory utilization and high-resolution reconstruction.
Quantitative evaluations on the iSeg-2019 dataset demonstrate
superior performance across key metrics such as Mean Squared Error
(MSE), Structural Similarity Index (SSIM), and R² compared to
baseline methods. Qualitative results highlight the model’s ability to
generate structurally accurate and clinically interpretable synthetic T2-
weighted images, making it a robust tool for both clinical and research applications.
Param Ahir1, Mehul Parikh2
Gujarat Technological University, India1, L. D. College of Engineering, India2
Magnetic Resonance Imaging, Deep Learning, Medical Imaging, Cross-Modality MRI Synthesis, Infant Brain MRI, 3D U-Net
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 15 , Issue: 3 , Pages: 3634 - 3638 )
Date of Publication :
January 2025
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