Keywords: Quantitative Imaging, Image Reconstruction, Pixel-wise fitting network; Model-based deep learning
Motivation: Pixel-wise fitting networks are robust to error and enhance quantitative MRI (qMRI) over classical fitting. Combining them with qMRI reconstruction can achieve high performance in accelerated qMRI.
Goal(s): We propose qDC-CNN, combining a pixel-wise fitting network with an unrolled reconstruction network, improving qMRI reconstruction performance.
Approach: We simulated multi-slice multi-echo data using the Brainweb database, comparing five models with different reconstruction and parameter fitting networks.
Results: qDC-CNN provided the highest-quality image reconstructions among all tested models.
Impact: The exceptional reconstruction performance of qDC-CNN, which combines a pixel-wise fitting network with an unrolled reconstruction network, has broad applications in accelerating various quantitative MRI tasks, offering superior results and potential advancements in medical imaging and beyond.
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