qDC-CNN: Model-based deep learning image reconstruction with a pixel-wise fitting network for accelerated quantitative MRI

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.