Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence
Motivation: 3D quantitative MRI presents a challenging inverse problem. The application of learned reconstruction methods is hindered by the need for extensive training data and the large size of high-resolution voxel representations of multi-dimensional data. Implicit neural fields have shown promise in cine imaging and slice-to-volume registration.
Goal(s): Explore the use of neural fields for representing 3D high-resolution quantitative parameters in qMRI.
Approach: We integrate motion correction, sensitivity map estimation, and 3D parameter neural fields into an end-to-end, scan-specific optimization without training data.
Results: Demonstration of feasibility in the context of cardiac qMRI and initial results of whole-heart 3D T1 maps.
Impact: Introduction of implicit neural fields into qMRI, allowing for continuous representation of the quantitative parameters in 3D space. Our novel end-to-end reconstruction with motion correction, sensitivity map estimation provides fast high-resolution, whole-heart T1-maps without relying on training data.
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