Improved 3D Prostate MR Fingerprinting with Cross-Domain Spatio-Temporal Reconstruction Network

Motivation: 3D Magnetic Resonance Fingerprinting is time-consuming, requiring full-time measurements. Shortening scan time while maintaining data quality enhances MRF's clinical utility. Goal(s): Our goal was to develop reconstruction process for prostate MRF based on the neural network. This approach aims to improve image quality and parameter map accuracy. Approach: We introduced the neural network composed of a combination of CNN and ANN and utilized compressed dictionary, enabling efficient cross-domain utilization of information. Results: Our approach enhances quality and accuracy of generated parameter maps, demonstrating the potential to expedite MRF scans for prostate imaging. Impact: Our proposed scheme for accelerated MRF reconstruction can improve quantitative imaging, thus providing faster and more accurate prostate diagnosis and treatment. This development has positive impacts on patient care, reduces scanning times, and promotes additional research in medical image reconstruction.