Quantitative Assessment of Synthetic MR with Deep Learning Reconstruction in Clinical Diagnosis of Nasopharyngeal Carcinoma

Motivation: To enhance nasopharyngeal carcinoma (NPC) diagnostics, this study aims to assess the accuracy and image quality of relaxometry maps using fast synthetic MRI with deep learning reconstruction. Goal(s): The primary goal is to evaluate the potential of DL Recon for NPC diagnosis, focusing on reduce scan time, improve image quality and quantitative accuracy to enable early lesion detection. Approach: Two protocols (Trad: lower acceleration rate without DL Recon, DLR: higher acceleration rate with DL Recon) was performed on twenty-four NPC patients to evaluated T1/T2/PD measurements and image quality. Results: Fast MAGiC acquisition with DL Recon can retain accuracy and improve image quality. Impact: With DL Recon, the MAGiC acquisition can achieve in shorter scan time, with enhanced image quality and maintained quantitative accuracy in NPC diagnosis use.