Deep image prior cine MR fingerprinting with B1+ spin history correction

Abstract

AbstractPurposeTo develop a deep image prior (DIP) reconstruction for B1+‐corrected 2D cine MR fingerprinting (MRF).MethodsThe proposed method combines low‐rank (LR) modeling with a DIP to generate cardiac phase‐resolved parameter maps without motion correction, employing self‐supervised training to enforce consistency with undersampled spiral k‐space data. Two implementations were tested: one approach (DIP) for cine T1, T2, and M0 mapping, and a second approach (DIP with effective B1+ estimation [DIP‐B1]) that also generated an effective B1+ map to correct for errors due to RF transmit inhomogeneities, through‐plane motion, and blood flow. Cine MRF data were acquired in 14 healthy subjects and four reconstructions were compared: LR, low‐rank motion‐corrected (LRMC), DIP, and DIP‐B1. Results were compared to diastolic ECG‐triggered MRF, MOLLI, and T2‐prep bSSFP. Additionally, bright‐blood and dark‐blood images calculated from cine MRF maps were used to quantify ventricular function and compared to reference cine measurements.ResultsDIP and DIP‐B1 outperformed other cine MRF reconstructions with improved noise suppression and delineation of high‐resolution details. Within‐segment variability in the myocardium (reported as the coefficient of variation for T1/T2) was lowest for DIP‐B1 (2.3/8.3%) followed by DIP (2.7/8.7%), LRMC (3.5/10.5%), and LR (15.3/39.6%). Spatial homogeneity improved with DIP‐B1 having the lowest intersegment variability (2.6/4.1%). The mean bias in ejection fraction was −1.1% compared to reference cine scans.ConclusionA DIP reconstruction for 2D cine MRF enabled cardiac phase‐resolved mapping of T1, T2, M0, and the effective B1+ with improved noise suppression and precision compared to LR and LRMC.