Abstract
BACKGROUND: Magnetic resonance fingerprinting (MRF) could provide joint T1, T2, and proton density mapping. Measuring diffusion encoding using the MRF framework is promising, given its capacity to generate self-aligned quantitative maps and contrast-weighted images from a single scan. It could avoid potential errors that arise from the registration of multiple MRI images and reduce the total scan time. However, the application of a strong diffusion gradient on the MRF sequence results in phase inconsistency between acquisitions, which could corrupt the reconstructed images. PURPOSE: To propose a distortion-free diffusion-weighted imaging module for MRF (DWI-MRF) method using a self-navigated subspace reconstruction on k-space data obtained from a dual-density spiral trajectory. METHODS: The proposed sequence consisted of two segments: inversion prepared steady-state free precession MRF for the first 800 time points and diffusion-weighted imaging (DWI) with two nominal b-values of 0 and 800 s/mm(2) for the following 200 time points. The temporal basis was acquired from the densely sampled central k-space during reconstruction. The subspace reconstruction was applied to generate aliasing-free and high-resolution images at each time point. The cardiac gating was retrospectively performed on the high-resolution and dynamic DWI images. Our T1, T2, and apparent diffusion coefficient (ADC) results were compared to conventional methods on a phantom and two healthy volunteers. RESULTS: Our method's T1, T2, and ADC values agreed reasonably with the reference values, with a slope of 0.88, 0.94, and 1.04 for T1, T2, and ADC, and an R(2) value of 0.97, 0.97, and 0.71, respectively. The T1, T2, and ADC maps from DWI-MRF exhibited pixel-by-pixel correspondence on phantom and in vivo (T1 and ADC: R(2 )= 0.75 on phantom and 0.84 in vivo; T2 and ADC: R(2 )= 0.79 and 0.83, respectively). Our method achieved high acquisition efficiency, requiring less than 20 s per slice. CONCLUSIONS: The proposed method was free of artifacts from cardiac pulsation and generated pixel-wise correspondent T1, T2, and ADC maps on both phantom and in vivo images.