  Magnetic Resonance Fingerprinting (MRF) is a time-efficient approach to
quantitative MRI for multiparametric tissue mapping. The reconstruction of
quantitative maps requires tailored algorithms for removing aliasing artefacts
from the compressed sampled MRF acquisitions. Within approaches found in the
literature, many focus solely on two-dimensional (2D) image reconstruction,
neglecting the extension to volumetric (3D) scans despite their higher
relevance and clinical value. A reason for this is that transitioning to 3D
imaging without appropriate mitigations presents significant challenges,
including increased computational cost and storage requirements, and the need
for large amount of ground-truth (artefact-free) data for training. To address
these issues, we introduce StoDIP, a new algorithm that extends the
ground-truth-free Deep Image Prior (DIP) reconstruction to 3D MRF imaging.
StoDIP employs memory-efficient stochastic updates across the multicoil MRF
data, a carefully selected neural network architecture, as well as faster
nonuniform FFT (NUFFT) transformations. This enables a faster convergence
compared against a conventional DIP implementation without these features.
Tested on a dataset of whole-brain scans from healthy volunteers, StoDIP
demonstrated superior performance over the ground-truth-free reconstruction
baselines, both quantitatively and qualitatively.
