Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Spatially Regularized Parametric Map Reconstruction for Fast Magnetic
  Resonance Fingerprinting   Magnetic resonance fingerprinting (MRF) provides a unique concept for
simultaneous and fast acquisition of multiple quantitative MR parameters.
Despite acquisition efficiency, adoption of MRF into the clinics is hindered by
its dictionary matching-based reconstruction, which is computationally
demanding and lacks scalability. Here, we propose a convolutional neural
network-based reconstruction, which enables both accurate and fast
reconstruction of parametric maps, and is adaptable based on the needs of
spatial regularization and the capacity for the reconstruction. We evaluated
the method using MRF T1-FF, an MRF sequence for T1 relaxation time of water
(T1H2O) and fat fraction (FF) mapping. We demonstrate the method's performance
on a highly heterogeneous dataset consisting of 164 patients with various
neuromuscular diseases imaged at thighs and legs. We empirically show the
benefit of incorporating spatial regularization during the reconstruction and
demonstrate that the method learns meaningful features from MR physics
perspective. Further, we investigate the ability of the method to handle highly
heterogeneous morphometric variations and its generalization to anatomical
regions unseen during training. The obtained results outperform the
state-of-the-art in deep learning-based MRF reconstruction. The method achieved
normalized root mean squared errors of 0.048 $\pm$ 0.011 for T1H2O maps and
0.027 $\pm$ 0.004 for FF maps when compared to the dictionary matching in a
test set of 50 patients. Coupled with fast MRF sequences, the proposed method
has the potential of enabling multiparametric MR imaging in clinically feasible
time.

22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
16
The categories of this paper are: stat