Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Model-based Synthetic Data-driven Learning (MOST-DL): Application in
  Single-shot T2 Mapping with Severe Head Motion Using Overlapping-echo
  Acquisition   Data-driven learning algorithms have been successfully applied to facilitate
reconstruction of medical imaging. However, real-world data needed for
supervised learning are typically unavailable or insufficient, especially in
the field of magnetic resonance imaging (MRI). Use of synthetic training
samples has provided a potential solution for such problems, while the
challenges brought by various non-ideal situations were usually encountered,
especially under complex experimental conditions. In this study, a general
framework, MOdel-based SyntheTic Data-driven Learning (MOST-DL), was proposed
to generate pairing data from the first principle to address the lack of
labeling in supervised learning scenarios. A challenging application is
demonstrated to verify the proposed framework and achieve robust T2 mapping
using overlapping-echo acquisition under severe head motion accompanied with
inhomogeneous RF field. We decomposed the process into two main steps: (1)
calibrationless parallel reconstruction and (2) end-to-end T2 mapping with
motion correction. The neural network was first trained in pure synthetic data
and then evaluated with in vivo human brain without fine-turning. Both
simulation and in vivo experiments showed that the MOST-DL method significantly
reduces ghosting and motion artifacts in T2 maps in the presence of random and
continuous subject movement. The proposed approach may open a door for solving
similar problems with other MRI acquisition methods and can be extended to
other areas of medical imaging.

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The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
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The categories of this paper are: econ
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org