Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Simultaneous Segmentation and Relaxometry for MRI through Multitask
  Learning   Purpose: This study demonstrated an MR signal multitask learning method for
3D simultaneous segmentation and relaxometry of human brain tissues. Materials
and Methods: A 3D inversion-prepared balanced steady-state free precession
sequence was used for acquiring in vivo multi-contrast brain images. The deep
neural network contained 3 residual blocks, and each block had 8 fully
connected layers with sigmoid activation, layer norm, and 256 neurons in each
layer. Online synthesized MR signal evolutions and labels were used to train
the neural network batch-by-batch. Empirically defined ranges of T1 and T2
values for the normal gray matter, white matter and cerebrospinal fluid (CSF)
were used as the prior knowledge. MRI brain experiments were performed on 3
healthy volunteers as well as animal (N=6) and prostate patient (N=1)
experiments. Results: In animal validation experiment, the differences/errors
(mean difference $\pm$ standard deviation of difference) between the T1 and T2
values estimated from the proposed method and the ground truth were 113 $\pm$
486 and 154 $\pm$ 512 ms for T1, and 5 $\pm$ 33 and 7 $\pm$ 41 ms for T2,
respectively. In healthy volunteer experiments (N=3), whole brain segmentation
and relaxometry were finished within ~5 seconds. The estimated apparent T1 and
T2 maps were in accordance with known brain anatomy, and not affected by coil
sensitivity variation. Gray matter, white matter, and CSF were successfully
segmented. The deep neural network can also generate synthetic T1 and T2
weighted images. Conclusion: The proposed multitask learning method can
directly generate brain apparent T1 and T2 maps, as well as synthetic T1 and T2
weighted images, in conjunction with segmentation of gray matter, white matter
and CSF.

17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
1
The categories of this paper are: cs
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin