MinJung Jang1, Seokha Jin1, MungSoo Kang1, SoHyun Han2, and HyungJoon Cho1
1Biomedical Engineering, Ulsan national institute of science and technology, Ulsan, Republic of Korea, 2Center for Neuroscience Imaging Research, Institute of Basic Science, Suwon, Republic of Korea
Synopsis
The purpose of this study is to
demonstrate automatic segmentation for reduced diffusion and perfusion areas in
ischemic stroke brains with pattern-recognition (constrained non-negative
matrix factorization (cNMF)) to directional intravoxel incoherent motion MRI
(IVIM-MRI). The robustness of region segmentation with pattern-recognition was
observed in both simulations and in vivo experiments. Using pattern-recognition
analysis of IVIM signal, white matter in which flow may have been aligned, and
lesion regions are automatically segmented in ischemic stroke brain. In this
study, we successfully implemented pattern-recognition to directional IVIM
signals.
Introduction
Changes in diffusion and perfusion are
important in ischemic stroke for accurate diagnosis and therapy1-4.
However, the conventional double-exponential fitting method is controversial due
to overfit problems5 and ambiguous standard for defining
double-exponentiality. In addition, flow direction may be an important factor
in ischemic stroke to study blood flow compensating function6.
Directionality studies using arterial spin labeling-MRI (ASL-MRI) have been proposed7,8,
however, ASL-MRI is limited to arterial information only. To avoid these
problems, we used constrained nonnegative matrix factorization (cNMF)-based pattern-recognition
on directional intravoxel incoherent motion (IVIM) signals for automatic
segmentation of lesion and white matter regions in stroke brain.Methods
Simulation
To mimic acute ischemic stroke rat brain,
the synthetic brain was separated into three regions9; core (low D and fD*),
penumbra (normal D, low fD*), and normal (normal D and fD*). Before
implementing cNMF-based pattern recognition10,11, two normalization
methods were used; (i) S0-normalized signal and (ii) scaled signal divided by
difference between the maximum and minimum of the signal. The accuracies of
segmentation for reduced perfusion region between double-exponential fitting
and cNMF results were compared. In addition, the robustness of segmentation
from cNMF results according to signal-to-noise (SNR) was verified. Monte-Carlo
simulation was performed to identify the IVIM signal pattern based on the
vascular orientation (randomized and aligned vessel)12. IVIM signals
from two vascular models were compared along diffusion gradient-direction.
MR experiment
The transient middle cerebral artery
occlusion (tMCAO) model was used for in-vivo experiments. IVIM signals were
acquired from a spin-echo echo planar imaging (SE-EPI) sequence with twenty
b-values for three orthogonal directions. Dynamic susceptibility contrast-MRI
(DSC-MRI) data was obtained by gradient-echo EPI (GE-EPI) sequence with
super-paramagnetic iron oxide nano-particle (SPION) to compare with cNMF
results. As the input signal of cNMF-based pattern recognition, two types of
signals were used (S0-normalized and scaled signal). Regions of interest (ROI) of
both white matter region (corpus callosum (CC) and cingulum (CG)) and ipsilateral
(left hemisphere) and contralateral (right hemisphere) region were selected from
the cNMF result. The averaged signal of each ROI was fitted (double-exponential
fitting and single-exponential fitting with b value > 200 s/mm2) and
compared to each region in x diffusion-gradient direction. Statistical analysis
was performed with normal (n=4) and tMCAO rat (n=4) for fD* ratio (ipsilateral
(left hemisphere)/contralateral (right hemisphere)) of auto-segmented regions
in cNMF results.
Results
Simulation
The input IVIM parameter map (first row)
and the input segmented brain region (second row) were shown in Fig 1A. Fig 1B
and 1C illustrate cNMF curves, cNMF maps for each pattern, and decision map
from S0-normalized and scaled IVIM input signals were shown, respectively. The pattern
1 attenuated faster than pattern 2 in Fig 1B. In the cNMF map of pattern 1, the
weight of penumbra is much closer to the normal region than the core region.
Therefore, the decision map corresponds well with the input D map. For the cNMF
curves with scaled signal (Fig 1C), there is a noticeable difference at low b
values. The weight of penumbra is much closer to core region than the normal
region. By changing the mixture threshold value (the designated value for determining
the pattern mixed state) and the fD* threshold value, the accuracy of cNMF and
double-exponential fitting for lesion segmentation was shown in Fig 2A. The
accuracy of cNMF method was higher than the double-exponential fitting. In
addition, the accuracy of cNMF method was higher than the double-exponential fitting
when the SNR was greater than ~30 (Fig 2B).
For the aligned vascular model (Fig 4A.1),
the IVIM signal with z diffusion-gradient direction has higher
double-exponentiality than that with the other diffusion gradient-direction
(Fig 4A.2). However, the IVIM signals (Fig 4B.2) are identical in three
diffusion-gradient directions when the vessel orientation is randomized (Fig
4B.1).
MR experiment
In the decision map with S0-normalized
IVIM signal (Fig 3A.2), the blue-colored region with white arrows matched the
lesions with white arrows in the D map (Fig 3A.3). The blue-colored region with
a white circle in decision map with scaled IVIM signal (Fig 3B.2) matched the reduced
flow region with a white circle in rCBF map (Fig 3B.3).
In Fig 5B, the average IVIM signal of CC
aligned to the x-direction has high double-exponentiality, however, the
double-exponentiality of CG aligned to z-direction is low in the x
diffusion-gradient direction. The double-exponentiality of ipsilateral region
(lesion) was lower than that of contralateral region (normal). In normal rat,
fD* values were similar between the left and right hemispheres. On the other
hand, the fD* value
of the ipsilateral region was significantly reduced than the fD* value of the
contralateral region in the tMCAO rat (Fig 5C).Discussion & Conclusions
In this study, we have successfully performed
automatic segmentation of lesion and white matter region via cNMF-based
pattern-recognition with two types of input IVIM signals along the diffusion
gradient-direction. In addition, we observed the robustness of segmentation
with cNMF analysis by comparing double-exponential fitting results in
simulation. The pattern-recognition analysis will be beneficial for disease in which
vessel structure and flow direction can change. In future studies, robust
pattern-recognition method of IVIM signals should be investigated for
multi-directionality, the number of b values, and multi-compartment models.Acknowledgements
This
research was supported by Basic Science Research Program through the National
Research Foundation of Korea (NRF) funded by the Ministry of Education
(2018R1A6A1A03025810)References
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