Shihui Chen^{1}, Mei-Lan Chu^{2}, Chun-Jung Juan^{3}, Liyuan Liang^{1}, and Hing-Chiu Chang^{1}

^{1}The University of Hong Kong, Hong Kong, Hong Kong, ^{2}Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, Taipei, Taiwan, ^{3}Department of Medical Imaging, China Medical University Hsinchu Hospital, Taiwan, Taipei, Taiwan

### Synopsis

Low SNR
and long acquisition time hinder the clinical feasibility of intra-voxel
incoherent motion (IVIM) diffusion-weighted imaging. In this work, we used a
joint reconstruction framework based on POCSMUSE algorithm to simultaneously
reconstruct under-sampled multi-b multi-shot DW-EPI data without undesired noise
amplification. A proposed parametric correction scheme (PCS) was incorporated
into POCSMUSE algorithm for robust reconstruction based on either conventional bi-exponential
or simplified IVIM model. The proposed method demonstrated that the
high-quality brain IVIM images could be reconstructed from highly accelerated
(R=4) data acquired with multi-b multi-shot DW-EPI.

### Introduction

Intra-voxel
incoherent motion (IVIM) diffusion-weighted imaging (DWI) is shown to be useful
in assessing diffusion and perfusion characteristics of brain tissue^{1, 2}. In practice, IVIM DWI utilizes single-shot
diffusion-weighted echo-planar imaging (ssDW-EPI) to acquire a data set with
multiple b-values^{1, 2}, and subsequently derives the
tissue parameters using a bi-exponential model. However, the use of ssDW-EPI leads
to suboptimal data quality, such as in the presence of geometric distortion and
limited spatial resolution. To improve the image quality, ssDW-EPI is combined
with SENSE^{3} to reduce the distortion and improve the spatial resolution,
but at the expense of noise amplification. Although multi-shot DW-EPI
(msDW-EPI) with POCSMUSE reconstruction can obtain high-resolution and
high-quality DWI data without undesired noise amplification^{4}, the prolonged scan time due to multi-shot
acquisition is impractical for the clinical acquisition of IVIM DWI. Recently, a
parametric POCSMUSE framework has been reported to simultaneous generate
high-quality multi-echo T2W images and accurate T2 maps from GRASE-EPI sequence
with high scan acceleration^{5}. In light of this, we proposed to improve the image
reconstruction of highly accelerated multi-b msDW-EPI data, by incorporating a
parametric correction scheme (PCS) into parametric POCSMUSE framework.### Methods

**Data Acquisition
and Hybrid-Simulation:**
All data
were collected from three healthy volunteers on a 1.5T MRI scanner (GE Explore
Lift) using a 12-channel head coil. For each subject, two brain IVIM datasets
were acquired by using a 4-shot DW-EPI sequence with and without
CSF-suppression, respectively. Seventeen b-values (0, 10, 20, 40, 80, 110, 140,
170, 200, 300, 400, 500, 600, 700, 800, 900, and 1000 s/mm2) with
three orthogonal diffusion directions for each b-value were acquired for each
IVIM dataset with the scan parameters shown as follow: TE/TR=84/4000ms, TI=1500ms,
matrix size=128x128, slice thickness=5mm, and scantime=13min. After acquisition, the fully-sampled 4-shot
DW-EPI data was used to simulate an under-sampled IVIM dataset with high
acceleration factor (i.e., R=4), by selecting 1 out of 4 k-space segments for each
multi-b data (Figure 1a).

**Image Reconstruction:
**
Figure 1b
shows the flowchart of parametric POCSMUSE algorithm with proposed PCS for simultaneously
reconstructing highly accelerated multi-b images of a single diffusion direction.
The fully-sampled b=0 image and corresponding ADC map (derived from
fully-sampled b=0 and b=600 images) served as intimal data at the beginning of
first iteration. After several iterations, either conventional bi-exponential or
simplified IVIM model was applied in PCS to perform IVIM curve fitting. The new
ADC, f, and D* derived from PSC at current iteration were used to update the diffusion-modulation
operator for next iteration (n is the number of b-values).
The reconstructed multi-b images were output from proposed framework when the iteration
converged. Afterward, the image data with three orthogonal diffusion directions
were averaged together for generated mean diffusion image for each b-value. In
addition, the fully-sampled and under-sampled IVIM data were respectively
reconstructed with MUSE and SENSE for comparison. The average multi-b DW images
produced by three different reconstruction methods were then used for estimating
IVIM parameters using either conventional bi-exponential IVIM model (with 16
b-values) or simplified model IVIM model (with 8 b-values). ### Results

Figure 2a
shows three representative images of 16 multi-b DW images reconstructed with
MUSE, proposed framework, and SENSE for comparison. Figure 2b shows difference
maps of reconstruction image between gold standard (fully-sampled multi-b DW
images reconstructed using MUSE algorithm) and the other two reconstruction methods.
Figures 3a and 4a show multi-b DW images reconstructed from the data with and
without CSF-suppression, using proposed POCSMUSE framework with conventional bi-exponential
IVIM model for PCS. Figures 3b and 4b show the difference maps of reconstructed
image between gold standard (MUSE) and proposed framework. Figure 5 shows ADC
maps and perfusion fraction maps derived from the multi-b DW images (with and
without CSF suppression) reconstructed with three different methods.### Discussion and Conclusion

Our
proposed parametric POCSMUSE algorithm with PCS can successfully eliminate
aliasing artifacts in highly under-sampled multi-b DW images without undesired
noise amplification due to SENSE reconstruction. The IVIM parameter mapping results
have demonstrated that the proposed framework is compatible with either
conventional bi-exponential or simplified IVIM model, and can obtain quantitative
maps close to the gold standards (4-shot fully-sampled data with MUSE). In
addition to reduced noise and improved geometric fidelity, the proposed
framework provides a substantial reduction in scantime compared to the data
acquired with multi-shot (i.e., 27% of the scantime for acquiring fully-sampled
data). Even though SENSE-produced multi-b DW images were aliasing-free and
distortion-mitigated, the undesired noise amplification may affect the accuracy
in the measurements of ADC and perfusion fraction (the bottom row in Figure 5).
In the
preliminary evaluation, the partial volume effect, such as the voxel involves
both brain tissue and CSF signals, can lead to the bias in IVIM parameter estimation
when using proposed framework. This limitation may be addressed by using CSF-suppression
for data acquisition. Our results also demonstrate the feasibility of deriving
IVIM parameter from CSF-suppressed data using proposed framework (Figures 3b vs
4b). In conclusion, the proposed framework can enable IVIM data acquisition
with high acceleration factor, thereby improving the geometric fidelity and
feasibility of IVIM for clinical application. ### Acknowledgements

The work was in part supported
by grants from Hong Kong Research Grant Council (GRF HKU17138616 and GRF
HKU17121517), and Hong Kong Innovation and Technology Commission (ITS/403/18).### References

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