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Arterial spin labeling perfusion MRI signal denoising using robust principal component analysis
Hancan Zhu1, Jian Zhang2, Ze Wang3
1School of Mathematics Physics and Information, Shaoxing University, Shaoxing, 312000, China.
Background:
Arterial spin labeling (ASL) perfusion MRI provides a non-invasive way to quantify regional cerebral blood flow (CBF) and has been increasingly used to characterize brain state changes due to disease or functional alterations. Its use in dynamic brain activity study, however, is still hampered by the relatively low signal-to-noise-ratio (SNR) of ASL data.
New Method:
The aim of this study was to validate a new temporal denoising strategy for ASL MRI. Robust principal component analysis (rPCA) was used to decompose the ASL CBF image series into a low-rank component and a sparse component. The former captures the slowly fluctuating perfusion patterns while the latter represents spatially incoherent spiky variations and was discarded as noise. While there still lacks a way to determine the parameter for controlling the balance between the low-rankness and sparsity of the decomposition, we designed a method to solve this problem based on the unique data structures of ASL MRI. Method evaluations were performed with ASL CBF-based functional connectivity (FC) analysis and a sensorimotor functional ASL MRI study.
Comparison With Existing Method(S):
The proposed method was compared with the component based noise correction method (CompCor).
Results:
The proposed method markedly increased temporal signal-to-noise-ratio (TSNR) and sensitivity of ASL CBF images for FC analysis and task activation detection.
Conclusions:
We proposed a new temporal ASL CBF image denoising method, and showed its benefit for the CBF time series-based FC analysis and task activation detection.
Insights
A new temporal denoising method using robust principal component analysis (rPCA) significantly improves signal-to-noise ratio (SNR) in arterial spin labeling (ASL) perfusion MRI. This enhances functional connectivity analysis and task activation detection in brain imaging studies.
Area of Science:
- Medical Imaging
- Neuroscience
- Biophysics
Background:
- Arterial spin labeling (ASL) perfusion MRI non-invasively quantifies regional cerebral blood flow (CBF).
- ASL MRI is valuable for characterizing brain state changes in disease and functional alterations.
- Low signal-to-noise ratio (SNR) currently limits dynamic brain activity studies using ASL.
Purpose of the Study:
- To validate a novel temporal denoising strategy for ASL MRI.
- To improve the utility of ASL for dynamic brain activity studies.
- To enhance functional connectivity (FC) analysis and task activation detection using ASL data.
Main Methods:
- Employed robust principal component analysis (rPCA) to decompose ASL CBF image series into low-rank and sparse components.
- Developed a method to determine the rPCA parameter balancing low-rankness and sparsity, tailored for ASL MRI data structures.
- Evaluated the method using ASL CBF-based functional connectivity (FC) analysis and a sensorimotor functional ASL MRI study.
- Compared the proposed method against component based noise correction (CompCor).
Main Results:
- The proposed temporal denoising method significantly increased the temporal signal-to-noise-ratio (TSNR) of ASL CBF images.
- Demonstrated marked improvements in the sensitivity of ASL CBF images for FC analysis.
- Showcased enhanced sensitivity for task activation detection in functional ASL studies.
Conclusions:
- Introduced a novel temporal denoising technique for ASL CBF images.
- Validated the method's effectiveness in improving CBF time series-based FC analysis.
- Confirmed the method's benefits for enhancing task activation detection in ASL studies.
