Dr. Yiran Li Profile

Dr. Yiran Li

at Univ of Maryland School of Medicine

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Proceedings Article | 2 April 2024 Poster + Paper

ASL MRI denoising via multi channel collaborative low-rank regularization

Hangfan Liu, Bo Li, Yiran Li, Rebecca Welsh, Ze Wang

Proceedings Volume 12926, 129261S (2024) https://doi.org/10.1117/12.3005223

KEYWORDS: Magnetic resonance imaging, Denoising, Signal to noise ratio, Matrices, Covariance matrices, Perfusion imaging, Image processing, Tunable filters, Neuroimaging

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Arterial spin labeling (ASL) perfusion MRI is the only non-invasive imaging technique for quantifying regional cerebral blood flow (CBF), which is a fundamental physiological variable. ASL MRI has a relatively low signal-to-noise-ratio (SNR). In this study, we proposed a novel ASL denoising method by simultaneously exploiting the inter- and intra-receive channel data correlations. MRI including ASL MRI data have been routinely acquired with multichannel coils but current denoising methods are designed for denoising the coil-combined data. Indeed, the concurrently acquired multi-channel images differ only by coil sensitivity weighting and random noise, resulting in a strong low-rank structure of the stacked multi-channel data matrix. In our method, this matrix was formed by stacking the vectorized slices from different channels. Matrix rank was then approximately measured through the logarithm-determinant of the covariance matrix. Notably, our filtering technique is applied directly to complex data, avoiding the need to separate magnitude and phase or divide real and imaginary data, thereby ensuring minimal information loss. The degree of low-rank regularization is controlled based on the estimated noise level, striking a balance between noise removal and texture preservation. A noteworthy advantage of our framework is its freedom from parameter tuning, distinguishing it from most existing methods. Experimental results on real-world imaging data demonstrate the effectiveness of our proposed approach in significantly improving ASL perfusion quality. By effectively mitigating noise while preserving important textural information, our method showcases its potential for enhancing the utility and accuracy of ASL perfusion MRI, paving the way for improved neuroimaging studies and clinical diagnoses.

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