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Related Experiment Video

Updated: Jul 2, 2026

Construction and Application of Cerebral Functional Region-Based Cerebral Blood Flow Atlas Using Magnetic Resonance Imaging-Arterial Spin Labeling

Construction and Application of Cerebral Functional Region-Based Cerebral Blood Flow Atlas Using Magnetic Resonance Imaging-Arterial Spin Labeling

Published on: May 31, 2024

Arterial spin labeling MRI denoising via locally adaptive regularization with structure-guided collaborative data

Hangfan Liu¹, Bo Li¹, John A Detre²

¹Center for Advanced Imaging Research, University of Maryland School of Medicine, Baltimore, MD, 21202, USA.

|June 30, 2026

Summary

This summary is machine-generated.

Arterial spin labeled (ASL) MRI denoising is improved with the novel LACS method. This unsupervised technique enhances ASL perfusion maps using fewer images, overcoming low signal-to-noise challenges.

Keywords:

ASL perfusion Arterial spin labeling Denoising Low rank MRI Sparsity

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Magnetic Resonance Imaging Quantification of Pulmonary Perfusion using Calibrated Arterial Spin Labeling

Magnetic Resonance Imaging Quantification of Pulmonary Perfusion using Calibrated Arterial Spin Labeling

Published on: May 30, 2011

Area of Science:

Medical Imaging
Biophysics

Background:

Arterial spin labeled (ASL) perfusion MRI is a non-invasive, non-radioactive method for measuring tissue perfusion.
ASL MRI suffers from low signal-to-noise ratio due to T1 decay, making denoising challenging without ground truth data.

Purpose of the Study:

To introduce an unsupervised Locally Adaptive regularization with Collaborative data Selection (LACS) scheme for ASL MRI denoising.
To improve the quality of ASL perfusion maps by addressing the intrinsic low signal-to-noise ratio.

Main Methods:

The LACS scheme utilizes the correlation between label/control (L/C) images to form low-rank matrices for regularization.
A non-convex surrogate (log-determinant of covariant matrices) for low-rank penalty was employed, exploiting sparsity without explicit training.
The method is unsupervised, requiring no ground-truth training data.

Main Results:

LACS significantly improved ASL perfusion map quality using only one L/C image pair.
The proposed method demonstrated superior performance compared to standard pipelines requiring multiple L/C pairs.
The regularization adapted better to local structures and was more robust to noise.

Conclusions:

The LACS scheme offers an effective solution for ASL MRI denoising, enhancing image quality and potentially setting a new benchmark.
This unsupervised approach overcomes limitations of traditional methods by requiring fewer data pairs and no ground truth.