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New publication in MIUA 2021: Medical Image Understanding and Analysis

Publication date: July 2021

Publication title: "Selective Motion Artefact Reduction via Radiomics and k-space Reconstruction for Improving Perivascular Space Quantification in Brain Magnetic Resonance Imaging".


Authors: Jose Bernal, William Xu, Maria d. C. Valdés-Hernández, Javier Escudero, Angela C. C. Jochems, Una Clancy, Fergus N. Doubal, Michael S. Stringer, Michael J. Thrippleton, Rhian M. Touyz, Joanna M. Wardlaw



Current evidence points towards perivascular spaces playing a key role in cerebral haemodynamics and waste clearance. Hence, their precise quantification may become a powerful tool for assessing brain health and further establishing their relationship with neurological diseases. Large strides have been made towards developing automatic tools to computationally assess the burden of perivascular spaces in MRI in recent years. However, their applicability depends to a large extent on the quality of the images. In this paper, we propose a pipeline to improve perivascular space quantification by means of radiomics-based image quality control and selective motion artefacts reduction. We demonstrate our method on a sample of patients with mild stroke (n = 60) with different extents of small vessel disease features and image quality. We show our proposal can differentiate high- and low-quality scans (AUROC = 0.98) and reduce imaging artefacts, which leads to greater correlations between visual and computational measurements, especially in the centrum semiovale (polyserial correlation: 0.86 [95% CI 0.85, 0.88] and 0.17 [95% CI 0.14, 0.21] with and without our proposal, respectively). Our preliminary results demonstrate the potential of our proposal for retaining clinically relevant information while reducing imaging artefacts.



Perivascular spaces,

Cerebral small vessel disease,

Image enhancement,

Imaging artefact reduction,

Brain magnetic resonance imaging 


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