Mark Chiew
PhD
Associate Professor
- Royal Academy of Engineering Research Fellow
- Head of Image Reconstruction
My research focuses on the development of methods and techniques for speeding up the acquisition of functional magnetic resonance imaging (FMRI) data. This is important for providing large amounts of finely sampled temporal information about the brain in shorter durations, reducing imaging times and facilitating research on the brain's functional architecture and dynamics.
I am currently exploring methods for acceleration using low-rank constraints and 3D measurement techniques at 3T and 7T magnetic field strengths to improve resting state FMRI data collection efficiency.
Recent publications
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Motion Compensated Structured Low-rank Reconstruction for 3D Multi-shot EPI
Journal article
Chen X. et al, (2024), Magnetic Resonance in Medicine
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Accelerated Cardiac Parametric Mapping Using Deep Learning-Refined Subspace Models
Conference paper
Sheagren CD. et al, (2024), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14507 LNCS, 369 - 379
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A Theoretical Framework for Self-Supervised MR Image Reconstruction Using Sub-Sampling via Variable Density Noisier2Noise.
Journal article
Millard C. and Chiew M., (2023), IEEE Trans Comput Imaging, 9, 707 - 720
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High-resolution 3D ultra-short echo time MRI with Rosette k-space pattern for brain iron content mapping.
Journal article
Shen X. et al, (2023), J Trace Elem Med Biol, 77
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Improving robustness of 3D multi-shot EPI by structured low-rank reconstruction of segmented CAIPI sampling for fMRI at 7T.
Journal article
Chen X. et al, (2023), Neuroimage, 267