Prof MICHAEL CHAPPELL's Outputs (6)

Synthetic cerebral blood vessel generator for training anatomically plausible deep learning models (2024)
Presentation / Conference Contribution
Kenyon, G., Lau, S., Perperidis, A., CHAPPELL, M., & Jenkinson, M. (2024, July). Synthetic cerebral blood vessel generator for training anatomically plausible deep learning models. Presented at MIUA - Medical Image Understanding and Analysis - 2024, Manchester

Blood vessel networks, with their complex geometrical and topological characteristics, play a significant role in diagnosing and understanding various cerebrovascular diseases. Deep learning (DL) segmentation methods can aid in analysing these struct... Read More about Synthetic cerebral blood vessel generator for training anatomically plausible deep learning models.

Recommendations for quantitative cerebral perfusion MRI using multi‐timepoint arterial spin labeling: Acquisition, quantification, and clinical applications (2024)
Journal Article
Woods, J. G., Achten, E., Asllani, I., Bolar, D. S., Dai, W., Detre, J. A., …the ISMRM Perfusion Study Group. (2024). Recommendations for quantitative cerebral perfusion MRI using multi‐timepoint arterial spin labeling: Acquisition, quantification, and clinical applications. Magnetic Resonance in Medicine, 92(2), 469-495. https://doi.org/10.1002/mrm.30091

Accurate assessment of cerebral perfusion is vital for understanding the hemodynamic processes involved in various neurological disorders and guiding clinical decision-making. This guidelines article provides a comprehensive overview of quantitative... Read More about Recommendations for quantitative cerebral perfusion MRI using multi‐timepoint arterial spin labeling: Acquisition, quantification, and clinical applications.

Bayesian Inference for Non-linear forward model by using a VAE-based neural network structure (2024)
Journal Article
Zhang, Y., Zheng, J.-Q., & Chappell, M. (2024). Bayesian Inference for Non-linear forward model by using a VAE-based neural network structure. IEEE Transactions on Signal Processing, 72, 1400-1411. https://doi.org/10.1109/TSP.2024.3374115

In this paper, a Variational Autoencoder (VAE) based framework is introduced to solve parameter estimation problems for non-linear forward models. In particular, we focus on applications in the field of medical imaging where many thousands of model-b... Read More about Bayesian Inference for Non-linear forward model by using a VAE-based neural network structure.

BASIL: A Toolbox for Perfusion Quantification using Arterial Spin Labelling (2023)
Journal Article
Chappell, M. A., Kirk, T. F., Craig, M. S., McConnell, F. A. K., Zhao, M. Y., MacIntosh, B. J., …Woolrich, M. W. (2023). BASIL: A Toolbox for Perfusion Quantification using Arterial Spin Labelling. Imaging Neuroscience, https://doi.org/10.1162/imag_a_00041

Arterial Spin Labelling (ASL) MRI is now an established non-invasive method to quantify cerebral blood flow and is increasingly being used in a variety of neuroimaging applications. With standard ASL acquisition protocols widely available, there is a... Read More about BASIL: A Toolbox for Perfusion Quantification using Arterial Spin Labelling.

Unified Surface and Volumetric Inference on Functional Imaging Data (2023)
Presentation / Conference Contribution
Kirk, T. F., Craig, M. S., & Chappell, M. A. (2023). Unified Surface and Volumetric Inference on Functional Imaging Data. In J. Duncan, H. Greenspan, A. Madabhushi, P. Mousavi, S. Salcudean, T. Syeda-Mahmood, & R. Taylor (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2023: 26th International Conference, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, Part VIII (399-408). https://doi.org/10.1007/978-3-031-43993-3_39

Surface-based analysis methods for functional imaging data have been shown to offer substantial benefits for the study of the human cortex, namely in the localisation of functional areas and the establishment of inter-subject correspondence. A new ap... Read More about Unified Surface and Volumetric Inference on Functional Imaging Data.

Brain perfusion imaging by multi‐delay arterial spin labeling: Impact of modeling dispersion and interaction with denoising strategies and pathology (2023)
Journal Article
Pires Monteiro, S., Pinto, J., Chappell, M. A., Fouto, A., Baptista, M. V., Vilela, P., & Figueiredo, P. (2023). Brain perfusion imaging by multi‐delay arterial spin labeling: Impact of modeling dispersion and interaction with denoising strategies and pathology. Magnetic Resonance in Medicine, 90(5), 1889-1904. https://doi.org/10.1002/mrm.29783

Purpose: Arterial spin labeling (ASL) acquisitions at multiple post-labeling delays may provide more accurate quantification of cerebral blood flow (CBF), by fitting appropriate kinetic models and simultaneously estimating relevant parameters such as... Read More about Brain perfusion imaging by multi‐delay arterial spin labeling: Impact of modeling dispersion and interaction with denoising strategies and pathology.