Prof MICHAEL CHAPPELL's Outputs (3)

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.