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Michael A. Chappell
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2020 – today
- 2024
- [j23]Yechuan Zhang
, Jian-Qing Zheng
Bayesian Inference for Non-Linear Forward Model by Using a VAE-Based Neural Network Structure. IEEE Trans. Signal Process. 72: 1400-1411 (2024) - 2023
- [c9]Thomas F. Kirk
Unified Surface and Volumetric Inference on Functional Imaging Data. MICCAI (8) 2023: 399-408 - [i2]Georgia Kenyon, Stephan Lau, Michael A. Chappell, Mark Jenkinson:
Segmentation method for cerebral blood vessels from MRA using hysteresis. CoRR abs/2303.05113 (2023) - 2021
- [j22]Caoilfhionn Ní Leidhin
Age-related normative changes in cerebral perfusion: Data from The Irish Longitudinal Study on Ageing (TILDA). NeuroImage 229: 117741 (2021) - [j21]Michael A. Chappell, Flora A. Kennedy McConnell
Partial volume correction in arterial spin labeling perfusion MRI: A method to disentangle anatomy from physiology or an analysis step too far? NeuroImage 238: 118236 (2021) - 2020
- [j20]Joseph G. Woods
Designing and comparing optimized pseudo-continuous Arterial Spin Labeling protocols for measurement of cerebral blood flow. NeuroImage 223: 117246 (2020) - [j19]Thomas F. Kirk
Toblerone: Surface-Based Partial Volume Estimation. IEEE Trans. Medical Imaging 39(5): 1501-1510 (2020) - [i1]Michael A. Chappell, Mark W. Woolrich:
The FMRIB Variational Bayesian Inference Tutorial II: Stochastic Variational Bayes. CoRR abs/2007.02725 (2020)
2010 – 2019
- 2019
- [j18]Dominic Carone, G. W. J. Harston, James Garrard
ICA-based denoising for ASL perfusion imaging. NeuroImage 200: 363-372 (2019) - [j17]Matthew T. Cherukara
Model-based Bayesian inference of brain oxygenation using quantitative BOLD. NeuroImage 202 (2019) - [j16]Antoine Saillant
Assessing Reliability of Myocardial Blood Flow After Motion Correction With Dynamic PET Using a Bayesian Framework. IEEE Trans. Medical Imaging 38(5): 1216-1226 (2019) - 2018
- [j15]Thais Roque
A DCE-MRI Driven 3-D Reaction-Diffusion Model of Solid Tumor Growth. IEEE Trans. Medical Imaging 37(3): 724-732 (2018) - 2017
- [j14]Marco Castellaro
A Variational Bayesian inference method for parametric imaging of PET data. NeuroImage 150: 136-149 (2017) - [j13]Moss Y. Zhao
A systematic study of the sensitivity of partial volume correction methods for the quantification of perfusion from pseudo-continuous arterial spin labeling MRI. NeuroImage 162: 384-397 (2017) - [j12]Hongfen Teng
Improving Rainfall Erosivity Estimates Using Merged TRMM and Gauge Data. Remote. Sens. 9(11): 1134 (2017) - [c8]G. P. Ralli, Daniel R. McGowan
4D-PET reconstruction of dynamic non-small cell lung cancer [18-F]-FMISO-PET data using adaptive-knot cubic B-splines. ISBI 2017: 1189-1192 - 2016
- [j11]Hongfen Teng
Assimilating satellite imagery and visible-near infrared spectroscopy to model and map soil loss by water erosion in Australia. Environ. Model. Softw. 77: 156-167 (2016) - [c7]Thais Roque, Bartlomiej W. Papiez
Tumor Growth Estimation via Registration of DCE-MRI Derived Tumor Specific Descriptors. CVPR Workshops 2016: 507-515 - [c6]Thais Roque, Veerle Kersemans
A DCE-MRI imaging-based model for simulation of vascular tumour growth. EMBC 2016: 5949-5952 - 2015
- [c5]Rutej R. Mehta, Michael A. Chappell
Quantification of errors in cerebral blood flow measurements due to dispersion in arterial spin labelling. EMBC 2015: 7917-7920 - 2014
- [j10]Eidrees Ghariq, Michael A. Chappell
Effects of background suppression on the sensitivity of dual-echo arterial spin labeling MRI for BOLD and CBF signal changes. NeuroImage 103: 316-322 (2014) - [c4]Monica Enescu, Mattias P. Heinrich
An MRF-Based Discrete Optimization Framework for Combined DCE-MRI Motion Correction and Pharmacokinetic Parameter Estimation. BAMBI 2014: 73-84 - [c3]Andre Hallack
A New Similarity Metric for Groupwise Registration of Variable Flip Angle Sequences for Improved T 10 Estimation in DCE-MRI. WBIR 2014: 154-163 - 2013
- [j9]Thomas W. Okell
A theoretical framework for quantifying blood volume flow rate from dynamic angiographic data and application to vessel-encoded arterial spin labeling MRI. Medical Image Anal. 17(8): 1025-1036 (2013) - [j8]Amit Mehndiratta, Bradley J. MacIntosh
A control point interpolation method for the non-parametric quantification of cerebral haemodynamics from dynamic susceptibility contrast MRI. NeuroImage 64: 560-570 (2013) - [c2]Manav Bhushan, Julia A. Schnabel
The Impact of Heterogeneity and Uncertainty on Prediction of Response to Therapy Using Dynamic MRI Data. MICCAI (1) 2013: 316-323 - 2012
- [j7]Michael A. Chappell
A fast analysis method for non-invasive imaging of blood flow in individual cerebral arteries using vessel-encoded arterial spin labelling angiography. Medical Image Anal. 16(4): 831-839 (2012) - [j6]Daniel P. Bulte, Michael Kelly, Michael A. Germuska, J. Xie, Michael A. Chappell, Thomas W. Okell
Quantitative measurement of cerebral physiology using respiratory-calibrated MRI. NeuroImage 60(1): 582-591 (2012)
2000 – 2009
- 2009
- [j5]Mark William Woolrich
Bayesian analysis of neuroimaging data in FSL. NeuroImage 45(1): S173-S186 (2009) - [j4]Adrian R. Groves, Michael A. Chappell
Combined spatial and non-spatial prior for inference on MRI time-series. NeuroImage 45(3): 795-809 (2009) - [j3]Samira M. Kazan, Michael A. Chappell
Modeling the Effects of Flow Dispersion in Arterial Spin Labeling. IEEE Trans. Biomed. Eng. 56(6): 1635-1643 (2009) - [j2]Michael A. Chappell
Variational Bayesian Inference for a Nonlinear Forward Model. IEEE Trans. Signal Process. 57(1): 223-236 (2009) - [c1]Michael A. Chappell
Vascular Territory Image Analysis Using Vessel Encoded Arterial Spin Labeling. MICCAI (1) 2009: 514-521 - 2007
- [j1]Michael A. Chappell
Modeling the Detachment and Transport of Bubbles From Nucleation Sites in Small Vessels. IEEE Trans. Biomed. Eng. 54(11): 2106-2108 (2007)
Coauthor Index
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