Overview
I am a final year PhD student in the CIG group at the Centre for Medical Image Computing (CMIC) at UCL. My supervisors are Prof Gary Zhang and Prof Parashkev Nachev. My work is focussed around developing and applying AI methods to diffusion magnetic resonance imaging (dMRI) data with application to Ischaemic Stroke. My ORCID and Google Scholar.
Spherical-CNN based diffusion MRI parameter estimation is robust to gradient schemes and equivariant to rotation
We demonstrate the advantages of spherical convolutional neural networks over conventional fully connected networks at estimating rotationally invariant microstructure indices. Fully-connected networks (FCN) have outperformed conventional model fitting for estimating microstructure indices, such as FA. However, these methods are not robust to changes diffusion weighted image sampling scheme nor are they rotationally equivariant. Recently spherical-CNN have been supposed as a solution to this problem. However, the advantages of spherical-CNNs have not been leveraged. We demonstrate both spherical-CNNs robust to new gradient schemes as well as the rotational equivariance. This has potential to decrease the number of training datapoints required.
Patch-CNN-DTI: Data-efficient high-fidelity tensor recovery from 6 direction diffusion weighted imaging
See the abstract presentation I gave at ISMRM 21 on youtube:
We present Patch-CNN-DTI, a deep-learning method to estimate diffusion tensors (DT) accurately from only 6 diffusion-weighted images. Early voxel-wise deep-learning methods can only estimate scalar measures of DT. Later work shows DT can be estimated using image-wise methods based on convolutional neural networks (CNN), but they require large training cohort. Patch-CNN-DTI can estimate DT with only one training subject, by pooling information from local neighbourhood of a voxel similar to the CNN but at a much smaller scale to minimise training data requirements. Results show it outperforms conventional model fitting with twice the number of diffusion directions.
Education
2019-onwards: PhD in Medical Imaging, UCL
2018-2019: MRes in Medical Imaging, UCL
2015-2018: BSc in Mathematics, University of Bristol