Publications
Fully automatic detection and quantification of new white matter lesions using deep learning
Accurate detection, segmentation, and quantification of lesion dynamics in longitudinal MRI is crucial for monitoring disease progression in Multiple Sclerosis, and for evaluating the efficacy of therapeutic interventions. We present an efficient, automatic method utilising deep learning to assess lesion changes in FLAIR MRI with a high degree of accuracy.
Neuroimaging to Facilitate Clinical Trials in Huntington’s Disease: Current Opinion from the EHDN Imaging Working Group - IOS Press
This comprehensive overview of the roles of structural, functional, and diffusion MRI, PET, MRS, and MEG serves as a resource for effectively integrating neuroimaging methodologies into the design and execution of Huntington's disease clinical trials. The paper discusses their applications in patient selection, safety monitoring, and demonstrating efficacy.
Volumetric change across the different Stages of the Huntington’s disease Integrated Staging System (HD-ISS) defined at baseline
The HD Integrated Staging System enables classification of people with HD into four disease stages based on quantitative landmark assessments. Here we characterized volume change over time in the caudate nucleus, putamen, lateral ventricles, and whole-brain across participants starting in each of the different HD-ISS Stages at baseline and compared to healthy controls.
Erosion of The Supratentorial White Matter Reference for Increased Power in Longitudinal Amyloid PET
The use of the supratentorial white matter as a reference region, alone 1 7 or in composite 3 can facilitate the detection of subtle changes in amyloid plaque burden Investigators often erode or otherwise restrict the sWM labels to reduce the influence of signal “spill in” from grey matter 2 4 8 but the optimal extent…
Association between regional volume change and clinical change in Huntington’s disease HD-ISS Stage 2 and Stage 3 participants
For HD-ISS Stage 2 and Stage 3 participants, whole-brain volume shows significant association with clinical change for all four clinical variables examined here. For caudate and putamen volume, the association depended on the clinical variable. Our results provide further evidence on the use of volume change as a surrogate endpoint.
A Deep Learning Framework For Clinical Trial Enrichment in Alzheimer’s Disease
The selection of participants at risk of cognitive decline in clinical trials, known as trial enrichment, increases the probability of trial success. It is estimated that by 2050, 153 million people worldwide will be living with a type of dementia. Hence, innovative trial recruitment strategies are necessary to accelerate treatment development.
Comparison of amyloid positivity and global cortical SUVR between black and white non-Hispanic participants in the GAP Bio-Hermes study
Summary: This poster shows our further investigation on the amyloid PET differences between Black and White participants in the GAP Bio-Hermes study and to better understand the relationship between Aβ+ status from visual read and quantitative SUVR.
A convolutional neural network-based framework for imaging biomarkers in MS - white matter hyperintensity & brain region volumes
Summary: We developed and validated scalable and robust methods for automated volumetric analysis of brain white matter hyperintensities (T2 lesions) and regions of interest in multiple sclerosis.
1-8 of 41 results