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Derek L. G. Hill

Researcher at University College London

Publications -  286
Citations -  38648

Derek L. G. Hill is an academic researcher from University College London. The author has contributed to research in topics: Image registration & Imaging phantom. The author has an hindex of 77, co-authored 285 publications receiving 36657 citations. Previous affiliations of Derek L. G. Hill include Indiana University & Critical Path Institute.

Papers
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Proceedings ArticleDOI

Detecting small anatomical change with 3D serial MR subtraction images

TL;DR: The results show a significant improvement in the detection of structural change and inter-observer agreement when aligned and subtracted images were used instead of unregistered ones.
Book ChapterDOI

Anisotropic wave propagation and apparent conductivity estimation in a fast electrophysiological model: application to XMR interventional imaging

TL;DR: This work presents a fast electrophysiological model incorporating anisotropy of the cardiac tissue, and demonstrates a qualitative match between the estimated conductivity parameter and possible pathology locations.
Journal ArticleDOI

Molecular Neuroimaging of the Dopamine Transporter as a Patient Enrichment Biomarker for Clinical Trials for Early Parkinson's Disease

TL;DR: The Critical Path for Parkinson's Imaging Biomarker and Modeling and Simulation working groups aimed to achieve qualification opinion by the European Medicines Agency (EMA) Committee for Medical Products for Human Use (CHMP) for the use of baseline dopamine transporter neuroimaging for patient selection in early Parkinson's disease clinical trials are described.
Proceedings ArticleDOI

XMR guided cardiac electrophysiology study and radio frequency ablation

TL;DR: The XMR registration technique allows the integration of anatomical, electrophysiological, and motion information for patients undergoing EPS/RFA and has assisted in interventional guidance and has been used to validate electromechanical models of the myocardium.
Proceedings ArticleDOI

Registration of multiple video images to preoperative CT for image-guided surgery

TL;DR: In this article, the authors used a simple iterative optimization scheme to maximize the mutual information between a video image and a rendering from the pre-operative data, and then registered five video views simultaneously to the 3D model.