Jamie R. McClelland
University College London
120 Papers
551 Citations
Jamie R. McClelland is an academic researcher from University College London. The author has contributed to research in topics: Image registration & Medicine. The author has an hindex of 28, co-authored 114 publications. Previous affiliations of Jamie R. McClelland include McGill University & King's College London.
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Papers
4D Motion Models over the Respiratory Cycle for use in Lung Cancer Radiotherapy Planning
Jamie R. McClelland,Adam G. Chandler,JM Blackall,S. Ahmad,David Landau,David J. Hawkes +5 more
- 12 Apr 2005
TL;DR: Results indicate that the motion modelling method shows considerable promise, offering significant improvement over current clinical practice, and potential advantages over alternative 4D CT imaging techniques.
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Super-resolution T2-weighted 4D MRI for image guided radiotherapy
Joshua N. Freedman,David J. Collins,Oliver J. Gurney-Champion,Jamie R. McClelland,Simeon Nill,Uwe Oelfke,Martin O. Leach,Andreas Wetscherek +7 more
TL;DR: A motion-modelling and super-resolution method was developed to calculate high quality 4D/midposition T2w MRI from orthogonal 2D-T2w MRIs, addressing current limitations of slice-selective implementations.
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Self-Aligning Manifolds for Matching Disparate Medical Image Datasets.
Christian F. Baumgartner,Alberto Gomez,Lisa M. Koch,James Housden,Christoph Kolbitsch,Jamie R. McClelland,Daniel Rueckert,Andrew P. King +7 more
- 28 Jun 2015
TL;DR: This work proposes a novel technique for the 'self-alignment' of manifolds (SAM) from multiple dissimilar imaging datasets without prior correspondences or inter-dataset image comparisons, which performs significantly better for 4DMR reconstruction than state-of-the-art image-based techniques.
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Evaluation of MRI-derived surrogate signals to model respiratory motion.
Elena Huong Tran,Björn Eiben,Andreas Wetscherek,Uwe Oelfke,Gustav Meedt,David J. Hawkes,Jamie R. McClelland +6 more
TL;DR: Results demonstrate that surrogate signals derived from 2D cine-MR images, including those generated by applying principal component analysis to the image intensities or control point displacements, can accurately model the motion of the internal anatomy within a single sagittal or coronal slice.
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First clinical investigation of CBCT and deformable registration for adaptive proton therapy of lung cancer
C. Veiga,Guillaume Janssens,Ching-Ling Teng,Thomas Baudier,L. Hotoiu,Jamie R. McClelland,Gary Royle,Liyong Lin,Lingshu Yin,James M. Metz,Timothy D. Solberg,Zelig Tochner,Charles B. Simone,James McDonough,Boon-Keng Kevin Teo +14 more
- 01 Jan 2016
TL;DR: This study describes the first clinical investigation of CBCT and deformable registration in adaptive lung proton therapy and its implications for patient positioning and routine computed tomography scans.
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