Chapter title |
A Statistical Motion Model Based on Biomechanical Simulations for Data Fusion during Image-Guided Prostate Interventions
|
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Chapter number | 88 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008
|
Published in |
Lecture notes in computer science, September 2008
|
DOI | 10.1007/978-3-540-85988-8_88 |
Pubmed ID | |
Book ISBNs |
978-3-54-085987-1, 978-3-54-085988-8
|
Authors |
Yipeng Hu, Dominic Morgan, Hashim Uddin Ahmed, Doug Pendsé, Mahua Sahu, Clare Allen, Mark Emberton, David Hawkes, Dean Barratt, Doug Pendsé, Hu, Yipeng, Morgan, Dominic, Ahmed, Hashim Uddin, Pendsé, Doug, Sahu, Mahua, Allen, Clare, Emberton, Mark, Hawkes, David, Barratt, Dean |
Abstract |
A method is described for generating a patient-specific, statistical motion model (SMM) of the prostate gland. Finite element analysis (FEA) is used to simulate the motion of the gland using an ultrasound-based 3D FE model over a range of plausible boundary conditions and soft-tissue properties. By applying principal component analysis to the displacements of the FE mesh node points inside the gland, the simulated deformations are then used as training data to construct the SMM. The SMM is used to both predict the displacement field over the whole gland and constrain a deformable surface registration algorithm, given only a small number of target points on the surface of the deformed gland. Using 3D transrectal ultrasound images of the prostates of five patients, acquired before and after imposing a physical deformation, to evaluate the accuracy of predicted landmark displacements, the mean target registration error was found to be less than 1.9 mm. |
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United Kingdom | 1 | 3% |
Japan | 1 | 3% |
Spain | 1 | 3% |
Unknown | 27 | 82% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 7 | 21% |
Researcher | 7 | 21% |
Student > Master | 5 | 15% |
Professor > Associate Professor | 4 | 12% |
Lecturer > Senior Lecturer | 2 | 6% |
Other | 2 | 6% |
Unknown | 6 | 18% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 8 | 24% |
Medicine and Dentistry | 8 | 24% |
Computer Science | 6 | 18% |
Immunology and Microbiology | 1 | 3% |
Psychology | 1 | 3% |
Other | 0 | 0% |
Unknown | 9 | 27% |