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Proceedings Paper

Elastic registration of prostate MR images based on state estimation of dynamical systems
Author(s): Bahram Marami; Suha Ghoul; Shahin Sirouspour; David W. Capson; Sean R. H. Davidson; John Trachtenberg; Aaron Fenster
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Paper Abstract

Magnetic resonance imaging (MRI) is being increasingly used for image-guided biopsy and focal therapy of prostate cancer. A combined rigid and deformable registration technique is proposed to register pre-treatment diagnostic 3T magnetic resonance (MR) images, with the identified target tumor(s), to the intra-treatment 1.5T MR images. The pre-treatment 3T images are acquired with patients in strictly supine position using an endorectal coil, while 1.5T images are obtained intra-operatively just before insertion of the ablation needle with patients in the lithotomy position. An intensity-based registration routine rigidly aligns two images in which the transformation parameters is initialized using three pairs of manually selected approximate corresponding points. The rigid registration is followed by a deformable registration algorithm employing a generic dynamic linear elastic deformation model discretized by the finite element method (FEM). The model is used in a classical state estimation framework to estimate the deformation of the prostate based on a similarity metric between pre- and intra-treatment images. Registration results using 10 sets of prostate MR images showed that the proposed method can significantly improve registration accuracy in terms of target registration error (TRE) for all prostate substructures. The root mean square (RMS) TRE of 46 manually identified fiducial points was found to be 2.40±1.20 mm, 2.51±1.20 mm, and 2.28±1.22mm for the whole gland (WG), central gland (CG), and peripheral zone (PZ), respectively after deformable registration. These values are improved from 3.15±1.60 mm, 3.09±1.50 mm, and 3.20±1.73mm in the WG, CG and PZ, respectively resulted from rigid registration. Registration results are also evaluated based on the Dice similarity coefficient (DSC), mean absolute surface distances (MAD) and maximum absolute surface distances (MAXD) of the WG and CG in the prostate images.

Paper Details

Date Published: 21 March 2014
PDF: 6 pages
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90340F (21 March 2014); doi: 10.1117/12.2043884
Show Author Affiliations
Bahram Marami, Robarts Research Institute (Canada)
The Univ. of Western Ontario (Canada)
McMaster Univ. (Canada)
Suha Ghoul, Robarts Research Institute (Canada)
Shahin Sirouspour, McMaster Univ. (Canada)
David W. Capson, Univ. of Victoria (Canada)
Sean R. H. Davidson, Ontario Cancer Institute (Canada)
John Trachtenberg, Univ. Health Network (Canada)
Aaron Fenster, Robarts Research Institute (Canada)
The Univ. of Western Ontario (Canada)


Published in SPIE Proceedings Vol. 9034:
Medical Imaging 2014: Image Processing
Sebastien Ourselin; Martin A. Styner, Editor(s)

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