Literature DB >> 16039535

Semiautomatic nonrigid registration for the prostate and pelvic MR volumes.

Baowei Fei1, Jeffrey L Duerk, D Bruce Sodee, David L Wilson.   

Abstract

RATIONALE AND
OBJECTIVES: Three-dimensional (3D) nonrigid image registration for potential applications in prostate cancer treatment and interventional magnetic resonance (iMRI) imaging-guided therapies were investigated.
MATERIALS AND METHODS: An almost fully automated 3D nonrigid registration algorithm using mutual information and a thin plate spline (TPS) transformation for MR images of the prostate and pelvis were created and evaluated. In the first step, an automatic rigid body registration with special features was used to capture the global transformation. In the second step, local feature points (FPs) were registered using mutual information. An operator entered only five FPs located at the prostate center, left and right hip joints, and left and right distal femurs. The program automatically determined and optimized other FPs at the external pelvic skin surface and along the femurs. More than 600 control points were used to establish a TPS transformation for deformation of the pelvic region and prostate. Ten volume pairs were acquired from three volunteers in the diagnostic (supine) and treatment positions (supine with legs raised).
RESULTS: Various visualization techniques showed that warping rectified the significant pelvic misalignment by the rigid-body method. Gray-value measures of registration quality, including mutual information, correlation coefficient, and intensity difference, all improved with warping. The distance between prostate 3D centroids was 0.7 +/- 0.2 mm after warping compared with 4.9 +/- 3.4 mm with rigid-body registration.
CONCLUSION: Semiautomatic nonrigid registration works better than rigid-body registration when patient position is changed greatly between acquisitions. It could be a useful tool for many applications in the management of prostate.

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Year:  2005        PMID: 16039535     DOI: 10.1016/j.acra.2005.03.063

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  22 in total

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2.  Automatic 3D Segmentation of the Kidney in MR Images Using Wavelet Feature Extraction and Probability Shape Model.

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4.  3D Prostate Segmentation of Ultrasound Images Combining Longitudinal Image Registration and Machine Learning.

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5.  Computer-aided diagnosis of prostate cancer with MRI.

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9.  A Molecular Image-directed, 3D Ultrasound-guided Biopsy System for the Prostate.

Authors:  Baowei Fei; David M Schuster; Viraj Master; Hamed Akbari; Aaron Fenster; Peter Nieh
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2012-02-16

10.  Non-rigid registration of breast surfaces using the laplace and diffusion equations.

Authors:  Rowena E Ong; Jao J Ou; Michael I Miga
Journal:  Biomed Eng Online       Date:  2010-02-12       Impact factor: 2.819

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