Elastic registration of prostate MR images based on estimation of deformation states
| Autor: | Suha Ghoul, Shahin Sirouspour, Jeremy Cepek, Bahram Marami, Aaron Fenster, John Trachtenberg, David W. Capson, Sean R.H. Davidson |
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| Rok vydání: | 2015 |
| Předmět: |
Male
Scanner Similarity (geometry) Image registration Health Informatics Elastic model-based registration Finite element method Focal ablation therapy Prostate MR image registration State estimation Sensitivity and Specificity Pattern Recognition Automated Imaging Three-Dimensional Position (vector) Prostate Artificial Intelligence Elastic Modulus Image Interpretation Computer-Assisted medicine Humans Radiology Nuclear Medicine and imaging Computer vision Rigid transformation Radiological and Ultrasound Technology medicine.diagnostic_test business.industry Perspective (graphical) Reproducibility of Results Magnetic resonance imaging Image Enhancement Computer Graphics and Computer-Aided Design medicine.anatomical_structure Subtraction Technique Medical Biophysics Elasticity Imaging Techniques Computer Vision and Pattern Recognition Artificial intelligence business Algorithms |
| Zdroj: | Medical Biophysics Publications |
| Popis: | Magnetic resonance imaging (MRI) is being used increasingly for image-guided targeted biopsy and focal therapy of prostate cancer. In this paper, a combined rigid and deformable registration technique is proposed to register pre-treatment diagnostic 3 T magnetic resonance (MR) images of the prostate, with the identified target tumor(s), to intra-treatment 1.5 T MR images. The pre-treatment T2-weighted MR images were acquired with patients in a supine position using an endorectal coil in a 3 T scanner, while the intra-treatment T2-weighted MR images were acquired in a 1.5 T scanner before insertion of the needle with patients in the semi-lithotomy position. Both the rigid and deformable registration algorithms employ an intensity-based distance metric defined based on the modality independent neighborhood descriptors (MIND) between images. The optimization routine for estimating the rigid transformation parameters is initialized using four pairs of manually selected approximate corresponding points on the boundaries of the prostate. In this paper, the problem of deformable image registration is approached from the perspective of state estimation for dynamical systems. The registration algorithm employs a rather generic dynamic linear elastic model of the tissue deformation discretized by the finite element method (FEM). We use the model in a classical state estimation framework to estimate the deformation of the prostate based on the distance metric between pre- and intra-treatment images. Our deformable registration results using 17 sets of prostate MR images showed that the proposed method yielded a target registration error (TRE) of 1.87 ± 0.94 mm, 2.03 ± 0.94 mm, and 1.70 ± 0.93 mm for the whole gland (WG), central gland (CG), and peripheral zone (PZ), respectively, using 76 manually-identified fiducial points. This was an improvement over the 2.67 ± 1.31 mm, 2.95 ± 1.43 mm, and 2.34 ± 1.11 mm, respectively for the WG, CG, and PZ after rigid registration alone. Dice similarity coefficients (DSC) in the WG, CG and PZ were 88.2 ± 5.3, 85.6 ± 7.6 and 68.7 ± 6.9 percent, respectively. Furthermore, the mean absolute distances (MAD) between surfaces was 1.26 ± 0.56 mm and 1.27 ± 0.55 mm in the WG and CG, after deformable registration. These results indicate that the proposed registration technique has sufficient accuracy for localizing prostate tumors in MRI-guided targeted biopsy or focal therapy of clinically localized prostate cancer. |
| Databáze: | OpenAIRE |
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