Elastic registration of prostate MR images based on estimation of deformation states

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 3T magnetic resonance (MR) images of the prostate, with the identified target tumor(s), to intra-treatment 1.5T MR images. The pre-treatment T2-weighted MR images were acquired with patients in a supine position using an endorectal coil in a 3T scanner, while the intra-treatment T2-weighted MR images were acquired in a 1.5T 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. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Marami, Bahram [verfasserIn] Sirouspour, Shahin Ghoul, Suha Cepek, Jeremy Davidson, Sean R H Capson, David W Trachtenberg, John Fenster, Aaron

Format:	Artikel
Sprache:	Englisch

Erschienen:	2015

Rechteinformationen:	Nutzungsrecht: Copyright © 2014 Elsevier B.V. All rights reserved.

Schlagwörter:	Elasticity Imaging Techniques - methods Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods Pattern Recognition, Automated - methods Prostate - pathology Prostate - physiopathology Image Enhancement - methods

Übergeordnetes Werk:	Enthalten in: Medical image analysis - Amsterdam [u.a.] : Elsevier, 1996, 21(2015), 1, Seite 87-103
Übergeordnetes Werk:	volume:21 ; year:2015 ; number:1 ; pages:87-103

Links:	Volltext Link aufrufen

DOI / URN:	10.1016/j.media.2014.12.007

Katalog-ID:	OLC1960853309

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520			\|a 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 3T magnetic resonance (MR) images of the prostate, with the identified target tumor(s), to intra-treatment 1.5T MR images. The pre-treatment T2-weighted MR images were acquired with patients in a supine position using an endorectal coil in a 3T scanner, while the intra-treatment T2-weighted MR images were acquired in a 1.5T 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.
540			\|a Nutzungsrecht: Copyright © 2014 Elsevier B.V. All rights reserved.
650		4	\|a Elasticity Imaging Techniques - methods
650		4	\|a Image Interpretation, Computer-Assisted - methods
650		4	\|a Imaging, Three-Dimensional - methods
650		4	\|a Pattern Recognition, Automated - methods
650		4	\|a Prostate - pathology
650		4	\|a Prostate - physiopathology
650		4	\|a Image Enhancement - methods
700	1		\|a Sirouspour, Shahin \|4 oth
700	1		\|a Ghoul, Suha \|4 oth
700	1		\|a Cepek, Jeremy \|4 oth
700	1		\|a Davidson, Sean R H \|4 oth
700	1		\|a Capson, David W \|4 oth
700	1		\|a Trachtenberg, John \|4 oth
700	1		\|a Fenster, Aaron \|4 oth
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allfields	10.1016/j.media.2014.12.007 doi PQ20160617 (DE-627)OLC1960853309 (DE-599)GBVOLC1960853309 (PRQ)c1277-6ee3db5094c562952f1afeb7307611ec9ca9e9ad823dc4ee4afb13df847c80930 (KEY)0392983320150000021000100087elasticregistrationofprostatemrimagesbasedonestima DE-627 ger DE-627 rakwb eng 004 ZDB Marami, Bahram verfasserin aut Elastic registration of prostate MR images based on estimation of deformation states 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier 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 3T magnetic resonance (MR) images of the prostate, with the identified target tumor(s), to intra-treatment 1.5T MR images. The pre-treatment T2-weighted MR images were acquired with patients in a supine position using an endorectal coil in a 3T scanner, while the intra-treatment T2-weighted MR images were acquired in a 1.5T 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. Nutzungsrecht: Copyright © 2014 Elsevier B.V. All rights reserved. Elasticity Imaging Techniques - methods Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods Pattern Recognition, Automated - methods Prostate - pathology Prostate - physiopathology Image Enhancement - methods Sirouspour, Shahin oth Ghoul, Suha oth Cepek, Jeremy oth Davidson, Sean R H oth Capson, David W oth Trachtenberg, John oth Fenster, Aaron oth Enthalten in Medical image analysis Amsterdam [u.a.] : Elsevier, 1996 21(2015), 1, Seite 87-103 (DE-627)223260010 (DE-600)1356436-5 (DE-576)080160034 1361-8415 nnns volume:21 year:2015 number:1 pages:87-103 http://dx.doi.org/10.1016/j.media.2014.12.007 Volltext http://www.ncbi.nlm.nih.gov/pubmed/25624044 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_4219 AR 21 2015 1 87-103
spelling	10.1016/j.media.2014.12.007 doi PQ20160617 (DE-627)OLC1960853309 (DE-599)GBVOLC1960853309 (PRQ)c1277-6ee3db5094c562952f1afeb7307611ec9ca9e9ad823dc4ee4afb13df847c80930 (KEY)0392983320150000021000100087elasticregistrationofprostatemrimagesbasedonestima DE-627 ger DE-627 rakwb eng 004 ZDB Marami, Bahram verfasserin aut Elastic registration of prostate MR images based on estimation of deformation states 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier 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 3T magnetic resonance (MR) images of the prostate, with the identified target tumor(s), to intra-treatment 1.5T MR images. The pre-treatment T2-weighted MR images were acquired with patients in a supine position using an endorectal coil in a 3T scanner, while the intra-treatment T2-weighted MR images were acquired in a 1.5T 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. Nutzungsrecht: Copyright © 2014 Elsevier B.V. All rights reserved. Elasticity Imaging Techniques - methods Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods Pattern Recognition, Automated - methods Prostate - pathology Prostate - physiopathology Image Enhancement - methods Sirouspour, Shahin oth Ghoul, Suha oth Cepek, Jeremy oth Davidson, Sean R H oth Capson, David W oth Trachtenberg, John oth Fenster, Aaron oth Enthalten in Medical image analysis Amsterdam [u.a.] : Elsevier, 1996 21(2015), 1, Seite 87-103 (DE-627)223260010 (DE-600)1356436-5 (DE-576)080160034 1361-8415 nnns volume:21 year:2015 number:1 pages:87-103 http://dx.doi.org/10.1016/j.media.2014.12.007 Volltext http://www.ncbi.nlm.nih.gov/pubmed/25624044 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_4219 AR 21 2015 1 87-103
allfields_unstemmed	10.1016/j.media.2014.12.007 doi PQ20160617 (DE-627)OLC1960853309 (DE-599)GBVOLC1960853309 (PRQ)c1277-6ee3db5094c562952f1afeb7307611ec9ca9e9ad823dc4ee4afb13df847c80930 (KEY)0392983320150000021000100087elasticregistrationofprostatemrimagesbasedonestima DE-627 ger DE-627 rakwb eng 004 ZDB Marami, Bahram verfasserin aut Elastic registration of prostate MR images based on estimation of deformation states 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier 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 3T magnetic resonance (MR) images of the prostate, with the identified target tumor(s), to intra-treatment 1.5T MR images. The pre-treatment T2-weighted MR images were acquired with patients in a supine position using an endorectal coil in a 3T scanner, while the intra-treatment T2-weighted MR images were acquired in a 1.5T 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. Nutzungsrecht: Copyright © 2014 Elsevier B.V. All rights reserved. Elasticity Imaging Techniques - methods Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods Pattern Recognition, Automated - methods Prostate - pathology Prostate - physiopathology Image Enhancement - methods Sirouspour, Shahin oth Ghoul, Suha oth Cepek, Jeremy oth Davidson, Sean R H oth Capson, David W oth Trachtenberg, John oth Fenster, Aaron oth Enthalten in Medical image analysis Amsterdam [u.a.] : Elsevier, 1996 21(2015), 1, Seite 87-103 (DE-627)223260010 (DE-600)1356436-5 (DE-576)080160034 1361-8415 nnns volume:21 year:2015 number:1 pages:87-103 http://dx.doi.org/10.1016/j.media.2014.12.007 Volltext http://www.ncbi.nlm.nih.gov/pubmed/25624044 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_4219 AR 21 2015 1 87-103
allfieldsGer	10.1016/j.media.2014.12.007 doi PQ20160617 (DE-627)OLC1960853309 (DE-599)GBVOLC1960853309 (PRQ)c1277-6ee3db5094c562952f1afeb7307611ec9ca9e9ad823dc4ee4afb13df847c80930 (KEY)0392983320150000021000100087elasticregistrationofprostatemrimagesbasedonestima DE-627 ger DE-627 rakwb eng 004 ZDB Marami, Bahram verfasserin aut Elastic registration of prostate MR images based on estimation of deformation states 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier 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 3T magnetic resonance (MR) images of the prostate, with the identified target tumor(s), to intra-treatment 1.5T MR images. The pre-treatment T2-weighted MR images were acquired with patients in a supine position using an endorectal coil in a 3T scanner, while the intra-treatment T2-weighted MR images were acquired in a 1.5T 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. Nutzungsrecht: Copyright © 2014 Elsevier B.V. All rights reserved. Elasticity Imaging Techniques - methods Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods Pattern Recognition, Automated - methods Prostate - pathology Prostate - physiopathology Image Enhancement - methods Sirouspour, Shahin oth Ghoul, Suha oth Cepek, Jeremy oth Davidson, Sean R H oth Capson, David W oth Trachtenberg, John oth Fenster, Aaron oth Enthalten in Medical image analysis Amsterdam [u.a.] : Elsevier, 1996 21(2015), 1, Seite 87-103 (DE-627)223260010 (DE-600)1356436-5 (DE-576)080160034 1361-8415 nnns volume:21 year:2015 number:1 pages:87-103 http://dx.doi.org/10.1016/j.media.2014.12.007 Volltext http://www.ncbi.nlm.nih.gov/pubmed/25624044 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_4219 AR 21 2015 1 87-103
allfieldsSound	10.1016/j.media.2014.12.007 doi PQ20160617 (DE-627)OLC1960853309 (DE-599)GBVOLC1960853309 (PRQ)c1277-6ee3db5094c562952f1afeb7307611ec9ca9e9ad823dc4ee4afb13df847c80930 (KEY)0392983320150000021000100087elasticregistrationofprostatemrimagesbasedonestima DE-627 ger DE-627 rakwb eng 004 ZDB Marami, Bahram verfasserin aut Elastic registration of prostate MR images based on estimation of deformation states 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier 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 3T magnetic resonance (MR) images of the prostate, with the identified target tumor(s), to intra-treatment 1.5T MR images. The pre-treatment T2-weighted MR images were acquired with patients in a supine position using an endorectal coil in a 3T scanner, while the intra-treatment T2-weighted MR images were acquired in a 1.5T 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. Nutzungsrecht: Copyright © 2014 Elsevier B.V. All rights reserved. Elasticity Imaging Techniques - methods Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods Pattern Recognition, Automated - methods Prostate - pathology Prostate - physiopathology Image Enhancement - methods Sirouspour, Shahin oth Ghoul, Suha oth Cepek, Jeremy oth Davidson, Sean R H oth Capson, David W oth Trachtenberg, John oth Fenster, Aaron oth Enthalten in Medical image analysis Amsterdam [u.a.] : Elsevier, 1996 21(2015), 1, Seite 87-103 (DE-627)223260010 (DE-600)1356436-5 (DE-576)080160034 1361-8415 nnns volume:21 year:2015 number:1 pages:87-103 http://dx.doi.org/10.1016/j.media.2014.12.007 Volltext http://www.ncbi.nlm.nih.gov/pubmed/25624044 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_4219 AR 21 2015 1 87-103
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source	Enthalten in Medical image analysis 21(2015), 1, Seite 87-103 volume:21 year:2015 number:1 pages:87-103
sourceStr	Enthalten in Medical image analysis 21(2015), 1, Seite 87-103 volume:21 year:2015 number:1 pages:87-103
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topic_facet	Elasticity Imaging Techniques - methods Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods Pattern Recognition, Automated - methods Prostate - pathology Prostate - physiopathology Image Enhancement - methods
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authorswithroles_txt_mv	Marami, Bahram @@aut@@ Sirouspour, Shahin @@oth@@ Ghoul, Suha @@oth@@ Cepek, Jeremy @@oth@@ Davidson, Sean R H @@oth@@ Capson, David W @@oth@@ Trachtenberg, John @@oth@@ Fenster, Aaron @@oth@@
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In this paper, a combined rigid and deformable registration technique is proposed to register pre-treatment diagnostic 3T magnetic resonance (MR) images of the prostate, with the identified target tumor(s), to intra-treatment 1.5T MR images. The pre-treatment T2-weighted MR images were acquired with patients in a supine position using an endorectal coil in a 3T scanner, while the intra-treatment T2-weighted MR images were acquired in a 1.5T 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.</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="a">Nutzungsrecht: Copyright © 2014 Elsevier B.V. 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author	Marami, Bahram
spellingShingle	Marami, Bahram ddc 004 misc Elasticity Imaging Techniques - methods misc Image Interpretation, Computer-Assisted - methods misc Imaging, Three-Dimensional - methods misc Pattern Recognition, Automated - methods misc Prostate - pathology misc Prostate - physiopathology misc Image Enhancement - methods Elastic registration of prostate MR images based on estimation of deformation states
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topic_title	004 ZDB Elastic registration of prostate MR images based on estimation of deformation states Elasticity Imaging Techniques - methods Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods Pattern Recognition, Automated - methods Prostate - pathology Prostate - physiopathology Image Enhancement - methods
topic	ddc 004 misc Elasticity Imaging Techniques - methods misc Image Interpretation, Computer-Assisted - methods misc Imaging, Three-Dimensional - methods misc Pattern Recognition, Automated - methods misc Prostate - pathology misc Prostate - physiopathology misc Image Enhancement - methods
topic_unstemmed	ddc 004 misc Elasticity Imaging Techniques - methods misc Image Interpretation, Computer-Assisted - methods misc Imaging, Three-Dimensional - methods misc Pattern Recognition, Automated - methods misc Prostate - pathology misc Prostate - physiopathology misc Image Enhancement - methods
topic_browse	ddc 004 misc Elasticity Imaging Techniques - methods misc Image Interpretation, Computer-Assisted - methods misc Imaging, Three-Dimensional - methods misc Pattern Recognition, Automated - methods misc Prostate - pathology misc Prostate - physiopathology misc Image Enhancement - methods
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title	Elastic registration of prostate MR images based on estimation of deformation states
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title_full	Elastic registration of prostate MR images based on estimation of deformation states
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title_sort	elastic registration of prostate mr images based on estimation of deformation states
title_auth	Elastic registration of prostate MR images based on estimation of deformation states
abstract	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 3T magnetic resonance (MR) images of the prostate, with the identified target tumor(s), to intra-treatment 1.5T MR images. The pre-treatment T2-weighted MR images were acquired with patients in a supine position using an endorectal coil in a 3T scanner, while the intra-treatment T2-weighted MR images were acquired in a 1.5T 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.
abstractGer	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 3T magnetic resonance (MR) images of the prostate, with the identified target tumor(s), to intra-treatment 1.5T MR images. The pre-treatment T2-weighted MR images were acquired with patients in a supine position using an endorectal coil in a 3T scanner, while the intra-treatment T2-weighted MR images were acquired in a 1.5T 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.
abstract_unstemmed	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 3T magnetic resonance (MR) images of the prostate, with the identified target tumor(s), to intra-treatment 1.5T MR images. The pre-treatment T2-weighted MR images were acquired with patients in a supine position using an endorectal coil in a 3T scanner, while the intra-treatment T2-weighted MR images were acquired in a 1.5T 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.
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title_short	Elastic registration of prostate MR images based on estimation of deformation states
url	http://dx.doi.org/10.1016/j.media.2014.12.007 http://www.ncbi.nlm.nih.gov/pubmed/25624044
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author2	Sirouspour, Shahin Ghoul, Suha Cepek, Jeremy Davidson, Sean R H Capson, David W Trachtenberg, John Fenster, Aaron
author2Str	Sirouspour, Shahin Ghoul, Suha Cepek, Jeremy Davidson, Sean R H Capson, David W Trachtenberg, John Fenster, Aaron
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up_date	2024-07-03T22:51:41.466Z
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In this paper, a combined rigid and deformable registration technique is proposed to register pre-treatment diagnostic 3T magnetic resonance (MR) images of the prostate, with the identified target tumor(s), to intra-treatment 1.5T MR images. The pre-treatment T2-weighted MR images were acquired with patients in a supine position using an endorectal coil in a 3T scanner, while the intra-treatment T2-weighted MR images were acquired in a 1.5T 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.</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="a">Nutzungsrecht: Copyright © 2014 Elsevier B.V. All rights reserved.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Elasticity Imaging Techniques - methods</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image Interpretation, Computer-Assisted - methods</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Imaging, Three-Dimensional - methods</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Pattern Recognition, Automated - methods</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Prostate - pathology</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Prostate - physiopathology</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image Enhancement - methods</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sirouspour, Shahin</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ghoul, Suha</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cepek, Jeremy</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Davidson, Sean R H</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Capson, David W</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Trachtenberg, John</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fenster, Aaron</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Medical image analysis</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier, 1996</subfield><subfield code="g">21(2015), 1, Seite 87-103</subfield><subfield code="w">(DE-627)223260010</subfield><subfield code="w">(DE-600)1356436-5</subfield><subfield code="w">(DE-576)080160034</subfield><subfield code="x">1361-8415</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:21</subfield><subfield code="g">year:2015</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:87-103</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1016/j.media.2014.12.007</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://www.ncbi.nlm.nih.gov/pubmed/25624044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-DE-84</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4219</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">21</subfield><subfield code="j">2015</subfield><subfield code="e">1</subfield><subfield code="h">87-103</subfield></datafield></record></collection>
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Elastic registration of prostate MR images based on estimation of deformation states

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