Advances and challenges in deformable image registration: From image fusion to complex motion modelling
Over the past 20 years, the field of medical image registration has significantly advanced from multi-modal image fusion to highly non-linear, deformable image registration for a wide range of medical applications and imaging modalities, involving the compensation and analysis of physiological organ...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Schnabel, Julia A [verfasserIn] |
|---|
| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Rechteinformationen: |
Nutzungsrecht: Copyright © 2016 Elsevier B.V. All rights reserved. |
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| Übergeordnetes Werk: |
Enthalten in: Medical image analysis - Amsterdam [u.a.] : Elsevier, 1996, 33(2016), Seite 145-148 |
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| Übergeordnetes Werk: |
volume:33 ; year:2016 ; pages:145-148 |
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| DOI / URN: |
10.1016/j.media.2016.06.031 |
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| 520 | |a Over the past 20 years, the field of medical image registration has significantly advanced from multi-modal image fusion to highly non-linear, deformable image registration for a wide range of medical applications and imaging modalities, involving the compensation and analysis of physiological organ motion or of tissue changes due to growth or disease patterns. While the original focus of image registration has predominantly been on correcting for rigid-body motion of brain image volumes acquired at different scanning sessions, often with different modalities, the advent of dedicated longitudinal and cross-sectional brain studies soon necessitated the development of more sophisticated methods that are able to detect and measure local structural or functional changes, or group differences. Moving outside of the brain, cine imaging and dynamic imaging required the development of deformable image registration to directly measure or compensate for local tissue motion. Since then, deformable image registration has become a general enabling technology. In this work we will present our own contributions to the state-of-the-art in deformable multi-modal fusion and complex motion modelling, and then discuss remaining challenges and provide future perspectives to the field. | ||
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10.1016/j.media.2016.06.031 doi PQ20161012 (DE-627)OLC1983582816 (DE-599)GBVOLC1983582816 (PRQ)c670-49f0c90f5aa178fafbec347a7106294c01b38b47ab65f70dbb8c45f609fb9d950 (KEY)0392983320160000033000000145advancesandchallengesindeformableimageregistration DE-627 ger DE-627 rakwb eng 004 ZDB Schnabel, Julia A verfasserin aut Advances and challenges in deformable image registration: From image fusion to complex motion modelling 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Over the past 20 years, the field of medical image registration has significantly advanced from multi-modal image fusion to highly non-linear, deformable image registration for a wide range of medical applications and imaging modalities, involving the compensation and analysis of physiological organ motion or of tissue changes due to growth or disease patterns. While the original focus of image registration has predominantly been on correcting for rigid-body motion of brain image volumes acquired at different scanning sessions, often with different modalities, the advent of dedicated longitudinal and cross-sectional brain studies soon necessitated the development of more sophisticated methods that are able to detect and measure local structural or functional changes, or group differences. Moving outside of the brain, cine imaging and dynamic imaging required the development of deformable image registration to directly measure or compensate for local tissue motion. Since then, deformable image registration has become a general enabling technology. In this work we will present our own contributions to the state-of-the-art in deformable multi-modal fusion and complex motion modelling, and then discuss remaining challenges and provide future perspectives to the field. Nutzungsrecht: Copyright © 2016 Elsevier B.V. All rights reserved. Heinrich, Mattias P oth Papież, Bartłomiej W oth Brady, Sir J Michael oth Enthalten in Medical image analysis Amsterdam [u.a.] : Elsevier, 1996 33(2016), Seite 145-148 (DE-627)223260010 (DE-600)1356436-5 (DE-576)080160034 1361-8415 nnns volume:33 year:2016 pages:145-148 http://dx.doi.org/10.1016/j.media.2016.06.031 Volltext http://www.ncbi.nlm.nih.gov/pubmed/27364430 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_4219 AR 33 2016 145-148 |
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10.1016/j.media.2016.06.031 doi PQ20161012 (DE-627)OLC1983582816 (DE-599)GBVOLC1983582816 (PRQ)c670-49f0c90f5aa178fafbec347a7106294c01b38b47ab65f70dbb8c45f609fb9d950 (KEY)0392983320160000033000000145advancesandchallengesindeformableimageregistration DE-627 ger DE-627 rakwb eng 004 ZDB Schnabel, Julia A verfasserin aut Advances and challenges in deformable image registration: From image fusion to complex motion modelling 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Over the past 20 years, the field of medical image registration has significantly advanced from multi-modal image fusion to highly non-linear, deformable image registration for a wide range of medical applications and imaging modalities, involving the compensation and analysis of physiological organ motion or of tissue changes due to growth or disease patterns. While the original focus of image registration has predominantly been on correcting for rigid-body motion of brain image volumes acquired at different scanning sessions, often with different modalities, the advent of dedicated longitudinal and cross-sectional brain studies soon necessitated the development of more sophisticated methods that are able to detect and measure local structural or functional changes, or group differences. Moving outside of the brain, cine imaging and dynamic imaging required the development of deformable image registration to directly measure or compensate for local tissue motion. Since then, deformable image registration has become a general enabling technology. In this work we will present our own contributions to the state-of-the-art in deformable multi-modal fusion and complex motion modelling, and then discuss remaining challenges and provide future perspectives to the field. Nutzungsrecht: Copyright © 2016 Elsevier B.V. All rights reserved. Heinrich, Mattias P oth Papież, Bartłomiej W oth Brady, Sir J Michael oth Enthalten in Medical image analysis Amsterdam [u.a.] : Elsevier, 1996 33(2016), Seite 145-148 (DE-627)223260010 (DE-600)1356436-5 (DE-576)080160034 1361-8415 nnns volume:33 year:2016 pages:145-148 http://dx.doi.org/10.1016/j.media.2016.06.031 Volltext http://www.ncbi.nlm.nih.gov/pubmed/27364430 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_4219 AR 33 2016 145-148 |
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10.1016/j.media.2016.06.031 doi PQ20161012 (DE-627)OLC1983582816 (DE-599)GBVOLC1983582816 (PRQ)c670-49f0c90f5aa178fafbec347a7106294c01b38b47ab65f70dbb8c45f609fb9d950 (KEY)0392983320160000033000000145advancesandchallengesindeformableimageregistration DE-627 ger DE-627 rakwb eng 004 ZDB Schnabel, Julia A verfasserin aut Advances and challenges in deformable image registration: From image fusion to complex motion modelling 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Over the past 20 years, the field of medical image registration has significantly advanced from multi-modal image fusion to highly non-linear, deformable image registration for a wide range of medical applications and imaging modalities, involving the compensation and analysis of physiological organ motion or of tissue changes due to growth or disease patterns. While the original focus of image registration has predominantly been on correcting for rigid-body motion of brain image volumes acquired at different scanning sessions, often with different modalities, the advent of dedicated longitudinal and cross-sectional brain studies soon necessitated the development of more sophisticated methods that are able to detect and measure local structural or functional changes, or group differences. Moving outside of the brain, cine imaging and dynamic imaging required the development of deformable image registration to directly measure or compensate for local tissue motion. Since then, deformable image registration has become a general enabling technology. In this work we will present our own contributions to the state-of-the-art in deformable multi-modal fusion and complex motion modelling, and then discuss remaining challenges and provide future perspectives to the field. Nutzungsrecht: Copyright © 2016 Elsevier B.V. All rights reserved. Heinrich, Mattias P oth Papież, Bartłomiej W oth Brady, Sir J Michael oth Enthalten in Medical image analysis Amsterdam [u.a.] : Elsevier, 1996 33(2016), Seite 145-148 (DE-627)223260010 (DE-600)1356436-5 (DE-576)080160034 1361-8415 nnns volume:33 year:2016 pages:145-148 http://dx.doi.org/10.1016/j.media.2016.06.031 Volltext http://www.ncbi.nlm.nih.gov/pubmed/27364430 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_4219 AR 33 2016 145-148 |
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Advances and challenges in deformable image registration: From image fusion to complex motion modelling |
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Over the past 20 years, the field of medical image registration has significantly advanced from multi-modal image fusion to highly non-linear, deformable image registration for a wide range of medical applications and imaging modalities, involving the compensation and analysis of physiological organ motion or of tissue changes due to growth or disease patterns. While the original focus of image registration has predominantly been on correcting for rigid-body motion of brain image volumes acquired at different scanning sessions, often with different modalities, the advent of dedicated longitudinal and cross-sectional brain studies soon necessitated the development of more sophisticated methods that are able to detect and measure local structural or functional changes, or group differences. Moving outside of the brain, cine imaging and dynamic imaging required the development of deformable image registration to directly measure or compensate for local tissue motion. Since then, deformable image registration has become a general enabling technology. In this work we will present our own contributions to the state-of-the-art in deformable multi-modal fusion and complex motion modelling, and then discuss remaining challenges and provide future perspectives to the field. |
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Over the past 20 years, the field of medical image registration has significantly advanced from multi-modal image fusion to highly non-linear, deformable image registration for a wide range of medical applications and imaging modalities, involving the compensation and analysis of physiological organ motion or of tissue changes due to growth or disease patterns. While the original focus of image registration has predominantly been on correcting for rigid-body motion of brain image volumes acquired at different scanning sessions, often with different modalities, the advent of dedicated longitudinal and cross-sectional brain studies soon necessitated the development of more sophisticated methods that are able to detect and measure local structural or functional changes, or group differences. Moving outside of the brain, cine imaging and dynamic imaging required the development of deformable image registration to directly measure or compensate for local tissue motion. Since then, deformable image registration has become a general enabling technology. In this work we will present our own contributions to the state-of-the-art in deformable multi-modal fusion and complex motion modelling, and then discuss remaining challenges and provide future perspectives to the field. |
| abstract_unstemmed |
Over the past 20 years, the field of medical image registration has significantly advanced from multi-modal image fusion to highly non-linear, deformable image registration for a wide range of medical applications and imaging modalities, involving the compensation and analysis of physiological organ motion or of tissue changes due to growth or disease patterns. While the original focus of image registration has predominantly been on correcting for rigid-body motion of brain image volumes acquired at different scanning sessions, often with different modalities, the advent of dedicated longitudinal and cross-sectional brain studies soon necessitated the development of more sophisticated methods that are able to detect and measure local structural or functional changes, or group differences. Moving outside of the brain, cine imaging and dynamic imaging required the development of deformable image registration to directly measure or compensate for local tissue motion. Since then, deformable image registration has become a general enabling technology. In this work we will present our own contributions to the state-of-the-art in deformable multi-modal fusion and complex motion modelling, and then discuss remaining challenges and provide future perspectives to the field. |
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| title_short |
Advances and challenges in deformable image registration: From image fusion to complex motion modelling |
| url |
http://dx.doi.org/10.1016/j.media.2016.06.031 http://www.ncbi.nlm.nih.gov/pubmed/27364430 |
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Heinrich, Mattias P Papież, Bartłomiej W Brady, Sir J Michael |
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10.1016/j.media.2016.06.031 |
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2024-07-03T21:23:38.689Z |
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