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Total Correlation-Based Groupwise Image Registration for Quantitative MRI. / Guyader, Jean Marie; Huizinga, Wyke; Fortunati, Valerio; Poot, Dirk H.; Kranenburg, Matthijs Van; Veenland, Jifke F.; Paulides, Margarethus M.; Niessen, Wiro J.; Klein, Stefan.

Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016. IEEE, 2016. p. 626-633 7789574.

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Harvard

Guyader, JM, Huizinga, W, Fortunati, V, Poot, DH, Kranenburg, MV, Veenland, JF, Paulides, MM, Niessen, WJ & Klein, S 2016, Total Correlation-Based Groupwise Image Registration for Quantitative MRI. in Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016., 7789574, IEEE, pp. 626-633, CVPRW 2016, Las Vegas, United States, 26/06/16. https://doi.org/10.1109/CVPRW.2016.84

APA

Guyader, J. M., Huizinga, W., Fortunati, V., Poot, D. H., Kranenburg, M. V., Veenland, J. F., ... Klein, S. (2016). Total Correlation-Based Groupwise Image Registration for Quantitative MRI. In Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016 (pp. 626-633). [7789574] IEEE. https://doi.org/10.1109/CVPRW.2016.84

Vancouver

Guyader JM, Huizinga W, Fortunati V, Poot DH, Kranenburg MV, Veenland JF et al. Total Correlation-Based Groupwise Image Registration for Quantitative MRI. In Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016. IEEE. 2016. p. 626-633. 7789574 https://doi.org/10.1109/CVPRW.2016.84

Author

Guyader, Jean Marie ; Huizinga, Wyke ; Fortunati, Valerio ; Poot, Dirk H. ; Kranenburg, Matthijs Van ; Veenland, Jifke F. ; Paulides, Margarethus M. ; Niessen, Wiro J. ; Klein, Stefan. / Total Correlation-Based Groupwise Image Registration for Quantitative MRI. Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016. IEEE, 2016. pp. 626-633

BibTeX

@inproceedings{44eb3bb7c78e4e9ab892e319bcd98fca,
title = "Total Correlation-Based Groupwise Image Registration for Quantitative MRI",
abstract = "In quantitative magnetic resonance imaging (qMRI), quantitative tissue properties can be estimated by fitting a signal model to the voxel intensities of a series of images acquired with different settings. To obtain reliable quantitative measures, it is necessary that the qMRI images are spatially aligned so that a given voxel corresponds in all images to the same anatomical location. The objective of the present study is to describe and evaluate a novel automatic groupwise registration technique using a dissimilarity metric based on an approximated form of total correlation. The proposed registration method is applied to five qMRI datasets of various anatomical locations, and the obtained registration performances are compared to these of a conventional pairwise registration based on mutual information. The results show that groupwise total correlation yields better registration performances than pairwise mutual information. This study also establishes that the formulation of approximated total correlation is quite analogous to two other groupwise metrics based on principal component analysis (PCA). Registration performances of total correlation and these two PCA-based techniques are therefore compared. The results show that total correlation yields performances that are analogous to these of the PCAbased techniques. However, compared to these PCA-based metrics, total correlation has two main advantages. Firstly, it is directly derived from a multivariate form of mutual information, while the PCA-based metrics were obtained empirically. Secondly, total correlation has the advantage of requiring no user-defined parameter.",
author = "Guyader, {Jean Marie} and Wyke Huizinga and Valerio Fortunati and Poot, {Dirk H.} and Kranenburg, {Matthijs Van} and Veenland, {Jifke F.} and Paulides, {Margarethus M.} and Niessen, {Wiro J.} and Stefan Klein",
year = "2016",
month = "12",
day = "16",
doi = "10.1109/CVPRW.2016.84",
language = "English",
pages = "626--633",
booktitle = "Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016",
publisher = "IEEE",
address = "United States",

}

RIS

TY - GEN

T1 - Total Correlation-Based Groupwise Image Registration for Quantitative MRI

AU - Guyader, Jean Marie

AU - Huizinga, Wyke

AU - Fortunati, Valerio

AU - Poot, Dirk H.

AU - Kranenburg, Matthijs Van

AU - Veenland, Jifke F.

AU - Paulides, Margarethus M.

AU - Niessen, Wiro J.

AU - Klein, Stefan

PY - 2016/12/16

Y1 - 2016/12/16

N2 - In quantitative magnetic resonance imaging (qMRI), quantitative tissue properties can be estimated by fitting a signal model to the voxel intensities of a series of images acquired with different settings. To obtain reliable quantitative measures, it is necessary that the qMRI images are spatially aligned so that a given voxel corresponds in all images to the same anatomical location. The objective of the present study is to describe and evaluate a novel automatic groupwise registration technique using a dissimilarity metric based on an approximated form of total correlation. The proposed registration method is applied to five qMRI datasets of various anatomical locations, and the obtained registration performances are compared to these of a conventional pairwise registration based on mutual information. The results show that groupwise total correlation yields better registration performances than pairwise mutual information. This study also establishes that the formulation of approximated total correlation is quite analogous to two other groupwise metrics based on principal component analysis (PCA). Registration performances of total correlation and these two PCA-based techniques are therefore compared. The results show that total correlation yields performances that are analogous to these of the PCAbased techniques. However, compared to these PCA-based metrics, total correlation has two main advantages. Firstly, it is directly derived from a multivariate form of mutual information, while the PCA-based metrics were obtained empirically. Secondly, total correlation has the advantage of requiring no user-defined parameter.

AB - In quantitative magnetic resonance imaging (qMRI), quantitative tissue properties can be estimated by fitting a signal model to the voxel intensities of a series of images acquired with different settings. To obtain reliable quantitative measures, it is necessary that the qMRI images are spatially aligned so that a given voxel corresponds in all images to the same anatomical location. The objective of the present study is to describe and evaluate a novel automatic groupwise registration technique using a dissimilarity metric based on an approximated form of total correlation. The proposed registration method is applied to five qMRI datasets of various anatomical locations, and the obtained registration performances are compared to these of a conventional pairwise registration based on mutual information. The results show that groupwise total correlation yields better registration performances than pairwise mutual information. This study also establishes that the formulation of approximated total correlation is quite analogous to two other groupwise metrics based on principal component analysis (PCA). Registration performances of total correlation and these two PCA-based techniques are therefore compared. The results show that total correlation yields performances that are analogous to these of the PCAbased techniques. However, compared to these PCA-based metrics, total correlation has two main advantages. Firstly, it is directly derived from a multivariate form of mutual information, while the PCA-based metrics were obtained empirically. Secondly, total correlation has the advantage of requiring no user-defined parameter.

UR - http://resolver.tudelft.nl/uuid:44eb3bb7-c78e-4e9a-b892-e319bcd98fca

UR - http://www.scopus.com/inward/record.url?scp=85010203053&partnerID=8YFLogxK

U2 - 10.1109/CVPRW.2016.84

DO - 10.1109/CVPRW.2016.84

M3 - Conference contribution

SP - 626

EP - 633

BT - Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016

PB - IEEE

ER -

ID: 16251238