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Aligning 3D time-of-flight MRA datasets for quantitative longitudinal studies: evaluation of rigid registration techniques

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Aligning 3D time-of-flight MRA datasets for quantitative longitudinal studies: evaluation of rigid registration techniques. / Verleger, Tobias; Schönfeld, Michael; Säring, Dennis; Siemonsen, Susanne; Fiehler, Jens; Forkert, Nils Daniel.

In: MAGN RESON IMAGING, Vol. 32, No. 10, 2014, p. 1390-5.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Verleger, T, Schönfeld, M, Säring, D, Siemonsen, S, Fiehler, J & Forkert, ND 2014, 'Aligning 3D time-of-flight MRA datasets for quantitative longitudinal studies: evaluation of rigid registration techniques', MAGN RESON IMAGING, vol. 32, no. 10, pp. 1390-5. https://doi.org/10.1016/j.mri.2014.08.011

APA

Verleger, T., Schönfeld, M., Säring, D., Siemonsen, S., Fiehler, J., & Forkert, N. D. (2014). Aligning 3D time-of-flight MRA datasets for quantitative longitudinal studies: evaluation of rigid registration techniques. MAGN RESON IMAGING, 32(10), 1390-5. https://doi.org/10.1016/j.mri.2014.08.011

Vancouver

Verleger T, Schönfeld M, Säring D, Siemonsen S, Fiehler J, Forkert ND. Aligning 3D time-of-flight MRA datasets for quantitative longitudinal studies: evaluation of rigid registration techniques. MAGN RESON IMAGING. 2014;32(10):1390-5. https://doi.org/10.1016/j.mri.2014.08.011

Bibtex

@article{280e98416dd7494f841fdc7531cb0a77,
title = "Aligning 3D time-of-flight MRA datasets for quantitative longitudinal studies: evaluation of rigid registration techniques",
abstract = "OBJECTIVE: 3D Time-of-flight (TOF) magnetic resonance angiography is commonly used for vascular analyses. A quantification of longitudinal morphological changes usually requires the registration of TOF image sequences acquired at different time points. The aim of this study was to evaluate the precision of different 3D rigid registration setups such that an optimal quantification of morphological changes can be achieved.METHODS: Eight different rigid registration techniques were implemented and evaluated in this study using the target registration error (TRE) calculated based on 554 landmarks defined in twenty TOF datasets. The registration techniques differed in integration of brain and vessel segmentation masks and usage of a multi-resolution framework. Furthermore, the benefit of a prior volume-of-interest definition for registration accuracy was evaluated.RESULTS: The results revealed that the highest registration accuracies can be achieved using a multi-resolution framework and a cerebrovascular segmentation as mask. Numerically, a mean TRE of 1.1mm was calculated. If applicable, a prior definition of a volume-of-interest allows a reduction of the TRE to only 0.6mm.CONCLUSION: TOF datasets should be registered using vessel segmentations as mask, multi-resolution framework and previous volume-of-interest definition if possible to obtain the highest registration precision. This is especially the case for longitudinal datasets that are separated by several months while the registration technique seems less important for datasets that are only separated by a few days.",
author = "Tobias Verleger and Michael Sch{\"o}nfeld and Dennis S{\"a}ring and Susanne Siemonsen and Jens Fiehler and Forkert, {Nils Daniel}",
note = "Verleger INTERN",
year = "2014",
doi = "10.1016/j.mri.2014.08.011",
language = "English",
volume = "32",
pages = "1390--5",
journal = "MAGN RESON IMAGING",
issn = "0730-725X",
publisher = "Elsevier Inc.",
number = "10",

}

RIS

TY - JOUR

T1 - Aligning 3D time-of-flight MRA datasets for quantitative longitudinal studies: evaluation of rigid registration techniques

AU - Verleger, Tobias

AU - Schönfeld, Michael

AU - Säring, Dennis

AU - Siemonsen, Susanne

AU - Fiehler, Jens

AU - Forkert, Nils Daniel

N1 - Verleger INTERN

PY - 2014

Y1 - 2014

N2 - OBJECTIVE: 3D Time-of-flight (TOF) magnetic resonance angiography is commonly used for vascular analyses. A quantification of longitudinal morphological changes usually requires the registration of TOF image sequences acquired at different time points. The aim of this study was to evaluate the precision of different 3D rigid registration setups such that an optimal quantification of morphological changes can be achieved.METHODS: Eight different rigid registration techniques were implemented and evaluated in this study using the target registration error (TRE) calculated based on 554 landmarks defined in twenty TOF datasets. The registration techniques differed in integration of brain and vessel segmentation masks and usage of a multi-resolution framework. Furthermore, the benefit of a prior volume-of-interest definition for registration accuracy was evaluated.RESULTS: The results revealed that the highest registration accuracies can be achieved using a multi-resolution framework and a cerebrovascular segmentation as mask. Numerically, a mean TRE of 1.1mm was calculated. If applicable, a prior definition of a volume-of-interest allows a reduction of the TRE to only 0.6mm.CONCLUSION: TOF datasets should be registered using vessel segmentations as mask, multi-resolution framework and previous volume-of-interest definition if possible to obtain the highest registration precision. This is especially the case for longitudinal datasets that are separated by several months while the registration technique seems less important for datasets that are only separated by a few days.

AB - OBJECTIVE: 3D Time-of-flight (TOF) magnetic resonance angiography is commonly used for vascular analyses. A quantification of longitudinal morphological changes usually requires the registration of TOF image sequences acquired at different time points. The aim of this study was to evaluate the precision of different 3D rigid registration setups such that an optimal quantification of morphological changes can be achieved.METHODS: Eight different rigid registration techniques were implemented and evaluated in this study using the target registration error (TRE) calculated based on 554 landmarks defined in twenty TOF datasets. The registration techniques differed in integration of brain and vessel segmentation masks and usage of a multi-resolution framework. Furthermore, the benefit of a prior volume-of-interest definition for registration accuracy was evaluated.RESULTS: The results revealed that the highest registration accuracies can be achieved using a multi-resolution framework and a cerebrovascular segmentation as mask. Numerically, a mean TRE of 1.1mm was calculated. If applicable, a prior definition of a volume-of-interest allows a reduction of the TRE to only 0.6mm.CONCLUSION: TOF datasets should be registered using vessel segmentations as mask, multi-resolution framework and previous volume-of-interest definition if possible to obtain the highest registration precision. This is especially the case for longitudinal datasets that are separated by several months while the registration technique seems less important for datasets that are only separated by a few days.

U2 - 10.1016/j.mri.2014.08.011

DO - 10.1016/j.mri.2014.08.011

M3 - SCORING: Journal article

C2 - 25131630

VL - 32

SP - 1390

EP - 1395

JO - MAGN RESON IMAGING

JF - MAGN RESON IMAGING

SN - 0730-725X

IS - 10

ER -