Optimized 3D co-registration of ultra-low-field and high-field magnetic resonance images

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Optimized 3D co-registration of ultra-low-field and high-field magnetic resonance images. / Guidotti, Roberto; Sinibaldi, Raffaele; De Luca, Cinzia; Conti, Allegra; Ilmoniemi, Risto J.; Zevenhoven, Koos C.J.; Magnelind, Per E.; Pizzella, Vittorio; Gratta, Cosimo Del; Romani, Gian Luca; Penna, Stefania Della.

In: PloS one, Vol. 13, No. 3, e0193890, 01.03.2018, p. 1-19.

Research output: Contribution to journalArticleScientificpeer-review

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Guidotti, R, Sinibaldi, R, De Luca, C, Conti, A, Ilmoniemi, RJ, Zevenhoven, KCJ, Magnelind, PE, Pizzella, V, Gratta, CD, Romani, GL & Penna, SD 2018, 'Optimized 3D co-registration of ultra-low-field and high-field magnetic resonance images' PloS one, vol. 13, no. 3, e0193890, pp. 1-19. https://doi.org/10.1371/journal.pone.0193890

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Guidotti, Roberto ; Sinibaldi, Raffaele ; De Luca, Cinzia ; Conti, Allegra ; Ilmoniemi, Risto J. ; Zevenhoven, Koos C.J. ; Magnelind, Per E. ; Pizzella, Vittorio ; Gratta, Cosimo Del ; Romani, Gian Luca ; Penna, Stefania Della. / Optimized 3D co-registration of ultra-low-field and high-field magnetic resonance images. In: PloS one. 2018 ; Vol. 13, No. 3. pp. 1-19.

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@article{5c504023297344e3865afb4ca510589c,
title = "Optimized 3D co-registration of ultra-low-field and high-field magnetic resonance images",
abstract = "The prototypes of ultra-low-field (ULF) MRI scanners developed in recent years represent new, innovative, cost-effective and safer systems, which are suitable to be integrated in multi-modal (Magnetoencephalography and MRI) devices. Integrated ULF-MRI and MEG scanners could represent an ideal solution to obtain functional (MEG) and anatomical (ULF MRI) information in the same environment, without errors that may limit source reconstruction accuracy. However, the low resolution and signal-to-noise ratio (SNR) of ULF images, as well as their limited coverage, do not generally allow for the construction of an accurate individual volume conductor model suitable for MEG localization. Thus, for practical usage, a high-field (HF) MRI image is also acquired, and the HF-MRI images are co-registered to the ULF-MRI ones. We address here this issue through an optimized pipeline (SWIM—Sliding WIndow grouping supporting Mutual information). The co-registration is performed by an affine transformation, the parameters of which are estimated using Normalized Mutual Information as the cost function, and Adaptive Simulated Annealing as the minimization algorithm. The sub-voxel resolution of the ULF images is handled by a sliding-window approach applying multiple grouping strategies to down-sample HF MRI to the ULF-MRI resolution. The pipeline has been tested on phantom and real data from different ULF-MRI devices, and comparison with well-known toolboxes for fMRI analysis has been performed. Our pipeline always outperformed the fMRI toolboxes (FSL and SPM). The HF–ULF MRI co-registration obtained by means of our pipeline could lead to an effective integration of ULF MRI with MEG, with the aim of improving localization accuracy, but also to help exploit ULF MRI in tumor imaging.",
author = "Roberto Guidotti and Raffaele Sinibaldi and {De Luca}, Cinzia and Allegra Conti and Ilmoniemi, {Risto J.} and Zevenhoven, {Koos C.J.} and Magnelind, {Per E.} and Vittorio Pizzella and Gratta, {Cosimo Del} and Romani, {Gian Luca} and Penna, {Stefania Della}",
year = "2018",
month = "3",
day = "1",
doi = "10.1371/journal.pone.0193890",
language = "English",
volume = "13",
pages = "1--19",
journal = "PloS one",
issn = "1932-6203",
number = "3",

}

RIS - Download

TY - JOUR

T1 - Optimized 3D co-registration of ultra-low-field and high-field magnetic resonance images

AU - Guidotti, Roberto

AU - Sinibaldi, Raffaele

AU - De Luca, Cinzia

AU - Conti, Allegra

AU - Ilmoniemi, Risto J.

AU - Zevenhoven, Koos C.J.

AU - Magnelind, Per E.

AU - Pizzella, Vittorio

AU - Gratta, Cosimo Del

AU - Romani, Gian Luca

AU - Penna, Stefania Della

PY - 2018/3/1

Y1 - 2018/3/1

N2 - The prototypes of ultra-low-field (ULF) MRI scanners developed in recent years represent new, innovative, cost-effective and safer systems, which are suitable to be integrated in multi-modal (Magnetoencephalography and MRI) devices. Integrated ULF-MRI and MEG scanners could represent an ideal solution to obtain functional (MEG) and anatomical (ULF MRI) information in the same environment, without errors that may limit source reconstruction accuracy. However, the low resolution and signal-to-noise ratio (SNR) of ULF images, as well as their limited coverage, do not generally allow for the construction of an accurate individual volume conductor model suitable for MEG localization. Thus, for practical usage, a high-field (HF) MRI image is also acquired, and the HF-MRI images are co-registered to the ULF-MRI ones. We address here this issue through an optimized pipeline (SWIM—Sliding WIndow grouping supporting Mutual information). The co-registration is performed by an affine transformation, the parameters of which are estimated using Normalized Mutual Information as the cost function, and Adaptive Simulated Annealing as the minimization algorithm. The sub-voxel resolution of the ULF images is handled by a sliding-window approach applying multiple grouping strategies to down-sample HF MRI to the ULF-MRI resolution. The pipeline has been tested on phantom and real data from different ULF-MRI devices, and comparison with well-known toolboxes for fMRI analysis has been performed. Our pipeline always outperformed the fMRI toolboxes (FSL and SPM). The HF–ULF MRI co-registration obtained by means of our pipeline could lead to an effective integration of ULF MRI with MEG, with the aim of improving localization accuracy, but also to help exploit ULF MRI in tumor imaging.

AB - The prototypes of ultra-low-field (ULF) MRI scanners developed in recent years represent new, innovative, cost-effective and safer systems, which are suitable to be integrated in multi-modal (Magnetoencephalography and MRI) devices. Integrated ULF-MRI and MEG scanners could represent an ideal solution to obtain functional (MEG) and anatomical (ULF MRI) information in the same environment, without errors that may limit source reconstruction accuracy. However, the low resolution and signal-to-noise ratio (SNR) of ULF images, as well as their limited coverage, do not generally allow for the construction of an accurate individual volume conductor model suitable for MEG localization. Thus, for practical usage, a high-field (HF) MRI image is also acquired, and the HF-MRI images are co-registered to the ULF-MRI ones. We address here this issue through an optimized pipeline (SWIM—Sliding WIndow grouping supporting Mutual information). The co-registration is performed by an affine transformation, the parameters of which are estimated using Normalized Mutual Information as the cost function, and Adaptive Simulated Annealing as the minimization algorithm. The sub-voxel resolution of the ULF images is handled by a sliding-window approach applying multiple grouping strategies to down-sample HF MRI to the ULF-MRI resolution. The pipeline has been tested on phantom and real data from different ULF-MRI devices, and comparison with well-known toolboxes for fMRI analysis has been performed. Our pipeline always outperformed the fMRI toolboxes (FSL and SPM). The HF–ULF MRI co-registration obtained by means of our pipeline could lead to an effective integration of ULF MRI with MEG, with the aim of improving localization accuracy, but also to help exploit ULF MRI in tumor imaging.

UR - http://www.scopus.com/inward/record.url?scp=85042917025&partnerID=8YFLogxK

U2 - 10.1371/journal.pone.0193890

DO - 10.1371/journal.pone.0193890

M3 - Article

VL - 13

SP - 1

EP - 19

JO - PloS one

JF - PloS one

SN - 1932-6203

IS - 3

M1 - e0193890

ER -

ID: 18276173