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Automated Registration of Freehand B-Mode Ultrasound and Magnetic Resonance Imaging of the Carotid Arteries Based on Geometric Features. / Carvalho, Diego D B; Arias Lorza, Andres Mauricio; Niessen, Wiro J.; de Bruijne, Marleen; Klein, Stefan.

In: Ultrasound in Medicine & Biology, Vol. 43, No. 1, 01.01.2017, p. 273-285.

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Carvalho, Diego D B ; Arias Lorza, Andres Mauricio ; Niessen, Wiro J. ; de Bruijne, Marleen ; Klein, Stefan. / Automated Registration of Freehand B-Mode Ultrasound and Magnetic Resonance Imaging of the Carotid Arteries Based on Geometric Features. In: Ultrasound in Medicine & Biology. 2017 ; Vol. 43, No. 1. pp. 273-285.

BibTeX

@article{de019295e7b743598a07d3c60a395b30,
title = "Automated Registration of Freehand B-Mode Ultrasound and Magnetic Resonance Imaging of the Carotid Arteries Based on Geometric Features",
abstract = "An automated method for registering B-mode ultrasound (US) and magnetic resonance imaging (MRI) of the carotid arteries is proposed. The registration uses geometric features, namely, lumen centerlines and lumen segmentations, which are extracted fully automatically from the images after manual annotation of three seed points in US and MRI. The registration procedure starts with alignment of the lumen centerlines using a point-based registration algorithm. The resulting rigid transformation is used to initialize a rigid and subsequent non-rigid registration procedure that jointly aligns centerlines and segmentations by minimizing a weighted sum of the Euclidean distance between centerlines and the dissimilarity between segmentations. The method was evaluated in 28 carotid arteries from eight patients and six healthy volunteers. First, the automated US lumen segmentation method was validated and optimized in a cross-validation experiment. Next, the effect of the weighting parameter of the proposed registration dissimilarity metric and the control point spacing in the non-rigid registration was evaluated. Finally, the proposed registration method was evaluated in comparison to an existing intensity-and-point-based method, a registration using only the centerlines and a registration using manual US lumen segmentations. Registration accuracy was measured in terms of the mean surface distance between manual US segmentations and the registered MRI segmentations. The average mean surface distance was 0.78 ± 0.34 mm for all subjects, 0.65 ± 0.09 mm for healthy volunteers and 0.87 ± 0.42 mm for patients. The results for the complete set were significantly better (Wilcoxon test, p <0.01) than the results for the intensity-and-point-based method and the centerline-based registration method. We conclude that the proposed method can robustly and accurately register US and MR images of the carotid artery, allowing multimodal analysis of the carotid plaque to improve plaque assessment.",
keywords = "Automatic segmentation, Centerline, Magnetic resonance imaging, Multimodal registration, Ultrasound",
author = "Carvalho, {Diego D B} and {Arias Lorza}, {Andres Mauricio} and Niessen, {Wiro J.} and {de Bruijne}, Marleen and Stefan Klein",
year = "2017",
month = "1",
day = "1",
doi = "10.1016/j.ultrasmedbio.2016.08.031",
language = "English",
volume = "43",
pages = "273--285",
journal = "Ultrasound in Medicine & Biology",
issn = "0301-5629",
publisher = "Elsevier",
number = "1",

}

RIS

TY - JOUR

T1 - Automated Registration of Freehand B-Mode Ultrasound and Magnetic Resonance Imaging of the Carotid Arteries Based on Geometric Features

AU - Carvalho, Diego D B

AU - Arias Lorza, Andres Mauricio

AU - Niessen, Wiro J.

AU - de Bruijne, Marleen

AU - Klein, Stefan

PY - 2017/1/1

Y1 - 2017/1/1

N2 - An automated method for registering B-mode ultrasound (US) and magnetic resonance imaging (MRI) of the carotid arteries is proposed. The registration uses geometric features, namely, lumen centerlines and lumen segmentations, which are extracted fully automatically from the images after manual annotation of three seed points in US and MRI. The registration procedure starts with alignment of the lumen centerlines using a point-based registration algorithm. The resulting rigid transformation is used to initialize a rigid and subsequent non-rigid registration procedure that jointly aligns centerlines and segmentations by minimizing a weighted sum of the Euclidean distance between centerlines and the dissimilarity between segmentations. The method was evaluated in 28 carotid arteries from eight patients and six healthy volunteers. First, the automated US lumen segmentation method was validated and optimized in a cross-validation experiment. Next, the effect of the weighting parameter of the proposed registration dissimilarity metric and the control point spacing in the non-rigid registration was evaluated. Finally, the proposed registration method was evaluated in comparison to an existing intensity-and-point-based method, a registration using only the centerlines and a registration using manual US lumen segmentations. Registration accuracy was measured in terms of the mean surface distance between manual US segmentations and the registered MRI segmentations. The average mean surface distance was 0.78 ± 0.34 mm for all subjects, 0.65 ± 0.09 mm for healthy volunteers and 0.87 ± 0.42 mm for patients. The results for the complete set were significantly better (Wilcoxon test, p <0.01) than the results for the intensity-and-point-based method and the centerline-based registration method. We conclude that the proposed method can robustly and accurately register US and MR images of the carotid artery, allowing multimodal analysis of the carotid plaque to improve plaque assessment.

AB - An automated method for registering B-mode ultrasound (US) and magnetic resonance imaging (MRI) of the carotid arteries is proposed. The registration uses geometric features, namely, lumen centerlines and lumen segmentations, which are extracted fully automatically from the images after manual annotation of three seed points in US and MRI. The registration procedure starts with alignment of the lumen centerlines using a point-based registration algorithm. The resulting rigid transformation is used to initialize a rigid and subsequent non-rigid registration procedure that jointly aligns centerlines and segmentations by minimizing a weighted sum of the Euclidean distance between centerlines and the dissimilarity between segmentations. The method was evaluated in 28 carotid arteries from eight patients and six healthy volunteers. First, the automated US lumen segmentation method was validated and optimized in a cross-validation experiment. Next, the effect of the weighting parameter of the proposed registration dissimilarity metric and the control point spacing in the non-rigid registration was evaluated. Finally, the proposed registration method was evaluated in comparison to an existing intensity-and-point-based method, a registration using only the centerlines and a registration using manual US lumen segmentations. Registration accuracy was measured in terms of the mean surface distance between manual US segmentations and the registered MRI segmentations. The average mean surface distance was 0.78 ± 0.34 mm for all subjects, 0.65 ± 0.09 mm for healthy volunteers and 0.87 ± 0.42 mm for patients. The results for the complete set were significantly better (Wilcoxon test, p <0.01) than the results for the intensity-and-point-based method and the centerline-based registration method. We conclude that the proposed method can robustly and accurately register US and MR images of the carotid artery, allowing multimodal analysis of the carotid plaque to improve plaque assessment.

KW - Automatic segmentation

KW - Centerline

KW - Magnetic resonance imaging

KW - Multimodal registration

KW - Ultrasound

UR - http://www.scopus.com/inward/record.url?scp=85002131964&partnerID=8YFLogxK

U2 - 10.1016/j.ultrasmedbio.2016.08.031

DO - 10.1016/j.ultrasmedbio.2016.08.031

M3 - Article

VL - 43

SP - 273

EP - 285

JO - Ultrasound in Medicine & Biology

T2 - Ultrasound in Medicine & Biology

JF - Ultrasound in Medicine & Biology

SN - 0301-5629

IS - 1

ER -

ID: 9624750