A brain MRI/SPECT registration system using an adaptive similarity metric: Application on the evaluation of parkinson's disease

Jiann Der Lee; Chung Hsien Huang; Cheng Wei Chen; Yi Hsin Weng; Kun Ju Lin; Chin Tu Chen

doi:10.1007/978-3-540-71457-6_22

A brain MRI/SPECT registration system using an adaptive similarity metric: Application on the evaluation of parkinson's disease

Jiann Der Lee^*, Chung Hsien Huang, Cheng Wei Chen, Yi Hsin Weng, Kun Ju Lin, Chin Tu Chen

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

Single photon emission computed tomography (SPECT) of dopamine transporters with ^99mTc-TRODAT-1 has recently been proposed to provide valuable information of assessing the dopaminergic system. In order to measure the binding ratio of the nuclear medicine, registering magnetic resonance imaging (MRI) and SPECT image is a significant process. Therefore, an automated MRI/SPECT image registration algorithm of using an adaptive similarity metric is proposed. This similarity metric combines anatomic features characterized by specific binding (SB), the mean counts per voxel within the specific tissues, of nuclear medicine and distribution of image intensity characterized by the Normalized Mutual Information (NMI). In addition, we have also built a computer-aid clinical diagnosis system which automates all the processes of MRI/SPECT registration for further evaluation of Parkinson's disease. Clinical MRI/SPECT data from eighteen healthy subjects and thirteen patients are involved to validate the performance of the proposed system. Comparing with the conventional NMI-based registration algorithm, our system reduces the target of registration error (TRE) from >7 mm to approximate 4 mm. From the view point of clinical evaluation, the error of binding ratio, the ratio of specific-to-non-specific ^99mTc-TRODAT-1 binding, is 0.20 in the healthy group and 0.13 in the patient group via the proposed system.

Original language	English
Title of host publication	Computer Vision/Computer Graphics Collaboration Techniques - Third International Conference, MIRAGE 2007, Proceedings
Publisher	Springer Verlag
Pages	235-246
Number of pages	12
ISBN (Print)	3540714561, 9783540714569
DOIs	https://doi.org/10.1007/978-3-540-71457-6_22
State	Published - 2007
Event	3rd International Conference, MIRAGE 2007: Computer Vision/Computer Graphics Collaboration Techniques - Rocquencourt, France Duration: 28 03 2007 → 30 03 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4418 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Conference, MIRAGE 2007: Computer Vision/Computer Graphics Collaboration Techniques
Country/Territory	France
City	Rocquencourt
Period	28/03/07 → 30/03/07

Keywords

MRI
Medical imaging
Registration
SPECT
Similarity metric

Access to Document

10.1007/978-3-540-71457-6_22

Cite this

Lee, J. D., Huang, C. H., Chen, C. W., Weng, Y. H., Lin, K. J., & Chen, C. T. (2007). A brain MRI/SPECT registration system using an adaptive similarity metric: Application on the evaluation of parkinson's disease. In Computer Vision/Computer Graphics Collaboration Techniques - Third International Conference, MIRAGE 2007, Proceedings (pp. 235-246). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4418 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-540-71457-6_22

Lee, Jiann Der ; Huang, Chung Hsien ; Chen, Cheng Wei et al. / A brain MRI/SPECT registration system using an adaptive similarity metric : Application on the evaluation of parkinson's disease. Computer Vision/Computer Graphics Collaboration Techniques - Third International Conference, MIRAGE 2007, Proceedings. Springer Verlag, 2007. pp. 235-246 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{8757817fc3174d5daffee071dda6e90e,

title = "A brain MRI/SPECT registration system using an adaptive similarity metric: Application on the evaluation of parkinson's disease",

abstract = "Single photon emission computed tomography (SPECT) of dopamine transporters with 99mTc-TRODAT-1 has recently been proposed to provide valuable information of assessing the dopaminergic system. In order to measure the binding ratio of the nuclear medicine, registering magnetic resonance imaging (MRI) and SPECT image is a significant process. Therefore, an automated MRI/SPECT image registration algorithm of using an adaptive similarity metric is proposed. This similarity metric combines anatomic features characterized by specific binding (SB), the mean counts per voxel within the specific tissues, of nuclear medicine and distribution of image intensity characterized by the Normalized Mutual Information (NMI). In addition, we have also built a computer-aid clinical diagnosis system which automates all the processes of MRI/SPECT registration for further evaluation of Parkinson's disease. Clinical MRI/SPECT data from eighteen healthy subjects and thirteen patients are involved to validate the performance of the proposed system. Comparing with the conventional NMI-based registration algorithm, our system reduces the target of registration error (TRE) from >7 mm to approximate 4 mm. From the view point of clinical evaluation, the error of binding ratio, the ratio of specific-to-non-specific 99mTc-TRODAT-1 binding, is 0.20 in the healthy group and 0.13 in the patient group via the proposed system.",

keywords = "MRI, Medical imaging, Registration, SPECT, Similarity metric",

author = "Lee, {Jiann Der} and Huang, {Chung Hsien} and Chen, {Cheng Wei} and Weng, {Yi Hsin} and Lin, {Kun Ju} and Chen, {Chin Tu}",

year = "2007",

doi = "10.1007/978-3-540-71457-6_22",

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Lee, JD, Huang, CH, Chen, CW, Weng, YH, Lin, KJ & Chen, CT 2007, A brain MRI/SPECT registration system using an adaptive similarity metric: Application on the evaluation of parkinson's disease. in Computer Vision/Computer Graphics Collaboration Techniques - Third International Conference, MIRAGE 2007, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4418 LNCS, Springer Verlag, pp. 235-246, 3rd International Conference, MIRAGE 2007: Computer Vision/Computer Graphics Collaboration Techniques, Rocquencourt, France, 28/03/07. https://doi.org/10.1007/978-3-540-71457-6_22

A brain MRI/SPECT registration system using an adaptive similarity metric: Application on the evaluation of parkinson's disease. / Lee, Jiann Der; Huang, Chung Hsien; Chen, Cheng Wei et al.
Computer Vision/Computer Graphics Collaboration Techniques - Third International Conference, MIRAGE 2007, Proceedings. Springer Verlag, 2007. p. 235-246 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4418 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A brain MRI/SPECT registration system using an adaptive similarity metric

T2 - 3rd International Conference, MIRAGE 2007: Computer Vision/Computer Graphics Collaboration Techniques

AU - Lee, Jiann Der

AU - Huang, Chung Hsien

AU - Chen, Cheng Wei

AU - Weng, Yi Hsin

AU - Lin, Kun Ju

AU - Chen, Chin Tu

PY - 2007

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N2 - Single photon emission computed tomography (SPECT) of dopamine transporters with 99mTc-TRODAT-1 has recently been proposed to provide valuable information of assessing the dopaminergic system. In order to measure the binding ratio of the nuclear medicine, registering magnetic resonance imaging (MRI) and SPECT image is a significant process. Therefore, an automated MRI/SPECT image registration algorithm of using an adaptive similarity metric is proposed. This similarity metric combines anatomic features characterized by specific binding (SB), the mean counts per voxel within the specific tissues, of nuclear medicine and distribution of image intensity characterized by the Normalized Mutual Information (NMI). In addition, we have also built a computer-aid clinical diagnosis system which automates all the processes of MRI/SPECT registration for further evaluation of Parkinson's disease. Clinical MRI/SPECT data from eighteen healthy subjects and thirteen patients are involved to validate the performance of the proposed system. Comparing with the conventional NMI-based registration algorithm, our system reduces the target of registration error (TRE) from >7 mm to approximate 4 mm. From the view point of clinical evaluation, the error of binding ratio, the ratio of specific-to-non-specific 99mTc-TRODAT-1 binding, is 0.20 in the healthy group and 0.13 in the patient group via the proposed system.

AB - Single photon emission computed tomography (SPECT) of dopamine transporters with 99mTc-TRODAT-1 has recently been proposed to provide valuable information of assessing the dopaminergic system. In order to measure the binding ratio of the nuclear medicine, registering magnetic resonance imaging (MRI) and SPECT image is a significant process. Therefore, an automated MRI/SPECT image registration algorithm of using an adaptive similarity metric is proposed. This similarity metric combines anatomic features characterized by specific binding (SB), the mean counts per voxel within the specific tissues, of nuclear medicine and distribution of image intensity characterized by the Normalized Mutual Information (NMI). In addition, we have also built a computer-aid clinical diagnosis system which automates all the processes of MRI/SPECT registration for further evaluation of Parkinson's disease. Clinical MRI/SPECT data from eighteen healthy subjects and thirteen patients are involved to validate the performance of the proposed system. Comparing with the conventional NMI-based registration algorithm, our system reduces the target of registration error (TRE) from >7 mm to approximate 4 mm. From the view point of clinical evaluation, the error of binding ratio, the ratio of specific-to-non-specific 99mTc-TRODAT-1 binding, is 0.20 in the healthy group and 0.13 in the patient group via the proposed system.

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KW - Medical imaging

KW - Registration

KW - SPECT

KW - Similarity metric

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DO - 10.1007/978-3-540-71457-6_22

M3 - 会议稿件

AN - SCOPUS:37149016205

SN - 3540714561

SN - 9783540714569

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Lee JD, Huang CH, Chen CW, Weng YH, Lin KJ, Chen CT. A brain MRI/SPECT registration system using an adaptive similarity metric: Application on the evaluation of parkinson's disease. In Computer Vision/Computer Graphics Collaboration Techniques - Third International Conference, MIRAGE 2007, Proceedings. Springer Verlag. 2007. p. 235-246. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-71457-6_22

A brain MRI/SPECT registration system using an adaptive similarity metric: Application on the evaluation of parkinson's disease

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