The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dalam imej otak berfungsi yang diperoleh dengan menganalisis fungsi otak manusia yang lebih tinggi menggunakan pengimejan resonans magnetik berfungsi (fMRI), satu masalah serius ialah imej ini menggambarkan kawasan pengaktifan palsu (artifak) yang terhasil daripada pergerakan fisiologi imej ke imej subjek semasa pemerolehan data fMRI. Untuk benar-benar mengesan kawasan pengaktifan berfungsi, adalah perlu untuk menghapuskan kesan pergerakan fisiologi subjek (iaitu, gerakan kepala kasar, aliran darah berdenyut dan cecair serebrospinal (CSF)) daripada data siri masa fMRI. Dalam makalah ini, kami mencadangkan satu kaedah untuk menghapuskan artifak disebabkan bukan sahaja gerakan badan tegar seperti gerakan kepala kasar, tetapi juga gerakan badan bukan tegar seperti ubah bentuk yang disebabkan oleh darah berdenyut dan aliran CSF. Kaedah yang dicadangkan menganggarkan pergerakan subjek dengan menggunakan kaedah kecerunan yang boleh mengesan aliran optik subpiksel. Kaedah kami menganggarkan pergerakan subjek pada asas "piksel demi piksel" dan mencapai anggaran tepat bagi gerakan badan tegar dan bukan badan tegar. Artifak dikurangkan dengan pembetulan berdasarkan anggaran pergerakan. Oleh itu, kawasan pengaktifan otak dikesan dengan tepat dalam imej otak berfungsi. Kami menunjukkan bahawa kaedah kami sah dengan menggunakannya pada data fMRI sebenar dan ia boleh meningkatkan pengesanan kawasan pengaktifan otak.
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Salinan
Seiji KUMAZAWA, Tsuyoshi YAMAMOTO, Yoshinori DOBASHI, "Motion Correction of Physiological Movements Using Optical Flow for fMRI Time Series" in IEICE TRANSACTIONS on Information,
vol. E85-D, no. 1, pp. 60-68, January 2002, doi: .
Abstract: In functional brain images obtained by analyzing higher human brain functions using functional magnetic resonance imaging (fMRI), one serious problem is that these images depict false activation areas (artifacts) resulting from image-to-image physiological movements of subject during fMRI data acquisition. In order to truly detect functional activation areas, it is necessary to eliminate the effects of physiological movements of subject (i.e., gross head motion, pulsatile blood and cerebrospinal fluid (CSF) flow) from fMRI time series data. In this paper, we propose a method for eliminating artifacts due to not only rigid-body motion such as gross head motion, but also non-rigid-body motion like the deformation caused by the pulsatile blood and CSF flow. The proposed method estimates subject movements by using gradient methods which can detect subpixel optical flow. Our method estimates the subject movements on a "pixel-by-pixel" basis, and achieves the accurate estimation of both rigid-body and non-rigid-body motion. The artifacts are reduced by correction based on the estimated movements. Therefore, brain activation areas are accurately detected in functional brain images. We demonstrate that our method is valid by applying it to real fMRI data and that it can improve the detection of brain activation areas.
URL: https://global.ieice.org/en_transactions/information/10.1587/e85-d_1_60/_p
Salinan
@ARTICLE{e85-d_1_60,
author={Seiji KUMAZAWA, Tsuyoshi YAMAMOTO, Yoshinori DOBASHI, },
journal={IEICE TRANSACTIONS on Information},
title={Motion Correction of Physiological Movements Using Optical Flow for fMRI Time Series},
year={2002},
volume={E85-D},
number={1},
pages={60-68},
abstract={In functional brain images obtained by analyzing higher human brain functions using functional magnetic resonance imaging (fMRI), one serious problem is that these images depict false activation areas (artifacts) resulting from image-to-image physiological movements of subject during fMRI data acquisition. In order to truly detect functional activation areas, it is necessary to eliminate the effects of physiological movements of subject (i.e., gross head motion, pulsatile blood and cerebrospinal fluid (CSF) flow) from fMRI time series data. In this paper, we propose a method for eliminating artifacts due to not only rigid-body motion such as gross head motion, but also non-rigid-body motion like the deformation caused by the pulsatile blood and CSF flow. The proposed method estimates subject movements by using gradient methods which can detect subpixel optical flow. Our method estimates the subject movements on a "pixel-by-pixel" basis, and achieves the accurate estimation of both rigid-body and non-rigid-body motion. The artifacts are reduced by correction based on the estimated movements. Therefore, brain activation areas are accurately detected in functional brain images. We demonstrate that our method is valid by applying it to real fMRI data and that it can improve the detection of brain activation areas.},
keywords={},
doi={},
ISSN={},
month={January},}
Salinan
TY - JOUR
TI - Motion Correction of Physiological Movements Using Optical Flow for fMRI Time Series
T2 - IEICE TRANSACTIONS on Information
SP - 60
EP - 68
AU - Seiji KUMAZAWA
AU - Tsuyoshi YAMAMOTO
AU - Yoshinori DOBASHI
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E85-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2002
AB - In functional brain images obtained by analyzing higher human brain functions using functional magnetic resonance imaging (fMRI), one serious problem is that these images depict false activation areas (artifacts) resulting from image-to-image physiological movements of subject during fMRI data acquisition. In order to truly detect functional activation areas, it is necessary to eliminate the effects of physiological movements of subject (i.e., gross head motion, pulsatile blood and cerebrospinal fluid (CSF) flow) from fMRI time series data. In this paper, we propose a method for eliminating artifacts due to not only rigid-body motion such as gross head motion, but also non-rigid-body motion like the deformation caused by the pulsatile blood and CSF flow. The proposed method estimates subject movements by using gradient methods which can detect subpixel optical flow. Our method estimates the subject movements on a "pixel-by-pixel" basis, and achieves the accurate estimation of both rigid-body and non-rigid-body motion. The artifacts are reduced by correction based on the estimated movements. Therefore, brain activation areas are accurately detected in functional brain images. We demonstrate that our method is valid by applying it to real fMRI data and that it can improve the detection of brain activation areas.
ER -