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
기능성 자기공명영상(fMRI)을 사용하여 인간의 고등 뇌 기능을 분석하여 얻은 기능적 뇌 영상에서 한 가지 심각한 문제는 이러한 영상이 fMRI 데이터 수집 중에 피험자의 영상 대 영상 생리적 움직임으로 인해 발생하는 허위 활성화 영역(인공물)을 묘사한다는 것입니다. 기능적 활성화 영역을 실제로 감지하려면 fMRI 시계열 데이터에서 피험자의 생리적 움직임(즉, 총 머리 움직임, 박동성 혈액 및 뇌척수액(CSF) 흐름)의 영향을 제거해야 합니다. 본 논문에서는 총 머리 움직임과 같은 강체 움직임뿐만 아니라 박동성 혈액 및 뇌척수액 흐름에 의한 변형과 같은 비강체 움직임으로 인한 인공물을 제거하는 방법을 제안합니다. 제안된 방법은 서브픽셀의 광학적 흐름을 감지할 수 있는 기울기 방법을 사용하여 피사체의 움직임을 추정합니다. 우리의 방법은 "픽셀별" 기반으로 대상 움직임을 추정하고 강체 및 비강체 모션 모두를 정확하게 추정합니다. 추정된 움직임을 기반으로 한 보정을 통해 아티팩트가 감소됩니다. 따라서 뇌의 기능적 영상에서는 뇌 활성화 영역이 정확하게 검출됩니다. 우리는 우리의 방법을 실제 fMRI 데이터에 적용함으로써 타당하고 뇌 활성화 영역의 탐지를 향상시킬 수 있음을 입증합니다.
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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
부
@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},}
부
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 -