Robust extraction of input function from H/sub 2//sup 15/O dynamic myocardial positron emission tomography using independent component analysis

Lee, J.S.; Ahn, Ji Young; Lee, D.S.; Han, JungHyun; Jang, Moonsuk; Chung, June‐Key; Lee, M.C.; Park, K.S.

doi:10.1109/nssmic.1999.845828

Cited by 13 publications

(20 citation statements)

References 9 publications

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“…ICA can extract independent components from several kinds of recorded signals (5,(16)(17)(18). It includes a group of algorithms that take advantage of independence in a complex signal to perform blind source separation (4,19).…”

Section: Discussionmentioning

confidence: 99%

Independent Component Analysis Removing Artifacts in Ictal Recordings

Urrestarazu¹,

Iriarte²,

Alegre³

et al. 2004

Epilepsia

110

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Summary:Purpose: Independent component analysis (ICA) is a novel algorithm able to separate independent components from complex signals. Studies in interictal EEG demonstrate its usefulness to eliminate eye, muscle, 50-Hz, electrocardiogram (ECG), and electrode artifacts. The goal of this study was to evaluate the usefulness of ICA in removing artifacts in ictal recordings with a known EEG onset.Methods: We studied 20 seizures of nine patients with focal epilepsy monitored in our video-EEG monitoring unit. ICA was applied to remove obvious artifacts in segments at the beginning of the seizure. The final EEGs were exported to the original format and were compared with the original EEG by two blinded examiners. We compared original recordings and the samples cleaned by digital filters (DFs), ICA and ICA plus digital filters (ICA + DFs), evaluating the possibility of finding an ictal pattern, the localization of the onset in area and time, and the global quality of the sample.Results: All the recordings except one (95%) improved after the use of ICA for the elimination of blinking and other artifacts. Three seizures were found in which in the original recordings did not permit us to detect an ictal pattern, and after ICA + DFs, an ictal onset was evident; in two of them, ICA alone was able to show this pattern. The best results in all the scores were obtained with ICA + DF. ICA was better than DFs. The agreement between the two reviewers was highly significant.Conclusions: ICA is useful to remove artifacts from ictal recordings. When applied to ictal recordings, it increases the quality of the recording. In some cases, ICA may be useful to show ictal onsets obscured by artifacts. ICA + DFs obtained the best results regarding removal of the artifacts.

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Section: Discussionmentioning

confidence: 99%

Independent Component Analysis Removing Artifacts in Ictal Recordings

Urrestarazu¹,

Iriarte²,

Alegre³

et al. 2004

Epilepsia

110

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“…It has been applied to electroencephalographic (EEG), magnetoencephalographic (MEG) and functional MRI data to identify independent signals in either space or time [16][17][18][19][20]. ICA was also used in dynamic PET to extract plasma time-activity curve for quantification [21][22]. Our proposed method is based on information maximization algorithm of Bell and Sejnowski [23].…”

Section: Independent Component Analysis (Ica)mentioning

confidence: 99%

Separation of Two Radionuclides in Simultaneous Dual-Isotope Imaging With Independent Component Analysis

Chang

Huang

et al. 2006

Biomed. Eng. Appl. Basis Commun.

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“…In recent times, Independent Component Analysis (ICA) received great interest in biomedical signals. In PET imaging, some works [11][12][13][14][15][16][17][18] have been published reporting usage of ICA in image decomposition, image segmentation or for seeking of blood sampling from the images. Recently, Mabrouk et al [18] used ICA to extract the IF and the myocardium TACs directly from dynamic positron emission tomography (PET) images in small animals.…”

Section: Introductionmentioning

confidence: 99%

Assessment of myocardial metabolic rate of glucose by means of Bayesian ICA and Markov Chain Monte Carlo methods in small animal PET imaging

Berradja

Boughanmi

2016

Polish Journal of Medical Physics and Engineering

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In dynamic cardiac PET FDG studies the assessment of myocardial metabolic rate of glucose (MMRG) requires the knowledge of the blood input function (IF). IF can be obtained by manual or automatic blood sampling and cross calibrated with PET. These procedures are cumbersome, invasive and generate uncertainties. The IF is contaminated by spillover of radioactivity from the adjacent myocardium and this could cause important error in the estimated MMRG. In this study, we show that the IF can be extracted from the images in a rat heart study with 18F-fluorodeoxyglucose (18F-FDG) by means of Independent Component Analysis (ICA) based on Bayesian theory and Markov Chain Monte Carlo (MCMC) sampling method (BICA). Images of the heart from rats were acquired with the Sherbrooke small animal PET scanner. A region of interest (ROI) was drawn around the rat image and decomposed into blood and tissue using BICA. The Statistical study showed that there is a significant difference (p < 0.05) between MMRG obtained with IF extracted by BICA with respect to IF extracted from measured images corrupted with spillover.

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Robust extraction of input function from H/sub 2//sup 15/O dynamic myocardial positron emission tomography using independent component analysis

Cited by 13 publications

References 9 publications

Independent Component Analysis Removing Artifacts in Ictal Recordings

Independent Component Analysis Removing Artifacts in Ictal Recordings

Separation of Two Radionuclides in Simultaneous Dual-Isotope Imaging With Independent Component Analysis

Assessment of myocardial metabolic rate of glucose by means of Bayesian ICA and Markov Chain Monte Carlo methods in small animal PET imaging

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