Characterization and Reduction of Noise in Dynamic PET Data Using Masked Volumewise Principal Component Analysis

Svensson, Per-Edvin; Olsson, Johan; Engbrant, Fredrik; Bengtsson, Ewert; Razifar, Pasha

doi:10.2967/jnmt.110.077347

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…The purpose of PCA filtering of the DCE-CT data is to reduce noise while preserving the time-intensity information that is essential for determining the kinetic parameter estimates. Several methods for denoising 4D data, including Gaussian process regression, 49 time-intensity profile similarity (TIPS) bilateral filter, 50 and PCA filters, 35,51,52 have been reported. Although the comparison in the results with other denoising methods is of great interest, the purpose of the study is to investigate the effectiveness of the previously published AIF estimation method on the filtered data.…”

Section: Discussionmentioning

confidence: 99%

Improved accuracy of quantitative parameter estimates in dynamic contrast-enhanced CT study with low temporal resolution

et al. 2015

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Section: Discussionmentioning

confidence: 99%

Improved accuracy of quantitative parameter estimates in dynamic contrast-enhanced CT study with low temporal resolution

et al. 2015

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“…As a popular multivariate statistical technique, principal component analysis (PCA) can be used to extract spatial information from the original data by computing principal components (PCs) which are linear combinations of the original data [16]. PCA has been widely used in various imaging technologies, such as computed tomography (CT) [17,18], positron emission tomography (PET) [19,20] and magnetic resonance imaging (MRI) [21]. In optical molecular imaging, PCA was used to analyze optical data varying with times [22,23], spectrums [12] and energies [7,8].…”

Section: Introductionmentioning

confidence: 99%

Spectral-resolved cone-beam X-ray luminescence computed tomography with principle component analysis

Gao

Rong

et al. 2018

Biomed. Opt. Express

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Cone-beam X-ray luminescence computed tomography (CB-XLCT) has become a promising technique for its higher utilization of X-ray and shorter scanning time compared to the narrow-beam XLCT, but it suffers from the low-spatial resolution that results in the insufficiency to resolve the adjacent multiple probes. In multispectral CB-XLCT, multiple probes show different emission behaviors in the dimension of the spectrum. In this work, a spectral-resolved CB-XLCT method combining multispectral CB-XLCT with principle component analysis (PCA) was proposed to improve the imaging resolution. Results of digital simulation and the phantom experiment illustrated that the proposed method was capable of resolving adjacent multiple probes accurately and had better performance than the common multispectral CB-XLCT with spectrum information .

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“…In order to achieve these goals, PCA computes new variables called principal components (PCs), which are linear combinations of the original variables [20]. PCA has been widely applied to computer tomography (CT) [21,22], positron emission tomography (PET) [23,24] and magnetic resonance imaging (MRI) [25]. In optical imaging, PCA was used to analyze dynamic optical projection data [26].…”

Section: Introductionmentioning

confidence: 99%

Separating structures of different fluorophore concentrations by principal component analysis on multispectral excitation-resolved fluorescence tomography images

Zhang

et al. 2013

Biomed. Opt. Express

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Multispectral excitation-resolved fluorescence tomography (MEFT) uses excitation light of different wavelengths to illuminate the fluorophores and obtains the reconstruction image frame which is fluorescence yield at each corresponding wavelength. For structures containing fluorophores of different concentrations, fluorescence yields show different variation trends with the excitation spectrum. In this study, principal component analysis (PCA) is used to analyze the MEFT reconstructed image frames. By taking advantage of the different variation trends of fluorescence yields, PCA can provide a set of principal components (PCs) in which structures containing different concentrations of fluorophores are shown separately. Simulations and experiments are both performed to test the performance of the proposed algorithm. The results suggest that the location and structure of fluorophores with different concentrations can be obtained and the contrast of fluorophores can be improved further by using this algorithm.

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Characterization and Reduction of Noise in Dynamic PET Data Using Masked Volumewise Principal Component Analysis

Abstract: Higher-order PC noise prenormalization has potential for improving the results from masked volumewise PCA on dynamic PET datasets independent of the type of reconstruction algorithm.

Cited by 6 publications

References 7 publications

Improved accuracy of quantitative parameter estimates in dynamic contrast-enhanced CT study with low temporal resolution

Improved accuracy of quantitative parameter estimates in dynamic contrast-enhanced CT study with low temporal resolution

Spectral-resolved cone-beam X-ray luminescence computed tomography with principle component analysis

Separating structures of different fluorophore concentrations by principal component analysis on multispectral excitation-resolved fluorescence tomography images

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