Journal Article10.1088/0031-9155/54/11/021
Principal component analysis with pre-normalization improves the signal-to-noise ratio and image quality in positron emission tomography studies of amyloid deposits in Alzheimer's disease
Pasha Razifar,Henry Engler,Gunnar Blomquist,Anna Ringheim,Sergio Estrada,Bengt Långström,Mats Bergström +6 more
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TL;DR: A new approach for the application of principal component analysis (PCA) with pre-normalization on dynamic positron emission tomography (PET) images with enhanced contrast and improved signal-to-noise ratio (SNR) and discrimination power (DP) compared to summed images and parametric images.
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Abstract: This study introduces a new approach for the application of principal component analysis (PCA) with pre-normalization on dynamic positron emission tomography (PET) images. These images are generated using the amyloid imaging agent N-methyl [(11)C]2-(4'-methylaminophenyl)-6-hydroxy-benzothiazole ([(11)C]PIB) in patients with Alzheimer's disease (AD) and healthy volunteers (HVs). The aim was to introduce a method which, by using the whole dataset and without assuming a specific kinetic model, could generate images with improved signal-to-noise and detect, extract and illustrate changes in kinetic behavior between different regions in the brain. Eight AD patients and eight HVs from a previously published study with [(11)C]PIB were used. The approach includes enhancement of brain regions where the kinetics of the radiotracer are different from what is seen in the reference region, pre-normalization for differences in noise levels and removal of negative values. This is followed by slice-wise application of PCA (SW-PCA) on the dynamic PET images. Results obtained using the new approach were compared with results obtained using reference Patlak and summed images. The new approach generated images with good quality in which cortical brain regions in AD patients showed high uptake, compared to cerebellum and white matter. Cortical structures in HVs showed low uptake as expected and in good agreement with data generated using kinetic modeling. The introduced approach generated images with enhanced contrast and improved signal-to-noise ratio (SNR) and discrimination power (DP) compared to summed images and parametric images. This method is expected to be an important clinical tool in the diagnosis and differential diagnosis of dementia.
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Citations
Principal component analysis of dynamic fluorescence diffuse optical tomography images.
TL;DR: A new method for detecting and visualizing organs with different kinetics utilizing principal component analysis (PCA) is presented, suggesting that it is able to extract and illustrate changes in ICG kinetic behavior between the heart and the lungs.
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Patent
Method and apparatus for motion correcting medical images
Pasha Razifar,Kris Filip Johan Jules Thielemans,Shailendra Pratap Singh Rathore +2 more
- 03 May 2011
TL;DR: In this article, a method for reducing motion related imaging artifacts is proposed, which includes obtaining an image dataset of a region of interest, generating a plurality of intermediate images using the image dataset, applying a multivariate data analysis technique to the plurality of the intermediate images to generate motion information, and sorting the intermediate image into a plurality based on the motion information.
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Separating structures of different fluorophore concentrations by principal component analysis on multispectral excitation-resolved fluorescence tomography images.
TL;DR: The results suggest that the location and structure of fluorophores with different concentrations can be obtained and the contrast of Fluorescence tomography can be improved further by using this algorithm.
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A Linear Correction for Principal Component Analysis of Dynamic Fluorescence Diffuse Optical Tomography Images
TL;DR: A new linear corrected method is proposed for modeling these time-varying fluorescence measurements before performing PCA, and results suggest that the principal component (PC) images generated using the new method improve SNR and discrimination capability, compared to the PC images generate using the uncorrected D-FDOT images.
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Spectral-resolved cone-beam X-ray luminescence computed tomography with principle component analysis.
TL;DR: 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 priori.
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