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Matlab 2010b for windows

Manufactured by MathWorks
Sourced in United States

MATLAB 2010b for Windows is a software application developed by MathWorks for numerical computing and visualization. It provides a programming environment for algorithm development, data analysis, and visualization. MATLAB 2010b supports a wide range of mathematical operations, including matrix manipulation, function plotting, and data analysis.

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4 protocols using matlab 2010b for windows

1

Preprocessing of 11C-PIB PET Data

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The preprocessing of the 11C-PIB imaging data was performed using Statistical Parametric Mapping 8 (SPM8) software and MATLAB 2010b for Windows (Mathworks, Natick, MA, USA). First, 11C-PIB integral images (data corrected for radioactive decay summed from 60 to 90 min post-injection) were created from the dynamic PET images (frames 32 to 34) and coregistered to the subject’s MRI images. Second, the MRI images were segmented into three classes (gray matter, white matter, and cerebrospinal fluid) in SPM8 using 16 non-linear iterations and 7 × 9 × 7 basis functions. Third, the PET images and gray matter magnetic resonance images were normalized using a T1-weighted MRI template that was delivered with SPM to obtain normalization parameters. The application of a 0.5 threshold to the gray matter probability map created a gray matter probability map in the MNI space. The gray matter probability map was then coregistered to the AAL template, and the PET counts were extracted from the gray matter probability map and ROIs. The mean values for all of the regions were calculated from the integral 11C-PIB image. Target-to-cerebellum ratios were subsequently calculated for 11 bilateral regions.
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2

PET Imaging Acquisition and Processing Protocols

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The acquisition and processing protocols for 18F-FDG and 11C-PIB PET imaging have been described in our previous study (Zhang et al., 2017 (link); Wang et al., 2019 (link)). Briefly, PET images were acquired in the three-dimensional scanning mode on a GE Discovery LS PET/CT 710 scanner. 11C-PIB was administered intravenously at a dose of 370-555MBq, and a 90-min dynamic PET scan was performed according to a predetermined protocol. One hour after the 11C-PiB PET scan, 185-259 MBq of 18F-FDG was then injected intravenously, A 10-min static PET emission scan was performed 40 min after FDG injection with the same scanning mode. FDG PET and PiB PET images were preprocessed using MRI data for partial volume effect correction and spatial normalization. PiB PET imaging analysis was performed using Statistical Parameter Mapping 8 (SPM8) software on MATLAB 2010b for Windows (Mathworks, Natick, MA, USA) or PMOD software (version 3.7, PMOD Technologies Ltd., Zurich, Switzerland), as described in our previous study. The average of all specific regions was calculated from the PiB integral image. FDG frames for each subject were summed and normalized to mean pons activity. It is then displayed on the NIH color scale and can be windowed and viewed on three planes according to the rater’s discretion.
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3

PET Imaging Data Preprocessing Protocol

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The preprocessing of the [11C]PIB imaging data was performed using Statistical Parametric Mapping 8 (SPM8) software and MATLAB 2010b for Windows (Mathworks, Natick, MA). The [11C]PIB integral images (data corrected for radioactive decay summed from 60 to 90 min post-injection) were created from the dynamic PET images (frames 32 to 34) and coregistered to the subject's MRI images. The MRI images were segmented into 3 classes (gray matter, white matter, and cerebrospinal fluid) in SPM8 using 16 nonlinear iterations and 7 × 9 × 7 basis functions. The PET images and gray matter MRI images were then normalized using a T1-weighted MRI template that was delivered with SPM to obtain normalization parameters. The application of a 0.5 threshold to the gray matter probability map created a gray matter probability map in MNI space. The gray matter probability map was then coregistered to the AAL template, and the PET counts were extracted from the gray matter probability map and ROIs. The mean values for all the regions were calculated from the integral [11C]PIB image. Target-to-cerebellum ratios were subsequently calculated for 11bilateral regions.
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4

Preprocessing of 11C-PIB PET Imaging Data

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The preprocessing of the 11C-PIB imaging data was performed using Statistical Parametric Mapping 8 (SPM8) software and MATLAB 2010b for Windows (Mathworks, Natick, MA, USA). First, 11C-PIB integral images (data corrected for radioactive decay summed from 60 to 90 min post-injection) were created from the dynamic PET images (frames 32 to 34) and coregistered to the subject’s MRI images. Second, the magnetic resonance images were segmented into three classes (gray matter, white matter, and cerebrospinal fluid) in SPM8 using 16 non-linear iterations and 7 × 9 × 7 basis functions. Third, the PET images and gray matter magnetic resonance images were normalized using a T1-weighted MRI template that was delivered with SPM to obtain normalization parameters. The application of a 0.5 threshold to the gray matter probability map created a gray matter probability map in the MNI space. The gray matter probability map was then coregistered to the AAL template, and the PET counts were extracted from the gray matter probability map and ROIs. The mean values for all of the regions were calculated from the integral 11C-PIB image. Target-to-cerebellum ratios were subsequently calculated for 11 bilateral regions.
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