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petmat

PET Image Preprocessing in MATLAB

https://github.com/taha-parsayan/petmat

Science Score: 44.0%

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Keywords

human-brain matlab medical-image-processing pet-mri petmat
Last synced: 10 months ago · JSON representation ·

Repository

PET Image Preprocessing in MATLAB

Basic Info
  • Host: GitHub
  • Owner: taha-parsayan
  • License: other
  • Default Branch: main
  • Homepage:
  • Size: 224 KB
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Topics
human-brain matlab medical-image-processing pet-mri petmat
Created over 1 year ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

PETMAT: PET Image Preprocessing in MATLAB

GitHub release (latest by tag) Static Badge Static Badge Static Badge Static Badge

PETMAT is a MATLAB-based app designed to facilitate the preprocessing of Positron Emission Tomography (PET) images using the Statistical Parametric Mapping (SPM) toolbox. The app supports operations like smoothing, co-registration, and normalization of PET images and provides an intuitive interface to work with multiple subject datasets.

Toolbox

Features

  • Smoothing: Option to smooth the PET images with different Full Width Half Maximum (FWHM) kernels.
  • Co-registration: Interpolation options for aligning PET images with anatomical scans.
  • Normalization: Rescale images to standard space with interpolation options.
  • Multiple subjects: Support for batch processing multiple subjects at once.
  • Progress tracking: Real-time completion percentage indicator during the preprocessing.

pipeline

Installation

  1. Clone the repository: bash git clone https://github.com/taha-parsayan/PETMAT.git

  2. Open MATLAB and navigate to the directory containing PETMAT.m.

  3. Ensure that you have the SPM toolbox installed and added to the MATLAB path. You can download SPM from SPM official website.

  4. Run the app by executing: matlab app = PETMAT;

Requirements

  • MATLAB (R2018b or later recommended)
  • SPM12 (must be installed and in the MATLAB path)
  • NIfTI PET and anatomical image files

Usage

  1. Input: Specify the folder containing the subject PET image files and the subject IDs. PET files should be in NIfTI format (.nii) and the anatomical scan should be named T1.nii.

  2. Smoothing Options: Choose the desired level of smoothing for the PET images from the list:

    • No smoothing
    • 5 FWHM
    • 8 FWHM
    • 10 FWHM

  3. Co-registration and Normalization: Select the desired interpolation methods for co-registration and normalization processes.

  4. Analyze: Click the "Analyze" button to start the preprocessing pipeline.

  5. Exit: Close the app by clicking "Exit."

Output

The processed images will be stored in the same subject directories, with names indicating the operation performed: - coregister_PET.nii: Co-registered PET images - std_T1.nii: Normalized anatomical scan - std_coregister_PET.nii: Normalized PET image - SUV.nii: Standard Uptake Value (SUV) image - SUV_GM.nii: SUV image masked by gray matter

Contributing

Contributions are welcome! If you encounter any bugs or have feature suggestions, feel free to open an issue or submit a pull request.

Acknowledgments

This app utilizes the SPM toolbox, developed by the Wellcome Centre for Human Neuroimaging. For more information, visit the SPM website.

Owner

  • Name: Taha Parsayan
  • Login: taha-parsayan
  • Kind: user
  • Location: Denmark - Odense

Ph.D. in Machine Learning and Pattern Recognition, University of Southern Denmark

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this code, please cite it as below."
title: "PETMAT"
authors:
  - family-names: "Parsayan"
    given-names: "Mohammadtaha"
    #orcid: "0000-0000-0000-0000"
date-released: 2024-10-01
version: "v2024-10"
#doi: "10.xxxx/your-doi"
url: "https://github.com.mcas.ms/taha-parsayan/PETMAT"
repository-code: "https://github.com.mcas.ms/taha-parsayan/PETMAT"
keywords: 
  - PET
  - MATLAB
  - Neuroimaging
license: SPM  # Or whichever license you choose

GitHub Events

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