Integrated MATLAB Toolbox for fMRI Visualization and Data Conversion


Jaber H. A., Aljobouri H. K., Koçak O. M., ALGIN O., ÇANKAYA İ.

Basic and Clinical Neuroscience, cilt.16, sa.4, ss.701-714, 2025 (ESCI, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 16 Sayı: 4
  • Basım Tarihi: 2025
  • Doi Numarası: 10.32598/bcn.2021.1694.1
  • Dergi Adı: Basic and Clinical Neuroscience
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, EMBASE, Directory of Open Access Journals
  • Sayfa Sayıları: ss.701-714
  • Anahtar Kelimeler: Brain imaging, Functional magnetic resonance imaging (fMRI), Information systems, Medical informatics computing
  • Ankara Üniversitesi Adresli: Evet

Özet

Introduction: Working with functional magnetic resonance imaging (fMRI) often involves engaging with multiple file formats and complex viewers. In this study, we developed a novel platform as a visualization and conversion fMRI (VCfMRI) MATLAB toolbox for fMRI data. Methods: The VCfMRI was developed to read and write 3D fMRI volumes in DICOM, NIfTI, ANALYZE, and MAT formats and convert between them, on a single user-friendly platform. It includes 62 functions across seven graphical user interface modules for conversion, batch read/write, and orthogonal viewing (sagittal, coronal, horizontal). This toolbox also supports overlaying statistical maps on anatomical images with adjustable thresholds. We built and tested VCfMRI using real datasets from a scanner (3T, Siemens Co.) at UMRAM, Bilkent University. Results: VCfMRI successfully converted and visualized all supported formats in one environment, enabling synchronized 3D views and functional overlays with interactive threshold control, streamlining previously fragmented steps. Conclusion: The VCfMRI toolbox provides a simple and efficient solution for fMRI data conversion and visualization. It simplifies the handling of fMRI datasets across different formats, which is especially beneficial for physicians, healthcare specialists, and researchers who face challenges in processing and visualizing multi-format neuroimaging data.