Feasibility of using Gramian angular field for preprocessing MR spectroscopy data in AI classification tasks: Differentiating glioblastoma from lymphoma
Spectroscopy data can be effectively transformed into distinct color graphical images using the Gramian angular field technique, enabling their use as input for deep learning algorithms. Accuracy tends to be higher when utilizing data derived from Fourier-transformed spectra compared to fitted spectra. This finding underscores the potential of using MR spectroscopy data in neural network-based classification purposes.
Authors
- Arsany Hakim (ORCID: https://orcid.org/0000-0001-9431-1069)
- Theoni Maragkou (ORCID: https://orcid.org/0000-0002-7774-760X)
- Johannes Slotboom (ORCID: https://orcid.org/0000-0001-5121-9852)
- Irena Zubak (ORCID: https://orcid.org/0000-0001-8474-6421)
- Michael Murek (ORCID: https://orcid.org/0000-0002-9874-1135)
- Christina Marx
- Thomas Rhomberg (ORCID: https://orcid.org/0000-0002-4382-999X)
Institutions
- University of Bern (CH)
- University Hospital of Bern (CH)
Publication Details
- Journal
- European Journal of Radiology
- Published
- 2025-01-29
- DOI
- https://doi.org/10.1016/j.ejrad.2025.111957
- Citations
- 1
- Primary Topic
- Advanced Neuroimaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.36