Feasibility of using Gramian angular field for preprocessing MR spectroscopy data in AI classification tasks: Differentiating glioblastoma from lymphoma.

OBJECTIVES

To convert 1D spectra into 2D images using the Gramian angular field, to be used as input for convolutional neural network for classification tasks such as glioblastoma versus lymphoma.

MATERIALS AND METHODS

Retrospective study including patients with histologically confirmed glioblastoma and lymphoma between 2009-2020 who underwent preoperative MR spectroscopy, using single voxel spectroscopy acquired with a short echo time (TE 30). We compared: 1) the Fourier-transformed raw spectra, and 2) the fitted spectra generated during post-processing. Both spectra were independently converted into images using the Gramian angular field, and then served as inputs for a pretrained neural network. We compared the classification performance using data from the Fourier-transformed raw spectra and the post-processed fitted spectra.

RESULTS

This feasibility study included 98 patients, of whom 65 were diagnosed with glioblastomas and 33 with lymphomas. For algorithm testing, 20 % of the cases (19 in total) were randomly selected. By applying the Gramian angular field technique to the Fourier-transformed spectra, we achieved an accuracy of 73.7 % and a sensitivity of 92 % in classifying glioblastoma versus lymphoma, slightly higher than the fitted spectra pathway.

CONCLUSION

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.