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MPRAGE to MP2RAGE UNI translation via generative adversarial network improves the automatic tissue and lesion
Francesco La Rosa1, Thomas Yu1, Germán Barquero1
1Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Switzerland; CIBM Center for Biomedical Imaging, Switzerland; Medical Image Analysis Laboratory (MIAL), Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Background And Objective:
Compared to the conventional magnetization-prepared rapid gradient-echo imaging (MPRAGE) MRI sequence, the specialized magnetization prepared 2 rapid acquisition gradient echoes (MP2RAGE) shows a higher brain tissue and lesion contrast in multiple sclerosis (MS) patients. The goal of this work is to retrospectively generate realistic-looking MP2RAGE uniform images (UNI) from already acquired MPRAGE images in order to improve the automatic lesion and tissue segmentation.
Methods:
For this task we propose a generative adversarial network (GAN). Multi-contrast MRI data of 12 healthy controls and 44 patients diagnosed with MS was retrospectively analyzed. Imaging was acquired at 3T using a SIEMENS scanner with MPRAGE, MP2RAGE, FLAIR, and DIR sequences. We train the GAN with both healthy controls and MS patients to generate synthetic MP2RAGE UNI images. These images were then compared to the real MP2RAGE UNI (considered as ground truth) analyzing the output of automatic brain tissue and lesion segmentation tools. Reference-based metrics as well as the lesion-wise true and false positives, Dice coefficient, and volume difference were considered for the evaluation. Statistical differences were assessed with the Wilcoxon signed-rank test.
Results:
The synthetic MP2RAGE UNI significantly improves the lesion and tissue segmentation masks in terms of Dice coefficient and volume difference (p-values < 0.001) compared to the MPRAGE. For the segmentation metrics analyzed no statistically significant differences are found between the synthetic and acquired MP2RAGE UNI.
Conclusion:
Synthesized MP2RAGE UNI images are visually realistic and improve the output of automatic segmentation tools.
