Within-Modality Synthesis and Novel Radiomic Evaluation of Brain MRI Scans

• Published in: Cancers Vol. 15; no. 14; p. 3565
• Main Authors: Rezaeijo, Seyed Masoud, Chegeni, Nahid, Baghaei Naeini, Fariborz, Makris, Dimitrios, Bakas, Spyridon
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 10.07.2023; MDPI
• Subjects: Brain research; Liquors; Magnetic resonance imaging; Medical imaging; Medical imaging equipment; Medical research; Medicine, Experimental; Neuroimaging; Patients; Performance evaluation; Radiomics; Germany; T2W; MRI synthesis; FLAIR; DC2Anet; radiomic; CycleGAN
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers15143565

One of the most common challenges in brain MRI scans is to perform different MRI sequences depending on the type and properties of tissues. In this paper, we propose a generative method to translate T2-Weighted (T2W) Magnetic Resonance Imaging (MRI) volume from T2-weight-Fluid-attenuated-Inversion-Recovery (FLAIR) and vice versa using Generative Adversarial Networks (GAN). To evaluate the proposed method, we propose a novel evaluation schema for generative and synthetic approaches based on radiomic features. For the evaluation purpose, we consider 510 pair-slices from 102 patients to train two different GAN-based architectures Cycle GAN and Dual Cycle-Consistent Adversarial network (DC Anet). The results indicate that generative methods can produce similar results to the original sequence without significant change in the radiometric feature. Therefore, such a method can assist clinics to make decisions based on the generated image when different sequences are not available or there is not enough time to re-perform the MRI scans.