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

Autor: Rezaeijo SM; Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran., Chegeni N; Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran., Baghaei Naeini F; Faculty of Engineering, Computing and the Environment, Kingston University, Penrhyn Road Campus, Kingston upon Thames, London KT1 2EE, UK., Makris D; Faculty of Engineering, Computing and the Environment, Kingston University, Penrhyn Road Campus, Kingston upon Thames, London KT1 2EE, UK., Bakas S; Faculty of Engineering, Computing and the Environment, Kingston University, Penrhyn Road Campus, Kingston upon Thames, London KT1 2EE, UK.; Richards Medical Research Laboratories, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Floor 7, 3700 Hamilton Walk, Philadelphia, PA 19104, USA.
Jazyk: angličtina
Zdroj: Cancers [Cancers (Basel)] 2023 Jul 10; Vol. 15 (14). Date of Electronic Publication: 2023 Jul 10.
DOI: 10.3390/cancers15143565
Abstrakt: 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 2 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.
Competing Interests: The authors declare no conflict of interest.
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje