Informatica

In recent years, Magnetic Resonance Imaging (MRI) has emerged as a prevalent medical imaging technique, offering comprehensive anatomical and functional information. However, the MRI data acquisition process presents several challenges, including time-consuming procedures, prone motion artifacts, and hardware constraints. To address these limitations, this study proposes a novel method that leverages the power of generative adversarial networks (GANs) to generate multi-domain MRI images from a single input MRI image. Within this framework, two primary generator architectures, namely ResUnet and StarGANs generators, were incorporated. Furthermore, the networks were trained on multiple datasets, thereby augmenting the available data, and enabling the generation of images with diverse contrasts obtained from different datasets, given an input image from another dataset. Experimental evaluations conducted on the IXI and BraTS2020 datasets substantiate the efficacy of the proposed method compared to an existing method, as assessed through metrics such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR) and Normalized Mean Absolute Error (NMAE). The synthesized images resulting from this method hold substantial potential as invaluable resources for medical professionals engaged in research, education, and clinical applications. Future research gears towards expanding experiments to larger datasets and encompassing the proposed approach to 3D images, enhancing medical diagnostics within practical applications.

We attempted to determine the most common localizations of epileptogenic foci by using common functional (EEG and PET/CT) and structural (MRI) imaging methods. Also, we compared the number of epileptogenic foci detected with all diagnostic methods and determined the success rate of surgery in the operated patients when the epileptogenic foci coincided on all three imaging methods. 35 patients (including children) with clinically proven refractory epilepsy were included into the study. All patients underwent an MRI scan with epilepsy protocol, Fluorodeoxyglucose-18-PET/CT scan, and an EEG prior to a PET study. 14 patients underwent neurosurgery for removal of epileptogenic foci. We found a statistically significant difference between the number of epileptogenic foci which were found in PET/CT and EEG studies but there was no significant difference between MRI and PET/CT lesion numbers. The most common localization of epileptogenic activity on EEG was right temporal lobe (54.3%); the most common lobe with structural changes on MRI was right temporal lobe (42.9%); the most common hypometabolism zone on PET/CT was in right temporal lobe (45.7%). 10 out of 14 patients who underwent surgery demonstrated excellent postsurgical outcomes, with no epileptic seizures one year or more after the operation; 3/14 patients had 1–2 seizures after surgery and one patient had the same count or more epileptic seizures in duration of one year or more. The measure of Agreement Kappa between PET/CT and EEG value was 0.613 $(p<0.05)$. Between PET/CT and MRI the value was 0.035 $(p>0.05)$. Surgical treatment may offer hope for patients with intractable epileptic seizures. PET/CT was an extremely useful imaging method to assist in the localization of epileptogenic zones. The dynamic functional information that brain PET/CT provides is complementary to anatomical imaging of MRI and functional information of EEG.