Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system, predominantly affecting the white matter, but also the grey matter. Aim of this study was to detect MS lesions with double inversion recovery (DIR), fluid-attenuated inversion recovery (FLAIR) and T2-weighted magnetic resonance (MR) techniques and determine the sensitivity of these techniques, and the correlation between the number of lesions and expanded disability state scale (EDSS) scores. Thirty-four patients with MS (20 females and 14 males) were included in this study. DIR and conventional MR (T2-A, FLAIR) sequences were obtained. Lesions were counted and classified as belonging to one of seven anatomical regions: cortical, juxtacortical, deep grey matter, deep white matter, mixed white matter-grey matter, periventricular white matter and infratentorial. The correlation between lesion number and EDSS scores was investigated. DIR images showed more intracortical and mixed white matter-grey matter lesions in comparison with both FLAIR and T2 sequences (p=0, p=0 respectively). There was a significant difference between mean lesion numbers at the juxtacortical region, obtained with DIR and T2-weighted images (p = 0.002). The total number of lesions obtained with all methods was similar. DIR brain imaging had the highest sensitivity in the detection of cortical and mixed white matter - grey matter lesions, compared with FLAIR and T2 sequences. In addition, the lesions obtained with DIR images were more easily visualized.

One proposed solution is to use artificial intelligence (AI)-based detection systems.With the help of machine learning, classification algorithms can be trained to predict results and outcomes, provided that enough training data are available. In 2017, we at the National Cancer Institute [7] proposed an AI system based on intensity and texture analysis and a random forest classification algorithm. This system was validated in a large multireader multicenter study in 2018 [8]. Results of that study revealed an increase in detection of transition zone lesions among moderately experienced readers only. Overall, however, the AI system was equivalent to conventional MRI interpretation [8]. In that study, color-coded prediction maps were used to draw attention to AI-detected lesions. Feedback from the study suggested that prediction maps compromised the interaction between the radiologists and the AI system with resultant decreased accuracy for some readers. To address this issue a new AI detection system with more expert annotated

Introduction. The limbic system primarily responsible for our emotional life and memories is known to undergo degradation with aging and diffusion tensor imaging (DTI) is capable of revealing the white matter integrity. The aim of this study is to investigate age-related changes of quantitative diffusivity parameters and fiber characteristics on limbic system in healthy volunteers. Methods. 31 healthy subjects aged 25–70 years were examined at 1,5 TMR. Quantitative fiber tracking was performed of fornix, cingulum, and the parahippocampal gyrus. The fractional anisotropy (FA) and apparent diffusion coefficient (ADC) measurements of bilateral hippocampus, amygdala, fornix, cingulum, and parahippocampal gyrus were obtained as related components. Results. The FA values of left hippocampus, bilateral parahippocampal gyrus, and fornix showed negative correlations with aging. The ADC values of right amygdala and left cingulum interestingly showed negative relation and the left hippocampus represented positive relation with age. The cingulum showed no correlation. The significant relative changes per decade of age were found in the cingulum and parahippocampal gyrus FA measurements. Conclusion. Our approach shows that aging affects hippocampus, parahippocampus, and fornix significantly but not cingulum. These findings reveal age-related changes of limbic system in normal population that may contribute to future DTI studies.

SUMMARY -Multiple sclerosis is a chronic inflammatory demyelinating disease of the central nervous system. Diffusion tensor magnetic resonance imaging (DTI) can yield important informa

Epilepsy is more than a grey-matter disorder affecting large white matter connections of the brain with seizure generation and propagation. The mechanism for such changes remains unclear. The purpose of this study was to investigate the microstructural changes in the corpus callosum in temporal lobe epilepsy (TLE) patients and whether these abnormalities are related to antiepileptic drug (AED) therapy. Ten TLE patients receiving AED therapy, ten TLE patients with no therapy and ten controls were included in the study. The regions of interest in the corpus callosum were outlined to each Witelson region (WR). Fractional anisotrophy (FA), apparent diffusion coefficient (ADC), three main diffusivity values (λ1, λ2, λ3) and tractography were acquired from each WR. DTI indices of these tracts and each WR were compared between the three subject groups and correlates examined with clinical variables that included duration of epilepsy, gender, AED type and AED therapy exposure. In TLE subjects with receiving AED therapy significantly (p<0.05) decreased FA and increased ADC values of corpus callosum were obtained when compared to the other groups. There was no significant relationship between AED type and DTI indices. Analysis of eigen values in the splenium of corpus callosum (WR7) showed λ1 values were significantly decreased in relation to AED medication duration (p<0.05). FA values of rostrum and corpus showed a reduction with duration of epilepsy. TLE is associated with abnormal integrity of corpus callosum white matter tracts. AED therapy may cause additional damage on secondary degeneration and medication time effects especially on the splenium of corpus callosum.