ParaPET: non-invasive deep learning method for direct parametric brain PET reconstruction using histoimages.

BACKGROUND: The indirect method for generating parametric images in positron emission tomography (PET) involves the acquisition and reconstruction of dynamic images and temporal modelling of tissue activity given a measured arterial input function. This approach is not robust, as noise in each dynamic image leads to a degradation in parameter estimation. Direct methods incorporate into the image reconstruction step both the kinetic and noise models, leading to improved parametric images. These methods require extensive computational time and large computing resources. Machine learning methods have demonstrated significant potential in overcoming these challenges. But they are limited by the requirement of a paired training dataset. A further challenge within the existing framework is the use of state-of-the-art arterial input function estimation via temporal arterial blood sampling, which is an invasive procedure, or an additional magnetic resonance imaging (MRI) scan for selecting a region where arterial blood signal can be measured from the PET image. We propose a novel machine learning approach for reconstructing high-quality parametric brain images from histoimages produced from time-of-flight PET data without requiring invasive arterial sampling, an MRI scan, or paired training data from standard field-of-view scanners. RESULT: The proposed is tested on a simulated phantom and five oncological subjects undergoing an 18F-FDG-PET scan of the brain using Siemens Biograph Vision Quadra. Kinetic parameters set in the brain phantom correlated strongly with the estimated parameters (K1, k2 and k3, Pearson correlation coefficient of 0.91, 0.92 and 0.93) and a mean squared error of less than 0.0004. In addition, our method significantly outperforms (p < 0.05, paired t-test) the conventional nonlinear least squares method in terms of contrast-to-noise ratio. At last, the proposed method was found to be 37% faster than the conventional method. CONCLUSION: We proposed a direct non-invasive DL-based reconstruction method and produced high-quality parametric maps of the brain. The use of histoimages holds promising potential for enhancing the estimation of parametric images, an area that has not been extensively explored thus far. The proposed method can be applied to subject-specific dynamic PET data alone.

7T GRE-MRI signal compartments are sensitive to dysplastic tissue in focal epilepsy.

Ultra-high field magnetic resonance imaging data obtained using a multi-echo gradient echo sequence has been shown to contain information on tissue microstructure. Quantitative assessment of water fraction, relaxation time and frequency shift using multi-compartment signal modelling may help improve our understanding of diseases and disorders affecting the human brain. In this study, we explored tissue microstructure information by analysing voxel compartment water fraction and frequency shifts derived from 7 T multi-echo gradient recalled echo MRI data. We aimed to test whether the parameters of a three compartment model could distinguish the normal cortex from the cortex affected by focal cortical dysplasia. We compartmentalised normal and dysplastic cortical regions in patients diagnosed with focal cortical dysplasia. We found the frequency shift parameter of the shortest T2⁎ signal compartment to be sensitive to regions of dysplastic tissue. We conclude that mathematical modelling of echo time dependent gradient recalled echo MRI signals in patients with focal cortical dysplasia can potentially delineate cortical areas that have undergone microstructural changes in comparison to normal tissue.

Intravenous immunoglobulin protects neurons against amyloid beta-peptide toxicity and ischemic stroke by attenuating multiple cell death pathways.

Intravenous immunoglobulin (IVIg) preparations obtained by fractionating blood plasma, are increasingly being used increasingly as an effective therapeutic agent in treatment of several inflammatory diseases. Its use as a potential therapeutic agent for treatment of stroke and Alzheimer's disease has been proposed, but little is known about the neuroprotective mechanisms of IVIg. In this study, we investigated the effect of IVIg on downstream signaling pathways that are involved in neuronal cell death in experimental models of stroke and Alzheimer's disease. Treatment of cultured neurons with IVIg reduced simulated ischemia- and amyloid ßpeptide (Aß)-induced caspase 3 cleavage, and phosphorylation of the cell death-associated kinases p38MAPK, c-Jun NH2 -terminal kinase and p65, in vitro. Additionally, Aß-induced accumulation of the lipid peroxidation product 4-hydroxynonenal was attenuated in neurons treated with IVIg. IVIg treatment also up-regulated the anti-apoptotic protein, Bcl2 in cortical neurons under ischemia-like conditions and exposure to Aß. Treatment of mice with IVIg reduced neuronal cell loss, apoptosis and infarct size, and improved functional outcome in a model of focal ischemic stroke. Together, these results indicate that IVIg acts directly on neurons to protect them against ischemic stroke and Aß-induced neuronal apoptosis by inhibiting cell death pathways and by elevating levels of the anti-apoptotic protein Bcl2.