Motivated by the great potential of deep learning in medical imaging, we propose an iterative positron emission tomography reconstruction framework using a deep learning-based prior. We utilized the denoising convolutional neural network (DnCNN) method and trained the network using full-dose images as the ground truth and low dose images reconstructed from downsampled data by Poisson thinning as input. Since most published deep networks are trained at a predetermined noise level, the noise level disparity of training and testing data is a major problem for their applicability as a generalized prior. In particular, the noise level significantly changes in each iteration, which can potentially degrade the overall performance of iterative reconstruction. Due to insufficient existing studies, we conducted simulations and evaluated the degradation of performance at various noise conditions. Our findings indicated that DnCNN produces additional bias induced by the disparity of noise levels. To address this issue, we propose a local linear fitting function incorporated with the DnCNN prior to improve the image quality by preventing unwanted bias. We demonstrate that the resultant method is robust against noise level disparities despite the network being trained at a predetermined noise level. By means of bias and standard deviation studies via both simulations and clinical experiments, we show that the proposed method outperforms conventional methods based on total variation and non-local means penalties. We thereby confirm that the proposed method improves the reconstruction result both quantitatively and qualitatively.

Advances in the new-generation of ultra-high-resolution, brain-dedicated positron emission tomography-magnetic resonance imaging (PET/MRI) systems have begun to provide many interesting insights into the molecular dynamics of the brain. First, the finely delineated structural information from ultra-high-field MRI can help us to identify accurate landmark structures, thereby making it easier to locate PET activation sites that are anatomically well-correlated with metabolic or ligand-specific organs in the neural structures in the brain. This synergistic potential of PET/MRI imaging is discussed in terms of neuroscience and neurological research from both translational and basic research perspectives. Experimental results from the hippocampus, thalamus, and brainstem obtained with (18)F-fluorodeoxyglucose and (11)C-3-amino-4-(2-dimethylaminomethylphenylsulfanyl)benzonitrile are used to demonstrate the potential of this new brain PET/MRI system.

Aim Patients with psychophysiological insomnia (PI) experience hyperarousal, especially as a reaction to sound stimuli. In the current study, we explored brain activity changes in response to sleep‐related sounds (SS) in patients with insomnia after cognitive behavioral therapy for insomnia (CBT‐I). Methods In 14 drug‐free PI patients, regional brain activity in response to SS, and to white noise sound (NS) as neutral stimuli, was investigated before and after individual CBT‐I using functional magnetic resonance imaging. Blood oxygen level‐dependent (BOLD) signals to SS and NS were compared before and after CBT‐I. In addition, the association between clinical improvement after CBT‐I and changes in brain activity in response to SS and NS was analyzed. Results Compared with baseline, regional brain activity in response to SS after CBT‐I decreased in the left middle temporal and left middle occipital gyrus. In regression analysis, a reduction in the Dysfunctional Beliefs and Attitudes about Sleep (DBAS) Scale score after CBT‐I was associated with decrease in brain activity in response to SS in both thalami. However, brain activity in response to NS showed no BOLD signal changes and no association with DBAS change. Conclusion Cortical hyperactivity, which may cause hyperarousal in PI, was found to decrease after CBT‐I. CBT‐I targeting changes in beliefs and attitudes about sleep may induce its therapeutic effects by reducing thalamic brain activity in response to sleep‐related stimuli.

Background Perturbed functional coupling between the metabotropic glutamate receptor-5 (mGluR5) and N-methyl-d-aspartate (NMDA) receptor-mediated excitatory glutamatergic neurotransmission may contribute to the pathophysiology of psychiatric disorders such as schizophrenia. We aimed to establish the functional interaction between mGluR5 and NMDA receptors in brain of mice with genetic ablation of the mGluR5. Methods We first measured the brain glutamate levels with magnetic resonance spectroscopy (MRS) in mGluR5 knockout (KO) and wild-type (WT) mice. Then, we assessed brain glucose metabolism with [18F]fluorodeoxyglucose ([18F]FDG) positron emission tomography before and after the acute administration of an NMDA antagonist, MK-801 (0.5 mg/kg), in the same mGluR5 KO and WT mice. Results Between-group comparisons showed no significant differences in [18F]FDG standardized uptake values (SUVs) in brain of mGluR5 KO and WT mice at baseline, but widespread reductions in mGluR5 KO mice compared to WT mice after MK-801 administration (p < 0.05). The baseline glutamate levels did not differ significantly between the two groups. However, there were significant negative correlations between baseline prefrontal glutamate levels and regional [18F]FDG SUVs in mGluR5 KO mice (p < 0.05), but no such correlations in WT mice. Fisher’s Z-transformation analysis revealed significant between-group differences in these correlations (p < 0.05). Conclusions This is the first multimodal neuroimaging study in mGluR5 KO mice and the first report on the association between cerebral glucose metabolism and glutamate levels in living rodents. The results indicate that mGluR5 KO mice respond to NMDA antagonism with reduced cerebral glucose metabolism, suggesting that mGluR5 transmission normally moderates the net effects of NMDA receptor antagonism on neuronal activity. The negative correlation between glutamate levels and glucose metabolism in mGluR5 KO mice at baseline may suggest an unmasking of an inhibitory component of the glutamatergic regulation of neuronal energy metabolism.

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