PET with ¹⁸F-FDG has been used for presurgical localization of epileptogenic foci; however, in nonsurgical patients, the correlation between cerebral glucose metabolism and clinical severity has not been fully understood. The aim of this study was to evaluate the glucose metabolic profile using ¹⁸F-FDG PET/CT imaging in patients with epilepsy. Methods: One hundred pediatric epilepsy patients who underwent ¹⁸F-FDG PET/CT, MRI, and electroencephalography examinations were included. Fifteen age-matched controls were also included. ¹⁸F-FDG PET images were analyzed by visual assessment combined with statistical parametric mapping (SPM) analysis. The absolute asymmetry index (|AI|) was calculated in patients with regional abnormal glucose metabolism. Results: Visual assessment combined with SPM analysis of ¹⁸F-FDG PET images detected more patients with abnormal glucose metabolism than visual assessment only. The |AI| significantly positively correlated with seizure frequency (P < 0.01) but negatively correlated with the time since last seizure (P < 0.01) in patients with abnormal glucose metabolism. The only significant contributing variable to the |AI| was the time since last seizure, in patients both with hypometabolism (P = 0.001) and with hypermetabolism (P = 0.005). For patients with either hypometabolism (P < 0.01) or hypermetabolism (P = 0.209), higher |AI| values were found in those with drug resistance than with seizure remission. In the post-1-y follow-up PET studies, a significant change of |AI| (%) was found in patients with clinical improvement compared with those with persistence or progression (P < 0.01). Conclusion: ¹⁸F-FDG PET imaging with visual assessment combined with SPM analysis could provide cerebral glucose metabolic profiles in nonsurgical epilepsy patients. |AI| might be used for evaluation of clinical severity and progress in these patients. Patients with a prolonged period of seizure freedom may have more subtle (or no) metabolic abnormalities on PET. The clinical value of PET might be enhanced by timing the scan closer to clinical seizures.

Positron emission tomography (PET) and magnetic resonance imaging (MRI) are established imaging modalities for the study of neurological disorders, such as epilepsy, dementia, psychiatric disorders and so on. Since these two available modalities vary in imaging principle and physical performance, each technique has its own advantages and disadvantages over the other. To acquire the mutual complementary information and reinforce each other, there is a need for the fusion of PET and MRI. This combined dual-modality (either sequential or simultaneous) could generate preferable soft tissue contrast of brain tissue, flexible acquisition parameters, and minimized exposure to radiation. The most unique superiority of PET/MRI is mainly manifested in MRI-based improvement for the inherent limitations of PET, such as motion artifacts, partial volume effect (PVE) and invasive procedure in quantitative analysis. Head motion during scanning significantly deteriorates the effective resolution of PET image, especially for the dynamic scan with lengthy time. Hybrid PET/MRI device can offer motion correction (MC) for PET data through MRI information acquired simultaneously. Regarding the PVE associated with limited spatial resolution, the process and reconstruction of PET data can be further optimized by using acquired MRI either sequentially or simultaneously. The quantitative analysis of dynamic PET data mainly relies upon an invasive arterial blood sampling procedure to acquire arterial input function (AIF). An image-derived input function (IDIF) method without the need of arterial cannulization, can serve as a potential alternative estimation of AIF. Compared with using PET data only, combining anatomical or functional information from MRI for improving the accuracy in IDIF approach has been demonstrated. Yet, due to the interference and inherent disparity between the two modalities, these methods for optimizing PET image based on MRI still have many technical challenges. This review discussed upon the most recent progress, current challenges and future directions of MRI-driven PET data optimization for neurological applications, with either sequential or simultaneous acquisition approach.

Major depressive disorder (MDD) is a common mental disorder worldwide, but now there is a lack of clinically effective assessment and management of MDD. In this study, we used technetium-99 m ethylcysteinate dimer ([^99mTc]ECD) SPECT/CT to characterize the regional cerebral blood flow (rCBF) status of MDD patients, and to explore an objective image assessment model of MDD which is non- or minimally-invasive, convenient and accurate in a clinical setting. The severity of MDD was assessed by three trained psychiatrists, based on scores obtained from HAMD and HAMA. [^99mTc]ECD rCBF SPECT/CT was performed in 20 healthy controls and 74 unipolar MDD patients before receiving the treatment. The CT attenuation-corrected SPECT images data were automatically registered, analyzed simultaneously by 3D-SSP and eZIS. The mean score of HAMD and HAMA in the MDD patients was 25.49 ± 6.00, and 23.12 ± 5.83, respectively. There was a positive correlation between two scores. The MDD women had higher HAMD scores than MDD men. The decreased rCBF of MDD patients in frontal lobes (bilateral B11, B47 and right B4, B6, B10, B46), temporal lobe (right B21, B41, B42) and cingulated cortex (bilateral B24, B33), while their increased rCBF in occipital lobe (bilateral B17, B19 and left B18). Additionally, the depression severity was negatively correlated with decreased rCBF in left ventral anterior cingulate cortex B24, and was positively correlated with decreased rCBF in left inferior prefrontal gyrus B47 and increased rCBF in right associative visual cortex B19. The anxiety severity was negatively correlated with decreased rCBF in left subgenual cortex B25. Although the mechanism underlying the correlation is not yet fully understood, our findings indicated that the rCBF SPECT/CT may provide an objective assessment for MDD severity. It might be used monitoring therapeutic efficacy in the management of MDD.