An iterative image-based inter-frame motion compensation method for dynamic brain PET imaging

As a non-invasive imaging tool, Positron Emission Tomography (PET) plays an important role in brain science and disease research. Dynamic acquisition is one way of brain PET imaging. Its wide application in clinical research has often been hindered by practical challenges, such as patient involuntary movement, which could degrade both image quality and the accuracy of the quantification. This is even more obvious in scans of patients with neurodegeneration or mental disorders. Conventional motion compensation methods were either based on images or raw measured data, were shown to be able to reduce the effect of motion on the image quality. As for a dynamic PET scan, motion compensation can be challenging as tracer kinetics and relatively high noise can be present in dynamic frames. In this work, we propose an image-based inter-frame motion compensation approach specifically designed for dynamic brain PET imaging. Our method has an iterative implementation that only requires reconstructed images, based on which the inter-frame subject movement can be estimated and compensated. The method utilized tracer-specific kinetic modelling and can deal with simple and complex movement patterns. The synthesized phantom study showed that the proposed method can compensate for the simulated motion in scans with 18F-FDG, 18F-Fallypride and 18F-AV45. Fifteen dynamic 18F-FDG patient scans with motion artifacts were also processed. The quality of the recovered image was superior to the one of the non-corrected images and the corrected images with other image-based methods. The proposed method enables retrospective image quality control for dynamic brain PET imaging, hence facilitates the applications of dynamic PET in clinics and research.

Perspectives of brain imaging with PET systems

In this partial review and partial attempt at vision of what may be the future of dedicated brain PET scanners, the key implementations of the PET technique, we postulate that we are still on a development path and there is still a lot to be done in order to develop optimal brain imagers. Optimized for particular imaging tasks and protocols, and also mobile, that can be used outside the PET center, in addition to the expected improvements in sensitivity and resolution. For this multi-application concept to be more practical, flexible, adaptable designs are preferred. This task is greatly facilitated by the improved TOF performance that allows for more open, adjustable, limited angular coverage geometries without creating image artifacts. As achieving uniform very high resolution in the whole body is not practical due to technological limits and high costs, hybrid systems using a moderate-resolution total body scanner (such as J-PET) combined with a very high performing brain imager could be a very attractive approach. As well, as using magnification inserts in the total body or long-axial length imagers to visualize selected targets with higher resolution. In addition, multigamma imagers combining PET with Compton imaging should be developed to enable multitracer imaging.

Frontal Cortex Segmentation of Brain PET Imaging Using Deep Neural Networks

18F-FDG positron emission tomography (PET) imaging of brain glucose use and amyloid accumulation is a research criteria for Alzheimer's disease (AD) diagnosis. Several PET studies have shown widespread metabolic deficits in the frontal cortex for AD patients. Therefore, studying frontal cortex changes is of great importance for AD research. This paper aims to segment frontal cortex from brain PET imaging using deep neural networks. The learning framework called Frontal cortex Segmentation model of brain PET imaging (FSPET) is proposed to tackle this problem. It combines the anatomical prior to frontal cortex into the segmentation model, which is based on conditional generative adversarial network and convolutional auto-encoder. The FSPET method is evaluated on a dataset of 30 brain PET imaging with ground truth annotated by a radiologist. Results that outperform other baselines demonstrate the effectiveness of the FSPET framework.

Population-specific brain [18F]-FDG PET templates of Chinese subjects for statistical parametric mapping

Statistical Parametric Mapping (SPM) is a computational approach for analysing functional brain images like Positron Emission Tomography (PET). When performing SPM analysis for different patient populations, brain PET template images representing population-specific brain morphometry and metabolism features are helpful. However, most currently available brain PET templates were constructed using the Caucasian data. To enrich the family of publicly available brain PET templates, we created Chinese-specific template images based on 116 [18F]-fluorodeoxyglucose ([18F]-FDG) PET images of normal participants. These images were warped into a common averaged space, in which the mean and standard deviation templates were both computed. We also developed the SPM analysis programmes to facilitate easy use of the templates. Our templates were validated through the SPM analysis of Alzheimer’s and Parkinson’s patient images. The resultant SPM t-maps accurately depicted the disease-related brain regions with abnormal [18F]-FDG uptake, proving the templates’ effectiveness in brain function impairment analysis.

A metabolically stable PET tracer for imaging synaptic vesicle protein 2A: synthesis and preclinical characterization of [18F]SDM-16

Purpose To quantify the synaptic vesicle glycoprotein 2A (SV2A) changes in the whole central nervous system (CNS) under pathophysiological conditions, a high affinity SV2A PET radiotracer with improved in vivo stability is desirable to minimize the potential confounding effect of radiometabolites. The aim of this study was to develop such a PET tracer based on the molecular scaffold of UCB-A, and evaluate its pharmacokinetics, in vivo stability, specific binding, and nonspecific binding signals in nonhuman primate brains, in comparison with [11C]UCB-A, [11C]UCB-J, and [18F]SynVesT-1. Methods The racemic SDM-16 (4-(3,5-difluorophenyl)-1-((2-methyl-1H-imidazol-1-yl)methyl)pyrrolidin-2-one) and its two enantiomers were synthesized and assayed for in vitro binding affinities to human SV2A. We synthesized the enantiopure [18F]SDM-16 using the corresponding enantiopure arylstannane precursor. Nonhuman primate brain PET scans were performed on FOCUS 220 scanners. Arterial blood was drawn for the measurement of plasma free fraction (fP), radiometabolite analysis, and construction of the plasma input function. Regional time-activity curves (TACs) were fitted with the one-tissue compartment (1TC) model to obtain the volume of distribution (VT). Nondisplaceable binding potential (BPND) was calculated using either the nondisplaceable volume of distribution (VND) or the centrum semiovale (CS) as the reference region. Results SDM-16 was synthesized in 3 steps with 44% overall yield and has the highest affinity (Ki = 0.9 nM) to human SV2A among all reported SV2A ligands. [18F]SDM-16 was prepared in about 20% decay-corrected radiochemical yield within 90 min, with greater than 99% radiochemical and enantiomeric purity. This radiotracer displayed high specific binding in monkey brains and was metabolically more stable than the other SV2A PET tracers. The fP of [18F]SDM-16 was 69%, which was higher than those of [11C]UCB-J (46%), [18F]SynVesT-1 (43%), [18F]SynVesT-2 (41%), and [18F]UCB-H (43%). The TACs were well described with the 1TC. The averaged test–retest variability (TRV) was 7 ± 3%, and averaged absolute TRV (aTRV) was 14 ± 7% for the analyzed brain regions. Conclusion We have successfully synthesized a novel SV2A PET tracer [18F]SDM-16, which has the highest SV2A binding affinity and metabolical stability among published SV2A PET tracers. The [18F]SDM-16 brain PET images showed superb contrast between gray matter and white matter. Moreover, [18F]SDM-16 showed high specific and reversible binding in the NHP brains, allowing for the reliable and sensitive quantification of SV2A, and has potential applications in the visualization and quantification of SV2A beyond the brain.

Translation of Preclinical PET Imaging Findings: Challenges and Motion Correction to Overcome the Confounding Effect of Anesthetics

Preclinical brain positron emission tomography (PET) in animals is performed using anesthesia to avoid movement during the PET scan. In contrast, brain PET scans in humans are typically performed in the awake subject. Anesthesia is therefore one of the principal limitations in the translation of preclinical brain PET to the clinic. This review summarizes the available literature supporting the confounding effect of anesthesia on several PET tracers for neuroscience in preclinical small animal scans. In a second part, we present the state-of-the-art methodologies to circumvent this limitation to increase the translational significance of preclinical research, with an emphasis on motion correction methods. Several motion tracking systems compatible with preclinical scanners have been developed, each one with its advantages and limitations. These systems and the novel experimental setups they can bring to preclinical brain PET research are reviewed here. While technical advances have been made in this field, and practical implementations have been demonstrated, the technique should become more readily available to research centers to allow for a wider adoption of the motion correction technique for brain research.

Performance evaluation in [18F]Florbetaben brain PET images classification using 3D Convolutional Neural Network

High accuracy has been reported in deep learning classification for amyloid brain scans, an important factor in Alzheimer’s disease diagnosis. However, the possibility of overfitting should be considered, as this model is fitted with sample data. Therefore, we created and evaluated an [18F]Florbetaben amyloid brain positron emission tomography (PET) scan classification model with a Dong-A University Hospital (DAUH) dataset based on a convolutional neural network (CNN), and performed external validation with the Alzheimer’s Disease Neuroimaging Initiative dataset. Spatial normalization, count normalization, and skull stripping preprocessing were performed on the DAUH and external datasets. However, smoothing was only performed on the external dataset. Three types of models were used, depending on their structure: Inception3D, ResNet3D, and VGG3D. After training with 80% of the DAUH dataset, an appropriate model was selected, and the rest of the DAUH dataset was used for model evaluation. The generalization potential of the selected model was then validated using the external dataset. The accuracy of the model evaluation for Inception3D, ResNet3D, and VGG3D was 95.4%, 92.0%, and 97.7%, and the accuracy of the external validation was 76.7%, 67.1%, and 85.3%, respectively. Inception3D and ResNet3D were retrained with the external dataset; then, the area under the curve was compared to determine the binary classification performance with a significance level of less than 0.05. When external validation was performed again after fine tuning, the performance improved to 15.3%p for Inception3D and 16.9%p for ResNet3D. In [18F]Florbetaben amyloid brain PET scan classification using CNN, the generalization potential can be seen through external validation. When there is a significant difference between the model classification performance and the external validation, changing the model structure or fine tuning the model can help improve the classification performance, and the optimal model can also be found by collaborating through a web-based open platform.

Combination of structural MRI, functional MRI and brain PET-CT provide more diagnostic and prognostic value in patients of cerebellar ataxia associated with anti-Tr/DNER: a case report

Background Brain magnetic resonance imaging (MRI) rarely reveals structural changes in patients with suspected anti-Tr/DNER encephalitis and thus provides very limited information. Here, we combined structural MRI, functional MRI, and positron emission tomography-computed tomography (PET-CT) findings to characterize this rare disorder in a patient. Case presentation A 43-year-old woman presented with progressive cerebellar ataxia, memory impairment, anxiety, and depression. Anti-Tr antibodies were detected in both her serum (1:10) and cerebrospinal fluid (1:10). A diagnosis of anti-Tr-positive autoimmune cerebellar ataxia was established. The patient’s symptoms were worse, but her brain MRI was normal. Meanwhile, voxel-based morphometry analysis showed bilateral reduced cerebellar volume, especially in the posterior lobe and uvula of the cerebellum and the middle of the left temporal lobe compared with 6 sex- and age-matched healthy subjects (6 females, 43 ± 2 years; p < 0.05). Using seed-based functional connectivity analysis, decreased connectivity between the posterior cingulate cortex/precuneus and left frontal lobe compared to the control group (p < 0.05) was detected. PET-CT revealed bilateral hypometabolism in the cerebellum and relative hypermetabolism in the cerebellar vermis and bilateral frontal lobe, but no malignant changes. Conclusions A combination of structural MRI, functional MRI, and brain PET-CT has higher diagnostic and prognostic value than conventional MRI in patients with suspected anti-Tr/DNER encephalitis.