Stable brain PET metabolic networks using a multiple sampling scheme

The correct interpretation of clinical positron emission tomography (PET) data depends largely on the physical limits of the PET scanner. The partial volume effect (PVE) is related to the size of the studied object compared to the spatial resolution. It represents one of the most important limiting factors in quantitative data analysis. This effect is increased in the case of atrophy, as in patients with Alzheimer disease (AD), and it influences measurement of the metabolic reduction generally seen in cerebral degeneration. In this case, interpretation can be biased, because cortical activity will be underestimated due to the atrophy. In general, anatomical images of AD patients have shown diffuse atrophy, while PET studies have found widespread hypometabolism affecting the parietal and temporal lobes. Although hypometabolic areas usually correspond to atrophic regions, they also occur without such changes. Thus, the aim is to differentiate authentic hypometabolism (decrease of glucose consumption per unit volume of gray matter) from that due to PVE from atrophy (cell loss). Consequently, we are using a method for three-dimensional (3D) correction of human PET data with 3D magnetic resonance imaging (MRI). We measured atrophy and metabolism by using both T1-weighted MR images and high and medium resolution PET scans. We injected 12 patients and controls with [18F]fluorodeoxyglucose for glucose consumption measurements. Atrophy was estimated in the following way. We isolated the cerebral structures, using a segmentation technique on the MRI scans, into gray matter (GM), white matter, and cerebrospinal fluid. We superimposed the PET images onto the MR images to obtain anatomo-functional correlations. We degraded the segmented MR images to the resolution of the PET images by a convolution process to create a PET image correction map. We corrected the metabolic PET data for the PVE. We studied the cerebral metabolic rate of glucose in the GM where metabolic variation is the most relevant to AD. By dealing with problems relating to the sensitivity to the segmentation and to the PET-MRI coregistration, computation of MRI convolution processes provided the degree of PVE on a pixel-by-pixel basis, allowing correction of hypometabolisms contained in GM PET values. Global cortical metabolism increased after correction for PVE by, on average, 29 and 24% for tomographs acquired with medium (TTV03 LETI) and high (ECAT 953B CTI/Siemens) resolution, respectively, whereas the cortical metabolism increased by 75 and 65% for the respective tomographs in AD patients. The difference of metabolism between scans after correction for PVE was less than before correction, decreasing from 31 to 17%. This difference was most marked in the frontal and temporal lobes. Fusion imaging allowed correction for PVE in metabolic data using 3D MRI and determination of whether a change in the apparent radiotracer concentration in PET data reflected an alteration in GM volume, a change in radiotracer concentration per unit volume of GM, or both.

Background: Changes in the metabolic and structural brain networks in mild cognitive impairment (MCI) have been widely researched. However, few studies have compared the differences in the topological properties of the metabolic and structural brain networks in patients with MCI.Methods: We analyzedmagnetic resonance imaging (MRI) and fluoro-deoxyglucose positron emission tomography (FDG-PET) data of 137 patients with MCI and 80 healthy controls (HCs). The HC group data comes from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The permutation test was used to compare the network parameters (characteristic path length, clustering coefficient, local efficiency, and global efficiency) between the two groups. Partial Pearson’s correlation analysis was used to calculate the correlations of the changes in gray matter volume and glucose intake in the key brain regions in MCI with the Alzheimer’s Disease Assessment Scale-Cognitive (ADAS-cog) sub-item scores.Results: Significant changes in the brain network parameters (longer characteristic path length, larger clustering coefficient, and lower local efficiency and global efficiency) were greater in the structural network than in the metabolic network (longer characteristic path length) in MCI patients than in HCs. We obtained the key brain regions (left globus pallidus, right calcarine fissure and its surrounding cortex, left lingual gyrus) by scanning the hubs. The volume of gray matter atrophy in the left globus pallidus was significantly positively correlated with comprehension of spoken language (p = 0.024) and word-finding difficulty in spontaneous speech item scores (p = 0.007) in the ADAS-cog. Glucose intake in the three key brain regions was significantly negatively correlated with remembering test instructions items in ADAS-cog (p = 0.020, p = 0.014, and p = 0.008, respectively).Conclusion: Structural brain networks showed more changes than metabolic brain networks in patients with MCI. Some brain regions with significant changes in betweenness centrality in both structural and metabolic networks were associated with MCI.

Small-animal positron emission tomography as a tool for neuropharmacology

Connectomics Sheds New Light on Alzheimer’s Disease

Pharmacokinetic analysis of Positron Emission Tomography (PET) data typically requires at least one hour of image acquisition, which poses a great disadvantage in clinical practice. In this work, we propose a novel approach for pharmacokinetic modelling with significantly reduced PET acquisition time, by incorporating the blood flow information from simultaneously acquired arterial spin labelling (ASL) magnetic resonance imaging (MRI). A relationship is established between blood flow, measured by ASL, and the transfer rate constant from plasma to tissue of the PET tracer, leading to modified PET kinetic models with ASL-derived flow information. Evaluation on clinical amyloid imaging data from an Alzheimer’s disease (AD) study shows that the proposed approach with the simplified reference tissue model can achieve amyloid burden estimation from 30 min [\(^{18}\)F]florbetapir PET data and 5 min simultaneous ASL MR data, which is comparable with the estimation from 60 min PET data (mean error\(\,=-0.03\)). Conversely, standardised uptake value ratio (SUVR), the alternative measure from the data showed a positive bias in areas of higher amyloid burden (mean error\(\,=0.07\)).

Brain Connectivity: A Journal of Clinical Neurology, Neuroscience, and Neuroimaging Advancing the Field of Neurology.

Positron Emission Tomography (PET) has been widely used in the early diagnosis and treatment monitoring of Alzheimer’s Disease (AD). As two radiotracers of neurodegeneration, [18F]Fluorodeoxyglucose ([18F]FDG) and [18F]Florbetapir ([18F]AV45) PET have been used to measure cerebral glucose metabolism and $\beta$-amyloid $(A\beta)$ deposition, respectively. The combination of different modality PET images, such as FDG PET and AV45 PET, can provide complementary information for clinical diagnosis and evaluation. However, compared to the actual and available FDG PET data, AV45 PET data is always deficient due to the institution-specific tracers. In this paper, we propose a lightweight Generative Adversarial Network (GAN)-based model, which is termed “dual perceptual loss based generative adversarial network (DPGAN) for fast 2. 5D-based cross-modality generation of AV45 PET from FDG PET. This model provides a potential supplementary solution to those clinical situations that only the FDG PET image is acquired, but the AV45 PET is missing. Our experimental results showed that the DPGAN outperformed recent CycleGAN and pGAN, given its stronger ability in capturing the $A \beta$ deposition patterns on the whole-brain scale. All qualitative and quantitative metrics demonstrated the strong similarity between the generated AV45 PET images using DPGAN and the original AV45 PET images.

On May 1-2, 1989, a PET Data Analysis Working Group convened to consider positron emission tomography (PET) methodology and data analysis. The papers presented and the recommendations of the Group are reviewed. The Group recommended that a standard phantom of the human brain be used by different institutions to examine machine and data reconstruction PET variables. Interinstitutional comparisons could be aided by using a standard three-dimensional coordinate system. Deformations within individual diseased or atypical brains would require nonlinear as well as linear transformations to the standard space, using magnetic resonance images in register with the PET images. Methods for intersubject averaging of pixel-by-pixel or region-of-interest data, as well as appropriate statistical methods, need to be developed. PET data may first be exploratory and hypothesis-generating (with less stringent statistical theory), then later used to test hypotheses (with more stringent statistical criteria). Common databases, obtained by computer simulation models with known inherent structure, or directly by PET measurements on different groups, could be used to compare analytical and statistical methods among institutions.

Multimodal neuroimaging has gained traction in Alzheimer's Disease (AD) diagnosis by integrating information from multiple imaging modalities to enhance classification accuracy. However, effectively handling heterogeneous data sources and overcoming the challenges posed by multiscale transform methods remains a significant hurdle. This article proposes a novel approach to address these challenges. To harness the power of diverse neuroimaging data, we employ a strategy that leverages optimized convolution techniques. These optimizations include varying kernel sizes and the incorporation of instance normalization, both of which play crucial roles in feature extraction from magnetic resonance imaging (MRI) and positron emission tomography (PET) images. Specifically, varying kernel sizes allow us to adapt the receptive field to different image characteristics, enhancing the model's ability to capture relevant information. Furthermore, we employ transposed convolution, which increases spatial resolution of feature maps, and it is optimized with varying kernel sizes and instance normalization. This heightened resolution facilitates the alignment and integration of data from disparate MRI and PET data. The use of larger kernels and strides in transposed convolution expands the receptive field, enabling the model to capture essential cross-modal relationships. Instance normalization, applied to each modality during the fusion process, mitigates potential biases stemming from differences in intensity, contrast, or scale between modalities. This enhancement contributes to improved model performance by reducing complexity and ensuring robust fusion. The performance of the proposed fusion method is assessed on three distinct neuroimaging datasets, which include: Alzheimer's Disease Neuroimaging Initiative (ADNI), consisting of 50 participants each at various stages of AD for both MRI and PET (Cognitive Normal, AD, and Early Mild Cognitive); Open Access Series of Imaging Studies (OASIS), consisting of 50 participants each at various stages of AD for both MRI and PET (Cognitive Normal, Mild Dementia, Very Mild Dementia); and whole-brain atlas neuroimaging (AANLIB) (consisting of 50 participants each at various stages of AD for both MRI and PET (Cognitive Normal, AD). To evaluate the quality of the fused images generated via our method, we employ a comprehensive set of evaluation metrics, including Structural Similarity Index Measurement (SSIM), which assesses the structural similarity between two images; Peak Signal-to-Noise Ratio (PSNR), which measures how closely the generated image resembles the ground truth; Entropy (E), which assesses the amount of information preserved or lost during fusion; the Feature Similarity Indexing Method (FSIM), which assesses the structural and feature similarities between two images; and Edge-Based Similarity (EBS), which measures the similarity of edges between the fused and ground truth images. The obtained fused image is further evaluated using a Mobile Vision Transformer. In the classification of AD vs. Cognitive Normal, the model achieved an accuracy of 99.00%, specificity of 99.00%, and sensitivity of 98.44% on the AANLIB dataset.

Respiratory motion blurs positron emission tomography (PET) images in thorax and abdominal regions, and can cause attenuation-related errors in quantitative parameters. In addition, this motion can cause localization error and disappearance of tumor(s) near the diaphragm. Respiratory gated PET imaging, or 4D PET imaging, is an attractive solution. For 4D PET, a surrogate measurement of the patient's breathing is made during the scan, and based on this measurement the list-mode PET data are sorted and reconstructed into multiple images. In several studies, it has been reported that 4D PET imaging recovers tumor volume more accurately than 3D PET imaging, and yields more accurate standard uptake values. In spite of its promise, 4D PET images suffer from statistical noise because only a fraction of the acquired data can be used in each image. Moreover, they also suffer from inconsistent attenuation correction due to mismatches between PET and CT images. In this paper, we propose a novel framework for phased attenuation correction and respiratory motion compensation for a 3D PET image by using a 3D CT image and multiple respiratory-phase 3D MR images. For phased attenuation correction, we generate and utilize 4D CT image obtained via non-rigid registrations among a 3D CT image and multiple respiratory-phase 3D MR images. A 3D PET image at a selected respiratory phase is then obtained by adding attenuation corrected multiple 3D PET images through non-rigid transformation. Experimental result for a clinical dataset shows that the proposed algorithm can provide much clearer organ boundary with improved quality than the conventional method in a 3D PET image.

Voxel-based analysis of Alzheimer's disease PET imaging using a triplet of radiotracers: PIB, FDDNP, and FDG

ImportancePrevious postmortem studies have long demonstrated that neurofibrillary tangles made of hyperphosphorylated tau proteins are closely associated with Alzheimer disease clinical phenotype and neurodegeneration pattern. Validating these associations in vivo will lead to new diagnostic tools for Alzheimer disease and better understanding of its neurobiology.ObjectiveTo examine whether topographical distribution and severity of hyperphosphorylated tau pathologic findings measured by fluorine 18–labeled AV-1451 ([18F]AV-1451) positron emission tomographic (PET) imaging are linked with clinical phenotype and cortical atrophy in patients with Alzheimer disease. Design, Setting, and ParticipantsThis observational case series, conducted from July 1, 2012, to July 30, 2015, in an outpatient referral center for patients with neurodegenerative diseases, included 6 patients: 3 with typical amnesic Alzheimer disease and 3 with atypical variants (posterior cortical atrophy, logopenic variant primary progressive aphasia, and corticobasal syndrome). Patients underwent [18F]AV-1451 PET imaging to measure tau burden, carbon 11–labeled Pittsburgh Compound B ([11C]PiB) PET imaging to measure amyloid burden, and structural magnetic resonance imaging to measure cortical thickness. Seventy-seven age-matched controls with normal cognitive function also underwent structural magnetic resonance imaging but not tau or amyloid PET imaging. Main Outcomes and MeasuresTau burden, amyloid burden, and cortical thickness.ResultsIn all 6 patients (3 women and 3 men; mean age 61.8 years), the underlying clinical phenotype was associated with the regional distribution of the [18F]AV-1451 signal. Furthermore, within 68 cortical regions of interest measured from each patient, the magnitude of cortical atrophy was strongly correlated with the magnitude of [18F]AV-1451 binding (3 patients with amnesic Alzheimer disease, r = –0.82; P < .001; r = –0.70; P < .001; r = –0.58; P < .001; and 3 patients with nonamnesic Alzheimer disease, r = –0.51; P < .001; r = –0.63; P < .001; r = –0.70; P < .001), but not of [11C]PiB binding.Conclusions and RelevanceThese findings provide further in vivo evidence that distribution of the [18F]AV-1451 signal as seen on results of PET imaging is a valid marker of clinical symptoms and neurodegeneration. By localizing and quantifying hyperphosphorylated tau in vivo, results of tau PET imaging will likely serve as a key biomarker that links a specific type of molecular Alzheimer disease neuropathologic condition with clinically significant neurodegeneration, which will likely catalyze additional efforts to develop disease-modifying therapeutics.

Pharmacokinetic modelling on dynamic positron emission tomography (PET) data is a quantitative technique. However, the long acquisition time is prohibitive for routine clinical use. Instead, the semi-quantitative standardised uptake value ratio (SUVR) from a shorter static acquisition is used, despite its sensitivity to blood flow confounding longitudinal analysis. A method has been proposed to reduce the dynamic acquisition time for quantification by incorporating cerebral blood flow (CBF) information from arterial spin labelling (ASL) magnetic resonance imaging (MRI) into the pharmacokinetic modelling. In this work, we optimise and validate this framework for a study of ageing and preclinical Alzheimer's disease. This methodology adapts the simplified reference tissue model (SRTM) for a reduced acquisition time (RT-SRTM) and is applied to [18F]-florbetapir PET data for amyloid-β quantification. Evaluation shows that the optimised RT-SRTM can achieve amyloid burden estimation from a 30-min PET/MR acquisition which is comparable with the gold standard SRTM applied to 60 min of PET data. Conversely, SUVR showed a significantly higher error and bias, and a statistically significant correlation with tracer delivery due to the influence of blood flow. The optimised RT-SRTM produced amyloid burden estimates which were uncorrelated with tracer delivery indicating its suitability for longitudinal studies.

Studying depressive phenotypes is challenging due to the brain’s complexity and isolation, but neuroimaging tools like Positron Emission Tomography (PET) can provide new insights. This thesis explored depressive phenotypes using PET imaging in animal models, focusing on two hypotheses for depression the ‘disconnection’ and ‘monoaminergic dysfunction’ using the repeated social defeat (RSD) paradigm to induce depressive-like phenotypes. <br/><br/>Brain connectivity was studied using [18F]FDG-PET. Key findings indicated alterations in brain connectivity in the default mode network simultaneously to occurrence of depressive-like behaviour; as well as, in the salience network after the remission. Hence, the alteration of connectivity in the brain networks may contribute to depressive symptoms. <br/><br/>PET was also used in this thesis to study neuroreceptors systems such the dopaminergic and serotoninergic. In one of these studies, agonistic and antagonistic treatments targeting the adenosinergic A2A receptor, caused an antidepressant effect and a reduction in the dopaminergic D2R receptor availability measured with [11C]raclopride-PET. Moreover, we found an increase in the serotonin transporter availability measured with [11C]DASB-PET in brain regions, such as amygdala, frontal and olfactory cortices and midbrain when the animals were sensitised to stress in early life. <br/><br/>Finally, we studied the optimal method for the quantification of the [11C]methylreboxetine-PET signal for its use in future studies. <br/> <br/>Overall, these studies showcase the importance of animal models and the use of PET methods to investigate depressive phenotypes for improving our understanding of depression’s biological mechanisms and guiding future research and therapies.

Multiplexed Positron Emission Tomography (PET) imaging allows simultaneous acquisition of multiple radiotracer signals, thus enhancing diagnostic capabilities, reducing scan times, and improving patient comfort. Traditional methods often require significant delays between tracer injections, leading to physiological changes and noise interference. Recent advancements, including multi-tracer compartment modeling and machine learning, provide promising solutions. This study explores the deep learning (DL)-based single-session triple-tracer brain PET imaging protocol, aiming at simplifying multi-tracer PET imaging, while reducing radiation exposure. The study uses the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, which includes cognitively normal (CN) patients, as well as patients with mild cognitive impairment (MCI) and dementia. The dataset also includes PET scans acquired with amyloid (18F-florbetaben [FBB] or 18F-florbetapir [FBP]), 18F-Fluorodeoxyglucose (FDG), and tau 18F-flortaucipir (FTP). To mimic the effect of simultaneous acquisition of multiple PET tracers, we generated synthetic dual- and triple-tracer images by summing FBP/FBB, FTP, and FDG scans. A DL model based on Swin Transformer architecture was developed to separate these signals, using five-fold cross-validation and mean squared error (MSE) loss. The synthetic PET images were evaluated using established image quality metrics, including MSE, structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). In addition, clinical evaluation was conducted by two nuclear medicine specialists to assess the amyloid and tau status in the synthetic and reference images. The proposed DL model effectively synthesized realistic FBB/FBP and FDG images from dual- and triple-tracer PET images. Although the proposed DL model's performance in generating FTP images was less successful, it remains promising. The clinical evaluation revealed that the amyloid status estimated from the synthetic images led to a sensitivity of 92% and specificity of 86% for FBB, while it showed a sensitivity of 93% and specificity of 67% for tau status using FBP extracted from the triple-tracer images. The calculated quantitative metrics showed that the mean error for synthetic amyloid images (FBB: 0.03 SUV, FBP: 0.00 SUV) was higher than FDG for FBB (0.02 SUV) but lower than FDG for FBP (-0.01 SUV), and comparable to FTP (FBB: 0.03 SUV, FBP: 0.00 SUV). Voxel-wise correlation analysis demonstrated strong correlation between synthetic and reference images, particularly for amyloid images (FBB: y = 0.98x + 0.00, R² = 0.85; FBP: y = 1.11x + 0.04, R² = 0.73), while FTP (FBB: y = 0.87x + 0.14, R² = 0.51; FBP: y = 0.98x + 0.09, R² = 0.59) and FDG images (FBB: y = 1.01x + 0.18, R² = 0.85; FBP: y = 0.96x + 1.37, R² = 0.77) showed moderate correlations. Our study demonstrates that the suggested DL model can separate the signals belonging to three different radiotracers from simultaneous triple-tracer PET scans. This method may make multiplex scanning feasible in the clinic, hence reducing the scanning time, radiation hazard and improving patient comfort.