From simultaneous to synergistic MR-PET brain imaging: A review of hybrid MR-PET imaging methodologies

Joint diffusion: mutual consistency-driven diffusion model for PET-MRI co-reconstruction

Objective. Positron Emission Tomography and Magnetic Resonance Imaging (PET-MRI) systems can obtain functional and anatomical scans. But PET suffers from a low signal-to-noise ratio, while MRI are time-consuming. To address time-consuming, an effective strategy involves reducing k-space data collection, albeit at the cost of lowering image quality. This study aims to leverage the inherent complementarity within PET-MRI data to enhance the image quality of PET-MRI.Approach. A novel PET-MRI joint reconstruction model, termed MC-Diffusion, is proposed in the Bayesian framework. The joint reconstruction problem is transformed into a joint regularization problem, where data fidelity terms of PET and MRI are expressed independently. The regular term, the derivative of the logarithm of the joint probability distribution of PET and MRI, employs a joint score-based diffusion model for learning. The diffusion model involves the forward diffusion process and the reverse diffusion process. The forward diffusion process adds noise to transform a complex joint data distribution into a known joint prior distribution for PET and MRI simultaneously, resembling a denoiser. The reverse diffusion process removes noise using a denoiser to revert the joint prior distribution to the original joint data distribution, effectively utilizing joint probability distribution to describe the correlations of PET and MRI for improved quality of joint reconstruction.Main results. Qualitative and quantitative improvements are observed with the MC-Diffusion model. Comparative analysis against LPLS and Joint ISAT-net on the ADNI dataset demonstrates superior performance by exploiting complementary information between PET and MRI. The MC-Diffusion model effectively enhances the quality of PET and MRI images.Significance. This study employs the MC-Diffusion model to enhance the quality of PET-MRI images by integrating the fundamental principles of PET and MRI modalities and leveraging their inherent complementarity. Furthermore, utilizing the diffusion model to learn the joint probability distribution of PET and MRI, thereby elucidating their latent correlation, facilitates a more profound comprehension of the priors obtained through deep learning, contrasting with black-box prior or artificially constructed structural similarities.

Topographic metabolism-function relationships in Alzheimer's disease: A simultaneous PET/MRI study

Disruptions of neural metabolism and function occur in parallel during Alzheimer's disease (AD). While many studies have shown diverse metabolic-functional relationships in specific brain regions, much less is known about how large-scale network-level functional activity is associated with the topology of metabolism in AD. In this study, we took the advantages of simultaneous PET/MRI and multivariate analyses to investigate the associations between AD-related stereotypical spatial patterns (topographies) of glucose metabolism, measured by fluorodeoxyglucose PET, and functional connectivity, measured by resting-state functional MRI. A total of 101 participants, including 37 patients with AD, 25 patients with mild cognitive impairment (MCI), and 39 cognitively normal controls, underwent PET/MRI scans and cognitive assessments. Three pairs of distinct but optimally correlated metabolic and functional topographies were identified, encompassing large-scale networks including the default-mode, executive and control, salience, attention, and subcortical networks. Importantly, the metabolic-functional associations were not only limited to one-to-one-corresponding regions, but also occur in remote and non-overlapping regions. Furthermore, both glucose metabolism and functional connectivity, as well as their linkages, exhibited various degrees of disruptions in patients with MCI and AD, and were correlated with cognitive decline. In conclusion, our results support distributed and heterogeneous topographic associations between metabolism and function, which are jeopardized by AD. Findings of this study may deepen our understanding of the pathological mechanism of AD through the perspectives of both local energy efficiency and long-term interactions between synaptic disruption and functional disconnection contributing to the clinical symptomatology in AD.

Deep learning based synthesis of MRI, CT and PET: Review and analysis

Medical image synthesis represents a critical area of research in clinical decision-making, aiming to overcome the challenges associated with acquiring multiple image modalities for an accurate clinical workflow. This approach proves beneficial in estimating an image of a desired modality from a given source modality among the most common medical imaging contrasts, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET). However, translating between two image modalities presents difficulties due to the complex and non-linear domain mappings. Deep learning-based generative modelling has exhibited superior performance in synthetic image contrast applications compared to conventional image synthesis methods. This survey comprehensively reviews deep learning-based medical imaging translation from 2018 to 2023 on pseudo-CT, synthetic MR, and synthetic PET. We provide an overview of synthetic contrasts in medical imaging and the most frequently employed deep learning networks for medical image synthesis. Additionally, we conduct a detailed analysis of each synthesis method, focusing on their diverse model designs based on input domains and network architectures. We also analyse novel network architectures, ranging from conventional CNNs to the recent Transformer and Diffusion models. This analysis includes comparing loss functions, available datasets and anatomical regions, and image quality assessments and performance in other downstream tasks. Finally, we discuss the challenges and identify solutions within the literature, suggesting possible future directions. We hope that the insights offered in this survey paper will serve as a valuable roadmap for researchers in the field of medical image synthesis.

Comparison of quantitative [11C]PE2I brain PET studies between an integrated PET/MR and a stand-alone PET system

PET/MR systems demanded great efforts for accurate attenuation correction (AC) but differences in technology, geometry and hardware attenuation may also affect quantitative results. Dedicated PET systems using transmission-based AC are regarded as the gold standard for quantitative brain PET. The study aim was to investigate the agreement between quantitative PET outcomes from a PET/MR scanner against a stand-alone PET system. Nine patients with Parkinsonism underwent two 80-min dynamic PET scans with the dopamine transporter ligand [11C]PE2I. Images were reconstructed with resolution-matched settings using 68Ge-transmission (stand-alone PET), and zero-echo-time MR (PET/MR) scans for AC. Non-displaceable binding potential (BPND) and relative delivery (R1) were evaluated using volumes of interest and voxel-wise analysis. Correlations between systems were high (r ≥ 0.85) for both quantitative outcome parameters in all brain regions. Striatal BPND was significantly lower on PET/MR than on stand-alone PET (-7%). R1 was significantly overestimated in posterior cortical regions (9%) and underestimated in striatal (-9%) and limbic areas (-6%). The voxel-wise evaluation revealed that the MR-safe headphones caused a negative bias in both parametric BPND and R1 images. Additionally, a significant positive bias of R1 was found in the auditory cortex, most likely due to the acoustic background noise during MR imaging. The relative bias of the quantitative [11C]PE2I PET data acquired from a SIGNA PET/MR system was in the same order as the expected test-retest reproducibility of [11C]PE2I BPND and R1, compared to a stand-alone ECAT PET scanner. MR headphones and background noise are potential sources of error in functional PET/MR studies.

New Perspectives in Radiological and Radiopharmaceutical Hybrid Imaging in Progressive Supranuclear Palsy: A Systematic Review

Progressive supranuclear palsy (PSP) is a neurodegenerative disease characterized by four-repeat tau deposition in various cell types and anatomical regions, and can manifest as several clinical phenotypes, including the most common phenotype, Richardson's syndrome. The limited availability of biomarkers for PSP relates to the overlap of clinical features with other neurodegenerative disorders, but identification of a growing number of biomarkers from imaging is underway. One way to increase the reliability of imaging biomarkers is to combine different modalities for multimodal imaging. This review aimed to provide an overview of the current state of PSP hybrid imaging by combinations of positron emission tomography (PET) and magnetic resonance imaging (MRI). Specifically, combined PET and MRI studies in PSP highlight the potential of [18F]AV-1451 to detect tau, but also the challenge in differentiating PSP from other neurodegenerative diseases. Studies over the last years showed a reduced synaptic density in [11C]UCB-J PET, linked [11C]PK11195 and [18F]AV-1451 markers to disease progression, and suggested the potential role of [18F]RO948 PET for identifying tau pathology in subcortical regions. The integration of quantitative global and regional gray matter analysis by MRI may further guide the assessment of reduced cortical thickness or volume alterations, and diffusion MRI could provide insight into microstructural changes and structural connectivity in PSP. Challenges in radiopharmaceutical biomarkers and hybrid imaging require further research targeting markers for comprehensive PSP diagnosis.

Improving portable low-field MRI image quality through image-to-image translation using paired low- and high-field images

Low-field portable magnetic resonance imaging (MRI) scanners are more accessible, cost-effective, sustainable with lower carbon emissions than superconducting high-field MRI scanners. However, the images produced have relatively poor image quality, lower signal-to-noise ratio, and limited spatial resolution. This study develops and investigates an image-to-image translation deep learning model, LoHiResGAN, to enhance the quality of low-field (64mT) MRI scans and generate synthetic high-field (3T) MRI scans. We employed a paired dataset comprising T1- and T2-weighted MRI sequences from the 64mT and 3T and compared the performance of the LoHiResGAN model with other state-of-the-art models, including GANs, CycleGAN, U-Net, and cGAN. Our proposed method demonstrates superior performance in terms of image quality metrics, such as normalized root-mean-squared error, structural similarity index measure, peak signal-to-noise ratio, and perception-based image quality evaluator. Additionally, we evaluated the accuracy of brain morphometry measurements for 33 brain regions across the original 3T, 64mT, and synthetic 3T images. The results indicate that the synthetic 3T images created using our proposed LoHiResGAN model significantly improve the image quality of low-field MRI data compared to other methods (GANs, CycleGAN, U-Net, cGAN) and provide more consistent brain morphometry measurements across various brain regions in reference to 3T. Synthetic images generated by our method demonstrated high quality both quantitatively and qualitatively. However, additional research, involving diverse datasets and clinical validation, is necessary to fully understand its applicability for clinical diagnostics, especially in settings where high-field MRI scanners are less accessible.

Clinical Value of Hybrid PET/MR Imaging: Brain Imaging Using PET/MR Imaging

Hybrid PET/MR imaging offers a unique opportunity to acquire MR imaging and PET information during a single imaging session. PET/MR imaging has numerous advantages, including enhanced diagnostic accuracy, improved disease characterization, and better treatment planning and monitoring. It enables the immediate integration of anatomic, functional, and metabolic imaging information, allowing for personalized characterization and monitoring of neurologic diseases. This review presents recent advances in PET/MR imaging and highlights advantages in clinical practice for neuro-oncology, epilepsy, and neurodegenerative disorders. PET/MR imaging provides valuable information about brain tumor metabolism, perfusion, and anatomic features, aiding in accurate delineation, treatment response assessment, and prognostication.

Impact of brain segmentation methods on regional metabolism quantification in 18F-FDG PET/MR analysis

BACKGROUND

Accurate analysis of quantitative PET data plays a crucial role in studying small, specific brain structures. The integration of PET and MRI through an integrated PET/MR system presents an opportunity to leverage the benefits of precisely aligned structural MRI and molecular PET images in both spatial and temporal dimensions. However, in many clinical workflows, PET studies are often performed without the aid of individually matched structural MRI scans, primarily for the sake of convenience in the data collection and brain segmentation possesses. Currently, two commonly employed segmentation strategies for brain PET analysis are distinguished: methods with or without MRI registration and methods employing either atlas-based or individual-based algorithms. Moreover, the development of artificial intelligence (AI)-assisted methods for predicting brain segmentation holds promise but requires further validation of their efficiency and accuracy for clinical applications. This study aims to compare and evaluate the correlations, consistencies, and differences among the above-mentioned brain segmentation strategies in quantification of brain metabolism in 18F-FDG PET/MR analysis.

RESULTS

Strong correlations were observed among all methods (r = 0.932 to 0.999, P < 0.001). The variances attributable to subject and brain region were higher than those caused by segmentation methods (P < 0.001). However, intraclass correlation coefficient (ICC)s between methods with or without MRI registration ranged from 0.924 to 0.975, while ICCs between methods with atlas- or individual-based algorithms ranged from 0.741 to 0.879. Brain regions exhibiting significant standardized uptake values (SUV) differences due to segmentation methods were the basal ganglia nuclei (maximum to 11.50 ± 4.67%), and various cerebral cortexes in temporal and occipital regions (maximum to 18.03 ± 5.52%). The AI-based method demonstrated high correlation (r = 0.998 and 0.999, P < 0.001) and ICC (0.998 and 0.997) with FreeSurfer, substantially reducing the time from 8.13 h to 57 s on per subject.

CONCLUSIONS

Different segmentation methods may have impact on the calculation of brain metabolism in basal ganglia nuclei and specific cerebral cortexes. The AI-based approach offers improved efficiency and is recommended for its enhanced performance.

Neurophysiological consequences of synapse loss in progressive supranuclear palsy

Synaptic loss occurs early in many neurodegenerative diseases and contributes to cognitive impairment even in the absence of gross atrophy. Currently, for human disease there are few formal models to explain how cortical networks underlying cognition are affected by synaptic loss. We advocate that biophysical models of neurophysiology offer both a bridge from preclinical to clinical models of pathology and quantitative assays for experimental medicine. Such biophysical models can also disclose hidden neuronal dynamics generating neurophysiological observations such as EEG and magnetoencephalography. Here, we augment a biophysically informed mesoscale model of human cortical function by inclusion of synaptic density estimates as captured by 11C-UCB-J PET, and provide insights into how regional synapse loss affects neurophysiology. We use the primary tauopathy of progressive supranuclear palsy (Richardson's syndrome) as an exemplar condition, with high clinicopathological correlations. Progressive supranuclear palsy causes a marked change in cortical neurophysiology in the presence of mild cortical atrophy and is associated with a decline in cognitive functions associated with the frontal lobe. Using parametric empirical Bayesian inversion of a conductance-based canonical microcircuit model of magnetoencephalography data, we show that the inclusion of regional synaptic density-as a subject-specific prior on laminar-specific neuronal populations-markedly increases model evidence. Specifically, model comparison suggests that a reduction in synaptic density in inferior frontal cortex affects superficial and granular layer glutamatergic excitation. This predicted individual differences in behaviour, demonstrating the link between synaptic loss, neurophysiology and cognitive deficits. The method we demonstrate is not restricted to progressive supranuclear palsy or the effects of synaptic loss: such pathology-enriched dynamic causal models can be used to assess the mechanisms of other neurological disorders, with diverse non-invasive measures of pathology, and is suitable to test the effects of experimental pharmacology.

Highlighted Advances in Therapies for Difficult-To-Treat Brain Tumours Such as Glioblastoma

Glioblastoma multiforme (GBM) remains a challenging disease, as it is the most common and deadly brain tumour in adults and has no curative solution and an overall short survival time. This incurability and short survival time means that, despite its rarity (average incidence of 3.2 per 100,000 persons), there has been an increased effort to try to treat this disease. Standard of care in newly diagnosed glioblastoma is maximal tumour resection followed by initial concomitant radiotherapy and temozolomide (TMZ) and then further chemotherapy with TMZ. Imaging techniques are key not only to diagnose the extent of the affected tissue but also for surgery planning and even for intraoperative use. Eligible patients may combine TMZ with tumour treating fields (TTF) therapy, which delivers low-intensity and intermediate-frequency electric fields to arrest tumour growth. Nonetheless, the blood–brain barrier (BBB) and systemic side effects are obstacles to successful chemotherapy in GBM; thus, more targeted, custom therapies such as immunotherapy and nanotechnological drug delivery systems have been undergoing research with varying degrees of success. This review proposes an overview of the pathophysiology, possible treatments, and the most (not all) representative examples of the latest advancements.

Analysis of Stable Chelate-free Gadolinium Loaded Titanium Dioxide Nanoparticles for MRI-Guided Radionuclide Stimulated Cancer Treatment

Background

Recent studies demonstrate that titanium dioxide nanoparticles (TiO₂ NPs) are an effective source of reactive oxygen species (ROS) for photodynamic therapy and radionuclide stimulated dynamic therapy (RaST). Unfortunately tracking the in vivo distribution of TiO₂ NPs noninvasively remains elusive.

Objective

Given the use of gadolinium (Gd) chelates as effective contrast agents for magnetic resonance imaging (MRI), this study aims to (1) develop hybrid TiO₂-Gd NPs that exhibit high relaxivity for tracking the NPs without loss of ROS generating capacity; and (2) establish a simple colorimetric assay for quantifying Gd loading and stability.

Methods

A chelate-free, heat-induced method was used to load Gd onto TiO₂ NPs, which was coated with transferrin (Tf). A sensitive colorimetric assay and inductively coupled plasma mass spectrometry (ICP-MS) were used to determine Gd loading and stability of the TiO₂-Gd-Tf NPs. Measurement of the relaxivity was performed on a 1.4 T relaxometer and a 4.7 T small animal magnetic resonance scanner to estimate the effects of magnetic field strength. ROS was quantified by activated dichlorodihydrofluorescein diacetate fluorescence. Cell uptake of the NPs and RaST were monitored by fluorescence microscopy. Both 3 T and 4.7 T scanners were used to image the in vivo distribution of intravenously injected NPs in tumor-bearing mice.

Results

A simple colorimetric assay accurately determined both the loading and stability of the NPs compared with the expensive and complex ICP-MS method. Coating of the TiO₂-Gd NPs with Tf stabilized the nanoconstruct and minimized aggregation. The TiO₂-Gd-Tf maintained ROS-generating capability without inducing cell death at a wide range of concentrations but induced significant cell death under RaST conditions in the presence of F-18 radiolabeled 2-fluorodeoxyglucose. The longitudinal (r1 = 10.43 mM^-1s^-1) and transverse (r2 = 13.43 mM^-1s^-1) relaxivity of TiO₂-Gd-Tf NPs were about twice and thrice, respectively, those of clinically used Gd contrast agent (Gd-DTPA; r1 = 3.77 mM^-1s^-1 and r2 = 5.51 mM^-1s^-1) at 1.4 T. While the r1 (8.13 mM^-1s^-1) reduced to about twice that of Gd-DTPA (4.89 mM^-1s^-1) at 4.7 T, the corresponding r2 (87.15 mM^-1s^-1) increased by a factor 22.6 compared to Gd-DTPA (r2 = 3.85). MRI of tumor-bearing mice injected with TiO₂-Gd-Tf NPs tracked the NPs distribution and accumulation in tumors.

Conclusion

This work demonstrates that Arsenazo III colorimetric assay can substitute ICP-MS for determining the loading and stability of Gd-doped TiO₂ NPs. The new nanoconstruct enabled RaST effect in cells, exhibited high relaxivity, and enhanced MRI contrast in tumors in vivo, paving the way for in vivo MRI-guided RaST.

CONN-NLM: A Novel CONNectome-Based Non-local Means Filter for PET-MRI Denoising

Background

Advancements in hybrid positron emission tomography-magnetic resonance (PET-MR) systems allow for combining the advantages of each modality. Integrating information from MRI and PET can be valuable for diagnosing and treating neurological disorders. However, combining diffusion MRI (dMRI) and PET data, which provide highly complementary information, has rarely been exploited in image post-processing. dMRI has the ability to investigate the white matter pathways of the brain through fibre tractography, which enables comprehensive mapping of the brain connection networks (the "connectome"). Novel methods are required to combine information present in the connectome and PET to increase the full potential of PET-MRI.

Methods

We developed a CONNectome-based Non-Local Means (CONN-NLM) filter to exploit synergies between dMRI-derived structural connectivity and PET intensity information to denoise PET images. PET-MR data are parcelled into a number of regions based on a brain atlas, and the inter-regional structural connectivity is calculated based on dMRI fibre-tracking. The CONN-NLM filter is then implemented as a post-reconstruction filter by combining the nonlocal means filter and a connectivity-based cortical smoothing. The effect of this approach is to weight voxels with similar PET intensity and highly connected voxels higher when computing the weighted-average to perform more informative denoising. The proposed method was first evaluated using a novel computer phantom framework to simulate realistic hybrid PET-MR images with different lesion scenarios. CONN-NLM was further assessed with clinical dMRI and tau PET examples.

Results

The results showed that CONN-NLM has the capacity to improve the overall PET image quality by reducing noise while preserving lesion contrasts, and it outperformed a range of filters that did not use dMRI information. The simulations demonstrate that CONN-NLM can handle various lesion contrasts consistently, as well as lesions with different levels of inter-connectivity.

Conclusion

CONN-NLM has unique advantages of providing more informative and accurate PET smoothing by adding complementary structural connectivity information from dMRI, representing a new avenue to exploit synergies between MRI and PET.

Disrupted coupling between salience network segregation and glucose metabolism is associated with cognitive decline in Alzheimer's disease - A simultaneous resting-state FDG-PET/fMRI study

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Hybrid PET/MRI was used to explore network segregation and glucose metabolism in AD.

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DMN, CEN, and SN showed reduced segregation in AD.

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In salience network, segregation coupled with glucose metabolism in CN group.

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The coupled segregation and glucose metabolism in CN disappeared in MCI and AD.

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Reduced segregation and hypometabolism were associated with cognitive impairments.

The aberrant organization and functioning of three core neurocognitive networks (NCNs), i.e., default-mode network (DMN), central executive network (CEN), and salience network (SN), are among the prominent features in Alzheimer’s disease (AD). The dysregulation of both intra- and inter-network functional connectivities (FCs) of the three NCNs contributed to AD-related cognitive and behavioral abnormalities. Brain functional network segregation, integrating intra- and inter-network FCs, is essential for maintaining the energetic efficiency of brain metabolism. The association of brain functional network segregation, together with glucose metabolism, with age-related cognitive decline was recently shown. Yet how these joint functional-metabolic biomarkers relate to cognitive decline along with mild cognitive impairment (MCI) and AD remains to be elucidated. In this study, under the framework of the triple-network model, we performed a hybrid FDG-PET/fMRI study to evaluate the concurrent changes of resting-state brain intrinsic FCs and glucose metabolism of the three NCNs across cognitively normal (CN) (N = 24), MCI (N = 21), and AD (N = 21) groups. Lower network segregation and glucose metabolism were observed in all three NCNs in patients with AD. More interestingly, in the SN, the coupled relationship between network segregation and glucose metabolism existed in the CN group (r = 0.523, p = 0.013) and diminished in patients with MCI (r = 0.431, p = 0.065) and AD (r = 0.079, p = 0.748). Finally, the glucose metabolism of the DMN (r = 0.380, p = 0.017) and the network segregation of the SN (r = 0.363, p = 0.023) were significantly correlated with the general cognitive status of the patients. Our findings suggest that the impaired SN segregation and its uncoupled relationship with glucose metabolism contribute to the cognitive decline in AD.

Monash DaCRA fPET-fMRI: A dataset for comparison of radiotracer administration for high temporal resolution functional FDG-PET

BACKGROUND

"Functional" [18F]-fluorodeoxyglucose positron emission tomography (FDG-fPET) is a new approach for measuring glucose uptake in the human brain. The goal of FDG-fPET is to maintain a constant plasma supply of radioactive FDG in order to track, with high temporal resolution, the dynamic uptake of glucose during neuronal activity that occurs in response to a task or at rest. FDG-fPET has most often been applied in simultaneous BOLD-fMRI/FDG-fPET (blood oxygenation level-dependent functional MRI fluorodeoxyglucose functional positron emission tomography) imaging. BOLD-fMRI/FDG-fPET provides the capability to image the 2 primary sources of energetic dynamics in the brain, the cerebrovascular haemodynamic response and cerebral glucose uptake.

FINDINGS

In this Data Note, we describe an open access dataset, Monash DaCRA fPET-fMRI, which contrasts 3 radiotracer administration protocols for FDG-fPET: bolus, constant infusion, and hybrid bolus/infusion. Participants (n = 5 in each group) were randomly assigned to each radiotracer administration protocol and underwent simultaneous BOLD-fMRI/FDG-fPET scanning while viewing a flickering checkerboard. The bolus group received the full FDG dose in a standard bolus administration, the infusion group received the full FDG dose as a slow infusion over the duration of the scan, and the bolus-infusion group received 50% of the FDG dose as bolus and 50% as constant infusion. We validate the dataset by contrasting plasma radioactivity, grey matter mean uptake, and task-related activity in the visual cortex.

CONCLUSIONS

The Monash DaCRA fPET-fMRI dataset provides significant reuse value for researchers interested in the comparison of signal dynamics in fPET, and its relationship with fMRI task-evoked activity.

Metabolic and functional connectivity provide unique and complementary insights into cognition-connectome relationships

A major challenge in current cognitive neuroscience is how functional brain connectivity gives rise to human cognition. Functional magnetic resonance imaging (fMRI) describes brain connectivity based on cerebral oxygenation dynamics (hemodynamic connectivity), whereas [18F]-fluorodeoxyglucose functional positron emission tomography (FDG-fPET) describes brain connectivity based on cerebral glucose uptake (metabolic connectivity), each providing a unique characterization of the human brain. How these 2 modalities differ in their contribution to cognition and behavior is unclear. We used simultaneous resting-state FDG-fPET/fMRI to investigate how hemodynamic connectivity and metabolic connectivity relate to cognitive function by applying partial least squares analyses. Results revealed that although for both modalities the frontoparietal anatomical subdivisions related the strongest to cognition, using hemodynamic measures this network expressed executive functioning, episodic memory, and depression, whereas for metabolic measures this network exclusively expressed executive functioning. These findings demonstrate the unique advantages that simultaneous FDG-PET/fMRI has to provide a comprehensive understanding of the neural mechanisms that underpin cognition and highlights the importance of multimodality imaging in cognitive neuroscience research.

Composite attenuation correction method using a 68Ge-transmission multi-atlas for quantitative brain PET/MR

In positron emission tomography (PET), ⁶⁸Ge-transmission scanning is considered the gold standard in attenuation correction (AC) though not available in current dual imaging systems. In this experimental study we evaluated a novel AC method for PET/magnetic resonance (MR) imaging which is essentially based on a composite database of multiple ⁶⁸Ge-transmission maps and T1-weighted (T1w) MR image-pairs (composite transmission, CTR-AC). This proof-of-concept study used retrospectively a database with 125 pairs of co-registered ⁶⁸Ge-AC maps and T1w MR images from anatomical normal subjects and a validation dataset comprising dynamic [¹¹C]PE2I PET data from nine patients with Parkinsonism. CTR-AC maps were generated by non-rigid image registration of all database T1w MRI to each subject's T1w, applying the same transformation to every ⁶⁸Ge-AC map, and averaging the resulting ⁶⁸Ge-AC maps. [¹¹C]PE2I PET images were reconstructed using CTR-AC and a patient-specific ⁶⁸Ge-AC map as the reference standard. Standardized uptake values (SUV) and quantitative parameters of kinetic analysis were compared, i.e., relative delivery (R₁) and non-displaceable binding potential (BP_ND). CTR-AC showed high accuracy for whole-brain SUV (mean %bias ± SD: 0.5 ± 3.5%), whole-brain R₁ (-0.1 ± 3.2%), and putamen BP_ND (3.7 ± 8.1%). SUV and R₁ precision (SD of %bias) were modest and lowest in the anterior cortex, with an R₁ %bias of -1.1 ± 6.4%). The prototype CTR-AC is capable of providing accurate MRAC-maps with continuous linear attenuation coefficients though still experimental. The method's accuracy is comparable to the best MRAC methods published so far, both in SUV and as found for ZTE-AC in quantitative parameters of kinetic modelling.

New Horizons-Cognitive Dysfunction Associated With Type 2 Diabetes

The prevalence of type 2 diabetes (T2D) and cognitive dysfunction increases with age. As society ages, clinicians will be increasingly tasked with managing older people who have both T2D and cognitive dysfunction. T2D is associated with an increased risk of cognitive dysfunction and hence there is increasing interest in whether T2D is a causal factor in the pathogenesis of cognitive decline and dementia. Recent advances in the use of sensitive measures of in vivo brain dysfunction in life-course studies can help understand potential mechanistic pathways and also help guide recommendations for clinical practice. In this article we will describe new horizons in the understanding of cognitive dysfunction associated with T2D. Coming from a clinical perspective, we discuss potential mechanisms and pathways linking the 2 conditions and the contribution of multimodal neuroimaging and study designs to advancing understanding in the field. We also highlight the important issues on the horizon that will need addressing in clinical identification, management, and risk reduction for people with coexistent T2D and cognitive dysfunction.

Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement

Image processing plays a crucial role in maximising diagnostic quality of positron emission tomography (PET) images. Recently, deep learning methods developed across many fields have shown tremendous potential when applied to medical image enhancement, resulting in a rich and rapidly advancing literature surrounding this subject. This review encapsulates methods for integrating deep learning into PET image reconstruction and post-processing for low-dose imaging and resolution enhancement. A brief introduction to conventional image processing techniques in PET is firstly presented. We then review methods which integrate deep learning into the image reconstruction framework as either deep learning-based regularisation or as a fully data-driven mapping from measured signal to images. Deep learning-based post-processing methods for low-dose imaging, temporal resolution enhancement and spatial resolution enhancement are also reviewed. Finally, the challenges associated with applying deep learning to enhance PET images in the clinical setting are discussed and future research directions to address these challenges are presented.

Integrated Positron Emission Tomography/Magnetic Resonance Imaging for Resting-State Functional and Metabolic Imaging in Human Brain: What Is Correlated and What Is Impacted

Integrated positron emission tomography (PET)/magnetic resonance imaging (MRI) could simultaneously obtain both functional MRI (fMRI) and ¹⁸F-fluorodeoxyglucose (FDG) PET and thus provide multiparametric information for the analysis of brain metabolism. In this study, we aimed to, for the first time, investigate the interplay of simultaneous fMRI and FDG PET scan using a randomized self-control protocol. In total, 24 healthy volunteers underwent PET/MRI scan for 30–40 min after the injection of FDG. A 22-min brain scan was separated into MRI-off mode (without fMRI pulsing) and MRI-on mode (with fMRI pulsing), with each one lasting for 11 min. We calculated the voxel-wise fMRI metrics (regional homogeneity, amplitude of low-frequency fluctuations, fractional amplitude of low-frequency fluctuations, and degree centrality), resting networks, relative standardized uptake value ratios (SUVr), SUVr slope, and regional cerebral metabolic rate of glucose (rCMRGlu) maps. Paired two-sample t-tests were applied to assess the statistical differences between SUVr, SUVr slope, correlation coefficients of fMRI metrics, and rCMRGlu between MRI-off and MRI-on modes, respectively. The voxel-wise whole-brain SUVr revealed no statistical difference (P > 0.05), while the SUVr slope was significantly elevated in sensorimotor, dorsal attention, ventral attention, control, default, and auditory networks (P < 0.05) during fMRI scan. The task-based group independent-component analysis revealed that the most active network components derived from the combined MRI-off and MRI-on static PET images were frontal pole, superior frontal gyrus, middle temporal gyrus, and occipital pole. High correlation coefficients were found among fMRI metrics with rCMRGlu in both MRI-off and MRI-on mode (P < 0.05). Our results systematically evaluated the impact of simultaneous fMRI scan on the quantification of human brain metabolism from an integrated PET/MRI system.

Task-evoked simultaneous FDG-PET and fMRI data for measurement of neural metabolism in the human visual cortex

Understanding how the living human brain functions requires sophisticated in vivo neuroimaging technologies to characterise the complexity of neuroanatomy, neural function, and brain metabolism. Fluorodeoxyglucose positron emission tomography (FDG-PET) studies of human brain function have historically been limited in their capacity to measure dynamic neural activity. Simultaneous [18 F]-FDG-PET and functional magnetic resonance imaging (fMRI) with FDG infusion protocols enable examination of dynamic changes in cerebral glucose metabolism simultaneously with dynamic changes in blood oxygenation. The Monash vis-fPET-fMRI dataset is a simultaneously acquired FDG-fPET/BOLD-fMRI dataset acquired from n = 10 healthy adults (18-49 yrs) whilst they viewed a flickering checkerboard task. The dataset contains both raw (unprocessed) images and source data organized according to the BIDS specification. The source data includes PET listmode, normalization, sinogram and physiology data. Here, the technical feasibility of using opensource frameworks to reconstruct the PET listmode data is demonstrated. The dataset has significant re-use value for the development of new processing pipelines, signal optimisation methods, and to formulate new hypotheses concerning the relationship between neuronal glucose uptake and cerebral haemodynamics.

Motion estimation and correction for simultaneous PET/MR using SIRF and CIL

SIRF is a powerful PET/MR image reconstruction research tool for processing data and developing new algorithms. In this research, new developments to SIRF are presented, with focus on motion estimation and correction. SIRF's recent inclusion of the adjoint of the resampling operator allows gradient propagation through resampling, enabling the MCIR technique. Another enhancement enabled registering and resampling of complex images, suitable for MRI. Furthermore, SIRF's integration with the optimization library CIL enables the use of novel algorithms. Finally, SPM is now supported, in addition to NiftyReg, for registration. Results of MR and PET MCIR reconstructions are presented, using FISTA and PDHG, respectively. These demonstrate the advantages of incorporating motion correction and variational and structural priors. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.

Towards lower-dose PET using physics-based uncertainty-aware multimodal learning with robustness to out-of-distribution data

Radiation exposure in positron emission tomography (PET) imaging limits its usage in the studies of radiation-sensitive populations, e.g., pregnant women, children, and adults that require longitudinal imaging. Reducing the PET radiotracer dose or acquisition time reduces photon counts, which can deteriorate image quality. Recent deep-neural-network (DNN) based methods for image-to-image translation enable the mapping of low-quality PET images (acquired using substantially reduced dose), coupled with the associated magnetic resonance imaging (MRI) images, to high-quality PET images. However, such DNN methods focus on applications involving test data that match the statistical characteristics of the training data very closely and give little attention to evaluating the performance of these DNNs on new out-of-distribution (OOD) acquisitions. We propose a novel DNN formulation that models the (i) underlying sinogram-based physics of the PET imaging system and (ii) the uncertainty in the DNN output through the per-voxel heteroscedasticity of the residuals between the predicted and the high-quality reference images. Our sinogram-based uncertainty-aware DNN framework, namely, suDNN, estimates a standard-dose PET image using multimodal input in the form of (i) a low-dose/low-count PET image and (ii) the corresponding multi-contrast MRI images, leading to improved robustness of suDNN to OOD acquisitions. Results on in vivo simultaneous PET-MRI, and various forms of OOD data in PET-MRI, show the benefits of suDNN over the current state of the art, quantitatively and qualitatively.

Application of PET Imaging in the Brain Regions of the Emotional Control Loop in Patients with Generalized Anxiety Disorder

Objective

This study uses PET imaging to observe the uptake and metabolism of 18F-fluorodeoxyglucose (¹⁸F-FDG) in the multibrain areas of the emotional control loop in patients with generalized anxiety disorder (GAD) and investigate the brain of GAD patient's functional abnormality mechanism.

Methods

The thesis clinically collected 20 GAD patients and 20 healthy subjects. Dynamic PET-CT scans were used. At the same time, ¹⁸F-FDG whole-brain uptake and metabolism data were collected. Image fusion and semiquantitative analysis were used to measure emotional control loops. The maximum standard uptake value (SUVmax) and dynamic uptake and metabolic changes of 11 time points in the brain area at 150 min were measured.

Results

Compared with the healthy control group, the peak uptake of the bilateral prefrontal cortex and the average uptake rate before the peak in GAD patients were significantly reduced (P < 0.05), and the average metabolic rate after the peak was significantly increased (P < 0.05). The peak uptake of the left striatum and the left hippocampus, the average uptake rate before the peak, and the average metabolic rate after the peak were all significantly reduced (P < 0.05); There were no obvious changes in the three indexes of the right striatum and the right hippocampus.

Conclusion

There are ¹⁸F-FDG uptake and metabolic disorders in multiple brain areas of the affective control loop of GAD patients. The abnormal peak and rate of uptake may be related to the pathogenesis of GAD.

Metabolic and Hemodynamic Resting-State Connectivity of the Human Brain: A High-Temporal Resolution Simultaneous BOLD-fMRI and FDG-fPET Multimodality Study

Simultaneous [18F]-fluorodeoxyglucose positron emission tomography functional magnetic resonance imaging (FDG-PET/fMRI) provides the capacity to image 2 sources of energetic dynamics in the brain-glucose metabolism and the hemodynamic response. fMRI connectivity has been enormously useful for characterizing interactions between distributed brain networks in humans. Metabolic connectivity based on static FDG-PET has been proposed as a biomarker for neurological disease, but FDG-sPET cannot be used to estimate subject-level measures of "connectivity," only across-subject "covariance." Here, we applied high-temporal resolution constant infusion functional positron emission tomography (fPET) to measure subject-level metabolic connectivity simultaneously with fMRI connectivity. fPET metabolic connectivity was characterized by frontoparietal connectivity within and between hemispheres. fPET metabolic connectivity showed moderate similarity with fMRI primarily in superior cortex and frontoparietal regions. Significantly, fPET metabolic connectivity showed little similarity with FDG-sPET metabolic covariance, indicating that metabolic brain connectivity is a nonergodic process whereby individual brain connectivity cannot be inferred from group-level metabolic covariance. Our results highlight the complementary strengths of fPET and fMRI in measuring the intrinsic connectivity of the brain and open up the opportunity for novel fundamental studies of human brain connectivity as well as multimodality biomarkers of neurological diseases.

Incorporation of anatomical MRI knowledge for enhanced mapping of brain metabolism using functional PET

Functional positron emission tomography (fPET) imaging using continuous infusion of [18F]-fluorodeoxyglucose (FDG) is a novel neuroimaging technique to track dynamic glucose utilization in the brain. In comparison to conventional static or dynamic bolus PET, fPET maintains a sustained supply of glucose in the blood plasma which improves sensitivity to measure dynamic glucose changes in the brain, and enables mapping of dynamic brain activity in task-based and resting-state fPET studies. However, there is a trade-off between temporal resolution and spatial noise due to the low concentration of FDG and the limited sensitivity of multi-ring PET scanners. Images from fPET studies suffer from partial volume errors and residual scatter noise that may cause the cerebral metabolic functional maps to be biased. Gaussian smoothing filters used to denoise the fPET images are suboptimal, as they introduce additional partial volume errors. In this work, a post-processing framework based on a magnetic resonance (MR) Bowsher-like prior was used to improve the spatial and temporal signal to noise characteristics of the fPET images. The performance of the MR guided method was compared with conventional denosing methods using both simulated and in vivo task fPET datasets. The results demonstrate that the MR-guided fPET framework denoises the fPET images and improves the partial volume correction, consequently enhancing the sensitivity to identify brain activation, and improving the anatomical accuracy for mapping changes of brain metabolism in response to a visual stimulation task. The framework extends the use of functional PET to investigate the dynamics of brain metabolic responses for faster presentation of brain activation tasks, and for applications in low dose PET imaging.

Magnetic Resonance Iron Imaging in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) results in progressive impairment of upper and lower motor neurons. Increasing evidence from both in vivo and ex vivo studies suggest that iron accumulation in the motor cortex is a neuropathological hallmark in ALS. An in vivo neuroimaging marker of iron dysregulation in ALS would be useful in disease diagnosis and prognosis. Magnetic resonance imaging (MRI), with its unique capability to generate a variety of soft tissue contrasts, provides opportunities to image iron distribution in the human brain with millimeter to sub-millimeter anatomical resolution. Conventionally, MRI T1-weighted, T2-weighted, and T2*-weighted images have been used to investigate iron dysregulation in the brain in vivo. Susceptibility weighted imaging has enhanced contrast for para-magnetic materials that provides superior sensitivity to iron in vivo. Recently, the development of quantitative susceptibility mapping (QSM) has realized the possibility of using quantitative assessments of magnetic susceptibility measures in brain tissues as a surrogate measurement of in vivo brain iron. In this review, we provide an overview of MRI techniques that have been used to investigate iron dysregulation in ALS in vivo. The potential uses, strengths, and limitations of these techniques in clinical trials, disease diagnosis, and prognosis are presented and discussed. We recommend further longitudinal studies with appropriate cohort characterization to validate the efficacy of these techniques. We conclude that quantitative iron assessment using recent advances in MRI including QSM holds great potential to be a sensitive diagnostic and prognostic marker in ALS. The use of multimodal neuroimaging markers in combination with iron imaging may also offer improved sensitivity in ALS diagnosis and prognosis that could make a major contribution to clinical care and treatment trials. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 3.

Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation

Multimodal fusion in neuroimaging combines data from multiple imaging modalities to overcome the fundamental limitations of individual modalities. Neuroimaging fusion can achieve higher temporal and spatial resolution, enhance contrast, correct imaging distortions, and bridge physiological and cognitive information. In this study, we analyzed over 450 references from PubMed, Google Scholar, IEEE, ScienceDirect, Web of Science, and various sources published from 1978 to 2020. We provide a review that encompasses (1) an overview of current challenges in multimodal fusion (2) the current medical applications of fusion for specific neurological diseases, (3) strengths and limitations of available imaging modalities, (4) fundamental fusion rules, (5) fusion quality assessment methods, and (6) the applications of fusion for atlas-based segmentation and quantification. Overall, multimodal fusion shows significant benefits in clinical diagnosis and neuroscience research. Widespread education and further research amongst engineers, researchers and clinicians will benefit the field of multimodal neuroimaging.

Estimation of simultaneous BOLD and dynamic FDG metabolic brain activations using a multimodality concatenated ICA (mcICA) method

Simultaneous magnetic resonance and positron emission tomography provides an opportunity to measure brain haemodynamics and metabolism in a single scan session, and to identify brain activations from multimodal measurements in response to external stimulation. However, there are few analysis methods available for jointly analysing the simultaneously acquired blood-oxygen-level dependant functional MRI (fMRI) and 18-F-fluorodeoxyglucose functional PET (fPET) datasets. In this work, we propose a new multimodality concatenated ICA (mcICA) method to identify joint fMRI-fPET brain activations in response to a visual stimulation task. The mcICA method produces a fused map from the multimodal datasets with equal contributions of information from both modalities, measured by entropy. We validated the method in silico, and applied it to an in vivo visual stimulation experiment. The mcICA method estimated the activated brain regions in the visual cortex modulated by both BOLD and FDG signals. The mcICA provides a fully data-driven analysis approach to analyse cerebral haemodynamic response and glucose uptake signals arising from exogenously induced neuronal activity.

Clinical utility of deep learning motion correction for T1 weighted MPRAGE MR images

PURPOSE

To evaluate the clinical utility of the application of a deep learning motion correction technique on 3D MPRAGE magnetic resonance images acquired in routine clinical practice.

METHODS

An encoder-decoder deep learning network inspired by InceptionResnet was trained on public datasets. The clinical utility of the trained network was evaluated retrospectively on 27 3D MPRAGE T1 weighted motion degraded MR images identified by radiologists during reporting. The assessment of image quality was performed by one board-certified radiologist and one senior radiology trainee for nine neuroanatomical regions of the brain using a five-point visual grading scale.

RESULTS

The deep learning motion correction technique resulted in reduced ghosting, ringing and blurring for all the brain regions investigated. The larger regions of interest such as ventricles improved the least (1.81 to 1.16, p-value: < 0.0001) while the smaller but complex regions such as the hippocampus improved most (3.0 to 1.67, p-value: < 0.0001). The Wilcox rank tests of image quality differences for the nine neuroanatomical regions were all statistically significant (p < 0.001). Overall, 60 % of the neuroanatomical regions were improved, 39 % were unchanged and 1 % were degraded. Out of the unchanged cases, 28 % were already scored at the highest image quality before motion correction. It was found that approximately 13 % of repeated scans could be avoided using the DL motion correction approach.

CONCLUSION

The deep learning motion correction technique improved the overall visual perception of the 3D T1 weighted MPRAGE brain images. This would improve the clinical utility of otherwise motion degraded images and allow visualisation of normal anatomy and even subtle pathology.

Simultaneous BOLD-fMRI and constant infusion FDG-PET data of the resting human brain

Simultaneous [18 F]-fluorodeoxyglucose positron emission tomography and functional magnetic resonance imaging (FDG-PET/fMRI) provides the capability to image two sources of energetic dynamics in the brain - cerebral glucose uptake and the cerebrovascular haemodynamic response. Resting-state fMRI connectivity has been enormously useful for characterising interactions between distributed brain regions in humans. Metabolic connectivity has recently emerged as a complementary measure to investigate brain network dynamics. Functional PET (fPET) is a new approach for measuring FDG uptake with high temporal resolution and has recently shown promise for assessing the dynamics of neural metabolism. Simultaneous fMRI/fPET is a relatively new hybrid imaging modality, with only a few biomedical imaging research facilities able to acquire FDG PET and BOLD fMRI data simultaneously. We present data for n = 27 healthy young adults (18-20 yrs) who underwent a 95-min simultaneous fMRI/fPET scan while resting with their eyes open. This dataset provides significant re-use value to understand the neural dynamics of glucose metabolism and the haemodynamic response, the synchrony, and interaction between these measures, and the development of new single- and multi-modality image preparation and analysis procedures.

MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation

Recent emerging hybrid technology of positron emission tomography/magnetic resonance (PET/MR) imaging has generated a great need for an accurate MR image-based PET attenuation correction. MR image segmentation, as a robust and simple method for PET attenuation correction, has been clinically adopted in commercial PET/MR scanners. The general approach in this method is to segment the MR image into different tissue types, each assigned an attenuation constant as in an X-ray CT image. Machine learning techniques such as clustering, classification and deep networks are extensively used for brain MR image segmentation. However, only limited work has been reported on using deep learning in brain PET attenuation correction. In addition, there is a lack of clinical evaluation of machine learning methods in this application. The aim of this review is to study the use of machine learning methods for MR image segmentation and its application in attenuation correction for PET brain imaging. Furthermore, challenges and future opportunities in MR image-based PET attenuation correction are discussed.

Contribution of PET-MRI in brain diseases in clinical practice

PURPOSE OF REVIEW

Hybrid PET- MRI is a technique that has the ability to improve diagnostic accuracy in many applications, whereas PET and MRI performed separately often fail to provide accurate responses to clinical questions. Here, we review recent studies and current developments in PET-MRI, focusing on clinical applications.

RECENT FINDINGS

The combination of PET and MRI imaging methods aims at increasing the potential of each individual modality. Combined methods of image reconstruction and correction of PET-MRI attenuation are being developed, and a number of applications are being introduced into clinical practice. To date, the value of PET-MRI has been demonstrated for the evaluation of brain tumours in epilepsy and neurodegenerative diseases. Continued advances in data analysis regularly improve the efficiency and the potential application of multimodal biomarkers.

SUMMARY

PET-MRI provides simultaneous of anatomical, functional, biochemical and metabolic information for the personalized characterization and monitoring of neurological diseases. In this review, we show the advantage of the complementarity of different biomarkers obtained using PET-MRI data. We also present the recent advances made in this hybrid imaging modality and its advantages in clinical practice compared with MRI and PET separately.

Advances in clinical MRI technology

Advances in MRI technologies have the potential to detect, characterize, and monitor a wide variety of diseases.

MR-PET head motion correction based on co-registration of multicontrast MR images

Head motion is a major source of image artefacts in neuroimaging studies and can lead to degradation of the quantitative accuracy of reconstructed PET images. Simultaneous magnetic resonance-positron emission tomography (MR-PET) makes it possible to estimate head motion information from high-resolution MR images and then correct motion artefacts in PET images. In this article, we introduce a fully automated PET motion correction method, MR-guided MAF, based on the co-registration of multicontrast MR images. The performance of the MR-guided MAF method was evaluated using MR-PET data acquired from a cohort of ten healthy participants who received a slow infusion of fluorodeoxyglucose ([18-F]FDG). Compared with conventional methods, MR-guided PET image reconstruction can reduce head motion introduced artefacts and improve the image sharpness and quantitative accuracy of PET images acquired using simultaneous MR-PET scanners. The fully automated motion estimation method has been implemented as a publicly available web-service.

Evaluation of Enzyme Substrate Radiotracers as PET/MRS Hybrid Imaging Agents

The development of a positron emission tomography (PET)/magnetic resonance spectroscopy (MRS) hybrid imaging agent allows for functional imaging by both methods with a single imaging agent. Enzyme substrates that are cleaved to form two metabolites present an interesting opportunity, as the unique metabolites generated might each be detected by a different modality. To be successful, such enzyme substrates would require administration of doses that (a) reach the in vivo target tissue at concentrations necessary for MRS imaging, (b) do not show substrate inhibition of tissue uptake or enzymatic activity, and (c) provide PET images that still reflect the action of the enzyme. We report in vitro and in vivo proof-of-concept studies of a carbon-11 small molecule substrate for brain monoamine oxidases that, upon enzyme-mediated cleavage, produces two metabolites, one detectable by PET and the other by MRS.

Accurate hybrid template-based and MR-based attenuation correction using UTE images for simultaneous PET/MR brain imaging applications

Background

Attenuation correction is one of the most crucial correction factors for accurate PET data quantitation in hybrid PET/MR scanners, and computing accurate attenuation coefficient maps from MR brain acquisitions is challenging. Here, we develop a method for accurate bone and air segmentation using MR ultrashort echo time (UTE) images.

Methods

MR UTE images from simultaneous MR and PET imaging of five healthy volunteers was used to generate a whole head, bone and air template image for inclusion into an improved MR derived attenuation correction map, and applied to PET image data for quantitative analysis. Bone, air and soft tissue were segmented based on Gaussian Mixture Models with probabilistic tissue maps as a priori information. We present results for two approaches for bone attenuation coefficient assignments: one using a constant attenuation correction value; and another using an estimated continuous attenuation value based on a calibration fit. Quantitative comparisons were performed to evaluate the accuracy of the reconstructed PET images, with respect to a reference image reconstructed with manually segmented attenuation maps.

Results

The DICE coefficient analysis for the air and bone regions in the images demonstrated improvements compared to the UTE approach, and other state-of-the-art techniques. The most accurate whole brain and regional brain analyses were obtained using constant bone attenuation coefficient values.

Conclusions

A novel attenuation correction method for PET data reconstruction is proposed. Analyses show improvements in the quantitative accuracy of the reconstructed PET images compared to other state-of-the-art AC methods for simultaneous PET/MR scanners. Further evaluation is needed with radiopharmaceuticals other than FDG, and in larger cohorts of participants.

An Introductory Guide to Organizational Neuroscience

The time is ripe for a renewed and interdisciplinary approach to organizational research that incorporates neuroscientific techniques. Like all methods, they have methodological, analytical, and interpretational limitations; however, the potential gains from using these techniques are far more considerable. We have therefore assembled a succinct yet authoritative collection of articles on the topic of neuroscience in organizational research, to serve as a solid introduction to the methods of neuroscience and what they can accomplish. The special topic is organized into two parts. The first includes a set of accessible reviews of the palette of brain imaging, mapping, and stimulation techniques (fMRI, fNIRS, EEG, MEG, and NIBS) as well as examples of the application of neuroscience methods to various disciplines including economics, marketing, finance, organizational behavior, neuroethology, as well an integrative translational critique on a variety of applications. The second is a collection of articles resulting from a competitive call for submissions that cover various neuroscience topics but also address important methodological and philosophical issues. The articles lay out a roadmap for the effective integration of neuroscientific methods into organizational research.

From simultaneous to synergistic MR-PET brain imaging: A review of hybrid MR-PET imaging methodologies

Simultaneous Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) scanning is a recent major development in biomedical imaging. The full integration of the PET detector ring and electronics within the MR system has been a technologically challenging design to develop but provides capacity for simultaneous imaging and the potential for new diagnostic and research capability. This article reviews state-of-the-art MR-PET hardware and software, and discusses future developments focusing on neuroimaging methodologies for MR-PET scanning. We particularly focus on the methodologies that lead to an improved synergy between MRI and PET, including optimal data acquisition, PET attenuation and motion correction, and joint image reconstruction and processing methods based on the underlying complementary and mutual information. We further review the current and potential future applications of simultaneous MR-PET in both systems neuroscience and clinical neuroimaging research. We demonstrate a simultaneous data acquisition protocol to highlight new applications of MR-PET neuroimaging research studies.