Automated radiosynthesis of [11C]CPPC for in-human PET imaging applications.

The macrophage colony-stimulating factor 1 receptor (CSF1R) is almost exclusively expressed in microglia, representing a biomarker target for imaging of microglia availability. [11C]CPPC has specific binding affinity to CSF1R and suitable kinetic properties for in vivo PET imaging of microglia. However, previous studies reported a low radiochemical yield, motivating additional research to optimize [11C]CPPC radiochemistry. In this work, we report an automated radiosynthesis of [11C]CPPC on a Synthra MeIPlus module with improved radiochemical yield. The final [11C]CPPC product was obtained with excellent chemical/radiochemical purities and molecular activity, facilitating high-quality in-human PET imaging applications.

The First Human Application of an F-18-Labeled Tryptophan Analog for PET Imaging of Cancer.

PURPOSE: Preclinical studies showed the tryptophan analog PET radiotracer 1-(2-18F-fluoroethyl)-L-tryptophan (18F-FETrp) to accumulate in various tumors, including gliomas, and being metabolized via the immunosuppressive kynurenine pathway. In this first-in-human study, we tested the use 18F-FETrp-PET in patients with neuroendocrine and brain tumors. PROCEDURES: We applied dynamic brain imaging in patients with gliomas (n = 2) and multi-pass 3D whole-body PET scans in patients with neuroendocrine tumors (n =4). Semiquantitative analysis of organ and tumor tracer uptake was performed using standardized uptake values (SUVs). In addition, organ dosimetry was performed based on extracted time-activity curves and the OLINDA software. RESULTS: Neuroendocrine tumors showed an early peak (10-min post-injection) followed by washout. Both gliomas showed prolonged 18F-FETrp accumulation plateauing around 40 min and showing heterogeneous uptake including non-enhancing tumor regions. Biodistribution showed moderate liver uptake and fast clearance of radioactivity into the urinary bladder; the estimated effective doses were similar to other 18F-labeled radioligands. CONCLUSIONS: The study provides proof-of-principle data for the safety and potential clinical value of 18F-FETrp-PET for molecular imaging of human gliomas.

Automated radiosynthesis of [18F]FMPEP-d2 for cannabinoid receptor PET imaging.

The cannabinoid subtype 1 receptor (CB1R) is highly expressed in the central nervous system and abnormalities in regional CB1R density are associated with neurodegenerative disorders. The PET tracer [18F]FMPEP-d2 is an inverse CB1R agonist which was shown to be suitable for non-invasive PET imaging. In this work, we reported the fully automated radiosynthesis of [18F]FMPEP-d2 on a Synthra RNplus research module. In a total synthesis time of 70 min, [18F]FMPEP-d2 was obtained in 2.2 ± 0.1 GBq (n = 3) with excellent radiochemical and chemical purity. Quality control test showed that [18F]FMPEP-d2 product meets all the release criteria for clinical patient use.

Depth and hierarchies in the predictive brain: From reaction to action.

The human brain is a prediction device, a view widely accepted in neuroscience. Prediction is a rational and efficient response that relies on the brain's ability to create and employ generative models to optimize actions over unpredictable time horizons. We argue that extant predictive frameworks while compelling, have not explicitly accounted for the following: (a) The brain's generative models must incorporate predictive depth (i.e., rely on degrees of abstraction to enable predictions over different time horizons); (b) The brain's implementation scheme to account for varying predictive depth relies on dynamic predictive hierarchies formed using the brain's functional networks. We show that these hierarchies incorporate the ascending processes (driven by reaction), and the descending processes (related to prediction), eventually driving action. Because they are dynamically formed, predictive hierarchies allow the brain to address predictive challenges in virtually any domain. By way of application, we explain how this framework can be applied to heretofore poorly understood processes of human behavioral thermoregulation. Although mammalian thermoregulation has been closely tied to deep brain structures engaged in autonomic control such as the hypothalamus, this narrow conception does not translate well to humans. In addition to profound differences in evolutionary history, the human brain is bestowed with substantially increased functional complexity (that itself emerged from evolutionary differences). We argue that behavioral thermoregulation in humans is possible because, (a) ascending signals shaped by homeostatic sub-networks, interject with (b) descending signals related to prediction (implemented in interoceptive and executive sub-networks) and action (implemented in executive sub-networks). These sub-networks cumulatively form a predictive hierarchy for human thermoregulation, potentiating a range of viable responses to known and unknown thermoregulatory challenges. We suggest that our proposed extensions to the predictive framework provide a set of generalizable principles that can further illuminate the many facets of the predictive brain. This article is categorized under: Neuroscience > Behavior Philosophy > Action Psychology > Prediction.

Fully Automated, Fast Motion Correction of Dynamic Whole-Body and Total-Body PET/CT Imaging Studies.

We introduce the Fast Algorithm for Motion Correction (FALCON) software, which allows correction of both rigid and nonlinear motion artifacts in dynamic whole-body (WB) images, irrespective of the PET/CT system or the tracer. Methods: Motion was corrected using affine alignment followed by a diffeomorphic approach to account for nonrigid deformations. In both steps, images were registered using multiscale image alignment. Moreover, the frames suited to successful motion correction were automatically estimated by calculating the initial normalized cross-correlation metric between the reference frame and the other moving frames. To evaluate motion correction performance, WB dynamic image sequences from 3 different PET/CT systems (Biograph mCT, Biograph Vision 600, and uEXPLORER) using 6 different tracers (18F-FDG, 18F-fluciclovine, 68Ga-PSMA, 68Ga-DOTATATE, 11C-Pittsburgh compound B, and 82Rb) were considered. Motion correction accuracy was assessed using 4 different measures: change in volume mismatch between individual WB image volumes to assess gross body motion, change in displacement of a large organ (liver dome) within the torso due to respiration, change in intensity in small tumor nodules due to motion blur, and constancy of activity concentration levels. Results: Motion correction decreased gross body motion artifacts and reduced volume mismatch across dynamic frames by about 50%. Moreover, large-organ motion correction was assessed on the basis of correction of liver dome motion, which was removed entirely in about 70% of all cases. Motion correction also improved tumor intensity, resulting in an average increase in tumor SUVs by 15%. Large deformations seen in gated cardiac 82Rb images were managed without leading to anomalous distortions or substantial intensity changes in the resulting images. Finally, the constancy of activity concentration levels was reasonably preserved (<2% change) in large organs before and after motion correction. Conclusion: FALCON allows fast and accurate correction of rigid and nonrigid WB motion artifacts while being insensitive to scanner hardware or tracer distribution, making it applicable to a wide range of PET imaging scenarios.

177Lu-DOTATATE Theranostics: Predicting Renal Dosimetry From Pretherapy 68Ga-DOTATATE PET and Clinical Biomarkers.

PURPOSE: Pretreatment predictions of absorbed doses can be especially valuable for patient selection and dosimetry-guided individualization of radiopharmaceutical therapy. Our goal was to build regression models using pretherapy 68Ga-DOTATATE PET uptake data and other baseline clinical factors/biomarkers to predict renal absorbed dose delivered by 177Lu-DOTATATE peptide receptor radionuclide therapy (177Lu-PRRT) for neuroendocrine tumors. We explore the combination of biomarkers and 68Ga PET uptake metrics, hypothesizing that they will improve predictive power over univariable regression. PATIENTS AND METHODS: Pretherapy 68Ga-DOTATATE PET/CTs were analyzed for 25 patients (50 kidneys) who also underwent quantitative 177Lu SPECT/CT imaging at approximately 4, 24, 96, and 168 hours after cycle 1 of 177Lu-PRRT. Kidneys were contoured on the CT of the PET/CT and SPECT/CT using validated deep learning-based tools. Dosimetry was performed by coupling the multi-time point SPECT/CT images with an in-house Monte Carlo code. Pretherapy renal PET SUV metrics, activity concentration per injected activity (Bq/mL/MBq), and other baseline clinical factors/biomarkers were investigated as predictors of the 177Lu SPECT/CT-derived mean absorbed dose per injected activity to the kidneys using univariable and bivariable models. Leave-one-out cross-validation (LOOCV) was used to estimate model performance using root mean squared error and absolute percent error in predicted renal absorbed dose including mean absolute percent error (MAPE) and associated standard deviation (SD). RESULTS: The median therapy-delivered renal dose was 0.5 Gy/GBq (range, 0.2-1.0 Gy/GBq). In LOOCV of univariable models, PET uptake (Bq/mL/MBq) performs best with MAPE of 18.0% (SD = 13.3%), and estimated glomerular filtration rate (eGFR) gives an MAPE of 28.5% (SD = 19.2%). Bivariable regression with both PET uptake and eGFR gives LOOCV MAPE of 17.3% (SD = 11.8%), indicating minimal improvement over univariable models. CONCLUSIONS: Pretherapy 68Ga-DOTATATE PET renal uptake can be used to predict post-177Lu-PRRT SPECT-derived mean absorbed dose to the kidneys with accuracy within 18%, on average. Compared with PET uptake alone, including eGFR in the same model to account for patient-specific kinetics did not improve predictive power. Following further validation of these preliminary findings in an independent cohort, predictions using renal PET uptake can be used in the clinic for patient selection and individualization of treatment before initiating the first cycle of PRRT.

Automated radiosynthesis of 1-(2-[18 F]fluoroethyl)-L-tryptophan ([18 F]FETrp) for positron emission tomography (PET) imaging of cancer in humans.

The radiotracer 1-(2-[18 F]fluoroethyl)-L-tryptophan (L-[18 F]FETrp or [18 F]FETrp) is a substrate of indoleamine 2,3-dioxygenase, the initial and key enzyme of the kynurenine pathway associated with tumoral immune resistance. In preclinical positron emission tomography studies, [18 F]FETrp is highly accumulated in a wide range of primary and metastatic cancers, such as lung cancer, prostate cancer, and gliomas. However, the clinical translation of this radiotracer into the first-in-human trial has not been reported, partially due to its racemization during radiofluorination which renders the purification of the final product challenging. However, efficient purification is essential for human studies in order to assure radiochemical and enantiomeric purity. In this work, we report a fully automated radiosynthesis of [18 F]FETrp on a Synthra RNPlus research module, including a one-pot two steps radiosynthesis, dual independent chiral and reverse-phase semipreparative high-performance liquid chromatography purifications, and solid-phase extraction-assisted formulation. The presented approach has led to its Investigational New Drug application and approval that allows the testing of this tracer in humans.

Feasibility of dose reduction for [18F]FDG-PET/MR imaging of patients with non-lesional epilepsy.

The aim of the study was to evaluate the effect of reduced injected [18F]FDG activity levels on the quantitative and diagnostic accuracy of PET images of patients with non-lesional epilepsy (NLE).Nine healthy volunteers and nine patients with NLE underwent 60-min dynamic list-mode (LM) scans on a fully-integrated PET/MRI system. Injected FDG activity levels were reduced virtually by randomly removing counts from the last 10-min of the LM data, so as to simulate the following activity levels: 50 %, 35 %, 20 %, and 10 % of the original activity. Four image reconstructions were evaluated: standard OSEM, OSEM with resolution recovery (PSF), the A-MAP, and the Asymmetrical Bowsher (AsymBowsher) algorithms. For the A-MAP algorithms, two weights were selected (low and high). Image contrast and noise levels were evaluated for all subjects while the lesion-to-background ratio (L/B) was only evaluated for patients. Patient images were scored by a Nuclear Medicine physician on a 5-point scale to assess clinical impression associated with the various reconstruction algorithms.The image contrast and L/B ratio characterizing all four reconstruction algorithms were similar, except for reconstructions based on only 10 % of total counts. Based on clinical impression, images with diagnostic quality can be achieved with as low as 35 % of the standard injected activity. The selection of algorithms utilizing an anatomical prior did not provide a significant advantage for clinical readings, despite a small improvement in L/B (< 5 %) using the A-MAP and AsymBowsher reconstruction algorithms.In patients with NLE who are undergoing [18F]FDG-PET/MR imaging, the injected [18F]FDG activity can be reduced to 35 % of the original dose levels without compromising.

Fully Automated, Semantic Segmentation of Whole-Body 18F-FDG PET/CT Images Based on Data-Centric Artificial Intelligence.

We introduce multiple-organ objective segmentation (MOOSE) software that generates subject-specific, multiorgan segmentation using data-centric artificial intelligence principles to facilitate high-throughput systemic investigations of the human body via whole-body PET imaging. Methods: Image data from 2 PET/CT systems were used in training MOOSE. For noncerebral structures, 50 whole-body CT images were used, 30 of which were acquired from healthy controls (14 men and 16 women), and 20 datasets were acquired from oncology patients (14 men and 6 women). Noncerebral tissues consisted of 13 abdominal organs, 20 bone segments, subcutaneous fat, visceral fat, psoas muscle, and skeletal muscle. An expert panel manually segmented all noncerebral structures except for subcutaneous fat, visceral fat, and skeletal muscle, which were semiautomatically segmented using thresholding. A majority-voting algorithm was used to generate a reference-standard segmentation. From the 50 CT datasets, 40 were used for training and 10 for testing. For cerebral structures, 34 18F-FDG PET/MRI brain image volumes were used from 10 healthy controls (5 men and 5 women imaged twice) and 14 nonlesional epilepsy patients (7 men and 7 women). Only 18F-FDG PET images were considered for training: 24 and 10 of 34 volumes were used for training and testing, respectively. The Dice score coefficient (DSC) was used as the primary metric, and the average symmetric surface distance as a secondary metric, to evaluate the automated segmentation performance. Results: An excellent overlap between the reference labels and MOOSE-derived organ segmentations was observed: 92% of noncerebral tissues showed DSCs of more than 0.90, whereas a few organs exhibited lower DSCs (e.g., adrenal glands [0.72], pancreas [0.85], and bladder [0.86]). The median DSCs of brain subregions derived from PET images were lower. Only 29% of the brain segments had a median DSC of more than 0.90, whereas segmentation of 60% of regions yielded a median DSC of 0.80-0.89. The results of the average symmetric surface distance analysis demonstrated that the average distance between the reference standard and the automatically segmented tissue surfaces (organs, bones, and brain regions) lies within the size of image voxels (2 mm). Conclusion: The proposed segmentation pipeline allows automatic segmentation of 120 unique tissues from whole-body 18F-FDG PET/CT images with high accuracy.

Effective connectivity of brain networks controlling human thermoregulation.

Homeostatic centers in the mammalian brainstem are critical in responding to thermal challenges. These centers play a prominent role in human thermoregulation, but humans also respond to thermal challenges through behavior modification. Behavioral modifications are presumably sub served by interactions between the brainstem and interoceptive, cognitive and affective elements in human brain networks. Prior evidence suggests that interoceptive regions such as the insula, and cognitive/affective regions such as the orbitofrontal cortex and anterior cingulate cortex are crucial. Here we used dynamic causal modeling (DCM) to discover likely generative network architectures and estimate changes in the effective connectivity between nodes in a hierarchically organized thermoregulatory network (homeostatic-interoceptive-cognitive/affective). fMRI data were acquired while participants (N = 20) were subjected to a controlled whole body thermal challenge that alternatingly evoked sympathetic and parasympathetic responses. Using a competitive modeling framework (ten competing modeling architectures), we demonstrated that sympathetic responses (evoked by whole-body cooling) resulted in more complex network interactions along two ascending pathways: (i) homeostatic interoceptive and (ii) homeostatic cognitive/affective. Analyses of estimated connectivity coefficients demonstrated that sympathetic responses evoked greater network connectivity in key pathways compared to parasympathetic responses. These results reveal putative mechanisms by which human thermoregulatory networks evince a high degree of contextual sensitivity to thermoregulatory challenges. The patterns of the discovered interactions also reveal how information propagation from homeostatic regions to both interoceptive and cognitive/affective regions sub serves the behavioral repertoire that is an important aspect of thermoregulatory defense in humans.

Conditional Generative Adversarial Networks Aided Motion Correction of Dynamic 18F-FDG PET Brain Studies.

This work set out to develop a motion-correction approach aided by conditional generative adversarial network (cGAN) methodology that allows reliable, data-driven determination of involuntary subject motion during dynamic 18F-FDG brain studies. Methods: Ten healthy volunteers (5 men/5 women; mean age ± SD, 27 ± 7 y; weight, 70 ± 10 kg) underwent a test-retest 18F-FDG PET/MRI examination of the brain (n = 20). The imaging protocol consisted of a 60-min PET list-mode acquisition contemporaneously acquired with MRI, including MR navigators and a 3-dimensional time-of-flight MR angiography sequence. Arterial blood samples were collected as a reference standard representing the arterial input function (AIF). Training of the cGAN was performed using 70% of the total datasets (n = 16, randomly chosen), which was corrected for motion using MR navigators. The resulting cGAN mappings (between individual frames and the reference frame [55-60 min after injection]) were then applied to the test dataset (remaining 30%, n = 6), producing artificially generated low-noise images from early high-noise PET frames. These low-noise images were then coregistered to the reference frame, yielding 3-dimensional motion vectors. Performance of cGAN-aided motion correction was assessed by comparing the image-derived input function (IDIF) extracted from a cGAN-aided motion-corrected dynamic sequence with the AIF based on the areas under the curves (AUCs). Moreover, clinical relevance was assessed through direct comparison of the average cerebral metabolic rates of glucose (CMRGlc) values in gray matter calculated using the AIF and the IDIF. Results: The absolute percentage difference between AUCs derived using the motion-corrected IDIF and the AIF was (1.2% + 0.9%). The gray matter CMRGlc values determined using these 2 input functions differed by less than 5% (2.4% + 1.7%). Conclusion: A fully automated data-driven motion-compensation approach was established and tested for 18F-FDG PET brain imaging. cGAN-aided motion correction enables the translation of noninvasive clinical absolute quantification from PET/MR to PET/CT by allowing the accurate determination of motion vectors from the PET data itself.

Potentials and caveats of AI in hybrid imaging.

State-of-the-art patient management frequently mandates the investigation of both anatomy and physiology of the patients. Hybrid imaging modalities such as the PET/MRI, PET/CT and SPECT/CT have the ability to provide both structural and functional information of the investigated tissues in a single examination. With the introduction of such advanced hardware fusion, new problems arise such as the exceedingly large amount of multi-modality data that requires novel approaches of how to extract a maximum of clinical information from large sets of multi-dimensional imaging data. Artificial intelligence (AI) has emerged as one of the leading technologies that has shown promise in facilitating highly integrative analysis of multi-parametric data. Specifically, the usefulness of AI algorithms in the medical imaging field has been heavily investigated in the realms of (1) image acquisition and reconstruction, (2) post-processing and (3) data mining and modelling. Here, we aim to provide an overview of the challenges encountered in hybrid imaging and discuss how AI algorithms can facilitate potential solutions. In addition, we highlight the pitfalls and challenges in using advanced AI algorithms in the context of hybrid imaging and provide suggestions for building robust AI solutions that enable reproducible and transparent research.

Directional Interactions Between Constituents of the Human Large-Scale Thermoregulatory Network.

In humans, dynamic thermoregulation is (presumably) underpinned by a complex hierarchy of functional interactions between constituents of the human thermoregulatory large-scale network. However, these interactions have not been quantified from in vivo fMRI signals acquired during the experimental delivery of whole-body thermal stress. Here, we used directed functional connectivity (dFC) analysis (based on multi-variate autoregressive models) to recover directed interactions within a single thermoregulatory network during an experimental paradigm that involved controlled exposure to whole-body cooling and warming. MRI studies were performed in 30 young adults (15 M/15F, mean age 25.1 ± 3.4 years). Gradient echo EPI fMRI data were acquired on a 3 T Siemens Verio system. The thermoregulatory challenge was applied using a specialized whole-body garment covering the entire body. Tubes lining the innards of the suit were infused with cold (2-4 °C) or neutral (31-34 °C) water to induce whole-body Cooling or Warming while fMRI data were contemporaneously acquired. dFC was estimated within and between the hierarchically organized homeostatic (midbrain, pons), interoceptive (insula) and executive (anterior cingulate, orbitofrontal and superior parietal cortices) sub-networks using multi-variate autoregressive models applied to the fMRI time series data. Estimates of directed interactions (akin to Granger Causality) between nodes were analyzed to recover ascending (homeostatic sub-network "upward"), descending (executive sub-network "downward"), and lateral (within sub-network) directional ("causal") effects. Both Cooling and Warming induced complex hierarchical interactions in the thermoregulatory large-scale network. Cooling induced ascending interactions from the homeostatic (midbrain) to both the executive (OFC) and interoceptive (insula) sub-networks, particularly to the superior parietal, ACC and the anterior and posterior insulae. In comparison, descending interactions were induced from the posterior insula. Warming induced ascending interactions from the homeostatic sub-network to notably the OFC (executive) and the insulae (interoceptive). Descending interactions were induced from the ACC and the OFC. Sparser effects appear from the executive to the interoceptive sub-network during warming. Our study demonstrates a hierarchical organization of thermoregulatory function between homeostatic, interoceptive and executive sub-networks. The observed information flow between/within these is consistent with a reentrant property of the hierarchical regulatory structure, characterized by the ongoing bi-directional exchange of signals along reciprocal axonal fibers linking the various nodes.

Fully Integrated PET/MR Imaging for the Assessment of the Relationship Between Functional Connectivity and Glucose Metabolic Rate.

In the past, determination of absolute values of cerebral metabolic rate of glucose (CMRGlc) in clinical routine was rarely carried out due to the invasive nature of arterial sampling. With the advent of combined PET/MR imaging technology, CMRGlc values can be obtained non-invasively, thereby providing the opportunity to take advantage of fully quantitative data in clinical routine. However, CMRGlc values display high physiological variability, presumably due to fluctuations in the intrinsic activity of the brain at rest. To reduce CMRGlc variability associated with these fluctuations, the objective of this study was to determine whether functional connectivity measures derived from resting-state fMRI (rs-fMRI) could be used to correct for these fluctuations in intrinsic brain activity. METHODS: We studied 10 healthy volunteers who underwent a test-retest dynamic [18F]FDG-PET study using a fully integrated PET/MR system (Siemens Biograph mMR). To validate the non-invasive derivation of an image-derived input function based on combined analysis of PET and MR data, arterial blood samples were obtained. Using the arterial input function (AIF), parametric images representing CMRGlc were determined using the Patlak graphical approach. Both directed functional connectivity (dFC) and undirected functional connectivity (uFC) were determined between nodes in six major networks (Default mode network, Salience, L/R Executive, Attention, and Sensory-motor network) using either a bivariate-correlation (R coefficient) or a Multi-Variate AutoRegressive (MVAR) model. In addition, the performance of a regional connectivity measure, the fractional amplitude of low frequency fluctuations (fALFF), was also investigated. RESULTS: The average intrasubject variability for CMRGlc values between test and retest was determined as (14 ±8%) with an average inter-subject variability of 25% at test and 15% at retest. The average CMRGlc value (umol/100 g/min) across all networks was 39 ±10 at test and increased slightly to 43 ±6 at retest. The R, MVAR and fALFF coefficients showed relatively large test-retest variability in comparison to the inter-subjects variability, resulting in poor reliability (intraclass correlation in the range of 0.11-0.65). More importantly, no significant relationship was found between the R coefficients (for uFC), MVAR coefficients (for dFC) or fALFF and corresponding CMRGlc values for any of the six major networks. DISCUSSION: Measurement of functional connectivity within established brain networks did not provide a means to decrease the inter- or intrasubject variability of CMRGlc values. As such, our results indicate that connectivity measured derived from rs-fMRI acquired contemporaneously with PET imaging are not suited for correction of CMRGlc variability associated with intrinsic fluctuations of resting-state brain activity. Thus, given the observed substantial inter- and intrasubject variability of CMRGlc values, the relevance of absolute quantification for clinical routine is presently uncertain.

Utility of Absolute Quantification in Non-lesional Extratemporal Lobe Epilepsy Using FDG PET/MR Imaging.

The purpose of this study was to establish a non-invasive clinical PET/MR protocol using [18F]-labeled deoxyglucose (FDG) that provides physicians with regional metabolic rate of glucose (MRGlc) values and to clarify the contribution of absolute quantification to clinical management of patients with non-lesional extratemporal lobe epilepsy (ETLE). The study included a group of 15 patients with non-lesional ETLE who underwent a dynamic FDG PET study using a fully-integrated PET/MRI system (Siemens Biograph). FDG tracer uptake images were converted to MRGlc (µmol/100 g/min) maps using an image derived input function that was extracted based on the combined analysis of PET and MRI data. In addition, the same protocol was applied to a group of healthy controls, yielding a normative database. Abnormality maps for ETLE patients were created with respect to the normative database, defining significant hypo- or hyper-metabolic regions that exceeded ±2 SD of normal regional mean MRGlc values. Abnormality maps derived from MRGlc images of ETLE patients contributed to the localization of hypo-metabolic areas against visual readings in 53% and increased the confidence in the original clinical readings in 33% of all cases. Moreover, quantification allowed identification of hyper-metabolic areas that are associated with frequently spiking cortex, rarely acknowledged in clinical readings. Overall, besides providing some confirmatory information to visual readings, quantitative PET imaging demonstrated only a moderate impact on clinical management of patients with complex pathology that leads to epileptic seizures, failing to provide new decisive information that would have changed classification of patients from being rejected to being considered for surgical intervention.

Promise of Fully Integrated PET/MRI: Noninvasive Clinical Quantification of Cerebral Glucose Metabolism.

We describe a fully automated processing pipeline to support the noninvasive absolute quantification of the cerebral metabolic rate for glucose (CMRGlc) in a clinical setting. This pipeline takes advantage of "anatometabolic" information associated with fully integrated PET/MRI. Methods: Ten healthy volunteers (5 men and /5 women; 27 ± 7 y old; 70 ± 10 kg) underwent a test-retest 18F-FDG PET/MRI examination of the brain. The imaging protocol consisted of a 60-min PET list-mode acquisition with parallel MRI acquisitions, including 3-dimensional time-of-flight MR angiography, MRI navigators, and a T1-weighted MRI scan. State-of-the-art MRI-based attenuation correction was derived from T1-weighted MRI (pseudo-CT [pCT]). For validation purposes, a low-dose CT scan was also performed. Arterial blood samples were collected as the reference standard (arterial input function [AIF]). The developed pipeline allows the derivation of an image-derived input function (IDIF), which is subsequently used to create CMRGlc maps by means of a Patlak analysis. The pipeline also includes motion correction using the MRI navigator sequence as well as a novel partial-volume correction that accounts for background heterogeneity. Finally, CMRGlc maps are used to generate a normative database to facilitate the detection of metabolic abnormalities in future patient scans. To assess the performance of the developed pipeline, IDIFs extracted by both CT-based attenuation correction (CT-IDIF) and MRI-based attenuation correction (pCT-IDIF) were compared with the reference standard (AIF) using the absolute percentage difference between the areas under the curves as well as the absolute percentage difference in regional CMRGlc values. Results: The absolute percentage differences between the areas under the curves for CT-IDIF and pCT-IDIF were determined to be 1.4% ± 1.0% and 3.4% ± 2.6%, respectively. The absolute percentage difference in regional CMRGlc values based on CT-IDIF and pCT-IDIF differed by less than 6% from the reference values obtained from the AIF. Conclusion: By taking advantage of the capabilities of fully integrated PET/MRI, we developed a fully automated computational pipeline that allows the noninvasive determination of regional CMRGlc values in a clinical setting. This methodology might facilitate the proliferation of fully quantitative imaging into the clinical arena and, as a result, might contribute to improved diagnostic efficacy.

Fluorine-18-Labeled PET Radiotracers for Imaging Tryptophan Uptake and Metabolism: a Systematic Review.

Due to its metabolism via the serotonin and kynurenine pathways, tryptophan plays a key role in multiple disease processes including cancer. Imaging tryptophan uptake and metabolism in vivo can be achieved with tryptophan derivative positron emission tomography (PET) radiotracers. While human studies with such tracers have been confined to C-11-labeled compounds, preclinical development of F-18-labeled tryptophan-based radiotracers has surged in recent years. We performed a systematic review of studies reporting on such F-18-labeled tryptophan tracers to summarize and compare their biological characteristics and their potential for tumor imaging, with a particular focus on key enzymes of the kynurenine pathway (indoleamine 2,3-dioxygenase [IDO] and tryptophan 2,3-dioxygenase [TDO]), which play an important role in tumoral immune resistance. From a PubMed search, English language articles including data on the preparation and radiochemical and/or biological characteristics of F-18-labeled tryptophan derivative radiotracers were reviewed. A total of 19 original papers included data on 15 unique radiotracers, the majority of which were synthesized with an adequate radiochemical yield. Automated synthesis was reported for 1-(2-[18F]fluoroethyl)-L-tryptophan, the most extensively evaluated tracer thus far. Biodistribution studies showed high uptake in the pancreas, while the L-type amino acid transporter was the dominant transport mechanism for most of the reviewed tracers. Tracers tested for tumor uptake showed accumulation in tumor cell lines in vitro and in xenografts in vivo, often with favorable tumor-to-background uptake ratios in comparison with clinically used F-18-labeled radiotracers. Five tracers showed promise for imaging IDO activity, including 1-(2-[18F]fluoroethyl)-L-tryptophan and a F-18-labeled analog of alpha-[11C]methyl-L-tryptophan tested clinically in previous studies. Two radiotracers were metabolized by TDO but showed defluorination in vivo. In summary, most F-18-labeled tryptophan derivative PET tracers share common transport mechanisms and biodistribution characteristics. Several reported tracers could be candidates for further testing and validation toward PET imaging applications in a variety of human diseases.

Hierarchical control systems for the regulation of physiological homeostasis and affect: Can their interactions modulate mood and anhedonia?

Predominant concepts assert that conscious willful processes do not assert a significant influence on autonomic functions associated with physiological homeostasis (e.g., thermal regulation). The singular purpose of this review is to promote a reappraisal of concepts regarding the circumscribed role of hierarchical control systems. To effect this reappraisal, we assess the interaction between top-down and bottom-up regulatory mechanisms, specifically by highlighting the intersection between the "physiological" (specifically thermoregulatory pathways) and the "psychological" (specifically mood/anhedonia related processes). This reappraisal suggests that the physiological and psychological processes can interact in unanticipated ways, and is grounded in multiple lines of recent experimental evidence. For example, behavioral techniques that through a combination of hormesis (forced breathing, cold exposure) and meditation appear to exert unusual effects on homeostatic function (cold tolerance) and suppression of aberrant auto-immune responses. The molecular correlates of these effects (the putative release of endogenous cannabinoids and endorphins) may exert salutary effects on mood/anhedonia, even more significant than those exerted by cognitive behavioral techniques or meditation alone. By focusing on this interaction, we present a putative mechanistic model linking physiology with psychology, with particular implications for disturbances of mood/anhedonia. We suggest that volitional changes in breathing patterns can activate primary control centers for descending pain/cold stimuli in periaqueductal gray, initiating a stress-induced analgesic response mediated by endocannabinoid/endorphin release. The analgesic effects, and the feelings of euphoria generated by endocannbinoid release are prolonged via a top-down "outcome expectancy" control mechanism regulated by cortical areas. By focusing on modification strategies that principally target homeostatic function (but may also exert ancillary effects on mood), we articulate a novel framework for how hierarchical control systems for the regulation of physiological homeostasis and affect interact. This interaction may allow practitioners of focused modification strategies to assert increased control over key components of the affective system, allowing for viable treatment approaches for patients with disturbances of mood/anhedonia.

Towards quantitative [18F]FDG-PET/MRI of the brain: Automated MR-driven calculation of an image-derived input function for the non-invasive determination of cerebral glucose metabolic rates.

Absolute quantification of PET brain imaging requires the measurement of an arterial input function (AIF), typically obtained invasively via an arterial cannulation. We present an approach to automatically calculate an image-derived input function (IDIF) and cerebral metabolic rates of glucose (CMRGlc) from the [18F]FDG PET data using an integrated PET/MRI system. Ten healthy controls underwent test-retest dynamic [18F]FDG-PET/MRI examinations. The imaging protocol consisted of a 60-min PET list-mode acquisition together with a time-of-flight MR angiography scan for segmenting the carotid arteries and intermittent MR navigators to monitor subject movement. AIFs were collected as the reference standard. Attenuation correction was performed using a separate low-dose CT scan. Assessment of the percentage difference between area-under-the-curve of IDIF and AIF yielded values within ±5%. Similar test-retest variability was seen between AIFs (9 ± 8) % and the IDIFs (9 ± 7) %. Absolute percentage difference between CMRGlc values obtained from AIF and IDIF across all examinations and selected brain regions was 3.2% (interquartile range: (2.4-4.3) %, maximum < 10%). High test-retest intravariability was observed between CMRGlc values obtained from AIF (14%) and IDIF (17%). The proposed approach provides an IDIF, which can be effectively used in lieu of AIF.

Molecular Imaging of Sirtuin1 Expression-Activity in Rat Brain Using Positron-Emission Tomography-Magnetic-Resonance Imaging with [18F]-2-Fluorobenzoylaminohexanoicanilide.

Sirtuin 1 (SIRT1) is a class III histone deacetylase that plays significant roles in the regulation of lifespan, metabolism, memory, and circadian rhythms and in the mechanisms of many diseases. However, methods of monitoring the pharmacodynamics of SIRT1-targeted drugs are limited to blood sampling because of the invasive nature of biopsies. For the noninvasive monitoring of the spatial and temporal dynamics of SIRT1 expression-activity in vivo by PET-CT-MRI, we developed a novel substrate-type radiotracer, [18F]-2-fluorobenzoylaminohexanoicanilide (2-[18F]BzAHA). PET-CT-MRI studies in rats demonstrated increased accumulation of 2-[18F]BzAHA-derived radioactivity in the hypothalamus, hippocampus, nucleus accumbens, and locus coeruleus, consistent with autoradiographic and immunofluorescent (IMF) analyses of brain-tissue sections. Pretreatment with the SIRT1 specific inhibitor, EX-527 (5 mg/kg, ip), resulted in about a 20% reduction of 2-[18F]BzAHA-derived-radioactivity accumulation in these structures. In vivo imaging of SIRT1 expression-activity should facilitate studies that improve the understanding of SIRT1-mediated regulation in the brain and aid in the development and clinical translation of SIRT1-targeted therapies.