Radiosynthesis automation, non-human primate biodistribution and dosimetry of K+ channel tracer [11C]3MeO4AP.

BACKGROUND: 4-Aminopyridine (4AP) is a medication for the symptomatic treatment of multiple sclerosis. Several 4AP-based PET tracers have been developed for imaging demyelination. In preclinical studies, [11C]3MeO4AP has shown promise due to its high brain permeability, high metabolic stability, high plasma availability, and high in vivo binding affinity. To prepare for the translation to human studies, we developed a cGMP-compatible automated radiosynthesis protocol and evaluated the whole-body biodistribution and radiation dosimetry of [11C]3MeO4AP in non-human primates (NHPs). METHODS: Automated radiosynthesis was carried out using a GE TRACERlab FX-C Pro synthesis module. One male and one female adult rhesus macaques were used in the study. A high-resolution CT from cranial vertex to knee was acquired. PET data were collected using a dynamic acquisition protocol with four bed positions and 13 passes over a total scan time of ~ 150 min. Based on the CT and PET images, volumes of interest (VOIs) were manually drawn for selected organs. Non-decay corrected time-activity curves (TACs) were extracted for each VOI. Radiation dosimetry and effective dose were calculated from the integrated TACs using OLINDA software. RESULTS: Fully automated radiosynthesis of [11C]3MeO4AP was achieved with 7.3 ± 1.2% (n = 4) of non-decay corrected radiochemical yield within 38 min of synthesis and purification time. [11C]3MeO4AP distributed quickly throughout the body and into the brain. The organs with highest dose were the kidneys. The average effective dose of [11C]3MeO4AP was 4.0 ± 0.6 µSv/MBq. No significant changes in vital signs were observed during the scan. CONCLUSION: A cGMP-compatible automated radiosynthesis of [11C]3MeO4AP was developed. The whole-body biodistribution and radiation dosimetry of [11C]3MeO4AP was successfully evaluated in NHPs. [11C]3MeO4AP shows lower average effective dose than [18F]3F4AP and similar average effective dose as other carbon-11 tracers.

Cross noise level PET denoising with continuous adversarial domain generalization.

Objective.Performing positron emission tomography (PET) denoising within the image space proves effective in reducing the variance in PET images. In recent years, deep learning has demonstrated superior denoising performance, but models trained on a specific noise level typically fail to generalize well on different noise levels, due to inherent distribution shifts between inputs. The distribution shift usually results in bias in the denoised images. Our goal is to tackle such a problem using a domain generalization technique.Approach.We propose to utilize the domain generalization technique with a novel feature space continuous discriminator (CD) for adversarial training, using the fraction of events as a continuous domain label. The core idea is to enforce the extraction of noise-level invariant features. Thus minimizing the distribution divergence of latent feature representation for different continuous noise levels, and making the model general for arbitrary noise levels. We created three sets of 10%, 13%-22% (uniformly randomly selected), or 25% fractions of events from 9718F-MK6240 tau PET studies of 60 subjects. For each set, we generated 20 noise realizations. Training, validation, and testing were implemented using 1400, 120, and 420 pairs of 3D image volumes from the same or different sets. We used 3D UNet as the baseline and implemented CD to the continuous noise level training data of 13%-22% set.Main results.The proposed CD improves the denoising performance of our model trained in a 13%-22% fraction set for testing in both 10% and 25% fraction sets, measured by bias and standard deviation using full-count images as references. In addition, our CD method can improve the SSIM and PSNR consistently for Alzheimer-related regions and the whole brain.Significance.To our knowledge, this is the first attempt to alleviate the performance degradation in cross-noise level denoising from the perspective of domain generalization. Our study is also a pioneer work of continuous domain generalization to utilize continuously changing source domains.

PET imaging of M4 muscarinic acetylcholine receptors in rhesus macaques using [11C]MK-6884: Quantification with kinetic modeling and receptor occupancy by CVL-231 (emraclidine), a novel positive allosteric modulator.

Stimulation of the M4 muscarinic acetylcholine receptor reduces striatal hyperdopaminergia, suggesting its potential as a therapeutic target for schizophrenia. Emraclidine (CVL-231) is a novel, highly selective, positive allosteric modulator (PAM) of M4 muscarinic acetylcholine receptors i.e. acts as a modulator that increases the response of these receptors. First, we aimed to further characterize the positron emission tomography (PET) imaging and quantification performance of a recently developed M4 PAM radiotracer, [11C]MK-6884, in non-human primates (NHPs). Second, we applied these results to determine the receptor occupancy of CVL-231 as a function of dose. Using paired baseline-blocking PET scans, we quantified total volume of distribution, binding potential, and receptor occupancy. Both blood-based and reference region-based methods quantified M4 receptor levels across brain regions. The 2-tissue 4-parameter kinetic model best fitted regional [11C]MK-6884-time activity curves. Only the caudate nucleus and putamen displayed statistically significant [11C]MK-6884 uptake and dose-dependent blocking by CVL-231. For binding potential and receptor occupancy quantification, the simplified reference tissue model using the grey cerebellum as a reference region was employed. CVL-231 demonstrated dose-dependent M4 receptor occupancy in the striatum of the NHP brain and shows promise for further development in clinical trials.

Acute Stress Increases Striatal Connectivity With Cortical Regions Enriched for µ and κ Opioid Receptors.

BACKGROUND: Understanding the neurobiological effects of stress is critical for addressing the etiology of major depressive disorder (MDD). Using a dimensional approach involving individuals with differing degree of MDD risk, we investigated 1) the effects of acute stress on cortico-cortical and subcortical-cortical functional connectivity (FC) and 2) how such effects are related to gene expression and receptor maps. METHODS: Across 115 participants (37 control, 39 remitted MDD, 39 current MDD), we evaluated the effects of stress on FC during the Montreal Imaging Stress Task. Using partial least squares regression, we investigated genes whose expression in the Allen Human Brain Atlas was associated with anatomical patterns of stress-related FC change. Finally, we correlated stress-related FC change maps with opioid and GABAA (gamma-aminobutyric acid A) receptor distribution maps derived from positron emission tomography. RESULTS: Results revealed robust effects of stress on global cortical connectivity, with increased global FC in frontoparietal and attentional networks and decreased global FC in the medial default mode network. Moreover, robust increases emerged in FC of the caudate, putamen, and amygdala with regions from the ventral attention/salience network, frontoparietal network, and motor networks. Such regions showed preferential expression of genes involved in cell-to-cell signaling (OPRM1, OPRK1, SST, GABRA3, GABRA5), similar to previous genetic MDD studies. CONCLUSIONS: Acute stress altered global cortical connectivity and increased striatal connectivity with cortical regions that express genes that have previously been associated with imaging abnormalities in MDD and are rich in µ and κ opioid receptors. These findings point to overlapping circuitry underlying stress response, reward, and MDD.

The role of 18F-FDG PET in minimizing variability in gross tumor volume delineation of soft tissue sarcomas.

BACKGROUND: Accurate gross tumor volume (GTV) delineation is a critical step in radiation therapy treatment planning. However, it is reader dependent and thus susceptible to intra- and inter-reader variability. GTV delineation of soft tissue sarcoma (STS) often relies on CT and MR images. PURPOSE: This study investigates the potential role of 18F-FDG PET in reducing intra- and inter-reader variability thereby improving reproducibility of GTV delineation in STS, without incurring additional costs or radiation exposure. MATERIALS AND METHODS: Three readers performed independent GTV delineation of 61 patients with STS using first CT and MR followed by CT, MR, and 18F-FDG PET images. Each reader performed a total of six delineation trials, three trials per imaging modality group. Dice Similarity Coefficient (DSC) score and Hausdorff distance (HD) were used to assess both intra- and inter-reader variability using generated simultaneous truth and performance level estimation (STAPLE) GTVs as ground truth. Statistical analysis was performed using a Wilcoxon signed-ranked test. RESULTS: There was a statistically significant decrease in both intra- and inter-reader variability in GTV delineation using CT, MR 18F-FDG PET images vs. CT and MR images. This was translated by an increase in the DSC score and a decrease in the HD for GTVs drawn from CT, MR and 18F-FDG PET images vs. GTVs drawn from CT and MR for all readers and across all three trials. CONCLUSION: Incorporation of 18F-FDG PET into CT and MR images decreased intra- and inter-reader variability and subsequently increased reproducibility of GTV delineation in STS.

Head-to-head comparison of [18F]-Flortaucipir, [18F]-MK-6240 and [18F]-PI-2620 postmortem binding across the spectrum of neurodegenerative diseases.

We and others have shown that [18F]-Flortaucipir, the most validated tau PET tracer thus far, binds with strong affinity to tau aggregates in Alzheimer's (AD) but has relatively low affinity for tau aggregates in non-AD tauopathies and exhibits off-target binding to neuromelanin- and melanin-containing cells, and to hemorrhages. Several second-generation tau tracers have been subsequently developed. [18F]-MK-6240 and [18F]-PI-2620 are the two that have garnered most attention. Our recent data indicated that the binding pattern of [18F]-MK-6240 closely parallels that of [18F]-Flortaucipir. The present study aimed at the direct comparison of the autoradiographic binding properties and off-target profile of [18F]-Flortaucipir, [18F]-MK-6240 and [18F]-PI-2620 in human tissue specimens, and their potential binding to monoamine oxidases (MAO). Phosphor-screen and high resolution autoradiographic patterns of the three tracers were studied in the same postmortem tissue material from AD and non-AD tauopathies, cerebral amyloid angiopathy, synucleopathies, transactive response DNA-binding protein 43 (TDP-43)-frontotemporal lobe degeneration and controls. Our results show that the three tracers show nearly identical autoradiographic binding profiles. They all strongly bind to neurofibrillary tangles in AD but do not seem to bind to a significant extent to tau aggregates in non-AD tauopathies pointing to their limited utility for the in vivo detection of non-AD tau lesions. None of them binds to lesions containing ß-amyloid, α-synuclein or TDP-43 but they all show strong off-target binding to neuromelanin and melanin-containing cells, as well as weaker binding to areas of hemorrhage. The autoradiographic binding signals of the three tracers are only weakly displaced by competing concentrations of selective MAO-B inhibitor deprenyl but not by MAO-A inhibitor clorgyline suggesting that MAO enzymes do not appear to be a significant binding target of any of them. These findings provide relevant insights for the correct interpretation of the in vivo behavior of these three tau PET tracers.

Association of Sleep Duration and Change Over Time With Imaging Biomarkers of Cerebrovascular, Amyloid, Tau, and Neurodegenerative Pathology.

BACKGROUND AND OBJECTIVES: Both short and long sleep duration were previously associated with incident dementia, but underlying mechanisms remain unclear. We evaluated how self-reported sleep duration and its change over time associate with (A)myloid, (T)au, (N)eurodegeneration, and (V)ascular neuroimaging markers of Alzheimer disease. METHODS: Two Framingham Heart Study overlapping samples were studied: participants who underwent 11C-Pittsburg Compound B amyloid and 18F-flortaucipir tau PET imaging and participants who underwent an MRI. MRI metrics estimated neurodegeneration (total brain volume) and cerebrovascular injuries (white matter hyperintensities [WMHs] volume, covert brain infarcts, free-water [FW] fraction). Self-reported sleep duration was assessed and split into categories both at the time of neuroimaging testing and approximately 13 years before: short ≤6 hours. average 7-8 hours, and long ≥9 hours. Logistic and linear regression models were used to examine sleep duration and neuroimaging metrics. RESULTS: The tested cohort was composed of 271 participants (age 53.6 ± 8.0 years; 51% male) in the PET imaging sample and 2,165 participants (age 61.3 ± 11.1 years; 45% male) in the MRI sample. No fully adjusted association was observed between cross-sectional sleep duration and neuroimaging metrics. In fully adjusted models compared with consistently sleeping 7-8 hours, groups transitioning to a longer sleep duration category over time had higher FW fraction (short to average ß [SE] 0.0062 [0.0024], p = 0.009; short to long ß [SE] 0.0164 [0.0076], p = 0.031; average to long ß [SE] 0.0083 [0.0022], p = 0.002), and those specifically going from average to long sleep duration also had higher WMH burden (ß [SE] 0.29 [0.11], p = 0.007). The opposite associations (lower WMH and FW) were observed in participants consistently sleeping ≥9 hours as compared with people consistently sleeping 7-8 hours in fully adjusted models (ß [SE] -0.43 [0.20], p = 0.028; ß [SE] -0.019 [0.004], p = 0.020). Each hour of increasing sleep (continuous, ß [SE] 0.12 [0.04], p = 0.003; ß [SE] 0.002 [0.001], p = 0.021) and extensive increase in sleep duration (≥2 hours vs 0 ± 1 hour change; ß [SE] 0.24 [0.10], p = 0.019; ß [SE] 0.0081 [0.0025], p = 0.001) over time was associated with higher WMH burden and FW fraction in fully adjusted models. Sleep duration change was not associated with PET amyloid or tau outcomes. DISCUSSION: Longer self-reported sleep duration over time was associated with neuroimaging biomarkers of cerebrovascular pathology as evidenced by higher WMH burden and FW fraction. A longer sleep duration extending over time may be an early change in the neurodegenerative trajectory.

PET image denoising based on denoising diffusion probabilistic model.

PURPOSE: Due to various physical degradation factors and limited counts received, PET image quality needs further improvements. The denoising diffusion probabilistic model (DDPM) was a distribution learning-based model, which tried to transform a normal distribution into a specific data distribution based on iterative refinements. In this work, we proposed and evaluated different DDPM-based methods for PET image denoising. METHODS: Under the DDPM framework, one way to perform PET image denoising was to provide the PET image and/or the prior image as the input. Another way was to supply the prior image as the network input with the PET image included in the refinement steps, which could fit for scenarios of different noise levels. 150 brain [[Formula: see text]F]FDG datasets and 140 brain [[Formula: see text]F]MK-6240 (imaging neurofibrillary tangles deposition) datasets were utilized to evaluate the proposed DDPM-based methods. RESULTS: Quantification showed that the DDPM-based frameworks with PET information included generated better results than the nonlocal mean, Unet and generative adversarial network (GAN)-based denoising methods. Adding additional MR prior in the model helped achieved better performance and further reduced the uncertainty during image denoising. Solely relying on MR prior while ignoring the PET information resulted in large bias. Regional and surface quantification showed that employing MR prior as the network input while embedding PET image as a data-consistency constraint during inference achieved the best performance. CONCLUSION: DDPM-based PET image denoising is a flexible framework, which can efficiently utilize prior information and achieve better performance than the nonlocal mean, Unet and GAN-based denoising methods.

Evaluation of trans- and cis-4-[18F]Fluorogabapentin for Brain PET Imaging.

Gabapentin, a selective ligand for the α2δ subunit of voltage-dependent calcium channels, is an anticonvulsant medication used in the treatment of neuropathic pain, epilepsy, and other neurological conditions. We recently described two radiofluorinated derivatives of gabapentin (trans-4-[18F]fluorogabapentin, [18F]tGBP4F, and cis-4-[18F]fluorogabapentin, [18F]cGBP4F) and showed that these compounds accumulate in the injured nerves in a rodent model of neuropathic pain. Given the use of gabapentin in brain diseases, here we investigate whether these radiofluorinated derivatives of gabapentin can be used for imaging α2δ receptors in the brain. Specifically, we developed automated radiosynthesis methods for [18F]tGBP4F and [18F]cGBP4F and conducted dynamic PET imaging in adult rhesus macaques with and without preadministration of pharmacological doses of gabapentin. Both radiotracers showed very high metabolic stability, negligible plasma protein binding, and slow accumulation in the brain. [18F]tGBP4F, the isomer with higher binding affinity, showed low brain uptake and could not be displaced, whereas [18F]cGBP4F showed moderate brain uptake and could be partially displaced. Kinetic modeling of brain regional time-activity curves using a metabolite-corrected arterial input function shows that a one-tissue compartment model accurately fits the data. Graphical analysis using Logan or multilinear analysis 1 produced similar results as compartmental modeling, indicating robust quantification. This study advances our understanding of how gabapentinoids work and provides an important advancement toward imaging α2δ receptors in the brain.

SUCCESSIVE SUBSPACE LEARNING FOR CARDIAC DISEASE CLASSIFICATION WITH TWO-PHASE DEFORMATION FIELDS FROM CINE MRI.

Cardiac cine magnetic resonance imaging (MRI) has been used to characterize cardiovascular diseases (CVD), often providing a noninvasive phenotyping tool. While recently flourished deep learning based approaches using cine MRI yield accurate characterization results, the performance is often degraded by small training samples. In addition, many deep learning models are deemed a "black box," for which models remain largely elusive in how models yield a prediction and how reliable they are. To alleviate this, this work proposes a lightweight successive subspace learning (SSL) framework for CVD classification, based on an interpretable feedforward design, in conjunction with a cardiac atlas. Specifically, our hierarchical SSL model is based on (i) neighborhood voxel expansion, (ii) unsupervised subspace approximation, (iii) supervised regression, and (iv) multi-level feature integration. In addition, using two-phase 3D deformation fields, including end-diastolic and end-systolic phases, derived between the atlas and individual subjects as input offers objective means of assessing CVD, even with small training samples. We evaluate our framework on the ACDC2017 database, comprising one healthy group and four disease groups. Compared with 3D CNN-based approaches, our framework achieves superior classification performance with 140× fewer parameters, which supports its potential value in clinical use.

Association of Pathologic and Volumetric Biomarker Changes With Cognitive Decline in Clinically Normal Adults.

BACKGROUND AND OBJECTIVES: Hippocampal volume (HV) atrophy is a well-known biomarker of memory impairment. However, compared with ß-amyloid (Aß) and tau imaging, it is less specific for Alzheimer disease (AD) pathology. This lack of specificity could provide indirect information about potential copathologies that cannot be observed in vivo. In this prospective cohort study, we aimed to assess the associations among Aß, tau, HV, and cognition, measured over a 10-year follow-up period with a special focus on the contributions of HV atrophy to cognition after adjusting for Aß and tau. METHODS: We enrolled 283 older adults without dementia or overt cognitive impairment in the Harvard Aging Brain Study. In this report, we only analyzed data from individuals with available longitudinal imaging and cognition data. Serial MRI (follow-up duration 1.3-7.0 years), neocortical Aß imaging on Pittsburgh Compound B PET scans (1.9-8.5 years), entorhinal and inferior temporal tau on flortaucipir PET scans (0.8-6.0 years), and the Preclinical Alzheimer Cognitive Composite (3.0-9.8 years) were prospectively collected. We evaluated the longitudinal associations between Aß, tau, volume, and cognition data and investigated sequential models to test the contribution of each biomarker to cognitive decline. RESULTS: We analyzed data from 128 clinically normal older adults, including 72 (56%) women and 56 (44%) men; median age at inclusion was 73 years (range 63-87). Thirty-four participants (27%) exhibited an initial high-Aß burden on PET imaging. Faster HV atrophy was correlated with faster cognitive decline (R2 = 0.28, p < 0.0001). When comparing all biomarkers, HV slope was associated with cognitive decline independently of Aß and tau measures, uniquely accounting for 10% of the variance. Altogether, 45% of the variance in cognitive decline was explained by combining the change measures in the different imaging biomarkers. DISCUSSION: In older adults, longitudinal hippocampal atrophy is associated with cognitive decline, independently of Aß or tau, suggesting that non-AD pathologies (e.g., TDP-43, vascular) may contribute to hippocampal-mediated cognitive decline. Serial HV measures, in addition to AD-specific biomarkers, may help evaluate the contribution of non-AD pathologies that cannot be measured otherwise in vivo.

A Comprehensive Primer on Radiation Oncology for Non-Radiation Oncologists.

Background: Multidisciplinary management is crucial in cancer diagnosis and treatment. Multidisciplinary teams include specialists in surgery, medical therapies, and radiation therapy (RT), each playing unique roles in oncology care. One significant aspect is RT, guided by radiation oncologists (ROs). This paper serves as a detailed primer for non-oncologists, medical students, or non-clinical investigators, educating them on contemporary RT practices. Methods: This report follows the process of RT planning and execution. Starting from the decision-making in multidisciplinary teams to the completion of RT and subsequent patient follow-up, it aims to offer non-oncologists an understanding of the RO's work in a comprehensive manner. Results: The first step in RT is a planning session that includes obtaining a CT scan of the area to be treated, known as the CT simulation. The patients are imaged in the exact position in which they will receive treatment. The second step, which is the primary source of uncertainty, involves the delineation of treatment targets and organs at risk (OAR). The objective is to ensure precise irradiation of the target volume while sparing the OARs as much as possible. Various radiation modalities, such as external beam therapy with electrons, photons, or particles (including protons and carbon ions), as well as brachytherapy, are utilized. Within these modalities, several techniques, such as three-dimensional conformal RT, intensity-modulated RT, volumetric modulated arc therapy, scattering beam proton therapy, and intensity-modulated proton therapy, are employed to achieve optimal treatment outcomes. The RT plan development is an iterative process involving medical physicists, dosimetrists, and ROs. The complexity and time required vary, ranging from an hour to a week. Once approved, RT begins, with image-guided RT being standard practice for patient alignment. The RO manages acute toxicities during treatment and prepares a summary upon completion. There is a considerable variance in practices, with some ROs offering lifelong follow-up and managing potential late effects of treatment. Conclusions: Comprehension of RT clinical effects by non-oncologists providers significantly elevates long-term patient care quality. Hence, educating non-oncologists enhances care for RT patients, underlining this report's importance.

Tau-PET abnormality as a biomarker for Alzheimer's disease staging and early detection: a topological perspective.

Alzheimer's disease can be detected early through biomarkers such as tau positron emission tomography (PET) imaging, which shows abnormal protein accumulations in the brain. The standardized uptake value ratio (SUVR) is often used to quantify tau-PET imaging, but topological information from multiple brain regions is also linked to tau pathology. Here a new method was developed to investigate the correlations between brain regions using subject-level tau networks. Participants with cognitive normal (74), early mild cognitive impairment (35), late mild cognitive impairment (32), and Alzheimer's disease (40) were included. The abnormality network from each scan was constructed to extract topological features, and 7 functional clusters were further analyzed for connectivity strengths. Results showed that the proposed method performed better than conventional SUVR measures for disease staging and prodromal sign detection. For example, when to differ healthy subjects with and without amyloid deposition, topological biomarker is significant with P < 0.01, SUVR is not with P > 0.05. Functionally significant clusters, i.e. medial temporal lobe, default mode network, and visual-related regions, were identified as critical hubs vulnerable to early disease conversion before mild cognitive impairment. These findings were replicated in an independent data cohort, demonstrating the potential to monitor the early sign and progression of Alzheimer's disease from a topological perspective for individual.

Quantifying Velopharyngeal Motion Variation in Speech Sound Production Using an Audio-Informed Dynamic MRI Atlas.

New developments in dynamic magnetic resonance imaging (MRI) facilitate high-quality data acquisition of human velopharyngeal deformations in real-time speech. With recently established speech motion atlases, group analysis is made possible via spatially and temporally aligned datasets in the atlas space from a desired population of interest. In practice, when analyzing motion characteristics from various subjects performing a designated speech task, it is observed that different subjects' velopharyngeal deformation patterns could vary during the pronunciation of the same utterance, regardless of the spatial and temporal alignment of their MRI. Since such variation can be subtle, identification and extraction of unique patterns out of these high-dimensional datasets is a challenging task. In this work, we present a method that computes and visualizes subtle deformation variation patterns as principal components of a subject group's dynamic motion fields in the atlas space. Coupled with the real-time speech audio recordings during image acquisition, the key time frames that contain maximum speech variations are identified by the principal components of temporally aligned audio waveforms, which in turn inform the temporal location of the maximum spatial deformation variation. Henceforth, the motion fields between the key frames and the reference frame for each subject are computed and warped into the common atlas space, enabling a direct extraction of motion variation patterns via quantitative analysis. The method was evaluated on a dataset of twelve healthy subjects. Subtle velopharyngeal motion differences were visualized quantitatively to reveal pronunciation-specific patterns among different subjects.

Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI.

Deep learning (DL) models for segmenting various anatomical structures have achieved great success via a static DL model that is trained in a single source domain. Yet, the static DL model is likely to perform poorly in a continually evolving environment, requiring appropriate model updates. In an incremental learning setting, we would expect that well-trained static models are updated, following continually evolving target domain data-e.g., additional lesions or structures of interest-collected from different sites, without catastrophic forgetting. This, however, poses challenges, due to distribution shifts, additional structures not seen during the initial model training, and the absence of training data in a source domain. To address these challenges, in this work, we seek to progressively evolve an "off-the-shelf" trained segmentation model to diverse datasets with additional anatomical categories in a unified manner. Specifically, we first propose a divergence-aware dual-flow module with balanced rigidity and plasticity branches to decouple old and new tasks, which is guided by continuous batch renormalization. Then, a complementary pseudo-label training scheme with self-entropy regularized momentum MixUp decay is developed for adaptive network optimization. We evaluated our framework on a brain tumor segmentation task with continually changing target domains-i.e., new MRI scanners/modalities with incremental structures. Our framework was able to well retain the discriminability of previously learned structures, hence enabling the realistic life-long segmentation model extension along with the widespread accumulation of big medical data.

Injectable ice slurry for reducing pericardial adipose tissue.

OBJECTIVES: Excess pericardial adipose tissue (PAT) is associated with a higher risk of cardiovascular diseases. Currently, available methods for reducing PAT volume include weight loss through diet and exercise, weight loss with medications, and bariatric surgery. However, these methods are all limited by low patient compliance to maintain the results. We have developed an injectable ice slurry that could selectively target and reduce subcutaneous adipose tissue volume. The aim of this study was to investigate the feasibility and safety of using injectable slurry to selectively reduce PAT volume in a preclinical large animal model. METHODS: PAT in Yucatan swine was injected with slurry or room temperature control solution. All animals were imaged with baseline chest computed tomography (CT) before slurry injection and at 2 months after injection to quantify PAT volume. Specimens from injected and noninjected PAT were harvested for histology. RESULTS: Slurry treatment of PAT was well tolerated in all animals. Slurry-induced selective cryolipolysis in treated PAT. CT imaging showed decrease in PAT volume in treated area at 8 weeks posttreatment compared to baseline, that was significantly different from control solution treated group (median [range]: -29.66 [-35.07 to -27.92]% vs. -1.50 [-11.69 to 8.69]% in control animals respectively, p < 0.05). CONCLUSIONS: This study demonstrated that slurry injection into PAT is feasible in a large animal model. Slurry injection was safe and effective in inducing selective cryolipolysis in PAT and reducing PAT volume. Slurry reduction of PAT could potentially serve as a novel treatment for cardiovascular diseases.

Fully Automated Radiosynthesis of [18F]mG4P027 for mGluR4 Imaging.

Fluorine-18 labeled N-(4-chloro-3-(((fluoro-18F)methyl-d2)thio)phenyl)picolinamide, [18F]mG4P027, is a potent positron emission tomography (PET) radiotracer for metabotropic glutamate receptor 4 (mGluR4). Our previous in vitro and in vivo evaluations have demonstrated that this tracer is promising for further translational studies. To automate the radiosynthesis of [18F]mG4P027, significant modifications were made to the manual process by carefully examining this process and addressing the root causes of the challenges associated with its automation. We successfully implemented its automated radiosynthesis using the TRACERlab FX2N module and consequently, obtained a high-purity radiolabeled [18F]mG4P027 in high yield, meeting the requirements for future human studies.

Association of Novelty-Related Locus Coeruleus Function With Entorhinal Tau Deposition and Memory Decline in Preclinical Alzheimer Disease.

BACKGROUND AND OBJECTIVES: The predictable Braak staging scheme suggests that cortical tau progression may be related to synaptically connected neurons. Animal and human neuroimaging studies demonstrated that changes in neuronal activity contribute to tau spreading. Whether similar mechanisms explain tau progression from the locus coeruleus (LC), a tiny noradrenergic brainstem nucleus involved in novelty, learning, and memory and among the earliest regions to accumulate tau, has not yet been established. We aimed to investigate whether novelty-related LC activity was associated with the accumulation of cortical tau and its implications for cognitive decline. METHODS: We combined functional MRI data of a novel vs repeated face-name learning paradigm, [18F]-FTP-PET, [11C]-PiB-PET, and longitudinal cognitive data from 92 well-characterized older individuals in the Harvard Aging Brain Study. We related novelty vs repetition LC activity to cortical tau deposition and to longitudinal decline in memory, executive function, and the Preclinical Alzheimer Disease Cognitive Composite (version 5; PACC5). Structural equation modeling was used to examine whether entorhinal cortical (EC) tau mediated the relationship between LC activity and cognitive decline and whether this depended on beta-amyloid deposition. RESULTS: The participants' average age at baseline was 69.67 ± 10.14 years. Fifty-one participants were female. Ninety-one participants were cognitively normal (CDR global = 0), and one participant had mild cognitive impairment (CDR global = 0.5) at baseline. Lower novelty-related LC activity was specifically related to greater tau deposition in the medial-lateral temporal cortex and steeper memory decline. LC activity during novelty vs repetition was not related to executive dysfunction or decline on the PACC5. The relationship between LC activity and memory decline was partially mediated by EC tau, particularly in individuals with elevated beta-amyloid deposition. DISCUSSION: Our results suggested that lower novelty-related LC activity is associated with the emergence of EC tau and that the downstream effects of this LC-EC pathway on memory decline also require the presence of elevated beta-amyloid. Longitudinal studies are required to investigate whether optimal LC activity has the potential to delay tau spread and memory decline, which may have implications for designing targeted interventions promoting resilience.

B1 inhomogeneity-corrected T1 mapping and quantitative magnetization transfer imaging via simultaneously estimating Bloch-Siegert shift and magnetization transfer effects.

PURPOSE: To introduce a method of inducing Bloch-Siegert shift and magnetization Transfer Simultaneously (BTS) and demonstrate its utilization for measuring binary spin-bath model parameters free pool spin-lattice relaxation ( T 1 F $$ {T}_1^{\mathrm{F}} $$ ), macromolecular fraction ( f $$ f $$ ), magnetization exchange rate ( k F $$ {k}_{\mathrm{F}} $$ ) and local transmit field ( B 1 + $$ {B}_1^{+} $$ ). THEORY AND METHODS: Bloch-Siegert shift and magnetization transfer is simultaneously induced through the application of off-resonance irradiation in between excitation and acquisition of an RF-spoiled gradient-echo scheme. Applying the binary spin-bath model, an analytical signal equation is derived and verified through Bloch simulations. Monte Carlo simulations were performed to analyze the method's performance. The estimation of the binary spin-bath parameters with B 1 + $$ {B}_1^{+} $$ compensation was further investigated through experiments, both ex vivo and in vivo. RESULTS: Comparing BTS with existing methods, simulations showed that existing methods can significantly bias T 1 $$ {T}_1 $$ estimation when not accounting for transmit B 1 $$ {B}_1 $$ heterogeneity and MT effects that are present. Phantom experiments further showed that the degree of this bias increases with increasing macromolecular proton fraction. Multi-parameter fit results from an in vivo brain study generated values in agreement with previous literature. Based on these studies, we confirmed that BTS is a robust method for estimating the binary spin-bath parameters in macromolecule-rich environments, even in the presence of B 1 + $$ {B}_1^{+} $$ inhomogeneity. CONCLUSION: A method of estimating Bloch-Siegert shift and magnetization transfer effect has been developed and validated. Both simulations and experiments confirmed that BTS can estimate spin-bath parameters ( T 1 F $$ {T}_1^{\mathrm{F}} $$ , f $$ f $$ , k F $$ {k}_{\mathrm{F}} $$ ) that are free from B 1 + $$ {B}_1^{+} $$ bias.

Attentive Continuous Generative Self-training for Unsupervised Domain Adaptive Medical Image Translation.

Self-training is an important class of unsupervised domain adaptation (UDA) approaches that are used to mitigate the problem of domain shift, when applying knowledge learned from a labeled source domain to unlabeled and heterogeneous target domains. While self-training-based UDA has shown considerable promise on discriminative tasks, including classification and segmentation, through reliable pseudo-label filtering based on the maximum softmax probability, there is a paucity of prior work on self-training-based UDA for generative tasks, including image modality translation. To fill this gap, in this work, we seek to develop a generative self-training (GST) framework for domain adaptive image translation with continuous value prediction and regression objectives. Specifically, we quantify both aleatoric and epistemic uncertainties within our GST using variational Bayes learning to measure the reliability of synthesized data. We also introduce a self-attention scheme that de-emphasizes the background region to prevent it from dominating the training process. The adaptation is then carried out by an alternating optimization scheme with target domain supervision that focuses attention on the regions with reliable pseudo-labels. We evaluated our framework on two cross-scanner/center, inter-subject translation tasks, including tagged-to-cine magnetic resonance (MR) image translation and T1-weighted MR-to-fractional anisotropy translation. Extensive validations with unpaired target domain data showed that our GST yielded superior synthesis performance in comparison to adversarial training UDA methods.