Associations of cerebral amyloid beta and tau with cognition from midlife.

INTRODUCTION: Understanding early neuropathological changes and their associations with cognition may aid dementia prevention. This study investigated associations of cerebral amyloid and tau positron emission tomography (PET) retention with cognition in a predominately middle-aged community-based cohort and examined factors that may modify these relationships. METHODS: 11C-Pittsburgh compound B amyloid and 18F-flortaucipir tau PET imaging were performed. Associations of amyloid and tau PET with cognition were evaluated using linear regression. Interactions with age, apolipoprotein E (APOE) ε4 status, and education were examined. RESULTS: Amyloid and tau PET were not associated with cognition in the overall sample (N = 423; mean: 57 ± 10 years; 50% female). However, younger age (< 55 years) and APOE ε4 were significant effect modifiers, worsening cognition in the presence of higher amyloid and tau. DISCUSSION: Higher levels of Aß and tau may have a pernicious effect on cognition among APOE ε4 carriers and younger adults, suggesting a potential role for targeted early interventions. HIGHLIGHTS: Risk and resilience factors influenced cognitive vulnerability due to Aß and tau. Higher fusiform tau associated with poorer visuospatial skills in younger adults. APOE ε4 interacted with Aß and tau to worsen cognition across multiple domains.

Joint spectral quantification of MR spectroscopic imaging using linear tangent space alignment-based manifold learning.

PURPOSE: To develop a manifold learning-based method that leverages the intrinsic low-dimensional structure of MR Spectroscopic Imaging (MRSI) signals for joint spectral quantification. METHODS: A linear tangent space alignment (LTSA) model was proposed to represent MRSI signals. In the proposed model, the signals of each metabolite were represented using a subspace model and the local coordinates of the subspaces were aligned to the global coordinates of the underlying low-dimensional manifold via linear transform. With the basis functions of the subspaces predetermined via quantum mechanics simulations, the global coordinates and the matrices for the local-to-global coordinate alignment were estimated by fitting the proposed LTSA model to noisy MRSI data with a spatial smoothness constraint on the global coordinates and a sparsity constraint on the matrices. RESULTS: The performance of the proposed method was validated using numerical simulation data and in vivo proton-MRSI experimental data acquired on healthy volunteers at 3T. The results of the proposed method were compared with the QUEST method and the subspace-based method. In all the compared cases, the proposed method achieved superior performance over the QUEST and the subspace-based methods both qualitatively in terms of noise and artifacts in the estimated metabolite concentration maps, and quantitatively in terms of spectral quantification accuracy measured by normalized root mean square errors. CONCLUSION: Joint spectral quantification using linear tangent space alignment-based manifold learning improves the accuracy of MRSI spectral quantification.

Light Spread Manipulation in Scintillators Using Laser Induced Optical Barriers.

We are using the Laser Induced Optical Barriers (LIOB) technique to fabricate scintillator detectors with combined performance characteristics of the two standard detector types, mechanically pixelated arrays and monolithic crystals. This is done by incorporation of so-called optical barriers that have a refractive index lower than that of the crystal bulk. Such barriers can redirect the scintillation light and allow for control of the light spread in the detector. Previous work has shown that the LIOB technique has the potential to achieve detectors with high transversal and depth of interaction (DOI) resolution simultaneously in a single-side readout configuration, suitable for high resolution PET imaging. However, all designs studied thus far present edge effect issues similarly as in the standard detector categories. In this work we take advantage of the inherent flexibility of the LIOB technique and investigate alternative barrier patterns with the aim to address this problem. Light transport simulations of barrier patterns in LYSO:Ce, with deeper barrier walls moving towards the detector edge show great promise in reducing the edge effect, however there is a trade-off in terms of achievable DOI information. Furthermore, fabrication and characterization of a 20 mm thick LYSO:Ce detector with optical barriers forming a pattern of 1 × 1 × 20mm3 pixel like structures show that light channeling in laser-processed detectors in agreement with optical barriers with refractive index between 1.2 and 1.4 is achievable.

Point-supervised Brain Tumor Segmentation with Box-prompted MedSAM.

Delineating lesions and anatomical structure is important for image-guided interventions. Point-supervised medical image segmentation (PSS) has great potential to alleviate costly expert delineation labeling. However, due to the lack of precise size and boundary guidance, the effectiveness of PSS often falls short of expectations. Although recent vision foundational models, such as the medical segment anything model (MedSAM), have made significant advancements in bounding-box-prompted segmentation, it is not straightforward to utilize point annotation, and is prone to semantic ambiguity. In this preliminary study, we introduce an iterative framework to facilitate semantic-aware point-supervised MedSAM. Specifically, the semantic box-prompt generator (SBPG) module has the capacity to convert the point input into potential pseudo bounding box suggestions, which are explicitly refined by the prototype-based semantic similarity. This is then succeeded by a prompt-guided spatial refinement (PGSR) module that harnesses the exceptional generalizability of MedSAM to infer the segmentation mask, which also updates the box proposal seed in SBPG. Performance can be progressively improved with adequate iterations. We conducted an evaluation on BraTS2018 for the segmentation of whole brain tumors and demonstrated its superior performance compared to traditional PSS methods and on par with box-supervised methods.

PET mapping of receptor occupancy using joint direct parametric reconstruction.

Receptor occupancy (RO) studies using PET neuroimaging play a critical role in the development of drugs targeting the central nervous system (CNS). The conventional approach to estimate drug receptor occupancy consists in estimation of binding potential changes between two PET scans (baseline and post-drug injection). This estimation is typically performed separately for each scan by first reconstructing dynamic PET scan data before fitting a kinetic model to time activity curves. This approach fails to properly model the noise in PET measurements, resulting in poor RO estimates, especially in low receptor density regions. OBJECTIVE: In this work, we evaluate a novel joint direct parametric reconstruction framework to directly estimate distributions of RO and other kinetic parameters in the brain from a pair of baseline and postdrug injection dynamic PET scans. METHODS: The proposed method combines the use of regularization on RO maps with alternating optimization to enable estimation of occupancy even in low binding regions. RESULTS: Simulation results demonstrate the quantitative improvement of this method over conventional approaches in terms of accuracy and precision of occupancy. The proposed method is also evaluated in preclinical in-vivo experiments using 11C-MK6884 and a muscarinic acetylcholine receptor 4 positive allosteric modulator drug, showing improved estimation of receptor occupancy as compared to traditional estimators. CONCLUSION: The proposed joint direct estimation framework improves RO estimation compared to conventional methods, especially in intermediate to low-binding regions. SIGNIFICANCE: This work could potentially facilitate the evaluation of new drug candidates targeting the CNS.

Free-breathing 3D cardiac extracellular volume (ECV) mapping using a linear tangent space alignment (LTSA) model.

$\textbf{Purpose:}$ To develop a new method for free-breathing 3D extracellular volume (ECV) mapping of the whole heart at 3T. $\textbf{Methods:}$ A free-breathing 3D cardiac ECV mapping method was developed at 3T. T1 mapping was performed before and after contrast agent injection using a free-breathing ECG-gated inversion-recovery sequence with spoiled gradient echo readout. A linear tangent space alignment (LTSA) model-based method was used to reconstruct high-frame-rate dynamic images from (k,t)-space data sparsely sampled along a random stack-of-stars trajectory. Joint T1 and transmit B1 estimation was performed voxel-by-voxel for pre- and post-contrast T1 mapping. To account for the time-varying T1 after contrast agent injection, a linearly time-varying T1 model was introduced for post-contrast T1 mapping. ECV maps were generated by aligning pre- and post-contrast T1 maps through affine transformation. $\textbf{Results:}$ The feasibility of the proposed method was demonstrated using in vivo studies with six healthy volunteers at 3T. We obtained 3D ECV maps at a spatial resolution of 1.9$\times$1.9$\times$4.5 $mm^{3}$ and a FOV of 308$\times$308$\times$144 $mm^{3}$, with a scan time of 10.1$\pm$1.4 and 10.6$\pm$1.6 min before and after contrast agent injection, respectively. The ECV maps and the pre- and post-contrast T1 maps obtained by the proposed method were in good agreement with the 2D MOLLI method both qualitatively and quantitatively. $\textbf{Conclusion:}$ The proposed method allows for free-breathing 3D ECV mapping of the whole heart within a practically feasible imaging time. The estimated ECV values from the proposed method were comparable to those from the existing method. $\textbf{Keywords:}$ cardiac extracellular volume (ECV) mapping, cardiac T1 mapping, linear tangent space alignment (LTSA), manifold learning.

Investigating muscle coordination patterns with Granger causality analysis in protrusive motion from tagged and diffusion MRI.

The human tongue exhibits an orchestrated arrangement of internal muscles, working in sequential order to execute tongue movements. Understanding the muscle coordination patterns involved in tongue protrusive motion is crucial for advancing knowledge of tongue structure and function. To achieve this, this work focuses on five muscles known to contribute to protrusive motion. Tagged and diffusion MRI data are collected for analysis of muscle fiber geometry and motion patterns. Lagrangian strain measurements are derived, and Granger causal analysis is carried out to assess predictive information among the muscles. Experimental results suggest sequential muscle coordination of protrusive motion among distinct muscle groups.

The United States Department of Energy and National Institutes of Health Collaboration: Medical Care Advances by Discovery in Radiation Detection.

A National Institutes of Health (NIH) and U.S. Department of Energy (DOE) Office of Science virtual workshop on shared general topics was held in July of 2021 and reported on in this publication in January of 2023. Following the inaugural 2021 joint meeting representatives from the DOE Office of Science and NIH met to discuss organizing a second joint workshop that would concentrate on radiation detection to bring together teams from both agencies and their grantee populations to stimulate collaboration and efficiency. To meet this scientific mission within the NIH and DOE radiation detection space, the organizers assembled workshop sessions covering the state-of-the-art in cameras, detectors, and sensors for radiation external and internal (diagnostic and therapeutic) to human, data acquisition and electronics, image reconstruction and processing, and the application of artificial intelligence. NIH and DOE are committed to continuing the process of convening a joint workshop every 12-24 months. This Special Report recaps the findings of this second workshop. Beyond showing only the innovations and areas of success, important gaps in our knowledge were defined and presented. We summarize by defining four areas of greatest opportunity and need that emerged from the unique, dynamic dialogue the in-person workshop provided the attendees.

Bias atlases for segmentation-based PET attenuation correction using PET-CT and MR.

This study was to obtain voxel-wise PET accuracy and precision using tissue-segmentation for attenuation correction. We applied multiple thresholds to the CTs of 23 patients to classify tissues. For six of the 23 patients, MR images were also acquired. The MR fat/in-phase ratio images were used for fat segmentation. Segmented tissue classes were used to create attenuation maps, which were used for attenuation correction in PET reconstruction. PET bias images were then computed using the PET reconstructed with the original CT as the reference. We registered the CTs for all the patients and transformed the corresponding bias images accordingly. We then obtained the mean and standard deviation bias atlas using all the registered bias images. Our CT-based study shows that four-class segmentation (air, lungs, fat, other tissues), which is available on most PET-MR scanners, yields 15.1%, 4.1%, 6.6%, and 12.9% RMSE bias in lungs, fat, non-fat soft-tissues, and bones, respectively. An accurate fat identification is achievable using fat/in-phase MR images. Furthermore, we have found that three-class segmentation (air, lungs, other tissues) yields less than 5% standard deviation of bias within the heart, liver, and kidneys. This implies that three-class segmentation can be sufficient to achieve small variation of bias for imaging these three organs. Finally, we have found that inter- and intra-patient lung density variations contribute almost equally to the overall standard deviation of bias within the lungs.

Synthesizing Audio from Tongue Motion During Speech Using Tagged MRI Via Transformer.

Investigating the relationship between internal tissue point motion of the tongue and oropharyngeal muscle deformation measured from tagged MRI and intelligible speech can aid in advancing speech motor control theories and developing novel treatment methods for speech related-disorders. However, elucidating the relationship between these two sources of information is challenging, due in part to the disparity in data structure between spatiotemporal motion fields (i.e., 4D motion fields) and one-dimensional audio waveforms. In this work, we present an efficient encoder-decoder translation network for exploring the predictive information inherent in 4D motion fields via 2D spectrograms as a surrogate of the audio data. Specifically, our encoder is based on 3D convolutional spatial modeling and transformer-based temporal modeling. The extracted features are processed by an asymmetric 2D convolution decoder to generate spectrograms that correspond to 4D motion fields. Furthermore, we incorporate a generative adversarial training approach into our framework to further improve synthesis quality on our generated spectrograms. We experiment on 63 paired motion field sequences and speech waveforms, demonstrating that our framework enables the generation of clear audio waveforms from a sequence of motion fields. Thus, our framework has the potential to improve our understanding of the relationship between these two modalities and inform the development of treatments for speech disorders.

Noise-Robust Sleep Staging via Adversarial Training With an Auxiliary Model.

Deep learning (DL)-based automatic sleep staging approaches have attracted much attention recently due in part to their outstanding accuracy. At the testing stage, however, the performance of these approaches is likely to be degraded, when applied in different testing environments, because of the problem of domain shift. This is because while a pre-trained model is typically trained on noise-free electroencephalogram (EEG) signals acquired from accurate medical equipment, deployment is carried out on consumer-level devices with undesirable noise. To alleviate this challenge, in this work, we propose an efficient training approach that is robust against unseen arbitrary noise. In particular, we propose to generate the worst-case input perturbations by means of adversarial transformation in an auxiliary model, to learn a wide range of input perturbations and thereby to improve reliability. Our approach is based on two separate training models: (i) an auxiliary model to generate adversarial noise and (ii) a target network to incorporate the noise signal to enhance robustness. Furthermore, we exploit novel class-wise robustness during the training of the target network to represent different robustness patterns of each sleep stage. Our experimental results demonstrated that our approach improved sleep staging performance on healthy controls, in the presence of moderate to severe noise levels, compared with competing methods. Our approach was able to effectively train and deploy a DL model to handle different types of noise, including adversarial, Gaussian, and shot noise.

Posterior estimation using deep learning: a simulation study of compartmental modeling in dynamic positron emission tomography.

BACKGROUND: In medical imaging, images are usually treated as deterministic, while their uncertainties are largely underexplored. PURPOSE: This work aims at using deep learning to efficiently estimate posterior distributions of imaging parameters, which in turn can be used to derive the most probable parameters as well as their uncertainties. METHODS: Our deep learning-based approaches are based on a variational Bayesian inference framework, which is implemented using two different deep neural networks based on conditional variational auto-encoder (CVAE), CVAE-dual-encoder, and CVAE-dual-decoder. The conventional CVAE framework, that is, CVAE-vanilla, can be regarded as a simplified case of these two neural networks. We applied these approaches to a simulation study of dynamic brain PET imaging using a reference region-based kinetic model. RESULTS: In the simulation study, we estimated posterior distributions of PET kinetic parameters given a measurement of the time-activity curve. Our proposed CVAE-dual-encoder and CVAE-dual-decoder yield results that are in good agreement with the asymptotically unbiased posterior distributions sampled by Markov Chain Monte Carlo (MCMC). The CVAE-vanilla can also be used for estimating posterior distributions, although it has an inferior performance to both CVAE-dual-encoder and CVAE-dual-decoder. CONCLUSIONS: We have evaluated the performance of our deep learning approaches for estimating posterior distributions in dynamic brain PET. Our deep learning approaches yield posterior distributions, which are in good agreement with unbiased distributions estimated by MCMC. All these neural networks have different characteristics and can be chosen by the user for specific applications. The proposed methods are general and can be adapted to other problems.

Manifold Learning via Linear Tangent Space Alignment (LTSA) for Accelerated Dynamic MRI With Sparse Sampling.

The spatial resolution and temporal frame-rate of dynamic magnetic resonance imaging (MRI) can be improved by reconstructing images from sparsely sampled k -space data with mathematical modeling of the underlying spatiotemporal signals. These models include sparsity models, linear subspace models, and non-linear manifold models. This work presents a novel linear tangent space alignment (LTSA) model-based framework that exploits the intrinsic low-dimensional manifold structure of dynamic images for accelerated dynamic MRI. The performance of the proposed method was evaluated and compared to state-of-the-art methods using numerical simulation studies as well as 2D and 3D in vivo cardiac imaging experiments. The proposed method achieved the best performance in image reconstruction among all the compared methods. The proposed method could prove useful for accelerating many MRI applications, including dynamic MRI, multi-parametric MRI, and MR spectroscopic imaging.

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

Purpose: 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, [ 11 C]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-compliant automated radiosynthesis protocol and evaluated the whole-body biodistribution and radiation dosimetry of [ 11 C]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 4 bed positions and 13 passes over a total scan time of ∼150 minutes. 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 [ 11 C]3MeO4AP was achieved with 7.3 ± 1.2 % (n = 4) of non-decay corrected radiochemical yield within 38 min of synthesis and purification time. [ 11 C]3MeO4AP distributed quickly throughout the body and into the brain. The organs with highest dose were the kidneys. The average effective dose of [ 11 C]3MeO4AP was 4.27 ± 0.57 µSv/MBq. No significant changes in vital signs were observed during the scan. Conclusion: The cGMP compliant automated radiosynthesis of [ 11 C]3MeO4AP was developed. The whole-body biodistribution and radiation dosimetry of [ 11 C]3MeO4AP was successfully evaluated in NHPs. [ 11 C]3MeO4AP shows lower average effective dose than [ 18 F]3F4AP and similar average effective dose as other carbon-11 tracers.

PET imaging studies to investigate functional expression of mGluR2 using [11C]mG2P001.

Metabotropic glutamate receptor 2 (mGluR2) has been extensively studied for the treatment of various neurological and psychiatric disorders. Understanding of the mGluR2 function is pivotal in supporting the drug discovery targeting mGluR2. Herein, the positive allosteric modulation of mGluR2 was investigated via the in vivo positron emission tomography (PET) imaging using 2-((4-(2-[11C]methoxy-4-(trifluoromethyl)phenyl)piperidin-1-yl)methyl)-1-methyl-1H-imidazo[4,5-b]pyridine ([11C]mG2P001). Distinct from the orthosteric compounds, pretreatment with the unlabeled mG2P001, a potent mGluR2 positive allosteric modulator (PAM), resulted in a significant increase instead of decrease of the [11C]mG2P001 accumulation in rat brain detected by PET imaging. Subsequent in vitro studies with [3H]mG2P001 revealed the cooperative binding mechanism of mG2P001 with glutamate and its pharmacological effect that contributed to the enhanced binding of [3H]mG2P001 in transfected CHO cells expressing mGluR2. The in vivo PET imaging and quantitative analysis of [11C]mG2P001 in non-human primates (NHPs) further validated the characteristics of [11C]mG2P001 as an imaging ligand for mGluR2. Self-blocking studies in primates enhanced accumulation of [11C]mG2P001. Altogether, these studies show that [11C]mG2P001 is a sensitive biomarker for mGluR2 expression and the binding is affected by the tissue glutamate concentration.

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 1-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 towards imaging α2δ receptors in the brain.

Kinetic evaluation and assessment of longitudinal changes in reference region and extracerebral [18F]MK-6240 PET uptake.

[18F]MK-6240 meningeal/extracerebral off-target binding may impact tau quantification. We examined the kinetics and longitudinal changes of extracerebral and reference regions. [18F]MK-6240 PET was performed in 24 cognitively-normal and eight cognitively-impaired subjects, with arterial samples in 13 subjects. Follow-up scans at 6.1 ± 0.5 (n = 25) and 13.3 ± 0.9 (n = 16) months were acquired. Extracerebral and reference region (cerebellar gray matter (CerGM)-based, cerebral white matter (WM), pons) uptake were evaluated using standardized uptake values (SUV90-110), spectral analysis, and distribution volume. Longitudinal changes in SUV90-110 were examined. The impact of reference region on target region outcomes, partial volume correction (PVC) and regional erosion were evaluated. Eroded WM and pons showed lower variability, lower extracerebral contamination, and lower longitudinal changes than CerGM-based regions. CerGM-based regions resulted larger cross-sectional effect sizes for group differentiation. Extracerebral signal was high in 50% of subjects and exhibited irreversible kinetics and nonsignificant longitudinal changes over one-year but was highly variable at subject-level. PVC resulted in higher variability in reference region uptake and longitudinal changes. Our results suggest that eroded CerGM may be preferred for cross-sectional, whilst eroded WM or pons may be preferred for longitudinal [18F]MK-6240 studies. For CerGM, erosion was necessary (preferred over PVC) to address the heterogenous nature of extracerebral signal.