Accelerated 3D metabolite T1 mapping of the brain using variable-flip-angle SPICE.

PURPOSE: To develop a practical method to enable 3D T1 mapping of brain metabolites. THEORY AND METHODS: Due to the high dimensionality of the imaging problem underlying metabolite T1 mapping, measurement of metabolite T1 values has been currently limited to a single voxel or slice. This work achieved 3D metabolite T1 mapping by leveraging a recent ultrafast MRSI technique called SPICE (spectroscopic imaging by exploiting spatiospectral correlation). The Ernst-angle FID MRSI data acquisition used in SPICE was extended to variable flip angles, with variable-density sparse sampling for efficient encoding of metabolite T1 information. In data processing, a novel generalized series model was used to remove water and subcutaneous lipid signals; a low-rank tensor model with prelearned subspaces was used to reconstruct the variable-flip-angle metabolite signals jointly from the noisy data. RESULTS: The proposed method was evaluated using both phantom and healthy subject data. Phantom experimental results demonstrated that high-quality 3D metabolite T1 maps could be obtained and used for correction of T1 saturation effects. In vivo experimental results showed metabolite T1 maps with a large spatial coverage of 240 × 240 × 72 mm3 and good reproducibility coefficients (< 11%) in a 14.5-min scan. The metabolite T1 times obtained ranged from 0.99 to 1.44 s in gray matter and from 1.00 to 1.35 s in white matter. CONCLUSION: We successfully demonstrated the feasibility of 3D metabolite T1 mapping within a clinically acceptable scan time. The proposed method may prove useful for both T1 mapping of brain metabolites and correcting the T1-weighting effects in quantitative metabolic imaging.

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.

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.

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.

Super-resolution in brain positron emission tomography using a real-time motion capture system.

Super-resolution (SR) is a methodology that seeks to improve image resolution by exploiting the increased spatial sampling information obtained from multiple acquisitions of the same target with accurately known sub-resolution shifts. This work aims to develop and evaluate an SR estimation framework for brain positron emission tomography (PET), taking advantage of a high-resolution infra-red tracking camera to measure shifts precisely and continuously. Moving phantoms and non-human primate (NHP) experiments were performed on a GE Discovery MI PET/CT scanner (GE Healthcare) using an NDI Polaris Vega (Northern Digital Inc), an external optical motion tracking device. To enable SR, a robust temporal and spatial calibration of the two devices was developed as well as a list-mode Ordered Subset Expectation Maximization PET reconstruction algorithm, incorporating the high-resolution tracking data from the Polaris Vega to correct motion for measured line of responses on an event-by-event basis. For both phantoms and NHP studies, the SR reconstruction method yielded PET images with visibly increased spatial resolution compared to standard static acquisitions, allowing improved visualization of small structures. Quantitative analysis in terms of SSIM, CNR and line profiles were conducted and validated our observations. The results demonstrate that SR can be achieved in brain PET by measuring target motion in real-time using a high-resolution infrared tracking camera.

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.

Human biodistribution and radiation dosimetry of the demyelination tracer [18F]3F4AP.

PURPOSE: [18F]3F4AP is a novel PET radiotracer that targets voltage-gated potassium (K+) channels and has shown promise for imaging demyelinated lesions in animal models of neurological diseases. This study aimed to evaluate the biodistribution, safety, and radiation dosimetry of [18F]3F4AP in healthy human volunteers. METHODS: Four healthy volunteers (2 females) underwent a 4-h dynamic PET scan from the cranial vertex to mid-thigh using multiple bed positions after administration of 368 ± 17.9 MBq (9.94 ± 0.48 mCi) of [18F]3F4AP. Volumes of interest for relevant organs were manually drawn guided by the CT, and PET images and time-activity curves (TACs) were extracted. Radiation dosimetry was estimated from the integrated TACs using OLINDA software. Safety assessments included measuring vital signs immediately before and after the scan, monitoring for adverse events, and obtaining a comprehensive metabolic panel and electrocardiogram within 30 days before and after the scan. RESULTS: [18F]3F4AP distributed throughout the body with the highest levels of activity in the kidneys, urinary bladder, stomach, liver, spleen, and brain and with low accumulation in muscle and fat. The tracer cleared quickly from circulation and from most organs. The clearance of the tracer was noticeably faster than previously reported in nonhuman primates (NHPs). The average effective dose (ED) across all subjects was 12.1 ± 2.2 µSv/MBq, which is lower than the estimated ED from the NHP studies (21.6 ± 0.6 µSv/MBq) as well as the ED of other fluorine-18 radiotracers such as [18F]FDG (~ 20 µSv/MBq). No differences in ED between males and females were observed. No substantial changes in safety assessments or adverse events were recorded. CONCLUSION: The biodistribution and radiation dosimetry of [18F]3F4AP in humans are reported for the first time. The average total ED across four subjects was lower than most 18F-labeled PET tracers. The tracer and study procedures were well tolerated, and no adverse events occurred.

A multi-pronged investigation of option generation using depression, PET and modafinil.

Option generation is a critical process in decision making, but previous studies have largely focused on choices between options given by a researcher. Consequently, how we self-generate options for behaviour remain poorly understood. Here, we investigated option generation in major depressive disorder and how dopamine might modulate this process, as well as the effects of modafinil (a putative cognitive enhancer) on option generation in healthy individuals. We first compared differences in self-generated options between healthy non-depressed adults [n = 44, age = 26.3 years (SD 5.9)] and patients with major depressive disorder [n = 54, age = 24.8 years (SD 7.4)]. In the second study, a subset of depressed individuals [n = 22, age = 25.6 years (SD 7.8)] underwent PET scans with 11C-raclopride to examine the relationships between dopamine D2/D3 receptor availability and individual differences in option generation. Finally, a randomized, double-blind, placebo-controlled, three-way crossover study of modafinil (100 mg and 200 mg), was conducted in an independent sample of healthy people [n = 19, age = 23.2 years (SD 4.8)] to compare option generation under different doses of this drug. The first study revealed that patients with major depressive disorder produced significantly fewer options [t(96) = 2.68, P = 0.009, Cohen's d = 0.54], albeit with greater uniqueness [t(96) = -2.54, P = 0.01, Cohen's d = 0.52], on the option generation task compared to healthy controls. In the second study, we found that 11C-raclopride binding potential in the putamen was negatively correlated with fluency (r = -0.69, P = 0.001) but positively associated with uniqueness (r = 0.59, P = 0.007). Hence, depressed individuals with higher densities of unoccupied putamen D2/D3 receptors in the putamen generated fewer but more unique options, whereas patients with lower D2/D3 receptor availability were likely to produce a larger number of similar options. Finally, healthy participants were less unique [F(2,36) = 3.32, P = 0.048, partial η2 = 0.16] and diverse [F(2,36) = 4.31, P = 0.021, partial η2 = 0.19] after taking 200 mg versus 100 mg and 0 mg of modafinil, while fluency increased linearly with dosage at a trend level [F(1,18) = 4.11, P = 0.058, partial η2 = 0.19]. Our results show, for the first time, that option generation is affected in clinical depression and that dopaminergic activity in the putamen of patients with major depressive disorder may play a key role in the self-generation of options. Modafinil was also found to influence option generation in healthy people by reducing the creativity of options produced.

Longitudinal Imaging in a Patient With Opioid-associated Amnestic Syndrome.

Since 2012, individuals with a history of opioid misuse have infrequently been observed to develop a sudden-onset amnestic syndrome associated with bilateral hippocampal-restricted diffusion on MRI. Follow-up imaging of this opioid-associated amnestic syndrome (OAS) has revealed persistent hippocampal abnormalities. Given these observations, as well as neuropathological studies demonstrating excessive tau deposition in the hippocampi and other brain regions of individuals with opioid misuse, we describe longitudinal imaging of a patient with a history of OAS from presentation through 53 months later, when tau positron emission tomography (PET) was performed. Our patient was a 21-year-old woman with a history of attention-deficit hyperactivity disorder and substance use disorder, including opioids (intravenous heroin), who was hospitalized for acute-onset, dense anterograde amnesia. Her urine toxicology screen was positive for opiates. On presentation, her brain MRI showed restricted diffusion as well as T2 and fluid-attenuated inversion recovery (FLAIR) hyperintensity of the hippocampi and globi pallidi. On day 3, magnetic resonance spectroscopy of a right hippocampal region of interest showed a mild reduction of N-acetyl aspartate/creatine, slight elevation of choline/creatine, and the appearance of lactate/lipid and glutamate/glutamine peaks. At 4.5 months, there was resolution of restricted diffusion on MRI, although a minimal anterior T2 and FLAIR hyperintense signal in the right hippocampus persisted. However, by 53 months, when mild memory loss was reported, the hippocampi appeared normal on MRI, and [ 18 F]T807 (tau) PET showed no uptake suggestive of tau deposition. This case report supports the investigation into the hypothesis that OAS may follow a trajectory of reversible metabolic injury.

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.

Detailed radiosynthesis of [18 F]mG4P027 as a positron emission tomography radiotracer for mGluR4.

We report here the detailed radiosynthesis of [18 F]mG4P027, a metabotropic glutamate receptor 4 (mGluR4) PET radiotracer, which showed superior properties to the currently reported mGluR4 radiotracers. The radiosynthesis in the automated system has been challenging, therefore we disclose here the major limiting factors for the synthesis via step-by-step examination. And we hope this thorough study will help its automation for human use in the future.

Increased Macrophage-Specific Arterial Infiltration Relates to Noncalcified Plaque and Systemic Immune Activation in People With Human Immunodeficiency Virus.

BACKGROUND: Persistent immune activation is thought to contribute to heightened atherosclerotic cardiovascular disease (ASCVD) risk among people with human immunodeficiency virus (PWH). METHODS: Participants (≥18 years) with or without human immunodeficiency virus (HIV) and without history of clinical ASCVD were enrolled. We hypothesized that increased macrophage-specific arterial infiltration would relate to plaque composition and systemic immune activation among PWH. We applied a novel targeted molecular imaging approach (technetium-99m [99mTc]-tilmanocept single photon emission computed tomography [SPECT]/CT) and comprehensive immune phenotyping. RESULTS: Aortic 99mTc-tilmanocept uptake was significantly higher among PWH (n = 20) than participants without HIV (n = 10) with similar 10-year ASCVD risk (P = .02). Among PWH, but not among participants without HIV, noncalcified aortic plaque volume related directly to aortic 99mTc-tilmanocept uptake at different uptake thresholds. An interaction (P = .001) was seen between HIV status and noncalcified plaque volume, but not calcified plaque (P = .83). Systemic levels of caspase-1 (P = .004), CD14-CD16+ (nonclassical/patrolling/homing) monocytes (P = .0004) and CD8+ T cells (P = .005) related positively and CD4+/CD8+ T-cell ratio (P = .02) inversely to aortic 99mTc-tilmanocept uptake volume. CONCLUSIONS: Macrophage-specific arterial infiltration was higher among PWH and related to noncalcified aortic plaque volume only among PWH. Key systemic markers of immune activation relating to macrophage-specific arterial infiltration may contribute to heightened ASCVD risk among PWH. CLINICAL TRIALS REGISTRATION: NCT02542371.

Free-breathing 3D cardiac T1 mapping with transmit B1 correction at 3T.

PURPOSE: To develop a cardiac T1 mapping method for free-breathing 3D T1 mapping of the whole heart at 3 T with transmit B1 ( B1+ ) correction. METHODS: A free-breathing, electrocardiogram-gated inversion-recovery sequence with spoiled gradient-echo readout was developed and optimized for cardiac T1 mapping at 3 T. High-frame-rate dynamic images were reconstructed from sparse (k,t)-space data acquired along a stack-of-stars trajectory using a subspace-based method for accelerated imaging. Joint T1 and flip-angle estimation was performed in T1 mapping to improve its robustness to B1+ inhomogeneity. Subject-specific timing of data acquisition was used in the estimation to account for natural heart-rate variations during the imaging experiment. RESULTS: Simulations showed that accuracy and precision of T1 mapping can be improved with joint T1 and flip-angle estimation and optimized electrocardiogram-gated spoiled gradient echo-based inversion-recovery acquisition scheme. The phantom study showed good agreement between the T1 maps from the proposed method and the reference method. Three-dimensional cardiac T1 maps (40 slices) were obtained at a 1.9-mm in-plane and 4.5-mm through-plane spatial resolution from healthy subjects (n = 6) with an average imaging time of 14.2 ± 1.6 minutes (heartbeat rate: 64.2 ± 7.1 bpm), showing myocardial T1 values comparable to those obtained from modified Look-Locker inversion recovery. The proposed method generated B1+ maps with spatially smooth variation showing 21%-32% and 11%-15% variations across the septal-lateral and inferior-anterior regions of the myocardium in the left ventricle. CONCLUSION: The proposed method allows free-breathing 3D T1 mapping of the whole heart with transmit B1 correction in a practical imaging time.

Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning.

Arterial spin labeling (ASL) imaging is a powerful magnetic resonance imaging technique that allows to quantitatively measure blood perfusion non-invasively, which has great potential for assessing tissue viability in various clinical settings. However, the clinical applications of ASL are currently limited by its low signal-to-noise ratio (SNR), limited spatial resolution, and long imaging time. In this work, we propose an unsupervised deep learning-based image denoising and reconstruction framework to improve the SNR and accelerate the imaging speed of high resolution ASL imaging. The unique feature of the proposed framework is that it does not require any prior training pairs but only the subject's own anatomical prior, such as T1-weighted images, as network input. The neural network was trained from scratch in the denoising or reconstruction process, with noisy images or sparely sampled k-space data as training labels. Performance of the proposed method was evaluated using in vivo experiment data obtained from 3 healthy subjects on a 3T MR scanner, using ASL images acquired with 44-min acquisition time as the ground truth. Both qualitative and quantitative analyses demonstrate the superior performance of the proposed txtc framework over the reference methods. In summary, our proposed unsupervised deep learning-based denoising and reconstruction framework can improve the image quality and accelerate the imaging speed of ASL imaging.

Identifying the individual metabolic abnormities from a systemic perspective using whole-body PET imaging.

INTRODUCTION: Distinct physiological states arise from complex interactions among the various organs present in the human body. PET is a non-invasive modality with numerous successful applications in oncology, neurology, and cardiology. However, while PET imaging has been applied extensively in detecting focal lesions or diseases, its potential in detecting systemic abnormalities is seldom explored, mostly because total-body imaging was not possible until recently. METHODS: In this context, the present study proposes a framework capable of constructing an individual metabolic abnormality network using a subject's whole-body 18F-FDG SUV image and a normal control database. The developed framework was evaluated in the patients with lung cancer, the one discharged after suffering from Covid-19 disease, and the one that had gastrointestinal bleeding with the underlying cause unknown. RESULTS: The framework could successfully capture the deviation of these patients from healthy subjects at the level of both system and organ. The strength of the altered network edges revealed the abnormal metabolic connection between organs. The overall deviation of the network nodes was observed to be highly correlated to the organ SUV measures. Therefore, the molecular connectivity of glucose metabolism was characterized at a single subject level. CONCLUSION: The proposed framework represents a significant step toward the use of PET imaging for identifying metabolic dysfunction from a systemic perspective. A better understanding of the underlying biological mechanisms and the physiological interpretation of the interregional connections identified in the present study warrant further research.

A cross-scanner and cross-tracer deep learning method for the recovery of standard-dose imaging quality from low-dose PET.

PURPOSE: A critical bottleneck for the credibility of artificial intelligence (AI) is replicating the results in the diversity of clinical practice. We aimed to develop an AI that can be independently applied to recover high-quality imaging from low-dose scans on different scanners and tracers. METHODS: Brain [18F]FDG PET imaging of 237 patients scanned with one scanner was used for the development of AI technology. The developed algorithm was then tested on [18F]FDG PET images of 45 patients scanned with three different scanners, [18F]FET PET images of 18 patients scanned with two different scanners, as well as [18F]Florbetapir images of 10 patients. A conditional generative adversarial network (GAN) was customized for cross-scanner and cross-tracer optimization. Three nuclear medicine physicians independently assessed the utility of the results in a clinical setting. RESULTS: The improvement achieved by AI recovery significantly correlated with the baseline image quality indicated by structural similarity index measurement (SSIM) (r = -0.71, p < 0.05) and normalized dose acquisition (r = -0.60, p < 0.05). Our cross-scanner and cross-tracer AI methodology showed utility based on both physical and clinical image assessment (p < 0.05). CONCLUSION: The deep learning development for extensible application on unknown scanners and tracers may improve the trustworthiness and clinical acceptability of AI-based dose reduction.

Organomediated cleavage of benzoyl group enables an efficient synthesis of 1-(6-nitropyridin-2-yl)thiourea and its application for developing 18F-labeled PET tracers.

A novel organomediated cleavage of benzoyl group using ethane-1,2-diamine and acetic acid under neutral condition enables an efficient synthesis of 1-(6-nitropyridin-2-yl)thiourea, which previously has been challenging to prepare by conventional methods. The successful synthesis of 1-(6-nitropyridin-2-yl)thiourea as a synthon permits development of a variety of 18F labeled heterocycles as PET imaging ligands such as N-(pyridin-2-yl)thiazol-2-amine derivatives. The utility of this synthon is demonstrated with the synthesis of a 18F-labeled PET tracer for studying prion disease. In vitro autoradiography using this PET tracer on sagittal rat brain slices showed highest accumulation of radioactivity in the hippocampus, cortex, and striatum, in accordance with reported immunostaining of PrPc in rat brain.

Magnetic resonance parameter mapping using model-guided self-supervised deep learning.

PURPOSE: To develop a model-guided self-supervised deep learning MRI reconstruction framework called reference-free latent map extraction (RELAX) for rapid quantitative MR parameter mapping. METHODS: Two physical models are incorporated for network training in RELAX, including the inherent MR imaging model and a quantitative model that is used to fit parameters in quantitative MRI. By enforcing these physical model constraints, RELAX eliminates the need for full sampled reference data sets that are required in standard supervised learning. Meanwhile, RELAX also enables direct reconstruction of corresponding MR parameter maps from undersampled k-space. Generic sparsity constraints used in conventional iterative reconstruction, such as the total variation constraint, can be additionally included in the RELAX framework to improve reconstruction quality. The performance of RELAX was tested for accelerated T1 and T2 mapping in both simulated and actually acquired MRI data sets and was compared with supervised learning and conventional constrained reconstruction for suppressing noise and/or undersampling-induced artifacts. RESULTS: In the simulated data sets, RELAX generated good T1 /T2 maps in the presence of noise and/or undersampling artifacts, comparable to artifact/noise-free ground truth. The inclusion of a spatial total variation constraint helps improve image quality. For the in vivo T1 /T2 mapping data sets, RELAX achieved superior reconstruction quality compared with conventional iterative reconstruction, and similar reconstruction performance to supervised deep learning reconstruction. CONCLUSION: This work has demonstrated the initial feasibility of rapid quantitative MR parameter mapping based on self-supervised deep learning. The RELAX framework may also be further extended to other quantitative MRI applications by incorporating corresponding quantitative imaging models.

Accelerated J-resolved 1 H-MRSI with limited and sparse sampling of ( k,t1,t2 -space.

PURPOSE: To accelerate the acquisition of J-resolved proton magnetic resonance spectroscopic imaging (1 H-MRSI) data for high-resolution mapping of brain metabolites and neurotransmitters. METHODS: The proposed method used a subspace model to represent multidimensional spatiospectral functions, which significantly reduced the number of parameters to be determined from J-resolved 1 H-MRSI data. A semi-LASER-based (Localization by Adiabatic SElective Refocusing) echo-planar spectroscopic imaging (EPSI) sequence was used for data acquisition. The proposed data acquisition scheme sampled k,t1,t2 -space in variable density, where t1 and t2 specify the J-coupling and chemical-shift encoding times, respectively. Selection of the J-coupling encoding times (or, echo time values) was based on a Cramer-Rao lower bound analysis, which were optimized for gamma-aminobutyric acid (GABA) detection. In image reconstruction, parameters of the subspace-based spatiospectral model were determined by solving a constrained optimization problem. RESULTS: Feasibility of the proposed method was evaluated using both simulated and experimental data from a spectroscopic phantom. The phantom experimental results showed that the proposed method, with a factor of 12 acceleration in data acquisition, could determine the distribution of J-coupled molecules with expected accuracy. In vivo study with healthy human subjects also showed that 3D maps of brain metabolites and neurotransmitters can be obtained with a nominal spatial resolution of 3.0 × 3.0 × 4.8 mm3 from J-resolved 1 H-MRSI data acquired in 19.4 min. CONCLUSIONS: This work demonstrated the feasibility of highly accelerated J-resolved 1 H-MRSI using limited and sparse sampling of k,t1,t2 -space and subspace modeling. With further development, the proposed method may enable high-resolution mapping of brain metabolites and neurotransmitters in clinical applications.

Attenuation correction using deep Learning and integrated UTE/multi-echo Dixon sequence: evaluation in amyloid and tau PET imaging.

PURPOSE: PET measures of amyloid and tau pathologies are powerful biomarkers for the diagnosis and monitoring of Alzheimer's disease (AD). Because cortical regions are close to bone, quantitation accuracy of amyloid and tau PET imaging can be significantly influenced by errors of attenuation correction (AC). This work presents an MR-based AC method that combines deep learning with a novel ultrashort time-to-echo (UTE)/multi-echo Dixon (mUTE) sequence for amyloid and tau imaging. METHODS: Thirty-five subjects that underwent both 11C-PiB and 18F-MK6240 scans were included in this study. The proposed method was compared with Dixon-based atlas method as well as magnetization-prepared rapid acquisition with gradient echo (MPRAGE)- or Dixon-based deep learning methods. The Dice coefficient and validation loss of the generated pseudo-CT images were used for comparison. PET error images regarding standardized uptake value ratio (SUVR) were quantified through regional and surface analysis to evaluate the final AC accuracy. RESULTS: The Dice coefficients of the deep learning methods based on MPRAGE, Dixon, and mUTE images were 0.84 (0.91), 0.84 (0.92), and 0.87 (0.94) for the whole-brain (above-eye) bone regions, respectively, higher than the atlas method of 0.52 (0.64). The regional SUVR error for the atlas method was around 6%, higher than the regional SUV error. The regional SUV and SUVR errors for all deep learning methods were below 2%, with mUTE-based deep learning method performing the best. As for the surface analysis, the atlas method showed the largest error (> 10%) near vertices inside superior frontal, lateral occipital, superior parietal, and inferior temporal cortices. The mUTE-based deep learning method resulted in the least number of regions with error higher than 1%, with the largest error (> 5%) showing up near the inferior temporal and medial orbitofrontal cortices. CONCLUSION: Deep learning with mUTE can generate accurate AC for amyloid and tau imaging in PET/MR.