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.

PET imaging of neurotransmission using direct parametric reconstruction.

Receptor ligand-based dynamic Positron Emission Tomography (PET) permits the measurement of neurotransmitter release in the human brain. For single-scan paradigms, the conventional method of estimating changes in neurotransmitter levels relies on fitting a pharmacokinetic model to activity concentration histories extracted after PET image reconstruction. However, due to the statistical fluctuations of activity concentration data at the voxel scale, parametric images computed using this approach often exhibit low signal-to-noise ratio, impeding characterization of neurotransmitter release. Numerous studies have shown that direct parametric reconstruction (DPR) approaches, which combine image reconstruction and kinetic analysis in a unified framework, can improve the signal-to-noise ratio of parametric mapping. However, there is little experience with DPR in imaging of neurotransmission and the performance of the approach in this application has not been evaluated before in humans. In this report, we present and evaluate a DPR methodology that computes 3-D distributions of ligand transport, binding potential (BPND) and neurotransmitter release magnitude (γ) from a dynamic sequence of PET sinograms. The technique employs the linear simplified reference region model (LSRRM) of Alpert et al. (2003), which represents an extension of the simplified reference region model that incorporates time-varying binding parameters due to radioligand displacement by release of neurotransmitter. Estimation of parametric images is performed by gradient-based optimization of a Poisson log-likelihood function incorporating LSRRM kinetics and accounting for the effects of head movement, attenuation, detector sensitivity, random and scattered coincidences. A 11C-raclopride simulation study showed that the proposed approach substantially reduces the bias and variance of voxel-wise γ estimates as compared to standard methods. Moreover, simulations showed that detection of release could be made more reliable and/or conducted using a smaller sample size using the proposed DPR estimator. Likewise, images of BPND computed using DPR had substantially improved bias and variance properties. Application of the method in human subjects was demonstrated using 11C-raclopride dynamic scans and a reward task, confirming the improved quality of the estimated parametric images using the proposed approach.

Orexin A alleviates neuroinflammation via OXR2/CaMKKß/AMPK signaling pathway after ICH in mice.

BACKGROUND: Orexins are two neuropeptides (orexin A, OXA; orexin B, OXB) secreted mainly from the lateral hypothalamus, which exert a wide range of physiological effects by activating two types of receptors (orexin receptor 1, OXR1; orexin receptor 2, OXR2). OXA has equal affinity for OXR1 and OXR2, whereas OXB binds preferentially to OXR2. OXA rapidly crosses the blood-brain barrier by simple diffusion. Many studies have reported OXA's protective effect on neurological diseases via regulating inflammatory response which is also a fundamental pathological process in intracerebral hemorrhage (ICH). However, neuroprotective mechanisms of OXA have not been explored in ICH. METHODS: ICH models were established using stereotactic injection of autologous arterial blood into the right basal ganglia of male CD-1 mice. Exogenous OXA was administered intranasally; CaMKKß inhibitor (STO-609), OXR1 antagonist (SB-334867), and OXR2 antagonist (JNJ-10397049) were administered intraperitoneally. Neurobehavioral tests, hematoma volume, and brain water content were evaluated after ICH. Western blot and ELISA were utilized to evaluate downstream mechanisms. RESULTS: OXA, OXR1, and OXR2 were expressed moderately in microglia and astrocytes and abundantly in neurons. Expression of OXA decreased whereas OXR1 and OXR2 increased after ICH. OXA treatment significantly improved not only short-term but also long-term neurofunctional outcomes and reduced brain edema in ipsilateral hemisphere. OXA administration upregulated p-CaMKKß, p-AMPK, and anti-inflammatory cytokines while downregulated p-NFκB and pro-inflammatory cytokines after ICH; this effect was reversed by STO-609 or JNJ-10397049 but not SB-334867. CONCLUSIONS: OXA improved neurofunctional outcomes and mitigated brain edema after ICH, possibly through alleviating neuroinflammation via OXR2/CaMKKß/AMPK pathway.

Motion compensation for brain PET imaging using wireless MR active markers in simultaneous PET-MR: phantom and non-human primate studies.

Brain PET scanning plays an important role in the diagnosis, prognostication and monitoring of many brain diseases. Motion artifacts from head motion are one of the major hurdles in brain PET. In this work, we propose to use wireless MR active markers to track head motion in real time during a simultaneous PET-MR brain scan and incorporate the motion measured by the markers in the listmode PET reconstruction. Several wireless MR active markers and a dedicated fast MR tracking pulse sequence module were built. Data were acquired on an ACR Flangeless PET phantom with multiple spheres and a non-human primate with and without motion. Motions of the phantom and monkey's head were measured with the wireless markers using a dedicated MR tracking sequence module. The motion PET data were reconstructed using list-mode reconstruction with and without motion correction. Static reference was used as gold standard for quantitative analysis. The motion artifacts, which were prominent on the images without motion correction, were eliminated by the wireless marker based motion correction in both the phantom and monkey experiments. Quantitative analysis was performed on the phantom motion data from 24 independent noise realizations. The reduction of bias of sphere-to-background PET contrast by active marker based motion correction ranges from 26% to 64% and 17% to 25% for hot (i.e., radioactive) and cold (i.e., non-radioactive) spheres, respectively. The motion correction improved the channelized Hotelling observer signal-to-noise ratio of the spheres by 1.2 to 6.9 depending on their locations and sizes. The proposed wireless MR active marker based motion correction technique removes the motion artifacts in the reconstructed PET images and yields accurate quantitative values.

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.

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.

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.

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.

Arterial spin labeled perfusion imaging with balanced steady-state free precession readout and radial sampling.

PURPOSE: To develop an arterial spin labeling (ASL) perfusion imaging method with balanced steady-state free precession (bSSFP) readout and radial sampling for improved SNR and robustness to motion and off-resonance effects. METHODS: An ASL perfusion imaging method was developed with pseudo-continuous arterial spin labeling (pCASL) and bSSFP readout. Three-dimensional (3D) k-space data were collected in segmented acquisitions following a stack-of-stars sampling trajectory. Multiple phase-cycling technique was utilized to improve the robustness to off-resonance effects. Parallel imaging with sparsity-constrained image reconstruction was used to accelerate imaging or increase the spatial coverage. RESULTS: ASL with bSSFP readout showed higher spatial and temporal SNRs of the gray matter perfusion signal compared to those from spoiled gradient-recalled acquisition (SPGR). Cartesian and radial sampling schemes showed similar spatial and temporal SNRs, regardless of the imaging readout. In case of severe B0 inhomogeneity, single-RF phase incremented bSSFP acquisitions showed banding artifacts. These artifacts were significantly reduced when multiple phase-cycling technique (N = 4) was employed. The perfusion-weighted images obtained by the Cartesian sampling scheme showed respiratory motion-related artifacts when a high segmentation number was used. The perfusion-weighted images obtained by the radial sampling scheme did not show these artifacts. Whole brain perfusion imaging was feasible in 1.15 min or 4.6 min for cases without and with phase-cycling (N = 4), respectively, using the proposed method with parallel imaging. CONCLUSIONS: The developed method allows non-invasive perfusion imaging of the whole-brain with relatively high SNR and robustness to motion and off-resonance effects in a practically feasible imaging time.

Posterior Estimation Using Deep Learning: A Simulation Study of Compartmental Modeling in Dynamic PET.

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, i.e., 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 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.

Impact of motion correction on [18F]-MK6240 tau PET imaging.

Objective. Positron emission tomography (PET) imaging of tau deposition using [18F]-MK6240 often involves long acquisitions in older subjects, many of whom exhibit dementia symptoms. The resulting unavoidable head motion can greatly degrade image quality. Motion increases the variability of PET quantitation for longitudinal studies across subjects, resulting in larger sample sizes in clinical trials of Alzheimer's disease (AD) treatment.Approach. After using an ultra-short frame-by-frame motion detection method based on the list-mode data, we applied an event-by-event list-mode reconstruction to generate the motion-corrected images from 139 scans acquired in 65 subjects. This approach was initially validated in two phantoms experiments against optical tracking data. We developed a motion metric based on the average voxel displacement in the brain to quantify the level of motion in each scan and consequently evaluate the effect of motion correction on images from studies with substantial motion. We estimated the rate of tau accumulation in longitudinal studies (51 subjects) by calculating the difference in the ratio of standard uptake values in key brain regions for AD. We compared the regions' standard deviations across subjects from motion and non-motion-corrected images.Main results. Individually, 14% of the scans exhibited notable motion quantified by the proposed motion metric, affecting 48% of the longitudinal datasets with three time points and 25% of all subjects. Motion correction decreased the blurring in images from scans with notable motion and improved the accuracy in quantitative measures. Motion correction reduced the standard deviation of the rate of tau accumulation by -49%, -24%, -18%, and -16% in the entorhinal, inferior temporal, precuneus, and amygdala regions, respectively.Significance. The list-mode-based motion correction method is capable of correcting both fast and slow motion during brain PET scans. It leads to improved brain PET quantitation, which is crucial for imaging AD.

Impact of reconstruction parameters on lesion detection and localization in joint ictal/inter-ictal SPECT reconstruction.

PURPOSE: Previously, a joint ictal/inter-ictal SPECT reconstruction was proposed to reconstruct a differential image representing the change of brain SPECT image from an inter-ictal to an ictal study. The so-called joint method yielded better performance for epileptic foci localization than the conventional subtraction method. In this study, we evaluated the performance of different reconstruction settings of the joint reconstruction of ictal/inter-ictal SPECT data, which creates a differential image showing the difference between ictal and inter-ictal images, in lesion detection and localization in epilepsy imaging. METHODS: Differential images reconstructed from phantom data using the joint and the subtraction methods were compared based on lesion detection performance (channelized Hotelling observer signal-to-noise ratio (SNRCHO) averaged across four lesion-to-background contrast levels) at the optimal iteration. The joint-initial method which was the joint method that was initialized by the subtraction method at optimal iteration was also used to reconstruct differential images. These three methods with respective optimal iteration and the subtraction method with four iterations were applied to epileptic patient datasets. A human observer lesion localization study was performed based on localization receiver operating characteristic (LROC) analysis. RESULTS: From the phantom study, at their respective optimal iteration, the joint method yielded an improvement in lesion detection performance over the subtraction method of 26%, which increased to 145% when using the joint-initial method. From the patient study, the joint-initial method yielded the highest area under the LROC curve as compared with those of the joint and the subtraction methods with optimal iteration and with 4 iterations (0.44 vs 0.41, 0.39 and 0.36, respectively). CONCLUSIONS: In lesion detection and localization, the joint method at optimal iteration outperformed the subtraction method at optimal iteration and at iteration typically used in clinical practice. Furthermore, initialization by the subtraction method improved the performance of the joint method.

Investigation of a Model-based Time-over-threshold Technique for Phoswich Crystal Discrimination.

The best crystal identification (CI) algorithms proposed so far for phoswich detectors are based on adaptive filtering and pulse shape discrimination (PSD). However, these techniques require free running analog to digital converters, which is no longer possible with the ever increasing pixelization of new detectors. We propose to explore the dual-threshold time-over-threshold (ToT) technique, used to measure events energy and time of occurence, as a more robust solution for crystal identification with broad energy windows in phoswich detectors. In this study, phoswich assemblies made of various combinations of LGSO and LYSO scintillators with decay times in the range 30 to 65 ns were investigated for the LabPET II detection front-end. The electronic readout is based on a 4 × 8 APD array where pixels are individually coupled to charge sensitive preamplifiers followed by first order CR-RC shapers with 75 ns peaking time. Crystal identification data were sorted out based on the measurements of likeliness between acquired signals and a time domain model of the analog front-end. Results demonstrate that crystal identification can be successfully performed using a dual-threshold ToT scheme with a discrimination accuracy of 99.1% for LGSO (30 ns)/LGSO (45 ns), 98.1% for LGSO (65 ns)/LYSO (40 ns) and 92.1% for LYSO (32 ns)/LYSO (47 ns), for an energy window of [350-650] keV. Moreover, the method shows a discrimination accuracy >97% for the two first pairs and ~90% for the last one when using a wide energy window of [250-650] keV.

Surgical Treatment and Clinical Outcomes of Petroclival Meningiomas: A Single-Center Experience of 107 Patients.

OBJECTIVE: This study aimed to establish optimal surgical strategies via reviewing the clinical outcomes of various surgical approaches for the pertroclival meningiomas (PCMs). METHODS: This retrospective study enrolled 107 patients with PCMs at the authors' institution from year 2010 to 2020. Patient demographics, the clinical characteristics, various operative approaches, major morbidity, post-operative cranial nerve deficits and tumor progression or recurrence were analyzed. RESULTS: The subtemporal transtentorial approach (STA), the Kawase approach (KA), the retrosigmoid approach (RSA) and the anterior sigmoid approach (ASA), namely the posterior petrosal approach (PPA) were adopted for 17 cases, 22 cases, 31 cases and 34 cases respectively. Total or subtotal resection was achieved in 96 cases (89.7%). The incidence of new-onset and aggravated cranial nerve dysfunction were 13.1% (14/107) and 10.4% (15/144), respectively. Furthermore, 14 cases suffered from intracranial infection, 9 cases had cerebrospinal fluid leakage, and 3 cases sustained intracranial hematoma (1 case underwent second operation). The mean preoperative and postoperative Karnofsky Performance Status (KPS) score was 80 (range 60-100) and 78.6 (range 0-100), but this was not statistically significant (P>0.05). After a mean follow-up of 5.1 years (range 0.3- 10.6 years), tumor progression or recurrence was confirmed in 23 cases. Two cases died from postoperative complications. CONCLUSIONS: For the treatment of PCMs, it is still a challenge to achieve total resection. With elaborate surgical plans and advanced microsurgical skills, most patients with PCMs can be rendered tumor resection with satisfactory extent and functional preservation, despite transient neurological deterioration during early postoperative periods.

Detecting lumbar lesions in 99m Tc-MDP SPECT by deep learning: Comparison with physicians.

PURPOSE: 99m Tc-MDP single-photon emission computed tomography (SPECT) is an established tool for diagnosing lumbar stress, a common cause of low back pain (LBP) in pediatric patients. However, detection of small stress lesions is complicated by the low quality of SPECT, leading to significant interreader variability. The study objectives were to develop an approach based on a deep convolutional neural network (CNN) for detecting lumbar lesions in 99m Tc-MDP scans and to compare its performance to that of physicians in a localization receiver operating characteristic (LROC) study. METHODS: Sixty-five lesion-absent (LA) 99m Tc-MDP studies performed in pediatric patients for evaluating LBP were retrospectively identified. Projections for an artificial focal lesion were acquired separately by imaging a 99m Tc capillary tube at multiple distances from the collimator. An approach was developed to automatically insert lesions into LA scans to obtain realistic lesion-present (LP) 99m Tc-MDP images while ensuring knowledge of the ground truth. A deep CNN was trained using 2.5D views extracted in LP and LA 99m Tc-MDP image sets. During testing, the CNN was applied in a sliding-window fashion to compute a 3D "heatmap" reporting the probability of a lesion being present at each lumbar location. The algorithm was evaluated using cross-validation on a 99m Tc-MDP test dataset which was also studied by five physicians in a LROC study. LP images in the test set were obtained by incorporating lesions at sites selected by a physician based on clinical likelihood of injury in this population. RESULTS: The deep learning (DL) system slightly outperformed human observers, achieving an area under the LROC curve (AUCLROC ) of 0.830 (95% confidence interval [CI]: [0.758, 0.924]) compared with 0.785 (95% CI: [0.738, 0.830]) for physicians. The AUCLROC for the DL system was higher than that of two readers (difference in AUCLROC [ΔAUCLROC ] = 0.049 and 0.053) who participated to the study and slightly lower than that of two other readers (ΔAUCLROC  = -0.006 and -0.012). Another reader outperformed DL by a more substantial margin (ΔAUCLROC  = -0.053). CONCLUSION: The DL system provides comparable or superior performance than physicians in localizing small 99m Tc-MDP positive lumbar lesions.

Quantitative PET in the 2020s: a roadmap.

Positron emission tomography (PET) plays an increasingly important role in research and clinical applications, catalysed by remarkable technical advances and a growing appreciation of the need for reliable, sensitive biomarkers of human function in health and disease. Over the last 30 years, a large amount of the physics and engineering effort in PET has been motivated by the dominant clinical application during that period, oncology. This has led to important developments such as PET/CT, whole-body PET, 3D PET, accelerated statistical image reconstruction, and time-of-flight PET. Despite impressive improvements in image quality as a result of these advances, the emphasis on static, semi-quantitative 'hot spot' imaging for oncologic applications has meant that the capability of PET to quantify biologically relevant parameters based on tracer kinetics has not been fully exploited. More recent advances, such as PET/MR and total-body PET, have opened up the ability to address a vast range of new research questions, from which a future expansion of applications and radiotracers appears highly likely. Many of these new applications and tracers will, at least initially, require quantitative analyses that more fully exploit the exquisite sensitivity of PET and the tracer principle on which it is based. It is also expected that they will require more sophisticated quantitative analysis methods than those that are currently available. At the same time, artificial intelligence is revolutionizing data analysis and impacting the relationship between the statistical quality of the acquired data and the information we can extract from the data. In this roadmap, leaders of the key sub-disciplines of the field identify the challenges and opportunities to be addressed over the next ten years that will enable PET to realise its full quantitative potential, initially in research laboratories and, ultimately, in clinical practice.

Motion correction for PET data using subspace-based real-time MR imaging in simultaneous PET/MR.

Image quality of positron emission tomography (PET) reconstructions is degraded by subject motion occurring during the acquisition. Magnetic resonance (MR)-based motion correction approaches have been studied for PET/MR scanners and have been successful at capturing regular motion patterns, when used in conjunction with surrogate signals (e.g. navigators) to detect motion. However, handling irregular respiratory motion and bulk motion remains challenging. In this work, we propose an MR-based motion correction method relying on subspace-based real-time MR imaging to estimate motion fields used to correct PET reconstructions. We take advantage of the low-rank characteristics of dynamic MR images to reconstruct high-resolution MR images at high frame rates from highly undersampled k-space data. Reconstructed dynamic MR images are used to determine motion phases for PET reconstruction and estimate phase-to-phase nonrigid motion fields able to capture complex motion patterns such as irregular respiratory and bulk motion. MR-derived binning and motion fields are used for PET reconstruction to generate motion-corrected PET images. The proposed method was evaluated on in vivo data with irregular motion patterns. MR reconstructions accurately captured motion, outperforming state-of-the-art dynamic MR reconstruction techniques. Evaluation of PET reconstructions demonstrated the benefits of the proposed method in terms of motion artifacts reduction, improving the contrast-to-noise ratio by up to a factor 3 and achieveing a target-to-background ratio up to 90% superior compared to standard/uncorrected methods. The proposed method can improve the image quality of motion-corrected PET reconstructions in clinical applications.

Dependence of fluorodeoxyglucose (FDG) uptake on cell cycle and dry mass: a single-cell study using a multi-modal radiography platform.

High glucose uptake by cancer compared to normal tissues has long been utilized in fluorodeoxyglucose-based positron emission tomography (FDG-PET) as a contrast mechanism. The FDG uptake rate has been further related to the proliferative potential of cancer, specifically the proliferation index (PI) - the proportion of cells in S, G2 or M phases. The underlying hypothesis was that the cells preparing for cell division would consume more energy and metabolites as building blocks for biosynthesis. Despite the wide clinical use, mixed reports exist in the literature on the relationship between FDG uptake and PI. This may be due to the large variation in cancer types or methods adopted for the measurements. Of note, the existing methods can only measure the average properties of a tumor mass or cell population with highly-heterogeneous constituents. In this study, we have built a multi-modal live-cell radiography system and measured the [18F]FDG uptake by single HeLa cells together with their dry mass and cell cycle phase. The results show that HeLa cells take up twice more [18F]FDG in S, G2 or M phases than in G1 phase, which confirms the association between FDG uptake and PI at a single-cell level. Importantly, we show that [18F]FDG uptake and cell dry mass have a positive correlation in HeLa cells, which suggests that high [18F]FDG uptake in S, G2 or M phases can be largely attributed to increased dry mass, rather than the activities preparing for cell division. This interpretation is consistent with recent observations that the energy required for the preparation of cell division is much smaller than that for maintaining house-keeping proteins.

MR-based PET attenuation correction using a combined ultrashort echo time/multi-echo Dixon acquisition.

PURPOSE: To develop a magnetic resonance (MR)-based method for estimation of continuous linear attenuation coefficients (LACs) in positron emission tomography (PET) using a physical compartmental model and ultrashort echo time (UTE)/multi-echo Dixon (mUTE) acquisitions. METHODS: We propose a three-dimensional (3D) mUTE sequence to acquire signals from water, fat, and short T2 components (e.g., bones) simultaneously in a single acquisition. The proposed mUTE sequence integrates 3D UTE with multi-echo Dixon acquisitions and uses sparse radial trajectories to accelerate imaging speed. Errors in the radial k-space trajectories are measured using a special k-space trajectory mapping sequence and corrected for image reconstruction. A physical compartmental model is used to fit the measured multi-echo MR signals to obtain fractions of water, fat, and bone components for each voxel, which are then used to estimate the continuous LAC map for PET attenuation correction. RESULTS: The performance of the proposed method was evaluated via phantom and in vivo human studies, using LACs from computed tomography (CT) as reference. Compared to Dixon- and atlas-based MRAC methods, the proposed method yielded PET images with higher correlation and similarity in relation to the reference. The relative absolute errors of PET activity values reconstructed by the proposed method were below 5% in all of the four lobes (frontal, temporal, parietal, and occipital), cerebellum, whole white matter, and gray matter regions across all subjects (n = 6). CONCLUSIONS: The proposed mUTE method can generate subject-specific, continuous LAC map for PET attenuation correction in PET/MR.

Quantitative simultaneous 99mTc/123I cardiac SPECT using MC-JOSEM.

Simultaneous rest 99mTc-Sestamibi/ 123I-BMIPP cardiac SPECT imaging has the potential to replace current clinical 99mTc-Sestamibi rest/stress imaging and therefore has great potential in the case of patients with chest pain presenting to the emergency department. Separation of images of these two radionuclides is difficult, however, because their emission energies are close. The authors previously developed a fast Monte Carlo (MC)-based joint ordered-subset expectation maximization (JOSEM) iterative reconstruction algorithm (MC-JOSEM), which simultaneously compensates for scatter and cross talk as well as detector response within the reconstruction algorithm. In this work, the authors evaluated the performance of MC-JOSEM in a realistic population of 99mTc/123I studies using cardiac phantom data on a Siemens e.cam system using a standard cardiac protocol. The authors also compared the performance of MC-JOSEM for estimation tasks to that of two other methods: standard OSEM using photopeak energy windows without scatter correction (NSC-OSEM) and standard OSEM using a Compton-scatter energy window for scatter correction (SC-OSEM). For each radionuclide the authors separately acquired high-count projections of radioactivity in the myocardium wall, liver, and soft tissue background compartments of a water-filled torso phantom, and they generated synthetic projections of various dual-radionuclide activity distributions. Images of different combinations of myocardium wall/background activity concentration ratios for each radionuclide were reconstructed by NSC-OSEM, SC-OSEM, and MC-JOSEM. For activity estimation in the myocardium wall, MC-JOSEM always produced the best relative bias and relative standard deviation compared with NSC-OSEM and SC-OSEM for all the activity combinations. On average, the relative biases after 100 iterations were 8.1% for 99mTc and 3.7% for 123I with MC-JOSEM, 39.4% for 99mTc and 23.7% for 123I with NSC-OSEM, and 20.9% for 99mTc with SC-OSEM. The relative standard deviations after 30 iterations were 0.7% for 99mTc and 1.0% for 123I with MC-JOSEM, as compared to 1.1% for 99mTc and 1.2% for 123I with NSC-OSEM and 1.3% for 99mTc with SC-OSEM. Finally, the authors compared the relative standard deviation after 30 iterations with the minimum theoretical variance on activity estimation, the Cramer-Rao lower bound (CRB), and with the biased CRB. The measured precision was larger than the biased bound values by factors of 2-4, suggesting that further improvement could be made to the method.