Performance Characteristics of the NeuroEXPLORER, a Next-Generation Human Brain PET/CT Imager.

The collaboration of Yale, the University of California, Davis, and United Imaging Healthcare has successfully developed the NeuroEXPLORER, a dedicated human brain PET imager with high spatial resolution, high sensitivity, and a built-in 3-dimensional camera for markerless continuous motion tracking. It has high depth-of-interaction and time-of-flight resolutions, along with a 52.4-cm transverse field of view (FOV) and an extended axial FOV (49.5 cm) to enhance sensitivity. Here, we present the physical characterization, performance evaluation, and first human images of the NeuroEXPLORER. Methods: Measurements of spatial resolution, sensitivity, count rate performance, energy and timing resolution, and image quality were performed adhering to the National Electrical Manufacturers Association (NEMA) NU 2-2018 standard. The system's performance was demonstrated through imaging studies of the Hoffman 3-dimensional brain phantom and the mini-Derenzo phantom. Initial 18F-FDG images from a healthy volunteer are presented. Results: With filtered backprojection reconstruction, the radial and tangential spatial resolutions (full width at half maximum) averaged 1.64, 2.06, and 2.51 mm, with axial resolutions of 2.73, 2.89, and 2.93 mm for radial offsets of 1, 10, and 20 cm, respectively. The average time-of-flight resolution was 236 ps, and the energy resolution was 10.5%. NEMA sensitivities were 46.0 and 47.6 kcps/MBq at the center and 10-cm offset, respectively. A sensitivity of 11.8% was achieved at the FOV center. The peak noise-equivalent count rate was 1.31 Mcps at 58.0 kBq/mL, and the scatter fraction at 5.3 kBq/mL was 36.5%. The maximum count rate error at the peak noise-equivalent count rate was less than 5%. At 3 iterations, the NEMA image-quality contrast recovery coefficients varied from 74.5% (10-mm sphere) to 92.6% (37-mm sphere), and background variability ranged from 3.1% to 1.4% at a contrast of 4.0:1. An example human brain 18F-FDG image exhibited very high resolution, capturing intricate details in the cortex and subcortical structures. Conclusion: The NeuroEXPLORER offers high sensitivity and high spatial resolution. With its long axial length, it also enables high-quality spinal cord imaging and image-derived input functions from the carotid arteries. These performance enhancements will substantially broaden the range of human brain PET paradigms, protocols, and thereby clinical research applications.

An ultrasensitive sandwich-type electrochemical immunosensor based on rGO-TEPA/ZIF67@ZIF8/Au and AuPdRu for the detection of tumor markers CA72-4.

Cancer antigen 72-4 (CA72-4) is an important marker of cancer detection, and accurate detection of CA72-4 is urgently required. Herein, a sandwich-type immunosensor was constructed for detection CA72-4 based on composite nanomaterial as the substrate material and trimetal nanoparticles as the nanoprobe. The composite nanomaterial rGO-TEPA/ZIF67@ZIF8/Au used as a selective bio-recognition element were modified on the glassy carbon electrode (GCE) surface. Meanwhile, the electrochemical nanoprobes were fabricated through the AuPdRu trimeric metal. After the target antigen 72-4 were captured, the nanoprobes were further assembled to form an antibody1 (Ab1)- antigen-antibody2 (Ab2) nanoprobes sandwich-like system on the electrode surface. Then, hybrid the substrate material rGO-TEPA/ZIF67@ZIF8/Au and the AuPdRu trimeric metal nanoprobes efficiently catalyzed the reduction of H2O2 and amplified the electrochemical signals. Cyclic voltammetry (CV), differential pulse voltammetry (DPV), electrochemical impedance spectroscopy (EIS), and Chronoamperometry (I-T) methods were used to characterize the performance and detection capabilities for CA72-4 of the prepared immunosensors. The results showed that the detection limit was 1.8 × 10-5 U/mL (S/N = 3), and the linear range was 0.001-1000 U/mL. This study provides a new signal amplification strategy for electrochemical sensors and a theoretical basis for the clinical application of immunosensor to detect other tumor markers.

Development and performance of SIAT bPET: a high-resolution and high-sensitivity MR-compatible brain PET scanner using dual-ended readout detectors.

PURPOSE: Positron emission tomography/magnetic resonance imaging (PET/MRI) is a powerful tool for brain imaging, but the spatial resolution of the PET scanners currently used for brain imaging can be further improved to enhance the quantitative accuracy of brain PET imaging. The purpose of this study is to develop an MR-compatible brain PET scanner that can simultaneously achieve a uniform high spatial resolution and high sensitivity by using dual-ended readout depth encoding detectors. METHODS: The MR-compatible brain PET scanner, named SIAT bPET, consists of 224 dual-ended readout detectors. Each detector contains a 26 × 26 lutetium yttrium oxyorthosilicate (LYSO) crystal array of 1.4 × 1.4 × 20 mm3 crystal size read out by two 10 × 10 silicon photomultiplier (SiPM) arrays from both ends. The scanner has a detector ring diameter of 376.8 mm and an axial field of view (FOV) of 329 mm. The performance of the scanner including spatial resolution, sensitivity, count rate, scatter fraction, and image quality was measured. Imaging studies of phantoms and the brain of a volunteer were performed. The mutual interferences of the PET insert and the uMR790 3 T MRI scanner were measured, and simultaneous PET/MRI imaging of the brain of a volunteer was performed. RESULTS: A spatial resolution of better than 1.5 mm with an average of 1.2 mm within the whole FOV was obtained. A sensitivity of 11.0% was achieved at the center FOV for an energy window of 350-750 keV. Except for the dedicated RF coil, which caused a ~ 30% reduction of the sensitivity of the PET scanner, the MRI sequences running had a negligible effect on the performance of the PET scanner. The reduction of the SNR and homogeneity of the MRI images was less than 2% as the PET scanner was inserted to the MRI scanner and powered-on. High quality PET and MRI images of a human brain were obtained from simultaneous PET/MRI scans. CONCLUSION: The SIAT bPET scanner achieved a spatial resolution and sensitivity better than all MR-compatible brain PET scanners developed up to date. It can be used either as a standalone brain PET scanner or a PET insert placed inside a commercial whole-body MRI scanner to perform simultaneous PET/MRI imaging.

MULTI-TASK DEEP LEARNING AND UNCERTAINTY ESTIMATION FOR PET HEAD MOTION CORRECTION.

Head motion occurring during brain positron emission tomography images acquisition leads to a decrease in image quality and induces quantification errors. We have previously introduced a Deep Learning Head Motion Correction (DL-HMC) method based on supervised learning of gold-standard Polaris Vicra motion tracking device and showed the potential of this method. In this study, we upgrade our network to a multi-task architecture in order to include image appearance prediction in the learning process. This multi-task Deep Learning Head Motion Correction (mtDL-HMC) model was trained on 21 subjects and showed enhanced motion prediction performance compared to our previous DL-HMC method on both quantitative and qualitative results for 5 testing subjects. We also evaluate the trustworthiness of network predictions by performing Monte Carlo Dropout at inference on testing subjects. We discard the data associated with a great motion prediction uncertainty and show that this does not harm the quality of reconstructed images, and can even improve it.

Markerless head motion tracking and event-by-event correction in brain PET.

Objective.Head motion correction (MC) is an essential process in brain positron emission tomography (PET) imaging. We have used the Polaris Vicra, an optical hardware-based motion tracking (HMT) device, for PET head MC. However, this requires attachment of a marker to the subject's head. Markerless HMT (MLMT) methods are more convenient for clinical translation than HMT with external markers. In this study, we validated the United Imaging Healthcare motion tracking (UMT) MLMT system using phantom and human point source studies, and tested its effectiveness on eight18F-FPEB and four11C-LSN3172176 human studies, with frame-based region of interest (ROI) analysis. We also proposed an evaluation metric, registration quality (RQ), and compared it to a data-driven evaluation method, motion-corrected centroid-of-distribution (MCCOD).Approach.UMT utilized a stereovision camera with infrared structured light to capture the subject's real-time 3D facial surface. Each point cloud, acquired at up to 30 Hz, was registered to the reference cloud using a rigid-body iterative closest point registration algorithm.Main results.In the phantom point source study, UMT exhibited superior reconstruction results than the Vicra with higher spatial resolution (0.35 ± 0.27 mm) and smaller residual displacements (0.12 ± 0.10 mm). In the human point source study, UMT achieved comparable performance as Vicra on spatial resolution with lower noise. Moreover, UMT achieved comparable ROI values as Vicra for all the human studies, with negligible mean standard uptake value differences, while no MC results showed significant negative bias. TheRQevaluation metric demonstrated the effectiveness of UMT and yielded comparable results to MCCOD.Significance.We performed an initial validation of a commercial MLMT system against the Vicra. Generally, UMT achieved comparable motion-tracking results in all studies and the effectiveness of UMT-based MC was demonstrated.

Spatio-Temporal Variations and Influencing Factors of Country-Level Carbon Emissions for Northeast China Based on VIIRS Nighttime Lighting Data.

This paper constructs a county-level carbon emission inversion model in Northeast China. We first fit the nighttime light data of the Visible Infrared Imaging Radiometer Suite (VIIRS) with local energy consumption statistics and carbon emissions data. We analyze the temporal and spatial characteristics of county-level energy-related carbon emissions in Northeast China from 2012 to 2020. At the same time, we use the geographic detector method to analyze the impact of various socio-economic factors on county carbon emissions under the single effect and interaction. The main results are as follows: (1) The county-level carbon emission model in Northeast China is relatively more accurate. The regression coefficient is 0.1217 and the determination coefficient R2 of the regression equation is 0.7722. More than 80% of the provinces have an error of less than 25%, meeting the estimation accuracy requirements. (2) From 2012 to 2020, the carbon emissions of county-level towns in Northeast China showed a trend of increasing first and then decreasing from 461.1159 million tons in 2012 to 405.752 million tons in 2020. It reached a peak of 486.325 million tons in 2014. (3) The regions with higher carbon emission growth rates are concentrated in the northern and coastal areas of Northeast China. The areas with low carbon emission growth rates are mainly distributed in some underdeveloped areas in the south and north in Northeast China. (4) Under the effect of the single factor urbanization rate, the added values of the secondary industry and public finance income have higher explanatory power to regional emissions. These factors promote the increase of county carbon emissions. When fiscal revenue and expenditure and the added value of the secondary industry and per capita GDP interact with the urbanization rate, respectively, the explanatory power of these factors on regional carbon emissions will be enhanced and the promotion of carbon emissions will be strengthened. The research results are helpful for exploring the changing rules and influencing factors of county carbon emissions in Northeast China and for providing data support for low-carbon development and decision making in Northeast China.

Fast Reconstruction for Deep Learning PET Head Motion Correction.

Head motion correction is an essential component of brain PET imaging, in which even motion of small magnitude can greatly degrade image quality and introduce artifacts. Building upon previous work, we propose a new head motion correction framework taking fast reconstructions as input. The main characteristics of the proposed method are: (i) the adoption of a high-resolution short-frame fast reconstruction workflow; (ii) the development of a novel encoder for PET data representation extraction; and (iii) the implementation of data augmentation techniques. Ablation studies are conducted to assess the individual contributions of each of these design choices. Furthermore, multi-subject studies are conducted on an 18F-FPEB dataset, and the method performance is qualitatively and quantitatively evaluated by MOLAR reconstruction study and corresponding brain Region of Interest (ROI) Standard Uptake Values (SUV) evaluation. Additionally, we also compared our method with a conventional intensity-based registration method. Our results demonstrate that the proposed method outperforms other methods on all subjects, and can accurately estimate motion for subjects out of the training set. All code is publicly available on GitHub: https://github.com/OnofreyLab/dl-hmc_fast_recon_miccai2023.

Cross-Attention for Improved Motion Correction in Brain PET.

Head movement during long scan sessions degrades the quality of reconstruction in positron emission tomography (PET) and introduces artifacts, which limits clinical diagnosis and treatment. Recent deep learning-based motion correction work utilized raw PET list-mode data and hardware motion tracking (HMT) to learn head motion in a supervised manner. However, motion prediction results were not robust to testing subjects outside the training data domain. In this paper, we integrate a cross-attention mechanism into the supervised deep learning network to improve motion correction across test subjects. Specifically, cross-attention learns the spatial correspondence between the reference images and moving images to explicitly focus the model on the most correlative inherent information - the head region the motion correction. We validate our approach on brain PET data from two different scanners: HRRT without time of flight (ToF) and mCT with ToF. Compared with traditional and deep learning benchmarks, our network improved the performance of motion correction by 58% and 26% in translation and rotation, respectively, in multi-subject testing in HRRT studies. In mCT studies, our approach improved performance by 66% and 64% for translation and rotation, respectively. Our results demonstrate that cross-attention has the potential to improve the quality of brain PET image reconstruction without the dependence on HMT. All code will be released on GitHub: https://github.com/OnofreyLab/dl_hmc_attention_mlcn2023.

Urban-Rural Fringe Long-Term Sequence Monitoring Based on a Comparative Study on DMSP-OLS and NPP-VIIRS Nighttime Light Data: A Case Study of Shenyang, China.

Urban-rural fringes, as special zones where urban and rural areas meet, are the most sensitive areas in the urbanization process. The quantitative identification of urban-rural fringes is the basis for studying the social structure, landscape pattern, and development gradient of fringes, and is also a prerequisite for quantitative analyses of the ecological effects of urbanization. However, few studies have been conducted to compare the identification accuracy of The US Air Force Defence Meteorological Satellite Program's (DMSP) and the Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data from the same year, subsequently enabling long time series monitoring of the urban-rural fringe. Therefore, in this study, taking Shenyang as an example, a K-means algorithm was used to delineate and compare the urban-rural fringe identification results of DMSP and VIIRS nighttime light data for 2013 and analyzed the changes between 2013 and 2020. The results of the study showed a high degree of overlap between the two types of data in 2013, with the overlap accounting for 75% of the VIIRS data identification results. Furthermore, the VIIRS identified more urban and rural details than the DMSP data. The area of the urban-rural fringe in Shenyang increased from 1872 km2 to 2537 km2, with the growth direction mainly concentrated in the southwest. This study helps to promote the study of urban-rural fringe identification from static identification to dynamic tracking, and from spatial identification to temporal identification. The research results can be applied to the comparative analysis of urban-rural differences and the study of the ecological and environmental effects of urbanization.

Enhancing cyclic and in-air stability of Ni-Rich cathodes through perovskite oxide surface coating.

Ni-rich layered oxides, such as LiNi0.8Co0.1Mn0.1O2 (NCM811), are promising cathode materials for high-energy lithium-ion batteries. However, the relatively high reactivity of Ni in NCM811 cathodes results in severe capacity fading originating from the undesired side reactions that occur at the cathode-electrolyte interface during prolonged cycling. Therefore, the trade-off between high capacity and long cycle life can obstruct the commercialization process of Ni-rich cathodes in modern lithium-ion batteries (LIBs). In addition, high sensitivity toward air upon storage greatly limits the commercial application. Herein, a facile surface modification strategy is introduced to enhance the cycling and in-air storage stability of NCM811. The NCM811 with a uniform SrTiO3 (STO) nano-coating layer exhibited outstanding electrochemical performances that could deliver a high discharge capacity of 173.5 mAh⋅g-1 after 200 cycles under 1C with a capacity retention of 90%. In contrast, the uncoated NCM811 only provided 65% capacity retention of 130.8 mAh⋅g-1 under the same conditions. Structural evolution analysis suggested that the STO coating acted as a buffer layer to suppress the dissolution of transition metal ions caused by the HF attack from the electrolyte and promote the lithium diffusion during the charge-discharge process. In addition, the constructed STO layer prevented the exposure of NCM811 to H2O and CO2 and thus effectively improved the in-air storage stability. This work offers an effective way to enhance the performance stability of Ni-rich oxides for high-performance cathodes of lithium-ion batteries.

The Effect of Urban Shrinkage on Carbon Dioxide Emissions Efficiency in Northeast China.

Climate change caused by CO2 emissions is a controversial topic in today's society; improving CO2 emission efficiency (CEE) is an important way to reduce carbon emissions. While studies have often focused on areas with high carbon and large economies, the areas with persistent contraction have been neglected. These regions do not have high carbon emissions, but are facing a continuous decline in energy efficiency; therefore, it is of great relevance to explore the impact and mechanisms of CO2 emission efficiency in shrinking areas or shrinking cities. This paper uses a super-efficiency slacks-based measure (SBM) model to measure the CO2 emission efficiency and potential CO2 emission reduction (PCR) of 33 prefecture-level cities in northeast China from 2006 to 2019. For the first time, a Tobit model is used to analyze the factors influencing CEE, using the level of urban shrinkage as the core variable, with socio-economic indicators and urban construction indicators as control variables, while the mediating effect model is applied to identify the transmission mechanism of urban shrinkage. The results show that the CEE index of cities in northeast China is decreasing by 1.75% per annum. For every 1% increase in urban shrinkage, CEE decreased by approximately 2.1458%, with urban shrinkage, industrial structure, and expansion intensity index (EII) being the main factors influencing CEE. At the same time, urban shrinkage has a further dampening effect on CEE by reducing research and development expenditure (R&D) and urban compactness (COMP), with each 1% increase in urban shrinkage reducing R&D and COMP by approximately 0.534% and 1.233%, respectively. This can be improved by making full use of the available built-up space, increasing urban density, and promoting investment in research.

Physical and imaging performance of SIAT aPET under different energy windows and timing windows.

PURPOSE: The performance of small animal PET scanners depends on the energy window (EW) and timing window (TW). In National Electrical Manufacturers Association (NEMA) Standards Publication NU 4-2008, detailed procedures of the performance measurements are defined, but the EW and TW are not specified. In this work, the effects of EW and TW on the physical and imaging performance of Shenzhen Institute of Advanced Technology small animal PET (SIAT aPET) will be evaluated. METHODS: First, the flood histogram, energy resolution, and timing resolution were measured for a detector of SIAT aPET. Second, the spatial resolutions were measured with different EWs. Third, the sensitivities, the scatter fractions (SFs), and noise equivalent count rates (NECRs) of a mouse-sized phantom and a rat-sized phantom, the recovery coefficients (RCs) of rods of different sizes, and the percentage standard deviation (%STD) of the NEMA image quality phantom were measured for different EWs and TWs. Last, images of a hot rod phantom, a mouse heart, and a rat brain were acquired from the scanner with different EWs. RESULTS: The SIAT aPET detectors provided good flood histograms such that all but the corner crystals can be resolved even with lower energies of 250-350 keV, an average energy resolution of 21.1 ± 1.9%, and an average timing resolution of 2.63 ± 0.69 ns. The average spatial resolutions obtained with EWs of 250-350 keV and 450-550 keV are 0.68 mm and 0.75 mm. For EWs of 250-750 keV, 350-750 keV, and 450-750 keV with a fixed TW of 12 ns, the sensitivities at the center of field of view (FOV) are 16.0%, 11.9%, and 8.2%, the peak NECRs of a mouse-sized phantom are 355.6 kcps, 324.4 kcps, and 249.4 kcps, and the peak NECRs of a rat-sized phantom are 148.5 kcps, 144.3 kcps, and 117.7 kcps, respectively. For the TWs of 4 ns, 8 ns,12 ns, and 20 ns with a fixed EW of 350-750 keV, the sensitivities at the center of FOV are 9.6%, 11.4%, 11.9%, and 12.2%, the peak NECRs of a mouse-sized phantom are 260.1 kcps, 311.5 kcps, 324.4 kcps and 324.9 kcps, and the peak NECRs of a rat-sized phantom are 110.5 kcps, 137.3 kcps, 144.3 kcps, and 142.6 kcps, respectively. Narrowing the EW and TW improves the RCs of rods of all sizes, and the %STD of images obtained with different EWs and TWs are similar. Rods with diameter down to 0.8 mm can be visually resolved from images of the hot rod phantom obtained with different EWs. Images of mouse heart with high spatial resolution and rat brain with detail brain structure were obtained with different EWs. Images of both phantom and in vivo animals obtained with different EWs only showed subtle difference. CONCLUSION: The performance of SIAT aPET under different EWs and TWs was compared. The EW and TW affect the sensitivity, SF, and NECR but not the spatial resolution and animal images of SIAT aPET, which imply that careful optimization of the EW and TW is not required.

Supervised Deep Learning for Head Motion Correction in PET.

Head movement is a major limitation in brain positron emission tomography (PET) imaging, which results in image artifacts and quantification errors. Head motion correction plays a critical role in quantitative image analysis and diagnosis of nervous system diseases. However, to date, there is no approach that can track head motion continuously without using an external device. Here, we develop a deep learning-based algorithm to predict rigid motion for brain PET by lever-aging existing dynamic PET scans with gold-standard motion measurements from external Polaris Vicra tracking. We propose a novel Deep Learning for Head Motion Correction (DL-HMC) methodology that consists of three components: (i) PET input data encoder layers; (ii) regression layers to estimate the six rigid motion transformation parameters; and (iii) feature-wise transformation (FWT) layers to condition the network to tracer time-activity. The input of DL-HMC is sampled pairs of one-second 3D cloud representations of the PET data and the output is the prediction of six rigid transformation motion parameters. We trained this network in a supervised manner using the Vicra motion tracking information as gold-standard. We quantitatively evaluate DL-HMC by comparing to gold-standard Vicra measurements and qualitatively evaluate the reconstructed images as well as perform region of interest standard uptake value (SUV) measurements. An algorithm ablation study was performed to determine the contributions of each of our DL-HMC design choices to network performance. Our results demonstrate accurate motion prediction performance for brain PET using a data-driven registration approach without external motion tracking hardware. All code is publicly available on GitHub: https://github.com/OnofreyLab/dl-hmc_miccai2022.

Light-matter interactions enhanced by quasi-bound states in the continuum in a graphene-dielectric metasurface.

In this paper, we propose a graphene-dielectric metasurface to enhance the light-matter interactions in graphene. The dielectric metasurface consists of periodically arranged silicon split rings placed on the silica substrate, which supports a symmetry-protected bound state in the continuum (BIC). When perturbation is introduced into the system to break symmetry, the BIC will transform into the quasi-BIC with high quality (Q)-factor. As the graphene layer is integrated with the dielectric metasurface, the absorption of graphene can be enhanced by the quasi-BIC resonance and a bandwidth-tunable absorber can be achieved by optimizing the Fermi energy of graphene and the asymmetry parameter of the metasurface to satisfy the critical coupling condition. By varying the Fermi energy of graphene, the quasi-BIC resonances can be effectively modulated and the max transmission intensity difference is up to 81% and a smaller asymmetry parameter will lead to better modulation performance. Our results may provide theoretical support for the design of absorber and modulator based on the quasi-BIC.

Effects of the Primary NbC Elimination on the SSCC Resistance of a HSLA Steel for Oil Country Tubular Goods.

Sulfide stress corrosion cracking (SSCC) has been of particular concern in high strength low alloyed (HSLA) steels used in the oil industry, and the non-metallic inclusions are usually considered as a detrimental factor to the SSCC resistance. In the present work, continuous casting (CC) and electroslag remelting (ESR) were adopted to fabricate a 125 ksi grade steel in order to evaluate the effect of microstructure with and without primary NbC carbides (inclusions) on the SSCC resistance in the steel. It was found that ESR could remove the primary NbC carbides, and hence, slightly increase the strength without deteriorating the SSCC resistance. The elimination of primary NbC carbides caused two opposite effects on the SSCC resistance in the studied steel. On the one hand, the elimination of primary NbC carbides increased the dislocation density and the proportion of high angle boundaries (HABs), which was not good to the SSCC resistance. On the other hand, the elimination of primary NbC carbides also induced more uniform nanosized secondary NbC carbides formed during tempering, providing many irreversible hydrogen traps. These two opposite effects on SSCC resistance due to the elimination of primary NbC carbides were assumed to be offset, and thus, the SSCC resistance was not greatly improved using ESR.

Design and system evaluation of a dual-panel portable PET (DP-PET).

BACKGROUND: Integrated whole-body PET/MR technology continues to mature and is now extensively used in clinical settings. However, due to the special design architecture, integrated whole-body PET/MR comes with a few inherent limitations. Firstly, whole-body PET/MR lacks sensitivity and resolution for focused organs. Secondly, broader clinical access of integrated PET/MR has been significantly restricted due to its prohibitively high cost. The MR-compatible PET insert is an independent and removable PET scanner which can be placed within an MRI bore. However, the mobility and configurability of all existing MR-compatible PET insert prototypes remain limited. METHODS: An MR-compatible portable PET insert prototype, dual-panel portable PET (DP-PET), has been developed for simultaneous PET/MR imaging. Using SiPM, digital readout electronics, novel carbon fiber shielding, phase-change cooling, and MRI compatible battery power, DP-PET was designed to achieve high-sensitivity and high-resolution with compatibility with a clinical 3-T MRI scanner. A GPU-based reconstruction method with resolution modeling (RM) has been developed for the DP-PET reconstruction. We evaluated the system performance on PET resolution, sensitivity, image quality, and the PET/MR interference. RESULTS: The initial results reveal that the DP-PET prototype worked as expected in the MRI bore and caused minimal compromise to the MRI image quality. The PET performance was measured to show a spatial resolution ≤ 2.5 mm (parallel to the detector panels), maximum sensitivity = 3.6% at the center of FOV, and energy resolution = 12.43%. MR pulsing introduces less than 2% variation to the PET performance measurement results. CONCLUSIONS: We developed a MR-compatible PET insert prototype and performed several studies to begin to characterize the performance of the proposed DP-PET. The results showed that the proposed DP-PET performed well in the MRI bore and would cause little influence on the MRI images. The Derenzo phantom test showed that the proposed reconstruction method could obtain high-quality images using DP-PET.

MRI-aided kernel PET image reconstruction method based on texture features.

We investigate the reconstruction of low-count positron emission tomography (PET) projection, which is an important, but challenging, task. Using the texture feature extraction method of radiomics, i.e. the gray-level co-occurrence matrix (GLCM), texture features can be extracted from magnetic resonance imaging images with high-spatial resolution. In this work, we propose a kernel reconstruction method combining autocorrelation texture features derived from the GLCM. The new kernel function includes the correlations of both the intensity and texture features from the prior image. By regarding the GLCM as a discrete approximation of a probability density function, the asymptotically gray-level-invariant autocorrelation texture feature is generated, which can maintain the accuracy of texture features extracted from small image regions by reducing the number of quantized image gray levels. A computer simulation shows that the proposed method can effectively reduce the noise in the reconstructed image compared to the maximum likelihood expectation maximum method and improve the image quality and tumor region accuracy compared to the original kernel method for low-count PET reconstruction. A simulation study on clinical patient images also shows that the proposed method can improve the whole image quality and that the reconstruction of a high-uptake lesion is more accurate than that achieved by the original kernel method.

Automatic rat brain image segmentation using triple cascaded convolutional neural networks in a clinical PET/MR.

The purpose of this work was to develop and evaluate a deep learning approach for automatic rat brain image segmentation of magnetic resonance imaging (MRI) images in a clinical PET/MR, providing a useful tool for analyzing studies of the pathology and progression of neurological disease and to validate new radiotracers and therapeutic agents. Rat brain PET/MR images (N = 56) were collected from a clinical PET/MR system using a dedicated small-animal imaging phased array coil. A segmentation method based on a triple cascaded convolutional neural network (CNN) was developed, where, for a rectangular region of interest covering the whole brain, the entire brain volume was outlined using a CNN, then the outlined brain was fed into the cascaded network to segment both the cerebellum and cerebrum, and finally the sub-cortical structures within the cerebrum including hippocampus, thalamus, striatum, lateral ventricles and prefrontal cortex were segmented out using the last cascaded CNN. The dice score coefficient (DSC) between manually drawn labels and predicted labels were used to quantitatively evaluate the segmentation accuracy. The proposed method achieved a mean DSC of 0.965, 0.927, 0.858, 0.594, 0.847, 0.674 and 0.838 for whole brain, cerebellum, hippocampus, lateral ventricles, striatum, prefrontal cortex and thalamus, respectively. Compared with the segmentation results reported in previous publications using atlas-based methods, the proposed method demonstrated improved performance in the whole brain and cerebellum segmentation. In conclusion, the proposed method achieved high accuracy for rat brain segmentation in MRI images from a clinical PET/MR and enabled the possibility of automatic rat brain image processing for small animal neurological research.

Design and performance of SIAT aPET: a uniform high-resolution small animal PET scanner using dual-ended readout detectors.

In this work, a small animal PET scanner named SIAT aPET was developed using dual-ended readout depth encoding detectors to simultaneously achieve high spatial resolution and high sensitivity. The scanner consists of four detector rings with 12 detector modules per ring; the ring diameter is 111 mm and the axial field of view (FOV) is 105.6 mm. The images are reconstructed using an ordered subset expectation maximization (OSEM) algorithm. The spatial resolution of the scanner was measured by using a 22Na point source at the center axial FOV with different radial offsets. The sensitivity of the scanner was measured at center axis of the scanner with different axial positions. The count rate performance of the system was evaluated by scanning mouse-sized and rat-sized phantoms. An ultra-micro hot-rods phantom and two mice injected with 18F-NaF and 18F-FDG were scanned on the scanner. An average depth of interaction (DOI) resolution of 1.96 mm, energy resolution of 19.1% and timing resolution of 1.20 ns were obtained for the detector. Average spatial resolutions of 0.82 mm and 1.16 mm were obtained up to a distance of 30 mm radially from the center of the FOV when reconstructing a point source in 1% and 10% warm backgrounds, respectively, using OSEM reconstruction with 16 subsets and 10 iterations. Sensitivities of 16.0% and 11.9% were achieved at center of the scanner for energy windows of 250-750 keV and 350-750 keV respectively. Peak noise equivalent count rates (NECRs) of 324 kcps and 144 kcps were obtained at an activity of 26.4 MBq for the mouse-sized and rat-sized phantoms. Rods of 1.0 mm diameter can be visually resolved from the image of the ultra-micro hot-rods phantom. The capability of the scanner was demonstrated by high quality in-vivo mouse images.

A GPU-accelerated fully 3D OSEM image reconstruction for a high-resolution small animal PET scanner using dual-ended readout detectors.

In this work, a GPU-accelerated fully 3D ordered-subset expectation maximization (OSEM) image reconstruction with point spread function (PSF) modeling was developed for a small animal PET scanner with a long axial field of view (FOV). Dual-ended readout detectors that provided high depth of interaction (DOI) resolution were used for the small animal PET scanner to simultaneously achieve uniform high spatial resolution and high sensitivity. First, we developed a novel sinogram generation method, in which the dimension of the sinogram was determined first and then an event was assigned to a few neighboring sinogram elements by using weights that are inversely proportional to the distance from the measured line of response (LOR) to the LOR of the sinogram elements. System geometric symmetry, precomputation of LOR-driven ray-tracing and texture memory were applied to accelerate the GPU-based reconstruction. We developed a spatially variant PSF model where the PSF parameters were obtained by using point source images measured at 18 positions in the FOV and a spatial invariant PSF model where the PSF parameters were obtained by using only one image measured at the center FOV. The performance of the image reconstruction method was evaluated by using simulated phantom data as well as phantom and in-vivo mouse data acquired on the scanner. The results showed that the proposed reconstruction method provided better spatial resolution, a higher contrast recovery coefficient and lower noise than the OSEM reconstruction and was more than 1000 times faster than the CPU-based reconstruction. The spatially variant PSF model did not result in any spatial resolution improvement compared to the spatial invariant PSF model, and thus, the latter that is much easier to implement in image reconstruction and can be used in a small animal PET scanner using detectors with very high DOI resolution. A whole body 18F-FDG mouse image with high resolution and a high contrast to noise ratio was obtained by using the proposed reconstruction method.