Development of a two-layer dense-pixel LYSO Compton camera prototype for prompt gamma imaging.

Objective.Lutetium-yttrium orthosilicate (LYSO)-based Compton camera (CC) has been proposed for prompt gamma imaging due to its high detection efficiency and position resolution. However, very few LYSO CC prototypes have been built and used for practical evaluation. In this study, we built a lightweight dense-pixel silicon photomultiplier-based two-layer LYSO CC prototype for future prompt gamma imaging.Approach.We attempt the first-ever effort to use the double-encoding with the thick light guide and coding circuit structure for 46 × 46 dense-pixel LYSO detectors construction and use pixel segmentation based on centroid mapping to obtain 4232 spectral calibrations. We also present a framework for list-mode projection data acquisition based on the decoding of the time series data obtained by data acquisition card in this study. Finally, the standard source calibration, ring-like22Na source with non-uniform intensity, and mixed point-like source with a wide energy spectrum experiments were implemented to evaluate the resolution metrics and imaging performance of the prototype.Main results.The lateral position resolution of the prototype was 1 mm, and the maximum measurement deviation is 2.5 mm and 5 mm in the depth direction for the scatterer and absorber, respectively. In the experiments, the measured energy resolution was 9.63% @ 1.33 MeV for the scatterer and 10.8% @ 1.33 MeV for the absorber. And the detection efficiency of the prototype for a spherical60Co source with a diameter of 2.8 mm at 10 cm far was 5.7 × 10-3@ 1.33 MeV and the full width at half maximum of the reconstruction was 5.5 mm. Besides, the spatial position offset within 2 mm of the radioactive source at 10 cm can be distinguished.Signification.The developed two-layer dense-pixel LYSO CC contributes to incorporating Compton imaging techniques for prompt gamma detection and multiple energy sources into nuclear medical imaging.

Technical note: Rapid and high-resolution deep learning-based radiopharmaceutical imaging with 3D-CZT Compton camera and sparse projection data.

BACKGROUND: The Compton camera (CC) has great potential in nuclear medicine imaging due to the high detection efficiency and the ability to simultaneously detect multi-energy radioactive sources. However, the finite resolution of the detectors will degrade the images that the real-world CC can obtain. Besides, the CC sometimes can be limited by the detection efficiency, leading to difficulty in using sparse projection data to realize high-resolution reconstruction with short-time measurement, which limits its clinical application for real-time or rapid radiopharmaceutical imaging. PURPOSE: To overcome the difficulty and promote the usage of the CC in radiopharmaceutical imaging, we present a deep learning (DL)-based CC reconstruction method to realize rapid and high-resolution imaging with short-time measurement. METHODS: We developed a DL-based algorithm MCBP-CCnet via Monte Carlo sampling-based back projection and a dedicated convolutional neural network, called CC-Net, to realize the rapid and high-resolution reconstruction with sparse projection data. A CC prototype based on a single three-dimensional position-sensitive CdZnTe (3D-CZT) detector was used to demonstrate the feasibility of our proposed method. The simulations and experiments of radiopharmaceutical imaging used the 3D-CZT CC and [18 F]NaF. A 3D-printing mouse phantom was also further used to evaluate the performance of the proposed method in animal molecular imaging. RESULTS: The simulation and experimental results showed that the proposed method could realize the images reconstruction within 5 s for list-mode projection data and realized a rapid reconstruction within 35 s for experimental radiopharmaceutical imaging based on the 3D-printing mouse phantom, as well as realized the high-resolution imaging with an accuracy of within 0.78 mm in terms of the sparse projection data that only contained hundreds of events. Besides, the deviations between the reconstructed radiative activities and the exact values were less than 1.51%. CONCLUSION: The results demonstrated that the proposed method could realize the rapid and high-resolution CC reconstruction with sparse projection data obtained by the 3D-CZT CC and realize the high-resolution radiopharmaceutical imaging. The study in this paper also demonstrated the potential and feasibility of future applications of a 3D-CZT CC for real-time high-resolution radiopharmaceutical imaging with short-time measurement.

Radiopharmaceutical imaging based on 3D-CZT Compton camera with 3D-printed mouse phantom.

PURPOSE: The study proposes the use of three-dimensional CdZnTe Compton camera (3D-CZT CC) for radiopharmaceutical imaging and investigates the influence factors using a 3D-printed mouse phantom. METHODS: The event selection method and image reconstruction algorithm are optimized by Monte Carlo simulations and mouse phantom experiments. RESULTS: Simulation results show that the intrinsic energy resolution and spatial resolution of 3D-CZT cause a certain deviation in the calculated Compton scattering angle and Compton axis. Such deviation causes the imaging quality to deteriorate. By selecting events whose distance between Compton and photoelectronic interactions are larger than 10 mm, the mean deviation of the Compton axis could be reduced to less than 10%. Using the ordered origin ensemble algorithm with resolution recovery, the artifacts around organs where the radiopharmaceutical was placed are reduced, and the quality of the reconstruction results are improved compared to the results with simple back projection and origin ensembles algorithms. The phantom study shows that the 3D-CZT CC imaging device could visualize the radiopharmaceuticals distribution by 15 min detection. CONCLUSIONS: Through the analysis of this study, the feasibility of 3D-CZT CC for in-vivo distribution measurement of radiopharmaceuticals is demonstrated, and the quality of reconstruction result has been improved.

Photoinitiated Multicomponent Anti-Markovnikov Alkoxylation over Graphene Oxide.

The development of graphene oxide-based heterogeneous materials with an economical and environmentally-friendly manner has the potential to facilitate many important organic transformations but proves to have few relevant reported reactions. Herein, we explore the synergistic role of catalytic systems driven by graphene oxide and visible light that form nucleophilic alkoxyl radical intermediates, which enable an anti-Markovnikov addition exclusively to the terminal alkenes, and then the produced benzyl radicals are subsequently added with N-methylquinoxalones. This photoinduced cascade radical difunctionalization of olefins offers a concise and applicable protocol for constructing alkoxyl-substituted N-methylquinoxalones.

Graphene oxide-catalyzed trifluoromethylation of alkynes with quinoxalinones and Langlois' reagent.

The direct C-H trifluoromethylation of alkynes and quinoxalinones has been achieved using a graphene oxide/Langlois' reagent system. This multi-component tandem reaction using graphene oxide as the catalyst and Langlois' reagent as the robust CF3 radical source results in the formation of olefinic C-CF3 to access a series of 3-trifluoroalkylated quinoxalin-2(1H)-ones.

U-net-based blocked artifacts removal method for dynamic computed tomography.

Airplane engines are vital aircraft components, so regular inspections of the engines are required to ensure their stable operation. A dynamic computed tomography (CT) system has been proposed by our group for in situ nondestructive testing of airplane engines, which takes advantage of the rotor's self-rotation. However, static parts of the engines cause blocked artifacts in the reconstructed image, leading to misinterpretations of the condition of engines. In this paper, in order to remove the artifacts produced by the projection of the static parts in CT reconstruction, two deep-learning-based methods are proposed, which use U-Net to perform correction in the projection domain. The projection of static parts can be estimated by a well-trained U-Net and subsequently can be subtracted from the projections of the engine. Finally, the rotor can be reconstructed from the corrected projections. The results shown in this paper indicate that the proposed methods are practical and effective for removing those blocked artifacts and recovering the details of rotating parts, which will, in turn, maximize the utilization of the dynamic CT system for in situ engine tests.

14-3-3/Tau Interaction and Tau Amyloidogenesis.

The major function of microtubule-associated protein tau is to promote microtubule assembly in the central nervous system. However, aggregation of abnormally phosphorylated tau is a hallmark of tauopathies. Although the molecular mechanisms of conformational transitions and assembling of tau molecules into amyloid fibril remain largely unknown, several factors have been shown to promote tau aggregation, including mutations, polyanions, phosphorylation, and interactions with other proteins. 14-3-3 proteins are a family of highly conserved, multifunctional proteins that are mainly expressed in the central nervous system. Being a scaffolding protein, 14-3-3 proteins interact with tau and regulate tau phosphorylation by bridging tau with various protein kinases. 14-3-3 proteins also directly regulate tau aggregation via specific and non-specific interactions with tau. In this review, we summarize recent advances in characterization of tau conformation and tau/14-3-3 interaction. We discuss the connection between 14-3-3 binding and tau aggregation with a special emphasis on the regulatory role of 14-3-3 on tau conformation.

Compton-based prompt gamma imaging using ordered origin ensemble algorithm with resolution recovery in proton therapy.

Prompt gamma ray (PG) imaging based on Compton camera (CC) is promising to realize in vivo verification during the proton therapy. However, the finite spatial and energy resolution of current CC, as well as the Doppler broaden effect, degrade the quality and resolution of PG images. In addition, due to the inherent geometrical complexity of Compton camera data, PG imaging can be time-consuming and difficult to reconstruct in real-time, while using standard techniques such as filtered back-projection or maximum likelihood-expectation maximization. In this paper, we propose three modifications of origin ensembles with resolution recovery (OE-RR) algorithm based on Markov chains to accelerate the convergence to equilibrium of OE-RR algorithm and improve the image quality. For evaluation, we performed a Monte Carlo simulation of a three-stage CZT Compton camera with resolution loss to detect the PG produced by a proton beam in a water phantom, and evaluate image quality of the gamma rays emitted during proton irradiation. The results show that our ordered OE-RR algorithm realized a good resolution recovery and accurate estimation of the position, including the peak and the distal falloff of the PG emission with remarkably faster reconstruction, thus demonstrating the feasibility of this new method in non-idealized PG-based proton range verification.