Time-gated single-pixel imaging of Cherenkov emission from a medical linear accelerator.

Cherenkov imaging is an ideal tool for real-time in vivo verification of a radiation therapy dose. Given that radiation is pulsed from a medical linear accelerator (LINAC) together with weak Cherenkov emissions, time-gated high-sensitivity imaging is required for robust measurements. Instead of using an expensive camera system with limited efficiency of detection in each pixel, a single-pixel imaging (SPI) approach that maintains promising sensitivity over the entire spectral band could be used to provide a low-cost and viable alternative. A prototype SPI system was developed and demonstrated here in Cherenkov imaging of LINAC dose delivery to a water tank. Validation experiments were performed using four regular fields and an intensity-modulated radiotherapy (IMRT) delivery plan. The Cherenkov image-based projection percent depth dose curves (pPDDs) were compared to pPDDs simulated by the treatment planning system (TPS), with an overall average error of 0.48, 0.42, 0.65, and 1.08% for the 3, 5, 7, and 9 cm square beams, respectively. The composite image of the IMRT plan achieved a 85.9% pass rate using 3%/3 mm gamma index criteria, in comparing Cherenkov intensity and TPS dose. This study validates the feasibility of applying SPI to the Cherenkov imaging of radiotherapy dose for the first time to our knowledge.

Photon-limited Cherenkov imaging of radiation therapy dose.

Cherenkov imaging is a unique verification tool that could provide both dosimetric and tissue functional information during radiation therapy. However, the number of interrogated Cherenkov photons in tissue is always limited and tangled with stray radiation photons, severely frustrating the measurement the signal-to-noise ratio (SNR). As such, here, a noise-robust photon-limited imaging technique is proposed by comprehensively exploiting the physical rationale of low-flux Cherenkov measurements together with the spatial correlations of the objects. Validation experiments confirmed that the Cherenkov signal could be promisingly recovered with high SNR by irradiating at as few as one x ray pulse from a linear accelerator (10 mGy dose), and the Cherenkov excited luminescence imaging depth can be extended by >100% on average, for most concentrations of phosphorescent probe. This approach demonstrates that improved applications in radiation oncology could be seen when signal amplitude, noise robustness, and temporal resolution are comprehensively considered in the image recovery process.

Representation Learning on Heterostructures via Heterogeneous Anonymous Walks.

Capturing structural similarity has been a hot topic in the field of network embedding (NE) recently due to its great help in understanding node functions and behaviors. However, existing works have paid very much attention to learning structures on homogeneous networks, while the related study on heterogeneous networks is still void. In this article, we try to take the first step for representation learning on heterostructures, which is very challenging due to their highly diverse combinations of node types and underlying structures. To effectively distinguish diverse heterostructures, we first propose a theoretically guaranteed technique called heterogeneous anonymous walk (HAW) and give two more applicable variants. Then, we devise the HAW embedding (HAWE) and its variants in a data-driven manner to circumvent using an extremely large number of possible walks and train embeddings by predicting occurring walks in the neighborhood of each node. Finally, we design and apply extensive and illustrative experiments on synthetic and real-world networks to build a benchmark on heterostructure learning and evaluate the effectiveness of our methods. The results demonstrate our methods achieve outstanding performance compared with both homogeneous and heterogeneous classic methods and can be applied on large-scale networks.

Neural network-based optimization of sub-diffuse reflectance spectroscopy for improved parameter prediction and efficient data collection.

In this study, a general and systematical investigation of sub-diffuse reflectance spectroscopy is implemented. A Gegenbauer-kernel phase function-based Monte Carlo is adopted to describe photon transport more efficiently. To improve the computational efficiency and accuracy, two neural network algorithms, namely, back propagation neural network and radial basis function neural network are utilized to predict the absorption coefficient µ a , reduced scattering coefficient µ s ' and sub-diffusive quantifier γ , simultaneously, at multiple source-detector separations (SDS). The predicted results show that the three parameters can be predicated accurately by selecting five SDSs or above. Based on the simulation results, a four wavelength (520, 650, 785 and 830 nm) measurement system using five SDSs is designed by adopting phase-lock-in technique. Furtherly, the trained neural-network models are utilized to extract optical properties from the phantom and in vivo experimental data. The results verify the feasibility and effectiveness of our proposed system and methods in mucosal disease diagnosis.

The human islet amyloid polypeptide reduces hippocampal tauopathy and behavioral impairments in P301S mice without inducing neurotoxicity or seeding amyloid aggregation.

Recent evidence suggests that human islet amyloid polypeptide (h-IAPP) accumulates in the brains of Alzheimer's disease (AD) patients and may interact with Aß or microtubule associated protein tau to associate with the neurodegenerative process. Increasing evidence indicates a potential protective effect of h-IAPP against Aß-induced neurotoxicity in AD mouse models. However, a direct therapeutic effect of h-IAPP supplementation on tauopathy has not been established. Here, we found that long-term h-IAPP treatment attenuated tau hyperphosphorylation levels and induced neuroinflammation and oxidative damage, prevented synaptic loss and neuronal degeneration in the hippocampus, and alleviated behavioral deficits in P301S transgenic mice (a mouse model of tauopathy). Restoration of insulin sensitization, glucose/energy metabolism, and activated BDNF signaling also contributed to the underlying mechanisms. These findings suggest that seemly h-IAPP has promise for the treatment of neurodegenerative disorders with tauopathy, such as AD.

Tikhonov regularization-based extended Kalman filter technique for robust and accurate reconstruction in diffuse optical tomography.

Diffuse optical tomography (DOT) is a non-invasive imaging modality that uses near-infrared light to probe the optical properties of tissue. In conventionally used deterministic methods for DOT inversion, the measurement errors were not taken into account, resulting in unsatisfactory noise robustness and, consequently, affecting the DOT image reconstruction quality. In order to overcome this defect, an extended Kalman filter (EKF)-based DOT reconstruction algorithm was introduced first, which improved the reconstruction results by incorporating a priori information and measurement errors to the model. Further, to mitigate the instability caused by the ill-condition of the observation matrix in the tomographic imaging problem, a new, to the best of our knowledge, estimation algorithm was derived by incorporating Tikhonov regularization to the EKF method. To verify the effectiveness of the EKF algorithm and Tikhonov regularization-based EKF algorithm for DOT imaging, a series of numerical simulations and phantom experiments were conducted, and the experimental results were quantitatively evaluated and compared with two conventionally used deterministic methods involving the algebraic reconstruction technique and Levenberg-Marquardt algorithm. The results show that the two EKF-based algorithms can accurately estimate the location and size of the target, and the imaging accuracy and noise robustness are obviously improved. Furthermore, the Tikhonov regularization-based EKF obtained optimal parameter estimations, especially under the circumstance of low absorption contrast (1.2) and high noise level (10%).

Prediction of acute lung injury in severe acute pancreatitis by routine clinical data.

AIM: Acute lung injury (ALI) is a common complication of severe acute pancreatitis (SAP) with a high mortality. Early prediction of patients at risk in initial stage can improve the long-term survival. METHODS: A total of 91 patients with SAP out of 1647 acute pancreatitis patients from January 2015 to December 2020 were considered. A predictive model for SAP-associated ALI was constructed based on the valuable risk factors identified from routine clinical characteristics and plasma biomarkers. The value of the model was evaluated and compared with Lung Injury Prediction Score (LIPS). A nomogram was built to visualize the model. RESULTS: Diabetes, oxygen supplementation, neutrophil count and D-dimer were found to be associated with ALI in SAP. The predictive model based on these factors had an area under the receiver operating characteristic curve [AUC: 0.88, 95% confidence interval (CI): 0.81-0.95], which was superior to LIPS (AUC: 0.71, 95% CI: 0.60-0.83), also with the higher sensitivity (65%) and specificity (96%) than LIPS (62%, 74%, respectively). Decision curve analysis of the model showed a higher net benefit than LIPS. Visualization by a nomogram facilitated the application of the model. CONCLUSION: Diabetes, oxygen supplementation, neutrophil count and D-dimer were risk factors for SAP-associated ALI. The combination of these routine clinical data and the model visualization by a nomogram provided a simple and effective way in predicting ALI in the early phase of SAP.

Studies on endoscopic submucosal dissection in the past 15 years: A bibliometric analysis.

Background and aims: Endoscopic submucosal dissection (ESD) is an advanced minimally invasive technique for en bloc resection of superficial gastrointestinal lesions, which is drawn an increasing attention from its emergence. This bibliometric analysis is to evaluate the origin, current hotspots, and research trends on ESD. Methods: A total of 2,131 publications on ESD from 2006 to 2020 were obtained from the Web of Science Core Collection (WoSCC) database. Bibliometric visualization analyses of countries/regions, institutes, authors, journals, references and keywords were performed by CiteSpace V.5.8.R3. Results: The quantity of publications on ESD increased significantly during the past 15 years. Japan occupied the leading position in terms of research power. Professor Yutaka Saito, together with the institute he belongs, the Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan, were the most productive author and institute, respectively. Colorectal ESD led the main thematic concentrations in ESD research. The most prolific journal was Gastrointestinal Endoscopy. European ESD Guideline was the most frequently co-cited reference. Guideline, meta-analysis, endoscopic resection, poly-glycolic acid sheet, Barrett's esophagus, fibrin glue, risk and colorectal neoplasm will be the active research hotspots in the future. Conclusions: These findings provide the trends and frontiers in the field of ESD, as well as valuable information for clinicians and scientists to discover the future perspectives with potential collaborators.

Combined multi-scale mesh and full-matrix inversion for enhancing time-domain breast diffuse optical tomography.

Time-domain diffuse optical tomography can efficiently reconstruct both absorption and reduced scattering coefficients but is heavily limited by the ill-posedness in its inverse problem and low spatial resolution. To deal with these adversities, the truncated singular value decomposition (TSVD)-based whole-weighting-matrix inversion scheme can be a particularly suitable implementation. Unfortunately, TSVD is subject to a storage challenge for three-dimensional imaging of a bulk region, such as breast. In this paper, a multi-scale mesh strategy based on computed tomography (CT) anatomical geometry is adopted to solve the storage challenge, where a fine mesh is used in forward calculation to ensure accuracy, and a coarse mesh in the inversion process to enable TSVD-based inversion of the whole-weighting matrix. We validate the proposed strategy using simulated data for a single lesion model from clinical positron emission tomography images of a breast cancer patient, and further, for a complex model that is constructed by setting dual lesions at different separations in the CT breast geometry.

Indirect and direct estimation of pharmacokinetic parameters in dynamic diffuse fluorescence tomography by adaptive extended Kalman filtering.

Pharmacokinetic parameter estimation with the support of dynamic diffuse fluorescence tomography (DFT) can provide helpful diagnostic information for tumor differentiation and monitoring. Adaptive extended Kalman filtering (AEKF) as a nonlinear filter method has the merits of high quantitativeness, noise robustness, and initialization independence. In this paper, indirect and direct AEKF schemes combining with a commonly used two-compartment model were studied to estimate the pharmacokinetic parameters based on our self-designed dynamic DFT system. To comprehensively compare the performances of both schemes, the selection of optimal noise covariance matrices affecting estimation results was first studied, then a series of numerical simulations with the metabolic time ranged from 4.16 min to 38 min was carried out and quantitatively evaluated. The comparison results show that the direct AEKF outperforms the indirect EKF in estimation accuracy at different metabolic velocity and demonstrates stronger stability at the large metabolic velocity. Furtherly, the in vivo experiment was conducted to achieve the indocyanine green pharmacokinetic-rate images in the mouse liver. The experimental results confirmed the capability of both schemes to estimate the pharmacokinetic-rate images and were in agreement with the theory predictions and the numerical simulation results.

Astrocyte-specific loss of lactoferrin influences neuronal structure and function by interfering with cholesterol synthesis.

Growing evidence indicates that circulating lactoferrin (Lf) is implicated in peripheral cholesterol metabolism disorders. It has emerged that the distribution of Lf changes in astrocytes of aging brains and those exhibiting neurodegeneration; however, its physiological and/or pathological role remains unknown. Here, we demonstrate that astrocyte-specific knockout of Lf (designated cKO) led to decreased body weight and cognitive abnormalities during early life in mice. Accordingly, there was a reduction in neuronal outgrowth and synaptic structure in cKO mice. Importantly, Lf deficiency in the primary astrocytes led to decreased sterol regulatory element binding protein 2 (Srebp2) activation and cholesterol production, and cholesterol content in cKO mice and/or in astrocytes was restored by exogenous Lf or a Srebp2 agonist. Moreover, neuronal dendritic complexity and total dendritic length were decreased after culture with the culture medium of the primary astrocytes derived from cKO mice and that this decrease was reversed after cholesterol supplementation. Alternatively, these alterations were associated with an activation of AMP-activated protein kinase (AMPK) and inhibition of SREBP2 nuclear translocation. These data suggest that astrocytic Lf might directly or indirectly control in situ cholesterol synthesis, which may be implicated in neurodevelopment and several neurological diseases.

Novel melatonin-trientine conjugate as potential therapeutic agents for Alzheimer's disease.

Researchers continue to explore drug targets to treat the characteristic pathologies of Alzheimer's disease (AD). Some drugs relieve the pathological processes of AD to some extent, but the failed clinical trials indicate that multifunctional agents seem more likely to achieve the therapy goals for this neurodegenerative disease. Herein, a novel compound named melatonin-trientine (TM) has been covalently synthesized with the natural antioxidant compounds melatonin and the metal ion chelator trientine. After toxicological and pharmacokinetic verification, we elucidated the effects of intraperitoneal administration of TM on AD-like pathology in 6-month-old mice that express both the ß-amyloid (Aß) precursor protein and presenilin-1 (APP/PS1). We found that TM significantly decreased Aß deposition and neuronal degeneration in the brains of the APP/PS1 double transgenic mice. This result may be due to the upregulation of iron regulatory protein-2 (IRP2), insulin degrading enzyme (IDE), and low density lipoprotein receptor related protein 1 (LRP1), which leads to decreases in APP and Aß levels. Additionally, TM may promote APP non-amyloidogenic processing by activating the melatonin receptor-2 (MT2)-dependent signaling pathways, but not MT1. In addition, TM plays an important role in blocking γ-secretase, tau hyperphosphorylation, neuroinflammation, oxidative stress, and metal ion dyshomeostasis. Our results suggest that TM may effectively maximize the therapeutic efficacy of targeting multiple mechanisms associated with AD pathology.

Transition to renewable energy and environmental technologies: The role of economic policy uncertainty in top five polluted economies.

The United Nations' "Sustainable Development Goals" (SDGs) express attention to climate action. Even though a considerable number of papers have targeted this issue, the literature on the top five, "China, India, Japan, Russia, and the United States" economies is uncommon. Therefore, this paper is targeted to examine the influence of renewable energy (RE), environmental technologies (ETs), and economic policy uncertainty (EPU) on carbon emissions. By using the most recent data available from 1992 to 2020, results are estimated with robust econometric techniques, i. e. "cross-sectionally augmented autoregressive distributed lag (CS-ARDL) and augmented mean group (AMG)". Findings reflect the harmful role of EPU. However, RE and ETs have a supportive role in the transition towards a sustainable environment. The findings are also strong in terms of policy implications for the top five polluters.

Single pixel imaging via unsupervised deep compressive sensing with collaborative sparsity in discretized feature space.

Single-pixel imaging (SPI) enables the use of advanced detector technologies to provide a potentially low-cost solution for sensing beyond the visible spectrum and has received increasing attentions recently. However, when it comes to sub-Nyquist sampling, the spectrum truncation and spectrum discretization effects significantly challenge the traditional SPI pipeline due to the lack of sufficient sparsity. In this work, a deep compressive sensing (CS) framework is built to conduct image reconstructions in classical SPIs, where a novel compression network is proposed to enable collaborative sparsity in discretized feature space while remaining excellent coherence with the sensing basis as per CS conditions. To alleviate the underlying limitations in an end-to-end supervised training, for example, the network typically needs to be re-trained as the basis patterns, sampling ratios and so on. change, the network is trained in an unsupervised fashion with no sensing physics involved. Validation experiments are performed both numerically and physically by comparing with traditional and cutting-edge SPI reconstruction methods. Particularly, fluorescence imaging is pioneered to preliminarily examine the in vivo biodistributions. Results show that the proposed method maintains comparable image fidelity to a sCMOS camera even at a sampling ratio down to 4%, while remaining the advantages inherent in SPI. The proposed technique maintains the unsupervised and self-contained properties that highly facilitate the downstream applications in the field of compressive imaging.

Enhanced phase retrieval via deep concatenation networks for in-line X-ray phase contrast imaging.

PURPOSE: In-line X-ray phase contrast imaging offers considerable additional information beyond that acquired from conventional absorption contrast X-ray imaging, showing promising potentials in clinical diagnosis, materials characterization and so on. Given the physically intractable factors tangled inside, conventional phase retrieval methods typically suffer from limited feasibility. A deep-learning-augmented reconstruction strategy is proposed to improve the phase retrieval in spatial resolution and noise compression. METHODS: The deep network is composed of a phase contrast refinement module and a phase retrieval module to stabilize and generalize the phase retrieval. The two modules are aggregated in a plug-and-play fashion with the final assembly finetuned using limited training data, essentially encouraging a semi-supervised training. Verification experiments were performed on simulated phase contrast images of histopathological images. The results were compared to those from conventional phase-attenuation duality method. RESULTS: The deep-learning-augmented reconstruction strategy increases structural similarity and peak signal-to-noise ratio of phase retrieval result by more than 8% and 30%, and reduces root mean squared error by 46% compared with conventional phase-attenuation duality method. CONCLUSIONS: The pilot study of deep learning deployment in in-line X-ray phase-contrast imaging exhibit advantages against conventional methods in terms of spatial resolution and noise robustness.

Differential Survival Outcomes Between De Novo and Secondary Acute Promyelocytic Leukemia: An Updated Population-based study.

BACKGROUND: Whether the characteristics and outcome of secondary acute promyelocytic leukemia (s-APL) are similar to de no APL (dn-APL) remains unknown. PATIENTS AND METHODS: Using the SEER database, we identified 3877 patients with APL diagnosed from 2000 to 2014, including 465 s-APL and 3412 dn-APL. RESULTS: Compared with dn-APL, s-APL werecharacterized by older median age, and a higher early mortality rate. Multivariate Cox model showed s-APL, older age, earlier year of diagnosis, and male gender were independently associated with worse survival. Notably, s-APL had a significantly inferior survival regardless of gender, race, marital status, and year of diagnosis. However, the difference between the 2 cohorts was only evident in younger patients (≤ 65 years) but was lost in older patients (> 65 years). Additionally, the majority of index cancer type was breast and prostate in female and male s-APL, respectively. Latency < 3 years was associated with superior survival in s-APL with breast index cancer. CONCLUSIONS: Inferior survival of s-APL points to the need for treatment improvement.

Human-level comparable control volume mapping with a deep unsupervised-learning model for image-guided radiation therapy.

PURPOSE: To develop a deep unsupervised learning method with control volume (CV) mapping from patient positioning daily CT (dCT) to planning computed tomography (pCT) for precise patient positioning. METHODS: We propose an unsupervised learning framework, which maps CVs from dCT to pCT to automatically generate the couch shifts, including translation and rotation dimensions. The network inputs are dCT, pCT and CV positions in the pCT. The output is the transformation parameter of the dCT used to setup the head and neck cancer (HNC) patients. The network is trained to maximize image similarity between the CV in the pCT and the CV in the dCT. A total of 554 CT scans from 158 HNC patients were used for the evaluation of the proposed model. At different points in time, each patient had many CT scans. Couch shifts are calculated for the testing by averaging the translation and rotation from the CVs. The ground-truth of the shifts come from bone landmarks determined by an experienced radiation oncologist. RESULTS: The system positioning errors of translation and rotation are less than 0.47 mm and 0.17°, respectively. The random positioning errors of translation and rotation are less than 1.13 mm and 0.29°, respectively. The proposed method enhanced the proportion of cases registered within a preset tolerance (2.0 mm/1.0°) from 66.67% to 90.91% as compared to standard registrations. CONCLUSIONS: We proposed a deep unsupervised learning architecture for patient positioning with inclusion of CVs mapping, which weights the CVs regions differently to mitigate any potential adverse influence of image artifacts on the registration. Our experimental results show that the proposed method achieved efficient and effective HNC patient positioning.

Improving spatial resolution with an edge-enhancement model for low-dose propagation-based X-ray phase-contrast computed tomography.

Propagation-based X-ray phase-contrast computed tomography (PB-PCCT) has been increasingly popular for distinguishing low contrast tissues. Phase retrieval is an important step to quantitatively obtain the phase information before the tomographic reconstructions, while typical phase retrieval methods in PB-PCCT, such as homogenous transport of intensity equation (TIE-Hom), are essentially low-pass filters and thus improve the signal to noise ratio at the expense of the reduced spatial resolution of the reconstructed image. To improve the reconstructed spatial resolution, measured phase contrast projections with high edge enhancement and the phase projections retrieved by TIE-Hom were weighted summed and fed into an iterative tomographic algorithm within the framework of the adaptive steepest descent projections onto convex sets (ASD-POCS), which was employed for suppressing the image noise in low dose reconstructions because of the sparse-view scanning strategy or low exposure time for single phase contrast projection. The merging strategy decreases the accuracy of the linear model of PB-PCCT and would finally lead to the reconstruction failure in iterative reconstructions. Therefore, the additive median root prior is also introduced in the algorithm to partly increase the model accuracy. The reconstructed spatial resolution and noise performance can be flexibly balanced by a pair of antagonistic hyper-parameters. Validations were performed by the established phase-contrast Feldkamp-Davis-Kress, phase-retrieved Feldkamp-Davis-Kress, conventional ASD-POCS and the proposed enhanced ASD-POCS with a numerical phantom dataset and experimental biomaterial dataset. Simulation results show that the proposed algorithm outperforms the conventional ASD-POCS in spatial evaluation assessments such as root mean square error (a ratio of 9.78%), contrast to noise ratio (CNR) (a ratio of 7.46%), and also frequency evaluation assessments such as modulation transfer function (a ratio of 66.48% of MTF50% (50% MTF value)), noise power spectrum (a ratio of 35.25% of f50% (50% value of the Nyquist frequency)) and noise equivalent quanta (1-2 orders of magnitude at high frequencies). Experimental results again confirm the superiority of proposed strategy relative to the conventional one in terms of edge sharpness and CNR (an average increase of 67.35%).

Deep learning-augmented radioluminescence imaging for radiotherapy dose verification.

PURPOSE: We developed a novel dose verification method using a camera-based radioluminescence imaging system (CRIS) combined with a deep learning-based signal processing technique. METHODS: The CRIS consists of a cylindrical chamber coated with scintillator material on the inner surface of the cylinder, coupled with a hemispherical mirror and a digital camera at the two ends. After training, the deep learning model is used for image-to-dose conversion to provide absolute dose prediction at multiple depths of a specific water phantom from a single CRIS image under the assumption of a good consistency between the TPS setting and actual beam energy. The model was trained using a set of captured radioluminescence images and the corresponding dose maps from the clinical treatment planning system (TPS) for the sake of acceptable data collection. To overcome the latent error and inconsistency that exists between the TPS calculation and the corresponding measurement, the model was trained in an unsupervised manner. Validation experiments were performed on five square fields (ranging from 2 × 2 to 10 × 10 cm2 ) and three clinical intensity-modulated radiation therapy (IMRT) cases. The results were compared to the TPS calculations in terms of gamma index at 1.5, 5, and 10 cm depths. RESULTS: The mean 2%/2 mm gamma pass rates were 100% for square fields and 97.2% (range from 95.5% to 99.5%) for the IMRT fields. Further validations were performed by comparing the CRIS results with measurements on various regular fields. The results show a mean gamma pass rate of 91% (1%/1 mm) for cross-profiles and a mean percentage deviation of 1.15% for percentage depth doses (PDDs). CONCLUSIONS: The system is capable of converting the irradiated radioluminescence image to corresponding water-based dose maps at multiple depths with a spatial resolution comparable to the TPS calculations.