Two-Dimensional Perovskite Single Crystals for High-Performance X-ray Imaging and Exploring MeV X-ray Detection

Scintillation semiconductors play increasingly important medical diagnosis and industrial inspection roles.Recently,two-dimensional(2D)perovskites have been shown to be promising materials for medical X-ray imaging,but they are mostly used in low-energy(≤130 keV)regions.Direct detection of MeV X-rays,which ensure thorough penetration of the thick shell walls of containers,trucks,and aircraft,is also highly desired in practical industrial applications.Unfortunately,scintillation semiconductors for high-energy X-ray detection are currently scarce.Here,This paper reports a 2D(C_(4)H_(9)NH_(3))_(2)PbBr_(4)single crystal with outstanding sensitivity and stability toward X-ray radiation that provides an ultra-wide detectable X-ray range of between 8.20 nGy_(air)s^(-1)(50 keV)and 15.24 mGy_(air)s^(-1)(9 MeV).The(C_(4)H_(9)NH_(3))_(2)PbBr_(4)single-crystal detector with a vertical structure is used for high-performance X-ray imaging,delivering a good spatial resolution of 4.3 Ip mm^(-1)in a plane-scan imaging system.Low ionic migration in the 2D perovskite enables the vertical device to be operated with hundreds of keV to MeV X-ray radiation at high bias voltages,leading to a sensitivity of 46.90μC Gy_(air)-1 cm^(-2)(-1.16 Vμm^(-1))with 9 MeV X-ray radiation,demonstrating that 2D perovskites have enormous potential for high-energy industrial applications.

Insights into the hydrogen evolution reaction in vanadium redox flow batteries:A synchrotron radiation based X-ray imaging study

The parasitic hydrogen evolution reaction(HER)in the negative half-cell of vanadium redox flow batteries(VRFBs)causes severe efficiency losses.Thus,a deeper understanding of this process and the accompanying bubble formation is crucial.This benchmarking study locally analyzes the bubble distribution in thick,porous electrodes for the first time using deep learning-based image segmentation of synchrotron X-ray micro-tomograms.Each large three-dimensional data set was processed precisely in less than one minute while minimizing human errors and pointing out areas of increased HER activity in VRFBs.The study systematically varies the electrode potential and material,concluding that more negative electrode potentials of-200 m V vs.reversible hydrogen electrode(RHE)and lower cause more substantial bubble formation,resulting in bubble fractions of around 15%–20%in carbon felt electrodes.Contrarily,the bubble fractions stay only around 2%in an electrode combining carbon felt and carbon paper.The detected areas with high HER activity,such as the border subregion with more than 30%bubble fraction in carbon felt electrodes,the cutting edges,and preferential spots in the electrode bulk,are potential-independent and suggest that larger electrodes with a higher bulk-to-border ratio might reduce HER-related performance losses.The described combination of electrochemical measurements,local X-ray microtomography,AI-based segmentation,and 3D morphometric analysis is a powerful and novel approach for local bubble analysis in three-dimensional porous electrodes,providing an essential toolkit for a broad community working on bubble-generating electrochemical systems.

Anisotropy of Trabecular Bone from Ultra-Distal Radius Digital X-Ray Imaging: Effects on Bone Mineral Density and Age

Background: When applied to trabecular bone X-ray images, the anisotropic properties of trabeculae located at ultra-distal radius were investigated by using the trabecular bone scores (TBS) calculated along directions parallel and perpendicular to the forearm. Methodology: Data from more than two hundred subjects were studied retrospectively. A DXA (GE Lunar Prodigy) scan of the forearm was performed on each subject to measure the bone mineral density (BMD) value at the location of ultra-distal radius, and an X-ray digital image of the same forearm was taken on the same day. The values of trabecular bone score along the direction perpendicular to the forearm, TBSx, and along the direction parallel to the forearm, TBSy, were calculated respectively. The statistics of TBSx and TBSy were calculated, and the anisotropy of the trabecular bone, which was defined as the ratio of TBSy to TBSx and changed with subjects’ BMD and age, was reported and analyzed. Results: The results show that the correlation coefficient between TBSx and TBSy was 0.72 (p BMD and age was reported. The results showed that decreased trabecular bone anisotropy was associated with deceased BMD and increased age in the subject group. Conclusions: This study shows that decreased trabecular bone anisotropy was associated with decreased BMD and increased age.

Using restored two-dimensional X-ray images to reconstruct the three-dimensional magnetopause

Astronomical imaging technologies are basic tools for the exploration of the universe,providing basic data for the research of astronomy and space physics.The Soft X-ray Imager(SXI)carried by the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)aims to capture two-dimensional(2-D)images of the Earth’s magnetosheath by using soft X-ray imaging.However,the observed 2-D images are affected by many noise factors,destroying the contained information,which is not conducive to the subsequent reconstruction of the three-dimensional(3-D)structure of the magnetopause.The analysis of SXI-simulated observation images shows that such damage cannot be evaluated with traditional restoration models.This makes it difficult to establish the mapping relationship between SXIsimulated observation images and target images by using mathematical models.We propose an image restoration algorithm for SXIsimulated observation images that can recover large-scale structure information on the magnetosphere.The idea is to train a patch estimator by selecting noise–clean patch pairs with the same distribution through the Classification–Expectation Maximization algorithm to achieve the restoration estimation of the SXI-simulated observation image,whose mapping relationship with the target image is established by the patch estimator.The Classification–Expectation Maximization algorithm is used to select multiple patch clusters with the same distribution and then train different patch estimators so as to improve the accuracy of the estimator.Experimental results showed that our image restoration algorithm is superior to other classical image restoration algorithms in the SXI-simulated observation image restoration task,according to the peak signal-to-noise ratio and structural similarity.The restoration results of SXI-simulated observation images are used in the tangent fitting approach and the computed tomography approach toward magnetospheric reconstruction techniques,significantly improving the reconstruction results.Hence,the proposed technology may be feasible for processing SXI-simulated observation images.

SMILE soft X-ray Imager flight model CCD370 pre-flight device characterisation

Throughout the SMILE mission the satellite will be bombarded by radiation which gradually damages the focal plane devices and degrades their performance.In order to understand the changes of the CCD370s within the soft X-ray Imager,an initial characterisation of the devices has been carried out to give a baseline performance level.Three CCDs have been characterised,the two flight devices and the flight spa re.This has been carried out at the Open University in a bespo ke cleanroom measure ment facility.The results show that there is a cluster of bright pixels in the flight spa re which increases in size with tempe rature.However at the nominal ope rating tempe rature(-120℃) it is within the procure ment specifications.Overall,the devices meet the specifications when ope rating at -120℃ in 6 × 6 binned frame transfer science mode.The se rial charge transfer inefficiency degrades with temperature in full frame mode.However any charge losses are recovered when binning/frame transfer is implemented.

Automated Algorithms for Detecting and Classifying X-Ray Images of Spine Fractures

This paper emphasizes a faster digital processing time while presenting an accurate method for identifying spinefractures in X-ray pictures. The study focuses on efficiency by utilizing many methods that include picturesegmentation, feature reduction, and image classification. Two important elements are investigated to reducethe classification time: Using feature reduction software and leveraging the capabilities of sophisticated digitalprocessing hardware. The researchers use different algorithms for picture enhancement, including theWiener andKalman filters, and they look into two background correction techniques. The article presents a technique forextracting textural features and evaluates three picture segmentation algorithms and three fractured spine detectionalgorithms using transformdomain, PowerDensity Spectrum(PDS), andHigher-Order Statistics (HOS) for featureextraction.With an emphasis on reducing digital processing time, this all-encompassing method helps to create asimplified system for classifying fractured spine fractures. A feature reduction program code has been built toimprove the processing speed for picture classification. Overall, the proposed approach shows great potential forsignificantly reducing classification time in clinical settings where time is critical. In comparison to other transformdomains, the texture features’ discrete cosine transform (DCT) yielded an exceptional classification rate, and theprocess of extracting features from the transform domain took less time. More capable hardware can also result inquicker execution times for the feature extraction algorithms.

Geant4 simulation of fast-electron bremsstrahlung imaging at the HL-3 tokamak

To further research on high-parameter plasma,we plan to develop a two-dimensional hard X-ray(HXR)imaging system at the HL-3 tokamak to measure HXRs with energies ranging from 20 to 300 keV.The application of an array-structured detector ensures that this system can measure HXR-radiation spectra from the entire plasma cross section.Therefore,it is suitable for the study of fast-electron physics,such as radio-frequency wave current drives,fast electrons driving instabilities,and plasma disruptions in fusion research.In this study,we develop a simulation for calculating fast-electron bremsstrahlung in the HL-3 tokamak based on the Monte Carlo simulation code Geant4,in which the plasma geometry and forward scattering of fast-electron bremsstrahlung are considered.The preliminary calculation results indicate that the HXR energy deposi-tion on the detector is symmetrically distributed,even though the plasma distribution is asymmetric owing to the toroidal effect.These simulation results are helpful in constructing the relationship between the energy deposition on the detector and parameter distribution on the plasma cross section during HL-3 experiments.This is beneficial for the reconstruction of the fast-electron-distribution function and for optimizing the design of the HXR-imaging system.

Structural control of magnetic nanoparticles for positive nuclear magnetic resonance imaging

In addition to the tens of millions of medical doses consumed annually around the world,a vast number of nuclear magnetic resonance imaging(MRI)contrast agents are being deployed in MRI research and development,offering precise diagnostic information,targeting capabilities,and analyte sensing.Superparamagnetic iron oxide nanoparticles(SPIONs)are notable among these agents,providing effective and versatile MRI applications while also being heavy-metal-free,bioconjugatable,and theranostic.We designed and implemented a novel two-pronged computational and experimental strategy to meet the demand for the efficient and rigorous development of SPION-based MRI agents.Our MATLAB-based modeling simulation and magnetic characterization revealed that extremely small maghemite SPIONs in the 1-3 nm range possess significantly reduced transversal relaxation rates(R_(2))and are therefore preferred for positive(T_(1)-weighted)MRI.Moreover,X-ray diffraction and X-ray absorption fine structure analyses demonstrated that the diffraction pattern and radial distribution function of our SPIONs matched those of the targeted maghemite crystals.In addition,simulations of the X-ray near-edge structure spectra indicated that our synthesized SPIONs,even at 1 nm,maintained a spherical structure.Furthermore,in vitro and in vivo MRI investigations showed that our 1-nm SPIONs effectively highlighted whole-body blood vessels and major organs in mice and could be cleared through the kidney route to minimize potential post-imaging side effects.Overall,our innovative approach enabled a swift discovery of the desired SPION structure,followed by targeted synthesis,synchrotron radiation spectroscopic studies,and MRI evaluations.The efficient and rigorous development of our high-performance SPIONs can set the stage for a computational and experimental platform for the development of future MRI agents.

Pulmonary Edema and Pleural Effusion Detection Using Efficient Net-V1-B4 Architecture and AdamW Optimizer from Chest X-Rays Images

This paper presents a novelmulticlass systemdesigned to detect pleural effusion and pulmonary edema on chest Xray images,addressing the critical need for early detection in healthcare.A new comprehensive dataset was formed by combining 28,309 samples from the ChestX-ray14,PadChest,and CheXpert databases,with 10,287,6022,and 12,000 samples representing Pleural Effusion,Pulmonary Edema,and Normal cases,respectively.Consequently,the preprocessing step involves applying the Contrast Limited Adaptive Histogram Equalization(CLAHE)method to boost the local contrast of the X-ray samples,then resizing the images to 380×380 dimensions,followed by using the data augmentation technique.The classification task employs a deep learning model based on the EfficientNet-V1-B4 architecture and is trained using the AdamW optimizer.The proposed multiclass system achieved an accuracy(ACC)of 98.3%,recall of 98.3%,precision of 98.7%,and F1-score of 98.7%.Moreover,the robustness of the model was revealed by the Receiver Operating Characteristic(ROC)analysis,which demonstrated an Area Under the Curve(AUC)of 1.00 for edema and normal cases and 0.99 for effusion.The experimental results demonstrate the superiority of the proposedmulti-class system,which has the potential to assist clinicians in timely and accurate diagnosis,leading to improved patient outcomes.Notably,ablation-CAM visualization at the last convolutional layer portrayed further enhanced diagnostic capabilities with heat maps on X-ray images,which will aid clinicians in interpreting and localizing abnormalities more effectively.

Segmentation of Head and Neck Tumors Using Dual PET/CT Imaging:Comparative Analysis of 2D,2.5D,and 3D Approaches Using UNet Transformer

The segmentation of head and neck(H&N)tumors in dual Positron Emission Tomography/Computed Tomogra-phy(PET/CT)imaging is a critical task in medical imaging,providing essential information for diagnosis,treatment planning,and outcome prediction.Motivated by the need for more accurate and robust segmentation methods,this study addresses key research gaps in the application of deep learning techniques to multimodal medical images.Specifically,it investigates the limitations of existing 2D and 3D models in capturing complex tumor structures and proposes an innovative 2.5D UNet Transformer model as a solution.The primary research questions guiding this study are:(1)How can the integration of convolutional neural networks(CNNs)and transformer networks enhance segmentation accuracy in dual PET/CT imaging?(2)What are the comparative advantages of 2D,2.5D,and 3D model configurations in this context?To answer these questions,we aimed to develop and evaluate advanced deep-learning models that leverage the strengths of both CNNs and transformers.Our proposed methodology involved a comprehensive preprocessing pipeline,including normalization,contrast enhancement,and resampling,followed by segmentation using 2D,2.5D,and 3D UNet Transformer models.The models were trained and tested on three diverse datasets:HeckTor2022,AutoPET2023,and SegRap2023.Performance was assessed using metrics such as Dice Similarity Coefficient,Jaccard Index,Average Surface Distance(ASD),and Relative Absolute Volume Difference(RAVD).The findings demonstrate that the 2.5D UNet Transformer model consistently outperformed the 2D and 3D models across most metrics,achieving the highest Dice and Jaccard values,indicating superior segmentation accuracy.For instance,on the HeckTor2022 dataset,the 2.5D model achieved a Dice score of 81.777 and a Jaccard index of 0.705,surpassing other model configurations.The 3D model showed strong boundary delineation performance but exhibited variability across datasets,while the 2D model,although effective,generally underperformed compared to its 2.5D and 3D counterparts.Compared to related literature,our study confirms the advantages of incorporating additional spatial context,as seen in the improved performance of the 2.5D model.This research fills a significant gap by providing a detailed comparative analysis of different model dimensions and their impact on H&N segmentation accuracy in dual PET/CT imaging.

Transparent and Accurate COVID-19 Diagnosis:Integrating Explainable AI with Advanced Deep Learning in CT Imaging

In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly significant.Despite its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is imperative.To bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning models.This integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data augmentation.Our approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model generalization.The pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s reasoning.This unique combination of an optimized Deep Neural Network(DNN)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic process.Our method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare systems.This innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.

Impact of computed tomography/magnetic resonance imaging registration on rehabilitation after percutaneous endoscopic decompression for lumbar stenosis: Retrospective study

BACKGROUND Percutaneous endoscopic lumbar decompression(PELD)shows promise for lumbar spinal stenosis(LSS)treatment,but its use is limited by the disease's complexity and procedural challenges.AIM In this study,the effects of preoperative planning and intraoperative guidance with computed tomography(CT)/magnetic resonance imaging(MRI)registration techniques on PELD for LSS and postoperative rehabilitation outcomes were evaluated.METHODS This retrospective study was conducted with data from patients who underwent PELD for LSS between January 2021 and December 2023.Patients were assigned to preoperative CT/MRI registration and control groups.Data collected included the operative time,length of hospital stay,visual analog scale(VAS)scores for low back and leg pain,and the Japanese Orthopaedic Association(JOA)lumbar spine score.Differences between groups were assessed using Student’s t test.RESULTS Data from 135 patients(71 in the CT/MRI registration group,64 in the control group)were analyzed.The operative time was significantly shorter in the CT/MRI registration group(P=0.007).At 2 months postoperatively,both groups showed significant reductions in VAS leg and low back pain scores(all P<0.001)and improvements in the JOA score(both P<0.001).No complication or death occurred.Preoperatively,pain and JOA scores were similar between groups(P=0.830,P=0.470,and P=0.287,respectively).At 2 months postoperatively,patients in the CT/MRI registration group reported lower leg and low back pain levels(P<0.001 and P=0.001,respectively)and had higher JOA scores(P=0.004)than did patients in the control group.CONCLUSION Preoperative CT/MRI registration for PELD for LSS reduced the operative time and VAS pain scores at 2 months and improved JOA scores,demonstrating enhanced effectiveness and safety.

Analysis of Imaging Characteristics and Dynamic Changes of 3 Cases of Severe Novel Coronavirus Pneumonia in Qinghai Province

Objective:To analyze the characteristics,dynamic changes,and outcomes of the first imaging manifestations of 3 patients with severe COVID-19 in our hospital.Methods:Computed tomography(CT)findings of 3 patients with severe COVID-19 who tested positive by the nucleic acid test in our hospital were selected,mainly focusing on the morphology,distribution characteristics,and dynamic changes of the first CT findings.Results:3 patients with severe pneumonia were older,with one aged 80.The first chest CT examination for all 3 patients differed.Imaging showed a leafy distribution of consolidation,primarily affecting the lower lobes of both lungs and extending subpleurally.A grid-like pattern was observed,along with changes in the consolidation and air bronchogram.These changes had slower absorption,especially in patients with underlying diseases.Conclusion:CT manifestations of severe COVID-19 have specific characteristics and the analysis of their characteristics and dynamic changes provide valuable insights for clinical treatment.

Analysis of The Value of Multi-Slice Spiral CT and Magnetic Resonance Imaging in The Diagnosis of Carpal Joint Injury

Objective:To analyze the value of multi-slice spiral computed tomography(CT)and magnetic resonance imaging(MRI)in the diagnosis of carpal joint injury.Methods:A total of 130 patients with suspected wrist injuries admitted to the Department of Orthopedics of our hospital from January 2023 to January 2024 were selected and randomly divided into a single group(n=65)and a joint group(n=65).The single group was diagnosed using multi-slice spiral CT,and the joint group was diagnosed using multi-slice spiral CT and magnetic resonance imaging,with pathological diagnosis as the gold standard.The diagnostic results of both groups were compared to the gold standard,and the diagnostic energy efficiency of both groups was compared.Results:The diagnostic results of the single group compared with the gold standard were significant(P<0.05).The diagnostic results of the joint group compared with the gold standard were not significant(P>0.05).The sensitivity and accuracy of diagnosis in the joint group were significantly higher than that in the single group(P<0.05).The specificity of diagnosis in the joint group was higher as compared to that in the single group(P>0.05).Conclusion:The combination of multi-slice spiral CT and MRI was highly accurate in diagnosing wrist injuries,and the misdiagnosis rate and leakage rate were relatively low.Hence,this diagnostic program is recommended to be popularized.

A fast and adaptive method for automatic weld defect detection in various real-time X-ray imaging systems

A first and effective method is proposed to detect weld deject adaptively in various Dypes of real-time X-ray images obtained in different conditions. After weld extraction and noise reduction, a proper template of median filter is used to estimate the weld background. After the weld background is subtracted from the original image, an adaptite threshold segmentation algorithm is proposed to obtain the binary image, and then the morphological close and open operation, labeling algorithm and fids＇e alarm eliminating algorithm are applied to pracess the binary image to obtain the defect, ct detection result. At last, a fast realization procedure jbr proposed method is developed. The proposed method is tested in real-time X-ray image,s obtairted in different X-ray imaging sutems. Experiment results show that the proposed method is effective to detect low contrast weld dejects with few .false alarms and is adaptive to various types of real-time X-ray imaging systems.

First commissioning results of the coherent scattering and imaging endstation at the Shanghai soft X-ray free-electron laser facility

The Shanghai soft X-ray free-electron laser(SXFEL)user facility project started in 2016 and is expected to be open to users by 2022.It aims to deliver ultra-intense coherent femtosecond X-ray pulses to five endstations covering a range of 100–620 eV for ultrafast X-ray science.Two undulator lines are designed and constructed,based on different lasing modes:self-amplified spontaneous emission and echo-enabled harmonic generation.The coherent scattering and imaging(CSI)endstation is the first of five endstations to be commissioned online.It focuses on high-resolution single-shot imaging and the study of ultrafast dynamic processes using coherent forward scattering techniques.Both the single-shot holograms and coherent diffraction patterns were recorded and reconstructed for nanoscale imaging,indicating the excellent coherence and high peak power of the SXFEL and the possibility of‘‘diffraction before destruction’’experiments at the CSI endstation.In this study,we report the first commissioning results of the CSI endstation.

Detecting Lumbar Implant and Diagnosing Scoliosis from Vietnamese X-Ray Imaging Using the Pre-Trained API Models and Transfer Learning

With the rapid growth of the autonomous system,deep learning has become integral parts to enumerate applications especially in the case of healthcare systems.Human body vertebrae are the longest and complex parts of the human body.There are numerous kinds of conditions such as scoliosis,vertebra degeneration,and vertebrate disc spacing that are related to the human body vertebrae or spine or backbone.Early detection of these problems is very important otherwise patients will suffer from a disease for a lifetime.In this proposed system,we developed an autonomous system that detects lumbar implants and diagnoses scoliosis from the modified Vietnamese x-ray imaging.We applied two different approaches including pre-trained APIs and transfer learning with their pre-trained models due to the unavailability of sufficient x-ray medical imaging.The results show that transfer learning is suitable for the modified Vietnamese x-ray imaging data as compared to the pre-trained API models.Moreover,we also explored and analyzed four transfer learning models and two pre-trained API models with our datasets in terms of accuracy,sensitivity,and specificity.

Nanofabrication of 50 nm zone plates through e-beam lithography with local proximity effect correction for x-ray imaging

High resolution Fresnel zone plates for nanoscale three-dimensional imaging of materials by both soft and hard x-rays are increasingly needed by the broad applications in nanoscience and nanotechnology.When the outmost zone-width is shrinking down to 50 nm or even below,patterning the zone plates with high aspect ratio by electron beam lithography still remains a challenge because of the proximity effect.The uneven charge distribution in the exposed resist is still frequently observed even after standard proximity effect correction(PEC),because of the large variety in the line width.This work develops a new strategy,nicknamed as local proximity effect correction(LPEC),efficiently modifying the deposited energy over the whole zone plate on the top of proximity effect correction.By this way,50 nm zone plates with the aspect ratio from 4:1 up to 15:1 and the duty cycle close to 0.5 have been fabricated.Their imaging capability in soft(1.3 keV)and hard(9 keV)x-ray,respectively,has been demonstrated in Shanghai Synchrotron Radiation Facility(SSRF)with the resolution of 50 nm.The local proximity effect correction developed in this work should also be generally significant for the generation of zone plates with high resolutions beyond 50 nm.

A new imaging mode based on X-ray CT as prior image and sparsely sampled projections for rapid clinical proton CT

Proton computed tomography(CT)has a distinct practical significance in clinical applications.It eliminates 3–5%errors caused by the transformation of Hounsfield unit(HU)to relative stopping power(RSP)values when using X-ray CT for positioning and treatment planning systems(TPSs).Following the development of FLASH proton therapy,there are increased requirements for accurate and rapid positioning in TPSs.Thus,a new rapid proton CT imaging mode is proposed based on sparsely sampled projections.The proton beam was boosted to 350 MeV by a compact proton linear accelerator(LINAC).In this study,the comparisons of the proton scattering with the energy of 350 MeV and 230 MeV are conducted based on GEANT4 simulations.As the sparsely sampled information associated with beam acquisitions at 12 angles is not enough for reconstruction,X-ray CT is used as a prior image.The RSP map generated by converting the X-ray CT was constructed based on Monte Carlo simulations.Considering the estimation of the most likely path(MLP),the prior image-constrained compressed sensing(PICCS)algorithm is used to reconstruct images from two different phantoms using sparse proton projections of 350 MeV parallel proton beam.The results show that it is feasible to realize the proton image reconstruction with the rapid proton CT imaging proposed in this paper.It can produce RSP maps with much higher accuracy for TPSs and fast positioning to achieve ultra-fast imaging for real-time image-guided radiotherapy(IGRT)in clinical proton therapy applications.

Simple phase extraction in x-ray differential phase contrast imaging

A fast and simple method to extract phase-contrast images from interferograms is proposed, and its effectiveness is demonstrated through simulation and experiment. For x-ray differential phase contrast imaging, a strong attenuation signal acts as an overwhelming background intensity that obscures the weak phase signal so that no obvious phase-gradient information is detectable in the raw image. By subtracting one interferogram from another, chosen at particular intervals,the phase signal can be isolated and magnified.