Short Imaging Time Research Articles

Restoration of X-ray phase-contrast imaging based on generative adversarial networks

For light-element materials, X-ray phase contrast imaging provides better contrast compared to absorption imaging. While the Fourier transform method has a shorter imaging time, it typically results in lower image quality; in contrast, the phase-shifting method offers higher image quality but is more time-consuming and involves a higher radiation dose. To rapidly reconstruct low-dose X-ray phase contrast images, this study developed a model based on Generative Adversarial Networks (GAN), incorporating custom layers and self-attention mechanisms to recover high-quality phase contrast images. We generated a simulated dataset using Kaggle’s X-ray data to train the GAN, and in simulated experiments, we achieved significant improvements in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). To further validate our method, we applied it to fringe images acquired from three phase contrast systems: a single-grating phase contrast system, a Talbot-Lau system, and a cascaded grating system. The current results demonstrate that our method successfully restored high-quality phase contrast images from fringe images collected in experimental settings, though it should be noted that these results were achieved using relatively simple sample configurations.

Nuclease‐Resistant L‐DNA Tension Probes Enable Long‐Term Force Mapping of Single Cells and Cell Consortia

AbstractDNA‐based tension probes with precisely programmable force responses provide important insights into cellular mechanosensing. However, their degradability in cell culture limits their use for long‐term imaging, for instance, when cells migrate, divide, and differentiate. This is a critical limitation for providing insights into mechanobiology for these longer‐term processes. Here, we present DNA‐based tension probes that are entirely designed based on the stereoisomer of biological D‐DNA, i.e., L‐DNA. We demonstrate that L‐DNA tension probes are essentially indestructible by nucleases and provide days‐long imaging without significant loss in image quality. We also show their superiority already for short imaging times commonly used for classical D‐DNA tension probes. We showcase the potential of these resilient probes to image minute movements, and for generating long term force maps of single cells and of collectively migrating cell populations.

The value of a deep learning image reconstruction algorithm in whole-brain computed tomography perfusion in patients with acute ischemic stroke.

Computed tomography perfusion (CTP) and computed tomography angiography (CTA) are valuable tools for diagnosing acute ischemic stroke (AIS). It is essential to obtain high-quality CTP and CTA images in short time. This study aimed to evaluate the image quality and diagnostic performance of brain CTP and CTA images generated from CTP reconstructed by a deep learning image reconstruction (DLIR) algorithm on patients with AIS. The study prospectively enrolled 54 patients with suspected AIS undergoing non-contrast CT and CTP within 24 hours. CTP datasets were reconstructed with three levels of adaptive statistical iterative reconstruction-Veo algorithm [ASIR-V 0% with filtered back projection (FBP), ASIR-V 40%, and ASIR-V 80%] and three levels of DLIR, including low (DLIR-L), medium (DLIR-M), and high (DLIR-H). CTA images were generated using the CTP datasets at the peak arterial phase. Objective parameters including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and noise reduction rate. Subjective evaluation was assessed according to Abels scoring system. Perfusion parameters and detection accuracy for infarction core lesions were evaluated. The objective and subjective image quality of CTA images were also evaluated. All reconstructions produced similar CT values (P>0.05). With the increase of ASIR-V and DLIR reconstruction strength, image noise decreased, while SNR and CNR increased for CTP images, especially in white matter. DLIR-H, DLIR-M, and ASIR-V80% yielded higher subjective scores than did ASIR-V40% and FBP. DLIR-H provided the highest noise reduction rate and detection accuracy. No significant difference was found in conventional parameters, the volume of infarct core, or ischemic penumbra among the 6 groups (P>0.05). The objective evaluation of reconstructed CTA images was comparable in DLIR-H, DLIR-M, and ASIR-V80% (P>0.05). The subjective scores of the DLIR-H and DLIR-M images were higher than those of the other groups, especially ASIR-V40% and FBP (P<0.05). Compared with FBP and ASIR-V40%, DLIR-H, DLIR-M, and ASIR-V80% improved the overall image quality of CTP and CTA images to varying degrees. Furthermore, DLIR-H and DLIR-M showed the best performance. DLIR-H is the best choice in diagnosing AIS with improved detection accuracy for cerebral infarction. Reconstructing CTA images using CTP datasets could reduce contrast agent and radiation dose.

Effective Mechanical Thrombectomy for Posterior Circulation Ischemia Using Magnetic Resonance Imaging-based Arterial Structures.

To improve the success of mechanical thrombectomy, three-dimensional turbo spin-echo (3D-TSE) sequences on T2WI can be employed to estimate the vascular structure of the posterior circulation. In addition to the short imaging time of 3D-TSE T2WI (33 sec), it can visualize the outer diameter of the main cerebral artery, including the occluded vessels. However, to date, the efficacy of mechanical thrombectomy in the posterior circulation remains unclear, and safer and more efficient mechanical thrombectomy procedures are required. Assessment of the anatomical variations in the posterior circulation using 3D-TSE T2WI is valuable for access decisions, device selection, and safe device guidance and retrieval techniques to the target vessel. Herein, we present representative cases of basilar artery and posterior cerebral artery occlusions in our institute and describe the utility of preoperative 3D-TSE T2WI in these patients.

Towards accurate 177Lu SPECT activity quantification and standardization using lesion-to-background voxel ratio

BackgroundConventional calibration of the gamma camera consists of the calculation of calibration factors (CFs) (ratio of counts/cc and true concentration activity) as the function of the volume of interest (VOI). However, such method shows inconsistent results when the background activity varies. The aim of the present study was to propose a new calibration method by considering the sphere-to-background counts/voxel ratio (SBVR) in addition to the VOI for CFs calculation. A PET cylindrical flood phantom, a NEMA IQ body phantom, a Data spectrum Torso Phantom (ECT/TOR/P) and a LK-S Kyoto Liver/Kidney phantom were used. The NEMA IQ phantom was used to calibrate the camera and to produce CFs for the different spheres volumes and for varying sphere-to-background activity ratios. The spheres were filled with a uniform activity concentration of 177Lu, while the background was first filled with cold water and activity was added between each SPECT scan. SPECT imaging was performed for 30-s, 20-s, and 10-s exposure per view. The calculated CFs were expressed as function of the sphere volume and SBVR. The obtained CFs were validated for an additional NEMA IQ acquisition with different activities in spheres and background and for the Torso and Liver/Kidney phantoms with inserted NEMA IQ spheres. The quantification accuracy was compared with the conventional method not taking SBVR into consideration.ResultsThe relative errors in quantification using the NEMA IQ phantom with the new calibration method were 0.16%, 5.77%, 9.34% for the large, medium and small sphere, respectively, for a time per view of 30-s. The conventional calibration method gave errors of 3.65%, 6.65%, 30.28% for 30-s. The LK-S Kyoto Liver/Kidney Phantom resulted in quantification errors of 3.40%, 2.14%, 11.18% for the large, medium and small spheres, respectively, for 30-s; compared to 11.31%, 17.54%, 14.43% for 30-s, respectively, for the conventional method. Similar results were obtained for shorter acquisitions times with 20-s and 10-s time per view.ConclusionThese results suggest that SBVR allows to improve quantification accuracy. The shorter time-per-view acquisitions had similar relative differences compared to the full-time acquisition which allows shorter imaging times with 177Lu and improved patient comfort. The SBVR method is simple to set up and can be proposed for standardization.

Mouthwash as a non-invasive method of indocyanine green delivery for near-infrared fluorescence dental imaging.

.Significance: X-ray imaging serves as the mainstream imaging in dentistry, but it involves risk of ionizing radiation.Aim: This study presents the feasibility of indocyanine green-assisted near-infrared fluorescence (ICG-NIRF) dental imaging with 785-nm NIR laser in the first (ICG-NIRF-I: 700 to 1000 nm) and second (ICG-NIRF-II: 1000 to 1700 nm) NIR wavelengths.Approach: Sprague Dawley rats with different postnatal days were used as animal models. ICG, as a fluorescence agent, was delivered to dental structures by subcutaneous injection (SC) and oral administration (OA).Results: For SC method, erupted and unerupted molars could be observed from ICG-NIRF images at a short imaging time (). ICG-NIRF-II could achieve a better image contrast in unerupted molars at 24 h after ICG injection. The OA could serve as a non-invasive method for ICG delivery; it could also cause the glow-in-dark effect in unerupted molars. For erupted molars, OA can be considered as mouthwash and exhibits outstanding performance for delivery of ICG dye; erupted molar structures could be observed at a short imaging time () and low ICG dose ().Conclusions: Overall, ICG-NIRF with mouthwash could perform in-vivo dental imaging in two NIR wavelengths at a short time and low ICG dose.

Etiology of Tinnitus on CT and CBCT: A Narrative Review.

Tinnitus is commonly depicted as a ringing within the ears, but it can sound like roaring, clicking, hissing, or buzzing. It is a symptom that shows something is wrong in the auditory system, which includes the sound-related nerve that interfaces the inward ear to the brain, and the parts of the brain that handle sound. Generally, the causes of tinnitus include: Otologic causes, Neurologic causes, temporomandibular joint, and masticatory muscle disorders. Causes of tinnitus can be diagnosed with CT and CBCT. A CT scan or computed tomography scan is a medical imaging technique used in radiology that can obtain detailed internal images of the brain and CBCT is a developing imaging technique designed to provide relatively low-dose high-spatial-resolution visualization of highcontrast structures in the head and neck and other anatomic areas. CBCT has a lower radiation dose, shorter imaging time, and better resolution than CT. This chapter reviews etiology of tinnitus on CT and CBCT.

A narrative review of biparametric MRI (bpMRI) implementation on screening, detection, and the overall accuracy for prostate cancer.

Prostate cancer is the most common malignancy in American men following skin cancer, with approximately one in eight men being diagnosed during their lifetime. Over the past several decades, the treatment of prostate cancer has evolved rapidly, so too has screening. Since the mid-2010s, magnetic resonance imaging (MRI)–guided biopsies or ‘targeted biopsies’ has been a rapidly growing topic of clinical research within the field of urologic oncology. The aim of this publication is to provide a review of biparametric MRI (bpMRI) utilization for the diagnosis of prostate cancer and a comparison to multiparametric MRI (mpMRI). Through single-centered studies and meta-analysis across all identified pertinent published literature, bpMRI is an effective tool for the screening and diagnosis of prostate cancer. When compared with the diagnostic accuracy of mpMRI, bpMRI identifies prostate cancer at comparable rates. In addition, when omitting dynamic contrast-enhanced (DCE) protocol to the MRI, patients incur reduced costs and shorter imaging time while providers can offer more tests to their patient population.

A methodology to develop part acceptance criteria model using non-destructive inspection technique for FDM printed part

Additive Manufacturing (AM) has transit over the years from simple prototyping to high-mix-low-volume production. Sectors such as Aerospace or Biomedical that have adopted Additive Manufacturing for parts production have stringent requirement for strength and minimal tolerance for sub-surface defects. The presence of embedded defects usually leads to premature failure and additional cost for reinstallation. The current gold standard for Non-Destructive Inspection (NDI) technique is the use of 3D micro-CT scan. 3D micro-CT scan although provide the best resolution for defect detection, it is expensive, time consuming and laborious. The defects found in polymer 3D printed component, e.g., Fused Deposition Modeling, may not require high resolution imaging for defect detection due to the nature of printing process. Therefore, 2D X-ray imaging known for its short imaging and processing time has been used in this study with Acrylonitrile Butadiene Styrene (ABS) as the model material. Defects of various void sizes were introduced to tensile test coupons to determine the effect of void sizes on tensile properties. Good linear agreement (R2 = 0.998) has been observed between the void sizes and tensile strength which was then used to develop the part acceptance criteria model. The part acceptance criteria model developed from 2D X-ray imaging and mechanical test data suggest the feasibility of using such methodology to perform quality checks on 3D printed part which obviates the need for time consuming 3D micro-CT scans and unsustainable destructive testing.

An artif\u0131cial \u0131ntelligence approach to automatic tooth detection and numbering in panoramic radiographs

BackgroundPanoramic radiography is an imaging method for displaying maxillary and mandibular teeth together with their supporting structures. Panoramic radiography is frequently used in dental imaging due to its relatively low radiation dose, short imaging time, and low burden to the patient. We verified the diagnostic performance of an artificial intelligence (AI) system based on a deep convolutional neural network method to detect and number teeth on panoramic radiographs.MethodsThe data set included 2482 anonymized panoramic radiographs from adults from the archive of Eskisehir Osmangazi University, Faculty of Dentistry, Department of Oral and Maxillofacial Radiology. A Faster R-CNN Inception v2 model was used to develop an AI algorithm (CranioCatch, Eskisehir, Turkey) to automatically detect and number teeth on panoramic radiographs. Human observation and AI methods were compared on a test data set consisting of 249 panoramic radiographs. True positive, false positive, and false negative rates were calculated for each quadrant of the jaws. The sensitivity, precision, and F-measure values were estimated using a confusion matrix.ResultsThe total numbers of true positive, false positive, and false negative results were 6940, 250, and 320 for all quadrants, respectively. Consequently, the estimated sensitivity, precision, and F-measure were 0.9559, 0.9652, and 0.9606, respectively.ConclusionsThe deep convolutional neural network system was successful in detecting and numbering teeth. Clinicians can use AI systems to detect and number teeth on panoramic radiographs, which may eventually replace evaluation by human observers and support decision making.

The Pharmaceutical Technology Approach on Imaging Innovations from Italian Research.

Many modern therapeutic approaches are based on precise diagnostic evidence, where imaging procedures play an essential role. To date, in the diagnostic field, a plethora of agents have been investigated to increase the selectivity and sensitivity of diagnosis. However, the most common drawbacks of conventional imaging agents reside in their non-specificity, short imaging time, instability, and toxicity. Moreover, routinely used diagnostic agents have low molecular weights and consequently a rapid clearance and renal excretion, and this represents a limitation if long-lasting imaging analyses are to be conducted. Thus, the development of new agents for in vivo diagnostics requires not only a deep knowledge of the physical principles of the imaging techniques and of the physiopathological aspects of the disease but also of the relative pharmaceutical and biopharmaceutical requirements. In this scenario, skills in pharmaceutical technology have become highly indispensable in order to respond to these needs. This review specifically aims to collect examples of newly developed diagnostic agents connoting the importance of an appropriate formulation study for the realization of effective products. Within the context of pharmaceutical technology research in Italy, several groups have developed and patented promising agents for fluorescence and radioactive imaging, the most relevant of which are described hereafter.

Defining the Basis of Cyanine Phototruncation Enables a New Approach to Single-Molecule Localization Microscopy.

The light-promoted conversion of extensively used cyanine dyes to blue-shifted emissive products has been observed in various contexts. However, both the underlying mechanism and the species involved in this photoconversion reaction have remained elusive. Here we report that irradiation of heptamethine cyanines provides pentamethine cyanines, which, in turn, are photoconverted to trimethine cyanines. We detail an examination of the mechanism and substrate scope of this remarkable two-carbon phototruncation reaction. Supported by computational analysis, we propose that this reaction involves a singlet oxygen-initiated multistep sequence involving a key hydroperoxycyclobutanol intermediate. Building on this mechanistic framework, we identify conditions to improve the yield of photoconversion by over an order of magnitude. We then demonstrate that cyanine phototruncation can be applied to super-resolution single-molecule localization microscopy, leading to improved spatial resolution with shorter imaging times. We anticipate these insights will help transform a common, but previously mechanistically ill-defined, chemical transformation into a valuable optical tool.

FDG-PET/CT in intensive care patients with bloodstream infection

Background2-Deoxy-2-[18F]fluoro-D-glucose (FDG) positron emission tomography (PET)/computed tomography (CT) is an advanced imaging technique that can be used to examine the whole body for an infection focus in a single examination in patients with bloodstream infection (BSI) of unknown origin. However, literature on the use of this technique in intensive care patients is scarce. The purpose of this study was to evaluate the diagnostic yield of FDG-PET/CT in intensive care patients with BSI.MethodsIn this retrospective cohort study, all intensive care patients from our Dutch university medical center who had culture-proven BSI between 2010 and 2020 and underwent FDG-PET/CT to find the focus of infection were included. Diagnostic performance was calculated and logistic regression analysis was performed to evaluate the association between FDG-PET/CT outcome and C-reactive protein level (CRP), leukocyte count, duration of antibiotic treatment, duration of ICU stay, quality of FDG-PET/CT, and dependency on mechanical ventilation. In addition, the impact of FDG-PET/CT on clinical treatment was evaluated.Results30 intensive care patients with BSI were included. In 21 patients, an infection focus was found on FDG-PET/CT which led to changes in clinical management in 14 patients. FDG-PET/CT achieved a sensitivity of 90.9% and specificity of 87.5% for identifying the focus of infection. Poor quality of the FDG-PET images significantly decreased the likelihood of finding an infection focus as compared to reasonable or good image quality (OR 0.16, P = 0.034). No other variables were significantly associated with FDG-PET/CT outcome. No adverse events during the FDG-PET/CT procedure were reported.ConclusionFDG-PET/CT has a high diagnostic yield for detecting the infection focus in patients with BSI admitted to intensive care. Poor PET image quality was significantly associated with a decreased likelihood of finding the infection focus in patients with BSI. This could be improved by adequate dietary preparation and cessation of intravenous glucose and glucose-regulating drugs. Recent advances in PET/CT technology enable higher image quality with shorter imaging time and may contribute to routinely performing FDG-PET/CT in intensive care patients with BSI of unknown origin.

Dual pH- and Glutathione-Responsive CO2-Generating Nanodrug Delivery System for Contrast-Enhanced Ultrasonography and Therapy of Prostate Cancer.

Ultrasonography (US) contrast imaging using US contrast agents has been widely applied for the diagnosis and differential diagnosis of tumors. Commercial US contrast agents have limited applications because of their large size and shorter imaging time. At the same time, the desired therapeutic purpose cannot be achieved by applying only conventional US contrast agents. The development of nanoscale US agents with US imaging and therapeutic functions has attracted increasing attention. In this study, we successfully developed DOX-loaded poly-1,6-hexanedithiol-sodium bicarbonate nanoparticles (DOX@HADT-SS-NaHCO3 NPs) with pH-responsive NaHCO3 and GSH-responsive disulfide linkages. DOX@HADT-SS-NaHCO3 NPs underwent acid-triggered decomposition of NaHCO3 to generate CO2 bubbles and a reduction of disulfide linkages to further promote the release of CO2 and DOX. The potential of DOX@HADT-SS-NaHCO3 NPs for contrast-enhanced US imaging and therapy of prostate cancer was thoroughly evaluated using in vitro agarose gel phantoms and a C4-2 tumor-bearing nude mice model. These polymeric NPs displayed significantly enhanced US contrast at acidic pH and antitumor efficacy. Therefore, the NaHCO3 and DOX-encapsulated polymeric NPs hold tremendous potential for effective US imaging and therapy of prostate cancer.

Diffusion analysis of fluid dynamics with incremental strength of motion proving gradient (DANDYISM) to evaluate cerebrospinal fluid dynamics

PurposeTo visualize and analyze the dynamics of cerebrospinal fluid (CSF) motion in the cranium, we evaluated the distribution of motion-related signal dephasing by CSF on Diffusion ANalysis of fluid DYnamics with Incremental Strength of Motion proving gradient (DANDYISM) method, a composite imaging method using various low b values.Materials and methodsThis study examined ten subjects aged 25–58. We acquired DWIs on a 3T clinical scanner with b values 0, 50, 100, 200, 300, 500, 700, and 1000 s/mm2 in total imaging time of 4 min. We constructed DANDYISM images and evaluated the CSF area distribution with decreased motion-dephasing signal using a scoring method.ResultsThe DANDYISM images showed statistically significant higher CSF scores in the ventral posterior fossa, suprasellar cistern, and Sylvian vallecula compared to the lateral ventricle and frontal and parietal CSF spaces, indicating greater CSF movement in the former areas.ConclusionThe results indicated prominent CSF motions in the ventral portion of the posterior fossa, suprasellar cistern, and Sylvian fissure but smaller motions in the lateral ventricles and parietal subarachnoid space. This method may provide information of CSF dynamics in the clinical settings within short imaging time.

Research on multispectral correlation imaging of moving target based on phase modulation

In order to overcome the inability of scanning multi-spectral imaging to capture multi-spectral data in dynamic scenes, a single-exposure multispectral imaging method for moving targets was proposed based on phase modulation. This method combined associated imaging technology, compressed sensing technology and spectral imaging, introduced a spatial random phase modulator into the imaging light path, modulated and compressed the three-dimensional map information data of the moving target object, then used the two-dimensional aliasing signal obtained by the detector to reconstruct the three-dimensional map information to achieve a single exposure and simultaneously obtained the three-dimensional map information of the moving target. It had the advantages of high utilization rate of light energy, short imaging time, and simple system structure. The experimental results show that when the average electron number of a single frame of CCD detection signal increases from 200 e− at intervals of 100 e− to 1300 e−, as the average rRMSE value of the electron number increases, the relative root mean square error of the reconstructed image decreases correspondingly, and the reconstruction improved image quality; when the stepper motor drives the target object to continuously move at a speed of 30 Hz, a multi-spectral reconstructed image of the moving object with better quality can be obtained; a spectrometer is used to test the spectral distribution curves of different spectrum bands in the target object, and the results obtained are basically consistent with the spectral distribution curves of the reconstructed image, which proves the effectiveness of the method. The research results provide a useful reference for the application of multi-spectral correlation imaging technology in UAV platforms, dynamic monitoring and other fields.

Sensitivity and specificity of CEST and NOE MRI in injured spinal cord in monkeys

PurposeThe sensitivity and accuracy of chemical exchange saturation transfer (CEST) and nuclear Overhauser enhancement (NOE) effects for assessing injury-associated changes in cervical spinal cords were evaluated in squirrel monkeys. Multiple interacting pools of protons, including one identified by an NOE at −1.6 ppm relative to water (NOE(-1.6)), were derived and quantified from fitting proton Z-spectra. The effects of down-sampled data acquisitions and corrections for non-specific factors including T1, semi-solid magnetization transfer, and direct saturation of free water (DS), were investigated. The overall goal is to develop a protocol for rapid data acquisition for assessing the molecular signatures of the injured spinal cord and its surrounding regions. MethodsMRI scans were recorded of anesthetized squirrel monkeys at 9.4 T, before and after a unilateral dorsal column sectioning of the cervical spinal cord. Z-spectral images at 51 different RF offsets were acquired. The amplitudes of CEST and NOE effects from multiple proton pools were quantified using a six-pool Lorenzian fitting of each Z-spectrum (MTRmfit). In addition, down-sampled data using reduced selections of RF offsets were analyzed and compared. An apparent exchange-dependent relaxation (AREXmfit) method was also used to correct for non-specific factors in quantifying regional spectra around lesion sites. ResultsThe parametric maps from multi-pool fitting using the complete sampling data (P51e) detected unilateral changes at and around the injury. The maps derived from selected twofold down-sampled data with appropriate interpolation (P26sI51) revealed quite similar spatial distributions of different pools as those obtained using P51e at each resonance shift. Across 10 subjects, both data acquisition schemes detected significant decreases in NOE(-3.5) and NOE(-1.6) and increases in DS(0.0) and CEST(3.5) at the lesion site relative to measures of the normal tissues before injury. AREXmfit of cysts and other abnormal tissues at and around the lesion site also exhibited significant changes, especially at 3.5, −1.6 and −3.5 ppm RF offsets. ConclusionThese results confirm that a reduced set of RF offsets and down sampling are adequate for CEST imaging of injured spinal cord and allow shorter imaging times and/or permit additional signal averaging. AREXmfit correction improved the accuracy of CEST and NOE measures. The results provide a rapid (~13 mins), sensitive, and accurate protocol for deriving multiple NOE and CEST effects simultaneously in spinal cord imaging at high field.

Multimodal imaging of muon based on scattering and secondary induced neutrons

Muon scattering imaging technology can be used to detect nuclear material and is of considerable significance in nuclear safety. However, it is difficult to distinguish special nuclear materials from high-Z objects effectively by using the existing muon scattering imaging technologies. Muon-induced neutrons emitted from special nuclear materials can help to identify the existence of special nuclear materials. However, this method has long imaging time and low imaging quality. Multimodal imaging of muon uses both the information about scattering muons penetrating the material and the information about muons stopped by material and generating secondary induced neutrons, which can overcome the shortcomings of single imaging method effectively. The detection model is set up based on Geant4. The simulation programs of muon imaging in coincidence with muon induced neutrons, scattering imaging of muon, and multimodal imaging of muon are developed by using Cosmic-ray Shower Library as particle source, and the imaging algorithms are implemented respectively on the basis of the simulated data. Two imaging models are designed for muon scattering imaging. The first one is a single &lt;sup&gt;235&lt;/sup&gt;U cube, and the second one is composed of four cubes, namely &lt;sup&gt;235&lt;/sup&gt;U cube, &lt;sup&gt;239&lt;/sup&gt;Pu cube, lead cube and aluminum cube. This simulation has completed muon scattering imaging of single cube and four cubes. In the part of muon imaging in coincidence with muon induced neutrons, the neutronic gain of the HEU (90% &lt;sup&gt;235&lt;/sup&gt;U) plate, LEU (20% &lt;sup&gt;235&lt;/sup&gt;U) plate, and DU (0.2% &lt;sup&gt;235&lt;/sup&gt;U) plate, as well as the relationship between the neutronic gain of these three uranium plates and the energy and charged properties of the muon are obtained by simulation, and then two imaging models are set up. The first one is composed of four cubes, namely &lt;sup&gt;235&lt;/sup&gt;U cube, &lt;sup&gt;239&lt;/sup&gt;Pu cube, lead cube, and aluminum cube, and the other is comprised of multilayer nuclear components. The 2D and 3D reconstruction results of multi-objects and multilayer nuclear components are obtained through muon imaging in coincidence with muon induced neutrons. Then the multimodal imaging of muon for three cubes is realized in the presence or absence of iron shielding shell. The imaging capabilities are compared with the muon scattering imaging capacities and muon imaging capacities in coincidence with muon induced neutrons. Simulation studies indicate that multimodal imaging of muon based on scattering and secondary induced neutrons can effectively combine the advantages of every single imaging method. The multimodal imaging of muon can take advantage of available information more efficiently, which is helpful in improving the imaging quality. Multimodal imaging of muon not only has the advantages of short imaging time and high imaging quality, but also can distinguish special nuclear material from other high-Z materials clearly, which is vital for detecting special nuclear materials.

Surface structure investigation by means of ion beam-induced luminescence imaging: A preliminary study

The idea of ionoluminescence 3-D imaging is introduced to provide surface structural information of luminescent samples from a depth of tens of micrometers. Application of broad ion beam in an in-air setup results in capturing high resolution 2-D images in short times. Therefore, ionoluminescence 3-D imaging is a non-destructive technique which can extract information such as inhomogeneity, porosity and roughness, especially from historical precious samples. In order to display the capabilities of the ionoluminescence 3-D imaging technique, the 3-D image of an inhomogeneous lapis lazuli sample is produced based on stereo-ionoluminescence approach. The results are indicative of high potentials of the applied imaging technique for revealing surface structural information of luminescent samples.

Designation of Thorax and Non-Thorax Regions for Lung Cancer Detection in CT Scan Images using Deep Learning

Lung cancer is a common cause of death among people throughout the world. Lung cancer detection can be done in several ways, such as radiography, magnetic resonance imaging (MRI) and computed tomography (CT). These methods take up a lot of resources in terms of time and money. However, CT has good for lung cancer detection, offers a lower cost, short imaging time and widespread availability. Early diagnosis of lung cancer can help doctors to treat patients in order to reduce the number of mortalities. This paper presents designation of thorax and nonthorax regions for lung cancer detection in CT Scan images using deep learning. The primary aim of this research is to propose an intelligent, fast and accurate method for lung cancer detection. As initial stage we proposed a thorax and non-thorax slice detection for CT scan images using deep convolutional neural network (DCNN) so that later it can be used to simplify the process of lung cancer detection. The proposed method involved the development of DCNN network architecture. It comprises the following steps which involves designed the convolution layer, activation function, max pooling, fully-connected layer and output size. We present three DCNN structures to find the most effective network for thorax and non-thorax region detection. All networks were trained using 12866 images and validate the performance using 5514 images. Simulation results showed that DCNN 2 and DCNN 3 were able to classify the thorax and non-thorax regions with good performance. The most efficient network is the DCNN with fivelayer structure (DCNN 2). This DCNN model achieved an accuracy of 99.42% with moderate duration of training time.