Retrospective analysis of pathological types and imaging features in pancreatic cancer: A comprehensive study

BACKGROUND Pancreatic cancer remains one of the most lethal malignancies worldwide,with a poor prognosis often attributed to late diagnosis.Understanding the correlation between pathological type and imaging features is crucial for early detection and appropriate treatment planning.AIM To retrospectively analyze the relationship between different pathological types of pancreatic cancer and their corresponding imaging features.METHODS We retrospectively analyzed the data of 500 patients diagnosed with pancreatic cancer between January 2010 and December 2020 at our institution.Pathological types were determined by histopathological examination of the surgical spe-cimens or biopsy samples.The imaging features were assessed using computed tomography,magnetic resonance imaging,and endoscopic ultrasound.Statistical analyses were performed to identify significant associations between pathological types and specific imaging characteristics.RESULTS There were 320(64%)cases of pancreatic ductal adenocarcinoma,75(15%)of intraductal papillary mucinous neoplasms,50(10%)of neuroendocrine tumors,and 55(11%)of other rare types.Distinct imaging features were identified in each pathological type.Pancreatic ductal adenocarcinoma typically presents as a hypodense mass with poorly defined borders on computed tomography,whereas intraductal papillary mucinous neoplasms present as characteristic cystic lesions with mural nodules.Neuroendocrine tumors often appear as hypervascular lesions in contrast-enhanced imaging.Statistical analysis revealed significant correlations between specific imaging features and pathological types(P<0.001).CONCLUSION This study demonstrated a strong association between the pathological types of pancreatic cancer and imaging features.These findings can enhance the accuracy of noninvasive diagnosis and guide personalized treatment approaches.

Design and performance evaluation of a large field-of-view dual-particle time-encoded imager based on a depth-of-interaction detector

Time-encoded imaging is useful for identifying potential special nuclear materials and other radioactive sources at a distance.In this study,a large field-of-view time-encoded imager was developed for gamma-ray and neutron source hotspot imaging based on a depth-of-interaction(DOI)detector.The imager primarily consists of a DOI detector system and a rotary dual-layer cylindrical coded mask.An EJ276 plastic scintillator coupled with two SiPMs was designed as the DOI detector to increase the field of view and improve the imager performance.The difference in signal time at both ends and the log of the signal amplitude ratio were used to calculate the interaction position resolution.The position resolution of the DOI detector was calibrated using a collimated Cs-137 source,and the full width at half maximum of the reconstruction position of the Gaussian fitting curve was approximately 4.4 cm.The DOI detector can be arbitrarily divided into several units to independently reconstruct the source distribution images.The unit length was optimized via Am-Be source-location experiments.A multidetector filtering method is proposed for image denoising.This method can effectively reduce image noise caused by poor DOI detector position resolution.The vertical field of view of the imager was(-55°,55°)when the detector was placed in the center of the coded mask.A DT neutron source at 20 m standoff could be located within 2400 s with an angular resolution of 3.5°.

Performance optimization of the neutron-sensitive image intensifier used in neutron imaging

As a non-destructive testing technology,neutron imaging plays an important role in various fields,including material science,nuclear engineering,and fundamental science.An imaging detector with a neutron-sensitive image intensifier has been developed and demonstrated to achieve good spatial resolution and timing resolution.However,the influence of the working voltage on the performance of the neutron-sensitive imaging intensifier has not been studied.To optimize the performance of the neutron-sensitive image intensifier at different voltages,experiments have been performed at the China Spallation Neutron Source(CSNS)neutron beamline.The change in the light yield and imaging quality with different voltages has been acquired.It is shown that the image quality benefits from the high gain of the microchannel plate(MCP)and the high accelerating electric field between the MCP and the screen.Increasing the accelerating electric field is more effective than increasing the gain of MCPs for the improvement of the imaging quality.Increasing the total gain of the MCP stack can be realized more effectively by improving the gain of the standard MCP than that of the n MCP.These results offer a development direction for image intensifiers in the future.

An Implementation of Multiscale Line Detection and Mathematical Morphology for Efficient and Precise Blood Vessel Segmentation in Fundus Images

Diagnosing various diseases such as glaucoma,age-related macular degeneration,cardiovascular conditions,and diabetic retinopathy involves segmenting retinal blood vessels.The task is particularly challenging when dealing with color fundus images due to issues like non-uniformillumination,low contrast,and variations in vessel appearance,especially in the presence of different pathologies.Furthermore,the speed of the retinal vessel segmentation system is of utmost importance.With the surge of now available big data,the speed of the algorithm becomes increasingly important,carrying almost equivalent weightage to the accuracy of the algorithm.To address these challenges,we present a novel approach for retinal vessel segmentation,leveraging efficient and robust techniques based on multiscale line detection and mathematical morphology.Our algorithm’s performance is evaluated on two publicly available datasets,namely the Digital Retinal Images for Vessel Extraction dataset(DRIVE)and the Structure Analysis of Retina(STARE)dataset.The experimental results demonstrate the effectiveness of our method,withmean accuracy values of 0.9467 forDRIVE and 0.9535 for STARE datasets,aswell as sensitivity values of 0.6952 forDRIVE and 0.6809 for STARE datasets.Notably,our algorithmexhibits competitive performance with state-of-the-art methods.Importantly,it operates at an average speed of 3.73 s per image for DRIVE and 3.75 s for STARE datasets.It is worth noting that these results were achieved using Matlab scripts containing multiple loops.This suggests that the processing time can be further reduced by replacing loops with vectorization.Thus the proposed algorithm can be deployed in real time applications.In summary,our proposed system strikes a fine balance between swift computation and accuracy that is on par with the best available methods in the field.

A Concise and Varied Visual Features-Based Image Captioning Model with Visual Selection

Image captioning has gained increasing attention in recent years.Visual characteristics found in input images play a crucial role in generating high-quality captions.Prior studies have used visual attention mechanisms to dynamically focus on localized regions of the input image,improving the effectiveness of identifying relevant image regions at each step of caption generation.However,providing image captioning models with the capability of selecting the most relevant visual features from the input image and attending to them can significantly improve the utilization of these features.Consequently,this leads to enhanced captioning network performance.In light of this,we present an image captioning framework that efficiently exploits the extracted representations of the image.Our framework comprises three key components:the Visual Feature Detector module(VFD),the Visual Feature Visual Attention module(VFVA),and the language model.The VFD module is responsible for detecting a subset of the most pertinent features from the local visual features,creating an updated visual features matrix.Subsequently,the VFVA directs its attention to the visual features matrix generated by the VFD,resulting in an updated context vector employed by the language model to generate an informative description.Integrating the VFD and VFVA modules introduces an additional layer of processing for the visual features,thereby contributing to enhancing the image captioning model’s performance.Using the MS-COCO dataset,our experiments show that the proposed framework competes well with state-of-the-art methods,effectively leveraging visual representations to improve performance.The implementation code can be found here:https://github.com/althobhani/VFDICM(accessed on 30 July 2024).

10B-doped MCP detector developed for neutron resonance imaging at Back-n white neutron source

Neutron resonance imaging(NRI)has recently emerged as an appealing technique for neutron radiography.Its complexity surpasses that of conventional transmission imaging,as it requires a high demand for both a neutron source and detector.Consequently,the progression of NRI technology has been sluggish since its inception in the 1980s,particularly considering the limited studies analyzing the neutron energy range above keV.The white neutron source(Back-n)at the China Spallation Neutron Source(CSNS)provides favorable beam conditions for the development of the NRI technique over a wide neutron energy range from eV to MeV.Neutron-sensitive microchannel plates(MCP)have emerged as a cutting-edge tool in the field of neutron detection owing to their high temporal and spatial resolutions,high detection efficiency,and low noise.In this study,we report the development of a 10B-doped MCP detector,along with its associated electronics,data processing system,and NRI experiments at the Back-n.Individual heavy elements such as gold,silver,tungsten,and indium can be easily identified in the transmission images by their characteristic resonance peaks in the 1–100 eV energy range;the more difficult medium-weight elements such as iron,copper,and aluminum with resonance peaks in the 1–100 keV energy range can also be identified.In particular,results in the neutron energy range of dozens of keV(Aluminum)are reported here for the first time.

Impact of Conjugated Shear Joint of“X”Type on Macroscopic Karstification on the Basis of the Analysis of Google Earth Images

As viewed from space remote-sensing images （e.g. Google Earth images） of South Guizhou and North Guangxi, the authors found that macroscopic karst landscape on the Earth＇s surface is strongly controlled by the Conjugated shear joint of ＂X＂ type. Joints of this kind constitute a huge infiltration network and act as channel-ways for the permeation of meteoric waters from the surface, thus, leading to the dissolution of carbonate rocks nearby. As a result, the karst landscape is formed, which is dominated by linear karst valleys. An ＂X＂ karst valley network structure appears in the area where horizontal strata are distributed, and a feather-like network structure appears in the area where vertical strata are distributed, respectively. When the water permeates downwards to the underground-water level, it will flow horizontally along the strike of ＂X＂ joints toward the local base level of erosion to form an ＂X＂ network system of underground conduits in the area where horizontal strata are distributed, but it is relatively complex, because of the joining of other joints. This is the first time we have made use of Google Earth images to study the karst environment. Therefore, it has been successful in research on the Earth＇s geomorphology, which could only rely on aerial photos and satellite photos in the past. Google Earth images provide low-cost and applicable imaging materials for the study of Earth＇s geomorphology and karst rocky desertification and its control.

Image quality evaluation of linear plastic scintillating fiber array detector for X-ray imaging

It is important to assess image quality, in order to ensure that the imaging system is performing optimally and also identify the weak points in an imaging system. Three parameters mostly leading to image degradation are contrast, spatial resolution and noise. There is always a trade-off between spatial resolution and signal to noise ratio, but in scintillating fiber array detectors spatial resolution is not as important as signal to noise ratio, so we paid more attention to contrast and SNR of the system. By using GEANT4 Monte Carlo detector simulation toolkit, some effec- tive parameters of the linear plastic scintillating fiber (PSF) array as an imaging detector were investigated. Finally we show that it is possible to use this kind of detector to take CT and DR (Digital Radiography) image under certain conditions.

Recent progress in the applications of presynaptic dopaminergic positron emission tomography imaging in parkinsonism

Nowadays,presynaptic dopaminergic positron emission tomography,which assesses deficiencies in dopamine synthesis,storage,and transport,is widely utilized for early diagnosis and differential diagnosis of parkinsonism.This review provides a comprehensive summary of the latest developments in the application of presynaptic dopaminergic positron emission tomography imaging in disorders that manifest parkinsonism.We conducted a thorough literature search using reputable databases such as PubMed and Web of Science.Selection criteria involved identifying peer-reviewed articles published within the last 5 years,with emphasis on their relevance to clinical applications.The findings from these studies highlight that presynaptic dopaminergic positron emission tomography has demonstrated potential not only in diagnosing and differentiating various Parkinsonian conditions but also in assessing disease severity and predicting prognosis.Moreover,when employed in conjunction with other imaging modalities and advanced analytical methods,presynaptic dopaminergic positron emission tomography has been validated as a reliable in vivo biomarker.This validation extends to screening and exploring potential neuropathological mechanisms associated with dopaminergic depletion.In summary,the insights gained from interpreting these studies are crucial for enhancing the effectiveness of preclinical investigations and clinical trials,ultimately advancing toward the goals of neuroregeneration in parkinsonian disorders.

A practical image reconstruction and processing method for symmetrically off-center detector CBCT system

CBCT scanners have been widely used in angiography,radiotherapy guidance,mammography and oral maxillofacial imaging.To cut detector size,reduce manufacturing costs and radiation dose while keeping a reasonable FOV,the flat panel detector can be placed off-center horizontally.This scanning configuration extends the FOV effectively.However,each projection is transversely truncated,bringing errors and artifacts in reconstruction.In this paper,a simple but practical method is proposed for this scanning geometry based on truncation compensation and the modified FDK algorithm.Numerical simulations with jaw phantom were conducted to evaluate the accuracy and practicability of the proposed method.A novel CBCT system for maxillofacial imaging is used for clinical test,which is equipped with an off-center small size flat panel detector.Results show that reconstruction accuracy is acceptable for clinical use,and the image quality appears sufficient for specific diagnostic requirements.It provides a novel solution for clinical CBCT system,in order to reduce radiation dose and manufacturing cost.

Spectral Imagers with Linear Detector Imager Systems Based on Spectrum Compressed

Traditional spectral imagers require 2-dimensional detectors. We present a new method to implement spectral imagers with linear detector imager systems based on spectrum compressed. Using 1-dimension detectors instead of 2-dimension detectors to get 3-dimensional data cubes, the spectral imagers could get both the spectral information and the spatial information of each ground object. By the method of characteristics decoupling, we make high precision reconstruction of compressed data. Theoretical analysis and simulations show that it not only ensures the imaging quality but also reduces the dimension of the detectors and complexity of imaging system greatly.

Hybrid Multi-Strategy Aquila Optimization with Deep Learning Driven Crop Type Classification on Hyperspectral Images

Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed scenes.Much spatial information and spectral signatures of hyperspectral images(HSIs)present greater potential for detecting and classifying fine crops.The accurate classification of crop kinds utilizing hyperspectral remote sensing imaging(RSI)has become an indispensable application in the agricultural domain.It is significant for the prediction and growth monitoring of crop yields.Amongst the deep learning(DL)techniques,Convolution Neural Network(CNN)was the best method for classifying HSI for their incredible local contextual modeling ability,enabling spectral and spatial feature extraction.This article designs a Hybrid Multi-Strategy Aquila Optimization with a Deep Learning-Driven Crop Type Classification(HMAODL-CTC)algorithm onHSI.The proposed HMAODL-CTC model mainly intends to categorize different types of crops on HSI.To accomplish this,the presented HMAODL-CTC model initially carries out image preprocessing to improve image quality.In addition,the presented HMAODL-CTC model develops dilated convolutional neural network(CNN)for feature extraction.For hyperparameter tuning of the dilated CNN model,the HMAO algorithm is utilized.Eventually,the presented HMAODL-CTC model uses an extreme learning machine(ELM)model for crop type classification.A comprehensive set of simulations were performed to illustrate the enhanced performance of the presented HMAODL-CTC algorithm.Extensive comparison studies reported the improved performance of the presented HMAODL-CTC algorithm over other compared methods.

An invariant interest point detector under image affine transformation

For vision-based mobile robot navigation, images of the same scene may undergo a general affine transformation in the case of significant viewpoint changes. So, a novel method for detecting affine invariant interest points is proposed to obtain the invariant local features, which is coined polynomial local orientation tensor(PLOT). The new detector is based on image local orientation tensor that is constructed from the polynomial expansion of image signal. Firstly, the properties of local orientation tensor of PLOT are analyzed, and a suitable tuning parameter of local orientation tensor is chosen so as to extract invariant features. The initial interest points are detected by local maxima search for the smaller eigenvalues of the orientation tensor. Then, an iterative procedure is used to allow the initial interest points to converge to affine invariant interest points and regions. The performances of this detector are evaluated on the repeatability criteria and recall versus 1-precision graphs, and then are compared with other existing approaches. Experimental results for PLOT show strong performance under affine transformation in the real-world conditions.

FITTING CORRECTION METHOD OF RING ARTIFACTS FOR RECONSTRUCTING CONE-BEAM CT IMAGES

In high-resolution cone-beam computed tomography （CBCT） using the flat-panel detector, imperfect or defect detector elements cause ring artifacts due to the none-uniformity of their X-ray response. They often disturb the image quality. A dedicated fitting correction method for high-resolution micro-CT is presented. The method converts each elementary X-ray response curve to an average one, and eliminates response inconsistency among pixels. Other factors of the method are discussed, such as the correction factor variability by different sampling frames and nonlinear factors over the whole spectrum. Results show that the noise and artifacts are both reduced in reconstructed images

Evaluation of Ventricular-vascular Coupling in Patients with Type 2 Diabetes Mellitus Using 2-Dimensional Speckle Tracking Imaging

The elastic and functional coupling of heart and vessels makes the stroke work （SW） of the heart optimal. Speckle tracking imaging （STI） can evaluate the myocardial strain and function. We studied ventricular-vascular coupling in 80 diabetic patients with different systolic function using STI. The patients were divided into two groups according to ejection fraction （EF）： the diabetes mel- litus with normal EF （DMN） group and the diabetes mellitus with abnormal EF （DMA） group. Forty-two volunteers served as control group. The relative wall thickness （RWT）, left ventricular mass index （LVMI）, stroke volume （SV）, SW, rate-pressure product （RPP）, systemic vascular resis- tance index （SVRI）, left ventricular end-systolic elastance （Ees）, effective arterial elasticity （Ea） and ventricular-vascular coupling index （VVI） were measured and calculated by conventional echocardi- ography. The longitudinal strain （LS） at basement （LSBA）, papillary muscle （LSvM） and cardiac apex （LSAv） was assessed with STI. It was found： （A） compared with control group, in DMN and DMA groups, LSBA, LSvM and LSAp decreased, and they were lower in DMA group. （B） VVI, RPP and SVRI increased, and they were higher in DMN group; Ees decreased, and it was lower in DMA group. （C） LSBA, LSpM, and LSAv had negative correlation with VVI. LSAp, RWT, LVMI and SW were independent predictors for VVI. The area under the receiver operating characteristic （ROC） curves was used for identification of DMA and DMN with LSBA, LSpM, and LSAp, and the area under the ROC of LSAp was the largest. This study supports that myocardial LS could reflect the ventricu- lar-vascular coupling. Different segments had an order to “respond to” the state of the coupling, and the cardiac apex might be the earliest.

CT dose and image quality in the last three scanner generations

AIM: To compare the computed tomography(CT) dose and image quality with the filtered back projection against the iterative reconstruction and CT with a minimal electronic noise detector. METHODS: A lung phantom(Chest Phantom N1 by Kyoto Kagaku) was scanned with 3 different CT scanners: the Somatom Sensation, the Definition Flash and the Definition Edge(all from Siemens, Erlangen, Germany). The scan parameters were identical to the Siemens presetting for THORAX ROUTINE(scan length 35 cm and FOV 33 cm). Nine different exposition levels were examined(reference mAs/peek voltage): 100/120, 100/100, 100/80, 50/120, 50/100, 50/80, 25/120, 25/100 and 25 mAs/80 kVp. Images from the SOMATOM Sensation were reconstructed using classic filtered back projection. Iterative reconstruction(SAFIRE, level 3) was performed for the two other scanners. A Stellar detector was used with the Somatom Definition Edge. The CT doses were represented by the dose length products(DLPs)(mGycm) provided by the scanners. Signal, contrast, noise and subjective image quality were recorded by two different radiologists with 10 and 3 years of experience in chest CT radiology. To determine the average dose reduction between two scanners, the integral of the dose difference was calculated from the lowest to the highest noise level. RESULTS: When using iterative reconstruction(IR) instead of filtered back projection(FBP), the average dose reduction was 30%, 52% and 80% for bone, soft tissue and air, respectively, for the same image quality(P 【 0.0001). The recently introduced Stellar detector(Sd) lowered the radiation dose by an additional 27%, 54% and 70% for bone, soft tissue and air, respectively(P 【 0.0001). The benefit of dose reduction was larger at lower dose levels. With the same radiation dose, an average of 34%(22%-37%) and 25%(13%-46%) more contrast to noise was achieved by changing from FBP to IR and from IR to Sd, respectively. For the same contrast to noise level, an average of 59%(46%-71%) and 51%(38%-68%) dose reduction was produced for IR and Sd, respectively. For the same subjective image quality, the dose could be reduced by 25%(2%-42%) and 44%(33%-54%) using IR and Sd, respectively. CONCLUSION: This study showed an average dose reduction between 27% and 70% for the new Stellar detector, which is equivalent to using IR instead of FBP.

Hard X-ray Imager (HXI) onboard the ASO-S mission

Hard X-ray Imager(HXI)is one of the three scientific instruments onboard the Advanced Spacebased Solar Observatory(ASO-S)mission,which is proposed for the 25th solar maximum by the Chinese solar community.HXI is designed to investigate the non-thermal high-energy electrons accelerated in solar flares by providing images of solar flaring regions in the energy range from 30 keV to 200 keV.The imaging principle of HXI is based on spatially modulated Fourier synthesis and utilizes about 91 sets of bi-grid sub-collimators and corresponding LaBr3 detectors to obtain Fourier components with a spatial resolution of about 3 arcsec and a time resolution better than 0.5 s.An engineering prototype has been developed and tested to verify the feasibility of design.In this paper,we present background,instrument design and the development and test status of the prototype.

High-resolution forward-looking imaging algorithm for missile-borne detectors

Aiming at a novel missile-borne detector in the optional burst height proximity fuze, a self-adaptive high-resolution forward-looking imaging algorithm (SAHRFL-IA) is presented. The echo data are captured by the missile-borne detector in the target regions;thereby the azimuth angulation accuracy at the same distance dimension is improved dynamically. Thus, azimuth information of the targets in the detection area may be obtained accurately. The proposed imaging algorithm breaks through the conventional misconception of merely using azimuth discrimination curves under ideal conditions during monopulse angulation. The real-time echo data from the target region are used to perform error correction for this discrimination curve, and finally the accuracy of the azimuth angulation may reach the optimum at the same distance dimension. A series of experiments demonstrate the validity, reliability and high performance of the proposed imaging algorithm. Azimuth angulation accuracy may reach ten times that of the detection beam width. Meanwhile, the running time of this algorithm satisfies the requirements of missile-borne platforms.

Low Crosstalk Three-Color Infrared Detector by Controlling the Minority Carriers Type of InAs/GaSb Superlattices for Middle-Long and Very-Long Wavelength

We report a type-Ⅱ InAs/GaSb superlattice three-color infrared detector for mid-wave （MW）, long-wave （LW）, and very long-wave （VLW） detections. The detector structure consists of three contacts of NIPIN architecture for MW and LW detections, and hetero-junction NIP architecture for VLW detection. It is found that the spectral crosstalks can be significantly reduced by controlling the minority carriers transport via doping beryllium in the two active regions of NIPIN section. The crosstalk detection at MW, LW, and VLW signals are achieved by selecting the bias voltages on the device. At 77K, the cutoff wavelengths of the three-color detection are 5.3μm （at OmV）, 141μm （at 300mV） and 19μm （at -20mV） with the detectivities of 4.6xlO11 cm.Hzl/ZW-1, 2.3×10^10 cm.Hzl/2W-1, and 1.0×10^10cm.Hzl/2W-1 for MW, LW and VLW. The crosstalks of the MW channel, LW channel, and VLW channel are almost 0, 0.25, and 0.6, respectively.