Improve Image Contrast Research Articles

HE‐DETR‐DC5 method to rapidly detect objects on passive THz images in human safety inspection

As worldwide counterterrorism operations grow, screening people for concealed objects in public places utilizing Terahertz security technology is becoming increasingly critical. In this letter, a passive THz Human security image Intelligent detection method (HE-DETR-DC5) is proposed that combines histogram equalization (HE) processing and DEtection TRansformer (DETR). The proposed method first performs histogram equalization on passive THz images to improve image contrast. In addition, the cavity convolution is added to the ResNet-50 backbone network to enhance the model's attention to the concealed object in the passive THz image and improve the accuracy. The corresponding model is called DETR-DC5. The experimental results show that the average precision mAP of the proposed method can achieve 99.9%. At the same time, compared to other image enhancement methods, the histogram equalization method used in this letter has better performance and higher detection accuracy. The detection speed of the proposed method is about 0.5 FPS, which can meet the demand for real-time detection. Therefore, the proposed model can efficiently and automatically detect hidden objects in passive THz human security images.

Effect of pulse duty ratio on temperature rise induced by focused ultrasound combined with magnetic microbubbles

Development of acoustic/magnetic contrast agent microbubbles with various diagnostic and therapeutic functions has attracted more and more attention in medical ultrasound, biomedical engineering and clinical applications. Superparamagnetic iron oxide nanoparticles (SPIO) have unique magnetic characteristics and wonderful biocompatibility, so they can be used as MRI contrast agents to improve image contrast, spatial resolution and diagnostic accuracy. Our previous work shows that the multimodal diagnostic and therapeutic microbubble agents can be successfully constructed by embedding SPIO particles into the coating shell of conventional ultrasound contrast agent (UCA) microbubbles, which in turn changes the size distribution and shell properties of UCA microbubbles, thereby affecting their acoustic scattering, cavitation and thermal effects. However, previous studies only considered the influence factors such as acoustic pressure and microbubble concentration. The relevant investigation regarding the influence of ultrasound temporal characteristics on the dynamic response of magnetic microbubbles is still lacking. This work systematically measures the temperature enhancement effect of the SPIO-albumin microbubble solution flowing in the vascular gel phantom exposed to pulsed ultrasound with various temporal settings (e.g. duty cycle, PRF and single pulse length). Meanwhile, a two-dimensional finite element model is developed to simulate and verify the experimental observations. The results show that the increase of duty cycle of pulse signal should be the crucial factor affecting the temperature enhancement effect of flowing SPIO-albumin microbubble solution under the exposure to high-intensity focused ultrasound. The current results help us to better understand the influence of different acoustic setting parameters on the thermal effect of dual-modal magnetic UCA microbubbles, and provide useful guidance for ensuring the safety and effectiveness of the application of SPIO-albumin microbubbles in clinics.

Synthesis and radiolabeling of a polar [125 I]I-1,2,4,5-tetrazine.

Pretargeting imaging has gained a lot of prominence, due to its excellent bioorthogonality and improved imaging contrast compared to conventional imaging. A new iodo tetrazine (Tz) derivative has been synthesized and further developed into the corresponding iodine-125 (125 I) analog (12), via the trimethylstannane precursor. Radiolabeling with either N-chlorosuccinimide or chloramine-T, in either MeCN or MeOH proceeded with a radiochemical conversion (RCC) of >80%. Subsequent deprotection only proved successful, among the tested conditions, when the radiolabeled Tz was stirred in 6-M HCl(aq.) at 60°C for 2.5h. To the best of our knowledge, this is the first H-tetrazine labeled with iodine. In vivo investigations on the pretargeting ability of 12 are currently under way.

The impact of the ClearRT™ upgrade on target motion tracking accuracy in Radixact® Synchrony® lung treatments.

The objective was to investigate the change in segmentation error of Radixact® Synchrony® lung treatment after its kV imaging system was upgraded from Generation 1 to Generation 2 in the ClearRT™ installation. Radixact® Lung Synchrony® plans were created for the Model 18023 Xsight® Lung Tracking "XLT" Phantom combined with different lung target inserts with densities of 0.280, 0.500, 0.943 and 1.093 g/cc. After Radixact® Synchrony® treatment delivery using the Generation 1 and Generation 2 kV systems according to each plan, the tracking performance of the two kV systems on each density insert was compared by calculating the root mean square (RMS) error (δRMS) between the Synchrony-predicted motion in the log file and the known phantom motion and by calculating δ95%, the maximum error within a 95% probability threshold. The δRMS and δ95% of Radixact® Synchrony® treatment for Gen1 kV systems deteriorated as the density of the target insert decreased, from 1.673 ± 0.064 mm and 3.049 ± 0.089 mm, respectively, for the 1.093 g/cc insert to 8.355 ± 5.873 mm and 15.297 ± 10.470 mm, respectively, for the 0.280 g/cc insert. In contrast, no such trend was observed in the δRMS or δ95% of Synchrony® treatment using the Gen2 kV system. The δRMS and δ95%, respectively, fluctuated slightly from 1.586 to 1.687 mm and from 2.874 to 2.971 mm when different target inserts were tracked by the Gen2 kV system. With improved image contrast in kV radiographs, the Gen2 kV imaging system can enhance the ability to track targets accurately in Radixact® Lung Synchrony® treatment and reduce the segmentation error. Our study showed that lung targets with density values as low as 0.280 cc/g could be tracked correctly in Synchrony treatment with the Gen2 kV imaging system.

All Two‐dimensional Integration‐Type Optoelectronic Synapse Mimicking Visual Attention Mechanism for Multi‐Target Recognition

AbstractThe human visual attention mechanism enables them to rapidly perceive important information and objects in a complex external scene; this effectively solves the problems of data redundancy, low‐resolution images, and substantial computing resources. The process by which the attention system reconstructs the visual information can be considered as integrating internal attention signals with external visual details in the postsynaptic neuron. However, electronic devices that simulate visual attention modulation by incorporating device characteristics into neuromorphic vision systems (NVSs) to achieve visual attention behavior are rarely reported. Herein, a synapse device that integrates optical and electrical stimulation is designed using ReS2/hBN/monolayer graphene heterojunction to mimic attention regulation and integrate multiple neuron signals successfully. The synapse array can imitate perceptual learning of the human visual system (HVS) to realize visual preprocessing, such as image contrast improvement and weak signal enhancement at the sensory terminal, and overcome data redundancy. Moreover, by applying gate voltage pulses, electric‐tunable synaptic plasticity is successfully observed, attributed to the carrier trapping and de‐trapping mechanism in the floating layer. Attention stabilization, fluctuation, distraction, and reinforcement are exhibited, simulating the attention behaviors of the HVS. Thus, an NVS with attention mechanism is established depending on the optoelectronic hybrid synaptic plasticity of the device, which successfully mimics the HVS to perform a multi‐target recognition task. Furthermore, the effect of device defects on the NVS is rarely evaluated, in which a method is provided to analyze the application results of the NVS when considering uniformity and fault rate. This study may provide new inspiration for developing neuromorphic vision systems for autonomous driving and brainwave control in the future.

Image Contrast Enhancement in CT SCAN for Determining Cervical Cancer Area

The quality of the contrast enhancement, which is deemed to be vague in contrast between one region and another, is a problem that many doctors face once identifying their patients with CT scan images. Image correction was used in this study to help doctors gain good CT scan images. In addition to reducing errors in the administering of radiation doses during treatment, accurate images are used to locate and assess the extent of cancer in patients. In this study, a computer application program to improve image contrast was created using the linear regression equation method. In this investigation, the cancer area is still being manually marked by doctors. Additionally, the proportion of the cancer area in the image that the doctor marked from the corrected image is calculated by comparing the ratio of cancer pixels to body pixels. The severity of the cancer is estimated using the proportion of the affected area. The error percentage for the average improvement in application performance is 0.15689%.

SELF-DEPENDENT ANTITUMOR EFFECTS PRODUCED BY METAL-CONTAINING NANOPARTICLES IN IN VIVO EXPERIMENTS

At present, the scope of research of the medical and biological effects produced by metal-containing nanoparticles (NPs) is very impressive. At the same time, the vast majority of scientific research therein is carried out to cover three main areas: visualization of neoplasms (improvement of image contrast in MRI), targeted delivery of various antitumor factors (cytostatics, vaccines, etc.) and magnetic fluid hyperthermia [1–3]. In the last decade, the fourth area of exploration is forming, associated with the study of the self-dependent effects of metal-containing NPs, which can be considered as a new option in cancer immunotherapy.

Development and comparison of 68Ga/18F/64Cu-labeled nanobody tracers probing Claudin18.2

Claudin 18.2 (CLDN18.2) is an emerging target for the treatment of gastric cancers. We aim to develop tracers to image the expression of CLDN18.2. A humanized nanobody targeting CLDN18.2 (clone hu19V3) was produced and labeled with 68Ga, 64Cu, and 18F. The tracers were investigated in subcutaneous and metastatic models established using two different mouse types (nude and Balb/c mice) and two different cell lines (CHO-CLDN18.2 and CT26-CLDN18.2). Gastric cancer patient-derived xenograft (PDX) models were further established for validation experiments. Three novel CLDN18.2-targeted tracers (i.e., [68Ga]Ga-NOTA-hu19V3, [64Cu]Cu-NOTA-hu19V3, and [18F]F-hu19V3) were developed with good radiochemical yields and excellent radiochemical purities. [68Ga]Ga-NOTA-hu19V3 immuno-positron emission tomography (immunoPET) rapidly delineated subcutaneous CHO-CLDN18.2 lesions and CT26-CLDN18.2 tumors, as well as showing excellent diagnostic value in PDX models naturally expressing CLDN18.2. While [68Ga]Ga-NOTA-hu19V3 had high kidney accumulation, [64Cu]Cu-NOTA-hu19V3 showed reduced kidney accumulation and improved image contrast at late time points. Moreover, [18F]F-hu19V3 was developed via click chemistry reaction under mild conditions and precisely disseminated CHO-CLDN18.2 lesions in the lungs. Furthermore, region of interest analysis, biodistribution study, and histopathological staining results correlated well with the invivo imaging results. Taken together, immunoPET imaging with the three tracers can reliably visualize CLDN18.2 expression.

A fusion denoising method based on homomorphic transform and 3D transform-domain collaborative filtering for laser speckle imaging of blood flow

Laser speckle contrast imaging (LSCI) is a real-time, full-field, non-contact optical imaging technique that has been widely used in blood flow imaging. However, its practical applications are limited due to its low signal-to-noise ratio (SNR) and low image contrast. In this study, a fusion denoising algorithm based on homomorphic transform and block-matching and 3D transform-domain collaborative filtering algorithm is proposed to reduce noise and improve image contrast. The reliability and applicability of the proposed method were evaluated by a phantom experiment and two in vivo experiments. The results show that the proposed algorithm can effectively remove the noise and enhance the image contrast. Consequently, the SNR is increased, the dynamic range of LSCI is expanded, and the estimation accuracy of blood flow is improved.

Experimentally unsupervised deconvolution for light-sheet microscopy with propagation-invariant beams

Deconvolution is a challenging inverse problem, particularly in techniques that employ complex engineered point-spread functions, such as microscopy with propagation-invariant beams. Here, we present a deep-learning method for deconvolution that, in lieu of end-to-end training with ground truths, is trained using known physics of the imaging system. Specifically, we train a generative adversarial network with images generated with the known point-spread function of the system, and combine this with unpaired experimental data that preserve perceptual content. Our method rapidly and robustly deconvolves and super-resolves microscopy images, demonstrating a two-fold improvement in image contrast to conventional deconvolution methods. In contrast to common end-to-end networks that often require 1000–10,000s paired images, our method is experimentally unsupervised and can be trained solely on a few hundred regions of interest. We demonstrate its performance on light-sheet microscopy with propagation-invariant Airy beams in oocytes, preimplantation embryos and excised brain tissue, as well as illustrate its utility for Bessel-beam LSM. This method aims to democratise learned methods for deconvolution, as it does not require data acquisition outwith the conventional imaging protocol.

Separation of mainlobe and sidelobe contributions to B-mode ultrasound images based on the aperture spectrum.

Isolating the mainlobe and sidelobe contribution to the ultrasound image can improve imaging contrast by removing off-axis clutter. Previous work achieves this separation of mainlobe and sidelobe contributions based on the covariance of received signals. However, the formation of a covariance matrix at each imaging point can be computationally burdensome and memory intensive for real-time applications. Our work demonstrates that the mainlobe and sidelobe contributions to the ultrasound image can be isolated based on the receive aperture spectrum, greatly reducing computational and memory requirements. The separation of mainlobe and sidelobe contributions to the ultrasound image is shown in simulation, in vitro, and in vivo using the aperture spectrum method and multicovariate imaging of subresolution targets (MIST). Contrast, contrast-to-noise-ratio (CNR), and speckle signal-to-noise-ratio are used to compare the aperture spectrum approach with MIST and conventional delay-and-sum (DAS) beamforming. The aperture spectrum approach improves contrast by 1.9 to 6.4dB beyond MIST and 8.9 to 13.5dB beyond conventional DAS B-mode imaging. However, the aperture spectrum approach yields speckle texture similar to DAS. As a result, the aperture spectrum-based approach has less CNR than MIST but greater CNR than conventional DAS. The CPU implementation of the aperture spectrum-based approach is shown to reduce computation time by a factor of 9 and memory consumption by a factor of 128 for a 128-element transducer. The mainlobe contribution to the ultrasound image can be isolated based on the receive aperture spectrum, which greatly reduces the computational cost and memory requirement of this approach as compared with MIST.

Artificial intelligence neural network analysis and application of CT imaging features to predict lymph node metastasis in non-small cell lung cancer.

Computed tomography (CT) is important in the diagnosing of lung cancer. The combination of CT features and artificial intelligence algorithm have been used in the diagnosis of various lung diseases. However, limited studies focused on the relationship between the combination of CT features and artificial intelligence algorithm and lymph node metastasis in non-small cell lung cancer (NSCLC). This study developed an algorithm for lung cancer CT image segmentation based on an artificial neural network model and investigated the role of a nomogram model based on CT images for predicting lymph node metastasis in lung cancer. Wiener filtering and fuzzy enhancement were first used to suppress image noise and improve image contrast. Next, texture features and fractal features were extracted. In the third step, the artificial neural network model was trained and tested according to the best parameters of the network. The area under the curve (AUC) of the constructed nomogram model on the training set and the test set were 0.859 (sensitivity, 0.810; specificity, 0.773) and 0.864 (sensitivity, 0.820; specificity, 0.753), respectively. The decision curve indicated that the model had good clinical application value. The lung cancer CT images contained 13 significant regional features of cancer. The best classification function obtained from training and testing data was Levenberg-Marquardt backpropagation. The sensitivity, specificity, and accuracy in the training stage could reach 98.4%, 100%, and 98.6%, respectively, and the corresponding indexes in the test stage reached 90.9%, 100%, and 95.1%, respectively. The image segmentation algorithm based on the artificial neural network model could extract CT lung cancer lesions efficiently and quasi-determinately, which could be used as an effective tool for radiologists to diagnose lung cancer. The nomogram model based on CT image features and related clinical indicators was an effective method for noninvasive prediction of lymph node metastasis in lung cancer.

Photoacoustic Imaging Probes for Theranostic Applications.

Photoacoustic imaging (PAI), an emerging biomedical imaging technology, capitalizes on a wide range of endogenous chromophores and exogenous contrast agents to offer detailed information related to the functional and molecular content of diseased biological tissues. Compared with traditional imaging technologies, PAI offers outstanding advantages, such as a higher spatial resolution, deeper penetrability in biological tissues, and improved imaging contrast. Based on nanomaterials and small molecular organic dyes, a huge number of contrast agents have recently been developed as PAI probes for disease diagnosis and treatment. Herein, we report the recent advances in the development of nanomaterials and organic dye-based PAI probes. The current challenges in the field and future research directions for the designing and fabrication of PAI probes are proposed.

Intelligent 3D Visual defect Detection for Industrial Applications

Currently, new energy industries such as electric vehicles and energy storage batteries are experiencing rapid growth throughout the world. As a recognized ideal energy storage element, lithium batteries have also attracted considerable interest. To improve image contrast, the industry employs image preprocessing algorithms. Due to the high requirement of image consistency in the template matching method, which is difficult to meet in practice, and the large apparent difference between defects, it is challenging to design features. Therefore, the framework for defect detection is based on a method of deep learning with strong feature expression capability. In light of the severe imbalance in the number of samples in defect detection, data augmentation and generation methods are used to simulate real samples in order to improve the training effect of deep neural networks and alleviate the burden of data annotation to some extent.

Prostate-Specific Membrane Antigen Targeted Deep Tumor Penetration of Polymer Nanocarriers.

Tumoral uptake of large-size nanoparticles is mediated by the enhanced permeability and retention (EPR) effect, with variable accumulation and heterogenous tumor tissue penetration depending on the tumor phenotype. The performance of nanocarriers via specific targeting has the potential to improve imaging contrast and therapeutic efficacy in vivo with increased deep tissue penetration. To address this hypothesis, we designed and synthesized prostate cancer-targeting starPEG nanocarriers (40 kDa, 15 nm), [89Zr]PEG-(DFB)3(ACUPA)1 and [89Zr]PEG-(DFB)1(ACUPA)3, with one or three prostate-specific membrane antigen (PSMA)-targeting ACUPA ligands. The in vitro PSMA binding affinity and in vivo pharmacokinetics of the targeted nanocarriers were compared with a nontargeted starPEG, [89Zr]PEG-(DFB)4, in PSMA+ PC3-Pip and PSMA- PC3-Flu cells, and xenografts. Increasing the number of ACUPA ligands improved the in vitro binding affinity of PEG-derived polymers to PC3-Pip cells. While both PSMA-targeted nanocarriers significantly improved tissue penetration in PC3-Pip tumors, the multivalent [89Zr]PEG-(DFB)1(ACUPA)3 showed a remarkably higher PC3-Pip/blood ratio and background clearance. In contrast, the nontargeted [89Zr]PEG-(DFB)4 showed low EPR-mediated accumulation with poor tumor tissue penetration. Overall, ACUPA conjugated targeted starPEGs significantly improve tumor retention with deep tumor tissue penetration in low EPR PC3-Pip xenografts. These data suggest that PSMA targeting with multivalent ACUPA ligands may be a generally applicable strategy to increase nanocarrier delivery to prostate cancer. These targeted multivalent nanocarriers with high tumor binding and low healthy tissue retention could be employed in imaging and therapeutic applications.

Schlieren Image Velocimetry of Swirl Sprays

Schlieren image velocimetry (SIV) is based on light deflection through flow heterogeneities and image cross-correlations. This is a low-cost and relatively low complexity technique that allows measurement of the droplet velocity field in a large region of a spray. A Z-type Toepler schlieren system with a high-speed camera was used to determine mean vertical and horizontal droplet velocities, as well as the cone angles of sprays produced by a pressure swirl injector with characteristic geometric constant K = 2. Different LEDs and digital filters were evaluated for edge detection and improvement of image contrast. Open software was adopted for digital image processing and velocimetry. Interrogation windows and overlaps of different sizes were tested to obtain an appropriate correlation for determination of the velocity field. The digital images were obtained with 5 × 103 fps and a resolution of 2.77 pixels/mm. Since the swirl sprays analysed presented instabilities, a number of 100 cross-correlations of images was required to reduce mean velocity fluctuations. Injection pressures varied from 0.05 to 7 bar and mass flow rates varied from 1.389 to 13.89 g/s, using water as test fluid. The wideband warm white LED with Laplacian or high-pass filters provided velocity data for a larger range of injection pressures. Mean axial velocities varied from 3.3 to 11.3 m/s, approximately, with mean horizontal velocities varying from around 0.17 to 3.3 m/s for pressures from 0.05 to 3.22 bar. The velocity data were compared to microscopic shadowgraphy results, showing a good agreement. Spray cone angles ranged from about 32.5o to 69.5°, for injection pressures from 0.05 to 7 bar, and results of triangulation with a blue LED were closer to semi-empirical data.

Improved Cone-Beam Reconstruction Using a Prior Breathing Motion Model

<h3>Purpose/Objective(s)</h3> To demonstrate whether incorporating a pre-measured breathing motion model into algebraic cone-beam CT (CBCT) reconstruction could improve daily target imaging for radiotherapy delivery of lung tumors. <h3>Materials/Methods</h3> We employed our clinic's 5DCT simulation technique to generate the breathing motion model. 5DCT provides a voxel-by-voxel representation of breathing motion as a function of breathing amplitude and rate, and the motion model was used to characterize the breathing motion during CBCT acquisition. We acquired CBCT projections before treatment and simultaneously acquired a breathing amplitude signal using a respiratory bellows. The 5DCT reference image was registered to the CBCT geometry using a rigid registration of the spine. To enable the use of the 5DCT motion model on the CBCT data, a correspondence between the simulation and CBCT surrogates needed to be made. The breathing surrogates of the simulation were correlated with the CBCT using the diaphragm shadow in the CBCT projections and the 5DCT reference image deformed using the motion model. By adjusting the 5DCT model breathing amplitude, the diaphragm shadow moved until the CBCT projection and 5DCT diaphragm shadows overlapped, yielding the correspondence between the two surrogates such that the 5DCT motion model could be directly applied to the CBCT reconstruction. To reconstruct the CBCT image, we modified the motion-compensated simultaneous algebraic reconstruction technique (MC-SART) by introducing the aligned and scaled 5DCT motion model parameters. The modified MC-SART compared the raw projections to corresponding model-generated projections with various levels of binning. To quantify the 5DCT model's ability to describe the breathing motion during the CBCT, we reconstructed the CBCT dividing the breathing amplitude into 1, 2, 4 and 8 bins. We examined the corresponding sharpness of the diaphragm to see if the diaphragm boundary continuously sharpened with increasing bins. <h3>Results</h3> We found that the diaphragm appeared most blurred when only 1 amplitude bin was used, appearing sharper as bins were added. At 8 bins, the diaphragm boundary was only 2 voxels, consistent with typical breath-hold CT reconstruction. This demonstrated that the motion model from the CT simulation could quantitatively track breathing motion during the CBCT. However, while the diaphragm appeared sharper, the reconstructed images with increasing bins also had poorer image contrast. <h3>Conclusion</h3> We have shown that using a previously determined breathing motion model with algebraic reconstruction can improve image sharpness. We will expand the current study to more patients and fractions and develop techniques to improve image contrast. However, since the purpose of this approach will be to localize lung tumors and characterize their motion at the treatment table, the image contrast may be sufficient. We will also compare our approach to published 4DCT approaches to determine if ours better manages irregular breathing.

Optimum Parameters for Imaging and Dose Calculation in Lung SBRT

<h3>Purpose/Objective(s)</h3> Efficiency, set up accuracy, patient comfort and accurate dose calculations are critical requirements in radiation therapy especially in stereotactic body radiotherapy (SBRT). There are several CT imaging parameters such as kV, mAs, image reconstruction algorithm filters, field of view and pitch that affect image quality used for volume delineation and dose calculation. Modifying the imaging parameters may lead to variation in Hounsfield Units (HU) that may affect the dose calculations. We investigated the relationship between the image reconstruction algorithms (Body (B) and Lung filter (L)), HU and dose distribution. We hypothesize that variation in the imaging parameters may lead to better image quality with clinically insignificant dose to target and organs at risk (OARs). <h3>Materials/Methods</h3> Ten lung SBRT patients were imaged on a 16 slice CT scanner. The standard clinical scan (C) is a B- reconstruction filter-based scan with low pitch. For selected patients, in additional to standard scan, sharp scans were acquired using greater pitch and L-filter algorithm to see the effect of density and dose calculation. Clinical workflow includes fusing the images acquired with B and L filter. Clinically B filter images are used for planning and treatment. For this study purposes, an additional plan was generated on L filtered images using appropriate HU to density curve. CT density phantom was scanned on a 16 slice CT scanner with standard Body (B) filter as well as standard images of CT density phantom were reconstructed with lung (L) filter algorithm to generate HU to physical density table for a treatment planning system and used for dose calculation. Image qualities for the two filter were evaluated visually by an attending radiation oncologist. The dosimetric differences in the target coverages were evaluated using the HU curve generated for B and L filter. <h3>Results</h3> For clinical cases, using lung SBRT protocols, planning target volume (PTV) coverage V100% were recorded. In addition to the target coverage mean physical density was recorded for the B and L filter and C-clinical scans. Average difference in the PTV physical density was 2.4%, 0.3% and 2.1% for B-C, L-C and B-L, where B stands for – B Filter density curve on the sharp scan, L – L Filter density curve on the sharp scan, C – Clinical scan. No significant differences were noted in PTV coverages for B-C 1.7% (p < 0.0004), L-C 2.03% (p < 0.0007) and B-L -0.29% (p < 0.0005) based plans for V100%. <h3>Conclusion</h3> The L filter high pitch combination improved image contrast and thus helped in contouring. No significant difference in the PTV coverage was observed when B filter HU curve was applied to sharp quality scan used for dose calculation. Thus, images acquired with L filter can be used clinically with existing B filter HU curve. Overall Differences in dosimetric parameters are insignificant compared to the small uncertainties in HU numbers.

Practical considerations for quantitative light sheet fluorescence microscopy.

Fluorescence microscopy has evolved from a purely observational tool to a platform for quantitative, hypothesis-driven research. As such, the demand for faster and less phototoxic imaging modalities has spurred a rapid growth in light sheet fluorescence microscopy (LSFM). By restricting the excitation to a thin plane, LSFM reduces the overall light dose to a specimen while simultaneously improving image contrast. However, the defining characteristics of light sheet microscopes subsequently warrant unique considerations in their use for quantitative experiments. In this Perspective, we outline many of the pitfalls in LSFM that can compromise analysis and confound interpretation. Moreover, we offer guidance in addressing these caveats when possible. In doing so, we hope to provide a useful resource for life scientists seeking to adopt LSFM to quantitatively address complex biological hypotheses.

Multi-Sensor Image Fusion Method for Defect Detection in Powder Bed Fusion.

Multi-sensor defect detection technology is a research hotspot for monitoring the powder bed fusion (PBF) processes, of which the quality of the captured defect images and the detection capability is the vital issue. Thus, in this study, we utilize visible information as well as infrared imaging to detect the defects in PBF parts that conventional optical inspection technologies cannot easily detect. A multi-source image acquisition system was designed to simultaneously acquire brightness intensity and infrared intensity. Then, a multi-sensor image fusion method based on finite discrete shearlet transform (FDST), multi-scale sequential toggle operator (MSSTO), and an improved pulse-coupled neural networks (PCNN) framework were proposed to fuse information in the visible and infrared spectra to detect defects in challenging conditions. The image fusion performance of the proposed method was evaluated with different indices and compared with other fusion algorithms. The experimental results show that the proposed method achieves satisfactory performance in terms of the averaged information entropy, average gradient, spatial frequency, standard deviation, peak signal-to-noise ratio, and structural similarity, which are 7.979, 0.0405, 29.836, 76.454, 20.078 and 0.748, respectively. Furthermore, the comparison experiments indicate that the proposed method can effectively improve image contrast and richness, enhance the display of image edge contour and texture information, and also retain and fuse the main information in the source image. The research provides a potential solution for defect information fusion and characterization analysis in multi-sensor detection systems in the PBF process.