Non-target Area Research Articles

Computed tomography myocardial perfusion imaging to detect myocardial ischemia in patients with anxiety and obstructive coronary heart disease post-exposure to mental stressors

This study aims to measure myocardial blood flow (MBF) using dynamic CT- myocardial perfusion imaging (CT-MPI) combined with mental stressors in patients with obstructive coronary artery disease (OCAD) and in patients with anxiety and no obstructive coronary artery disease (ANOCAD). A total of 30 patients with OCAD with 30 patients with ANOCAD were included in this analysis. Using the 17-segment model, the rest and stress phase MBF of major coronary arteries in participants were recorded respectively. Compared with ANOCAD patients, OCAD patients were more likely to have localized reduction of MBF (p < 0.05). For patients with ANOCAD, both global MBF and MBF of the main coronary arteries in the stress phase were lower than those in the rest phase (all p < 0.05), but there was no significant difference in MBF among the main coronary arteries in the rest or stress phase (p = 0.25, p = 0.15). For patients with OCAD, the MBF of the target area was lower than that of the non-target area in both the rest and stress phase, and the MBF of the target area in the stress phase was lower than that in the rest phase (all p < 0.05). However, there was no significant difference in MBF between the rest or stress phase in the non-target area (p = 0.73). Under mental stress, the decrease in MBF in ANOCAD patients was diffuse, while the decrease in MBF in OCAD patients was localized. Dynamic CT-MPI combined with mental stressors can be used to detect MBF changes in anxiety patients.

Performance of artificial intelligence-based software for the automatic detection of lung lesions on chest radiographs of patients with suspected lung cancer

PurposeThis study aimed to evaluate the performance of the commercially available artificial intelligence-based software CXR-AID for the automatic detection of pulmonary nodules on the chest radiographs of patients suspected of having lung cancer.Materials and methodsThis retrospective study included 399 patients with clinically suspected lung cancer who underwent CT and chest radiography within 1 month between June 2020 and May 2022. The candidate areas on chest radiographs identified by CXR-AID were categorized into target (properly detected areas) and non-target (improperly detected areas) areas. The non-target areas were further divided into non-target normal areas (false positives for normal structures) and non-target abnormal areas. The visibility score, characteristics and location of the nodules, presence of overlapping structures, and background lung score and presence of pulmonary disease were manually evaluated and compared between the nodules detected or undetected by CXR-AID. The probability indices calculated by CXR-AID were compared between the target and non-target areas.ResultsAmong the 450 nodules detected in 399 patients, 331 nodules detected in 313 patients were visible on chest radiographs during manual evaluation. CXR-AID detected 264 of these 331 nodules with a sensitivity of 0.80. The detection sensitivity increased significantly with the visibility score. No significant correlation was observed between the background lung score and sensitivity. The non-target area per image was 0.85, and the probability index of the non-target area was lower than that of the target area. The non-target normal area per image was 0.24. Larger and more solid nodules exhibited higher sensitivities, while nodules with overlapping structures demonstrated lower detection sensitivities.ConclusionThe nodule detection sensitivity of CXR-AID on chest radiographs was 0.80, and the non-target and non-target normal areas per image were 0.85 and 0.24, respectively. Larger, solid nodules without overlapping structures were detected more readily by CXR-AID.

Safer neighborhoods? Violent crime and trauma volume pre/post targeted police interventions in Dallas, Texas

BackgroundProject Safe Neighborhoods: Dallas (PSND) is part of a national initiative that partners federal, state, and local stakeholders and law enforcement to reduce violent crime, especially firearm violence, in select communities. The authors’ hospital is located centrally in PSND's target areas, and the trauma center's service area fully covers the target areas. This cohort study evaluated PSND's effectiveness by examining if PSND's April 2018 launch was associated with decreases in (1) violent crime or (2) the rate of patients presenting with firearm and assaultive injuries. MethodsData on violent crime (murder/non-negligent homicide, robbery, and aggravated assault) were obtained from all municipalities in the county for January 2015 – December 2020. Patient volume data were queried from the trauma registry for the same period. Nonlinear spatiotemporal models were used to calculate estimated rates and confidence intervals; derivatives were used to determine periods of significant change. Spatial point pattern tests assessed potential relocation of criminal activity. Given the importance of reducing violent crime, alpha was set at 0.05. ResultsThe target areas’ violent crime rate remained higher than the non-target areas’ throughout the study period, and target area violent crime non-significantly increased after PSND's launch. Violent crime in the non-target area increased significantly between February 2018 and February 2019. Part of this increase was attributable to a 7-fold increase (odds ratio [95% confidence interval] = 7.32 [2.58, 30.65], p < 0.001) in one police patrol beat just outside of the target areas. After years of decreases, rates of patients presenting with assaultive injuries or firearm injuries began significantly increasing and nearly doubled within two years of PSND. ConclusionsResults suggest PSND was ineffective. Criminal activity in the target area was not reduced relative to its pre-intervention levels or relative to the non-target areas. Offenders may have moved outside the target areas to evade increased scrutiny. Additionally, rates of firearm and assaultive injuries increased. As trusted third parties, trauma centers should consider evaluating local crime and injury prevention programs for effectiveness.

Anti-Drift Technology Progress of Plant Protection Applied to Orchards: A Review

In orchard plant protection application, an anti-drift strategy can effectively reduce drift in the non-target area, reduce spray drift in the environment, and avoid spray leakage and overspraying. To clarify the future development direction of orchard plant protection mechanization technology, this review introduces the development status of an anti-drift spray nozzle and the impact of different types of spray nozzles on the potential of drift, and then, it analyzes the research progress on air-assisted spraying, recycling spraying, profiling spraying, target variable spraying technologies, and plant protection UAVs. It also provides a general analysis of the above spraying technologies on the amount of drift and the impact of pesticide deposition. Finally, combined with the characteristics of orchard plant protection, the paper presents the research and development of anti-drift nozzles, pesticide adjuvant, air-assisted spraying technology, electrostatic, recycling spraying technology, profiling and target variable spraying technology, and plant protection UAVs. The review provides a reference for the development of an anti-drift strategy for orchard plant protection production.

The Next Generation of Cotton Defoliation Sprayer

Chemical spraying is one of the most important and frequently performed intercultural agricultural operations. It is imperative to select the appropriate spraying technology as a selection of ineffective one leads to the wastage of a considerable volume of applied chemicals to the non-target area. Many precision technologies have been developed in the past few decades, such as image processing based on real-time variable-rate chemical spraying systems, autonomous chemical sprayers using machine vision and nozzle control, and use of unmanned aerial and ground vehicles. Cotton defoliation is a natural physiological process, but untimely and inadequate leaf defoliation by natural process hinders the mechanical cotton harvest. Induced defoliation is practiced by applying defoliants to address the issue with the natural process of defoliation. This paper covers spraying technologies in agriculture, cotton plants, cotton defoliation, new defoliant spraying systems, and the recent field test. The new spraying system attached to an autonomous mobile robot aims to improve the delivery of defoliant chemicals by adding a spray unit on the side of the plant. Preliminary results of the water-sensitive paper test at the field showed adequate penetration with low flow rates. This is a huge development as there is a huge potential to save on the cost of applying defoliant chemicals.

Study on Spray Deposition and Drift Characteristics of UAV Agricultural Sprayer for Application of Insecticide in Redgram Crop (Cajanus cajan L. Millsp.)

Insecticide applications are typically being carried out with traditional manual spraying equipment in redgram, which leads to inadequate control of insects due to higher crop height. The modern deployment of tractor-drawn spray machines causes serious damage to the crop. In this connection, unmanned aerial vehicle (UAV) spray technology has great potential for precise insecticide application in redgram crops. One of the important machine parameters influencing droplet deposition and drift characteristics in UAV sprayers is downwash airflow generated by a multi-rotor propeller. A field experiment was carried out at the redgram research field (N11.01, E76.92), Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, during 2021–2022 to study the spray drift and deposition characteristics of an autonomous UAV sprayer. The Imidacloprid (a.i. 17.8SL) insecticide mixed with water in a ratio of 1 mL per liter was sprayed with a UAV sprayer. Water-sensitive paper samples were kept at upper, middle, and bottom positions on the leaves, and data were analyzed for the spray droplet size, deposition rate, droplet density, and area coverage both in target and non-target areas using Spray Deposit Scanner software. UAV spray droplet deposition rate (2.93 ± 0.17, 2.01 ± 0.08, and 2.21 ± 0.162.38 μL cm−2), droplet density (47 ± 4.04, 53 ± 3.61, and 52 ± 8.74 droplets cm−2), and area coverage (15.72 ± 0.39, 16.60 ± 0.71, and 14.99 ± 0.39%) were highest in the upper layer as compared to the middle layer (droplet deposition rate: 1.21 ± 0.08, 1.07 ± 0.03, and 0.77 ± 0.02 μL cm−2; droplet density: 42 ± 2.52, 43 ± 8.50, and 38 ± 2.52 droplets cm−2; area coverage: 10.95 ± 0.81, 11.22 ± 0.56, and 8.57 ± 0.44%) and bottom layer (droplet deposition rate: 0.41 ± 0.06, 0.35 ± 0.03, and 0.33 ± 0.03 μL cm−2; droplet density: 22 ± 4.36, 17 ± 3.51, and 19 ± 4.51 droplets cm−2; area coverage: 2.78 ± 0.29, 2.95 ± 0.45, and 2.46 ± 0.20%, respectively). In the spray drift test, there was a higher droplet deposition rate (1.63 ± 0.09, 1.93 ± 0.05, and 1.82 ± 0.06 μL cm−2), area coverage (14.40 ± 0.07, 17.54 ± 0.36, and 16.42 ± 0.30%), and droplet density (46 ± 3.61, 54 ± 2.08, and 45 ± 3.21 No’s cm−2) in the target area as compared to the non-target area (droplet deposition rate: 0.88 ± 0.02, 0.46 ± 0.03, 0.22 ± 0.05, and 0.00 μL cm−2; droplet density: 23 ± 1.53, 11 ± 2.08, 6 ± 1.53, and 0.00 droplets cm−2; area coverage: 7.58 ± 0.34, 4.41 ± 0.19, 2.16 ± 0.05, and 0.00%, respectively), which may have been due to the downwash airflow produced by the multi-rotor propeller of the UAV sprayer. Finally, the UAV-based spraying technology results showed that the downwash air produced by the six-rotor propeller improved the penetrability of insecticide to crop leaves and led to a higher droplet deposition rate, droplet density, area coverage, and droplet penetrability on the upper layer, middle layer, and bottom layer of the plants.

Spray drift-based pesticide residues on untreated edible crops grown near agricultural areas

We aimed to quantify spray drift-based exposure of fruits and vegetables grown in gardens or allotments next to agricultural areas to plant protection products (PPP). The amount of spray drift transported into gardens during the treatment of tall growing crops or field crops was simulated. Two different test systems in an outdoor wind tunnel were used, approximating conditions for the application to both crop types. For the experiments, strawberries, tomatoes and lettuce were used representing non-target food crops in gardens. After spraying, distance-related residues of the tracer pyranine were measured on the three food crops positioned 1–15 m downwind in the non-target area. Additionally, petri dishes were placed in front of the food crops to measure the ground deposition concurrently. For both scenarios, good correlation of residues on the non-target food crops and the ground deposition was found (linear regression model, R2 = 0.88–0.97). But unlike the field crops scenario, the experimental setup of the tall growing crops shows large deviations from the field situation, not allowing the transfer of the results to the field situation. The results of the wind tunnel experiments and of field trials on ground deposition were used to estimate the amount of PPP residues on food crops cultivated near agricultural fields. For example, application of a pesticide (1.3 kg active ingredient per ha−1) to field crops was estimated to result in residue levels of 0.39 mg kg−1 on lettuce, 0.32 mg kg−1 on strawberries, and 0.06 mg kg−1 on tomatoes cultivated 5 m from the field, thus indicating an exceedance of the default maximum residue level (MRL) (0.01 mg kg−1). Therefore, further in-depth studies are required to broaden the range of non-target crops and to refine the tall growing crop scenario to allow estimations of spray drift-based residues.

Preliminary Evaluation of Potential Impacts Associated with Small Cetacean Remote Biopsy Sampling by Controlled Testing on Stranded Common Bottlenose Dolphins (Tursiops truncatus)

To explore the potential macroscopic tissue effects of select remote biopsy tools to common bottlenose dolphins (Tursiops truncatus), carcasses were darted and their traumatic effects on the anatomy in target and non-target areas of the body were described. In total, 87 samples were collected (target area, n = 19; non-target area, n = 68) within standardized grid partitions from five carcasses of sub-adult to adult age classes with a range of body condition scores. We broadly classified impacts penetrating completely through the blubber into muscle or deeper internal tissues as over-penetrations (n = 51/87, 59%). For samples collected in the defined target area, there was a low number of over-penetrations (n = 5/51; 10%). However, for samples collected in the defined, non-target areas, a much higher number of over-penetrations occurred (n = 45/51 88%). A visual examination of some samples indicated that sample length and appearance may not be reliable guides to assess the penetration depth of the wounds. These preliminary results suggest samples collected in non-targeted areas could pose much higher risk to the individual. We encourage other researchers considering the use of remote biopsy tools to conduct similar assessments prior to field sampling to better understand the potential consequences of misplaced samples with a view towards continually improving remote biopsy tools and techniques for the benefit of cetacean welfare.

Reduced nontarget embolization and increased targeted delivery with a reflux-control microcatheter in a swine model

PurposeTo evaluate the potential differences in non-target embolization and vessel microsphere filling of a reflux-control microcatheter (RCM) compared to a standard end-hole microcatheter (SEHM) in a swine model. Materials and methodsRadiopaque microspheres were injected with both RCM and SEHM (2.4-Fr and 2.7-Fr) in the kidneys of a preclinical swine model. Transarterial renal embolization procedures with RCM or SEHM were performed in both kidneys of 14 pigs. Renal arteries were selectively embolized with an automated injection protocol of radio-opaque microspheres. Ex-vivo X-ray microtomography images of the kidneys were utilized to evaluate the embolization by quantification of the deposition of injected microspheres in the target vs. the non-target area of injection. X-ray microtomography images were blindly analyzed by five interventional radiologists. The degree of vessel filling and the non-target embolization were quantified using a scale from 1 to 5 for each parameter. An analysis of variance was used to compare the paired scores. ResultsTotal volumes of radio-opaque microspheres injected were similar for RCM (11.5±3.6 [SD] mL; range: 6–17mL) and SEHM (10.6±5.2 [SD] mL; range: 4–19mL) (P=0.38). The voxels enhanced ratio in the target (T) vs. non-target (NT) areas was greater with RCM (T=98.3% vs. NT=1.7%) than with SEHM (T=89% vs. NT=11%) but the difference was not significant (P=0.30). The total score blindly given by the five interventional radiologists was significantly different between RCM (12.3±2.1 [SD]; range: 6–15) and the standard catheter (11.3±2.5 [SD]; range: 4–15) (P=0.0073), with a significant decrease of non-target embolization for RCM (3.8±1.3 [SD]; range: 3.5–4.2) compared to SEHM (3.2±1.5 [SD]; range: 2.9–3.5) (P=0.014). ConclusionIn an animal model, RCM microcatheters reduce the risk of non-target embolization from 11% to 1.7%, increasing the delivery of microspheres of 98% to the target vessels, compared to SEHM microcatheters.

Non-target effect of pesticides in rice environment

Agriculture has always been affected by intractable pests. Conventional agriculture still relies on the use of several pesticides to control various pests to ensure the yield of the crop and to feed a constantly growing population. To reduce the pest menace, the fatuous behavior of people as well as a natural proclivity towards the use of pesticides in a non-judicious manner, has caused several ecological backlashes such as secondary pest outbreak, pest resurgence and non-target organism's exploitation. Effects of pesticides on non-target organisms area worldwide concern and contention are the basis for most legislation regarding usage of specific pesticides. Pesticides, besides their effect on pests, would also affect several beneficial non-targets. The effect of pesticides could be lethal and may prove catastrophic or sublethal and stimulate the growth and development of non targeted biota's. Hence, this review approaches well-analyzed hindsight regarding the effect of pesticides on the natural enemies as well as other non-target organisms present in the rice agroecosystem.

Adaptive Localizing Region-Based Level Set for Segmentation of Maxillary Sinus Based on Convolutional Neural Networks.

In this paper, we propose a novel method, an adaptive localizing region-based level set using convolutional neural network, for improving performance of maxillary sinus segmentation. The healthy sinus without lesion inside is easy for conventional algorithms. However, in practice, most of the cases are filled with lesions of great heterogeneity which lead to lower accuracy. Therefore, we provide a strategy to avoid active contour from being trapped into a nontarget area. First, features of lesion and maxillary sinus are studied using a convolutional neural network (CNN) with two convolutional and three fully connected layers in architecture. In addition, outputs of CNN are devised to evaluate possibilities of zero level set location close to lesion or not. Finally, the method estimates stable points on the contour by an interactive process. If it locates in the lesion, the point needs to be paid a certain speed compensation based on the value of possibility via CNN, assisting itself to escape from the local minima. If not, the point preserves current status till convergence. Capabilities of our method have been demonstrated on a dataset of 200 CT images with possible lesions. To illustrate the strength of our method, we evaluated it against state-of-the-art methods, FLS and CRF-FCN. For all cases, our method, as assessed by Dice similarity coefficients, performed significantly better compared with currently available methods and obtained a significant Dice improvement, 0.25 than FLS and 0.12 than CRF-FCN, respectively, on an average.

Virtual Try-on Network With Attribute Transformation and Local Rendering

A virtual try-on network has gradually become a popular topic in recent years. It aims to transfer images of in-shop clothes onto the image of a target person. Owing to the diversity of clothing attributes, developing an image-based virtual try-on network is a complicated task for computers to perform and requires significant effort. Existing methods are unsatisfactory as they cannot preserve the characteristics of the clothes or the target person's identity well, thereby affecting the perception of the generated images; therefore, further research is required. To address this problem, we propose a novel try-on method that combines attribute transformation and local rendering. First, we employ pixel-level semantic segmentation to identify the try-on area and provide implementation conditions for local rendering. Second, we construct a learnable attribute transformation module to complete the try-on task for different attributes. Third, we use a learnable clothing warping module to fit the pose and figure of the target person well and establish a novel loss function, called modified style loss (M-SL), to handle clothes with rich details. Finally, we adopt a local rendering strategy, using which only renders the clothing area to ensure that the details of the non-target area are not lost. Extensive experiments are performed to test our method. The results demonstrate that our method outperforms other state-of-the-art methods.

Automatic Segmentation of Remote Sensing Images on Water Bodies Based on Image Enhancement

By virtue of high-resolution remote sensing satellites, there is a possibility to analyze remote sensing images on water bodies through digital image processing (DIP). In many remote sensing images, however, the water bodies have similar gray values as other ground objects. To effectively distinguish water bodies from other ground objects in these images, this paper proposes a logarithmic enhancement method for remote sensing images on water bodies based on adaptive morphology. The proposed method can filter the noise of non-target area, and enhance the water body in the original image. On this basis, a morphology-based segmentation method was designed for remote sensing images on water bodies. Experimental results show that our method achieved a high segmentation accuracy, controlling the mean segmentation error at below 1.32%.

Intelligent IoT-based large-scale inverse planning system considering postmodulation factors

The model and algorithm of intensity-modulated radiotherapy (IMRT) are updated increasingly quickly, but the hardware upgrade of primary hospitals often lags behind. The new generation of intelligent precise radiotherapy platforms provides users with intelligent medical consortium services using big data, artificial intelligence and industrial Internet of Things technology. This technology can ensure that under the real-time guidance of a professional medical consortium, primary hospitals can realize rapid large-scale reverse planning design and can more accurately consider many factors of postprocessing. Although large-scale healthcare systems, such as volumetric-modulated arc therapy and other accurate radiotherapy technologies, have developed rapidly, the development of step-and-shoot-mode IMRT technology is still very important for developing countries. For software, in addition to the conformity of the dose distribution, the modulation speed, convenience and stability of the later dose delivery should also be considered in inverse planning. Therefore, this paper analyzes the main problems in conventional IMRT inverse planning, including the smoothing of the fluence map, the selection of the gantry angle and the dose leakage of tongue–groove effects. To address these issues, a novel Intelligent IoT-based large-scale inverse planning strategy with the key factors of the postmodulation is developed, and a detailed flow chart is also provided. The scheme consists of two steps. The first step is to obtain a relatively optimal combination of gantry angles by considering the dose distribution requirements and constraints and the modulation requirements and constraints. The second step is to optimize the intensity map, to smooth the map based on prior knowledge according to the determined angles, and to obtain the final modulation scheme according to the relevant objectives and constraints of the map decomposition (leaf sequencing). In an experiment, we calculate and validate the clinical head and neck case. Because of the special gantry angle selection, the angle combination is optimized from the initial equivalent distribution to adapt to the target area and protect the nontarget area. The value of the objective function varies greatly after the optimization, especially in the target area, and the target value decreases by approximately 10%. On this basis, we smooth the fluence map by a partial differential equation with prior knowledge and a minimization of the total number of monitor units. It is also shown from the objective function value that the target value is essentially unchanged for the target area, while for the nontarget area, the value decreases by 16%, which is very impressive.

Effect of Droplet Size Parameters on Droplet Deposition and Drift of Aerial Spraying by Using Plant Protection UAV

In the field of pesticide spraying, droplet size is one of the most important factors affecting droplet deposition and drift. In order to study the effect of different droplet size parameters on droplet deposition distribution and drift of aerial spraying by using plant protection UAV, an aerial spraying test with the same spraying rate and different size droplets in rice canopy was carried out by using multi-rotor unmanned aerial vehicles (UAV) and four TEEJET nozzles with different orifice sizes (these droplets with a volume median diameter (VMD) of 95.21, 121.43, 147.28, and 185.09 μm, respectively), and the deposition distribution and penetration of droplets in the target area and the drift distribution of droplets in the non-target area were compared and analyzed. The results showed that the deposition distribution and penetration of droplets in the target area and the drift distribution of droplets in the non-target area were influenced by the droplet size. The droplet deposition rate in the upper and lower rice canopies were increased in the target area with the increase of droplet size. The penetration results of droplets also increased with the increase of droplet size, and that of droplets with a VMD of 185.09 μm was the best, reaching 38.13%. The average values of the cumulative drift rate of droplets in the rice canopy in the four tests were 73.87%, 50.26%, 35.91%, and 23.06%, respectively, and the cumulative drift rate and the drift distance of droplets decreased with the increase of droplet size, which indicated that the increase of droplet size can effectively reduce droplet drift. It demonstrated that the droplet size is one of the most important factors affecting droplet deposition and drift for pesticide spraying by plant protection UAV, and for the application of plant protection UAV with extra-low volume spraying, the use of droplets with VMD less than 160 μm should be avoided and a more than 10 m buffer zone should be considered downwind of the spraying field to avoid drug damage caused by pesticide drift. The results have fully revealed the effect of droplet size parameters on droplet deposition and drift of aerial spraying. Moreover, the influence of the wind field below the rotors on the distribution of droplet deposition was surmised and analyzed from the perspective of plant protection UAV. It is important for optimizing the droplet parameters of aerial spraying, increasing the spraying efficiency, and realizing precision agricultural aviation spray.

Multiplexed Superresolution CRISPR Imaging of Chromatin in Living Cells

Multiplexed Superresolution CRISPR Imaging of Chromatin in Living Cells

Nearshore Ship Detection on High-Resolution Remote Sensing Image via Scene-Mask R-CNN

Deep convolutional neural network (DCNN) can achieve ship detection mission on the high-resolution remote sensing images. However, the false alarms caused by the onshore ship-like objects may decrease the accuracy and feasibility of these DCNN-based detection frameworks. In our work, an end-to-end method, named as Scene Mask R-CNN, is proposed to reduce the onshore false alarms. The scene mask extraction network (SMEN), as a network branch for scene segmentation, is innovatively introduced into the detection framework. The non-target area is marked out by an inferenced scene mask which is used to assist the ship detection. Combining the feature map originated from feature extraction network (FEN) with the inferenced scene mask by using the edge probability weighted (EPW) merging method, the false candidate targets in the non-target area are excluded. This novel mechanism of DCNN-based ship detection not only maintains the detection accuracy, but also effectively suppresses the false alarms in the non-target area. Finally, the validity and accuracy of this method are verified on a ship dataset generated by the high-resolution optical remote sensing images.

A Novel Definition of Equivalent Uniform Dose Based on Volume Dose Curve

With the improvement of mobile device performance, the requirement of equivalent dose description in intensity-modulated radiation therapy is increasing in mobile multimedia for healthcare. The emergence of mobile cloud computing will provide cloud servers and storage for intensity-modulated radiotherapy (IMRT) mobile applications, thus realizing visualized radiotherapy in a real sense. Equivalent uniform dose (EUD) is a biomedical indicator based on the dose measure. In this paper, the dose volume histogram is used to describe the dose distribution of different tissues in target and nontarget regions. The traditional definition of EUD, such as the exponential form and the linear form, has only a few parameters in the model for fast calculation. However, there is no close relationship between this traditional definition and the dose volume histogram. In order to establish the consistency between the EUD and the dose volume histogram, this paper proposes a novel definition of EUD based on the volume dose curve, called VD-EUD. By using a unique organic volume weight curve, it is easy to calculate VD-EUD for different dose distributions. In definition, different weight curves are used to represent the biological effects of different organs. For the target area, we should be more careful about those voxels with a low dose (cold point); thus, the weight curve is monotonically decreasing. While for the nontarget area, the curve is monotonically increasing. Furthermore, we present the curves for parallel, serial, and mixed organs of nontarget areas separately, and we define the weight curve form with only two parameters. Medical doctors can adjust the curve interactively according to different patients and organs. We also propose a fluence map optimization model with the VD-EUD constraint, which means that the proposed EUD constraint will lead to a large feasible solution space. We compare the generalized EUD (gEUD) and the proposed VD-EUD by experiments, which show that the VD-EUD has a closer relationship with the dose volume histogram. If the biological survival probability is equivalent to the VD-EUD, the feasible solution space would be large, and the target areas can be covered. By establishing a personalized organic weight curve, medical doctors can have a unique VD-EUD for each patient. By using the flexible and adjustable EUD definition, we can establish the VD-EUD-based fluence map optimization model, which will lead to a larger solution space than the traditional dose volume constraint-based model. The VD-EUD is a new definition; thus, we need more clinical testing and verification.

Effect of Photon Energy on Conventional Intensity-Modulated Radiotherapy and Rapid Arc Radiotherapy Planning for Deep-Seated Targets in Carcinoma Cervix

Introduction To evaluate the dosimetric effect of photon energies on fixed field intensity-modulated radiotherapy (IMRT) and dual arc (DA) planning and to compare the dosimetric differences between conventional IMRT and DA radiotherapy planning. Materials and Methods IMRT and DA plans were generated for 15 patients having cervical cancer using different photon energies. IMRT and DA plans were generated using seven fields and double arcs, respectively. Dosimetric comparison was done in terms of planning target volume (PTV) coverage, sparing of organ at risk (OAR), homogeneity index (HI), conformity index (CI), and monitor units (MUs). Photo-neutron (energy ≤10MV) contribution was not considered for this study. Near region (NR) and far region (FR) were contoured to evaluate the dose deposited in nontarget area. Results No significant difference was observed (p &gt; 0.05) in PTV coverage for conventional IMRT and DA; however, 6 MV yielded significantly better coverage over 15 MV (p &lt; 0.05) for both the treatment modalities. Mean bladder dose was significantly more for conventional IMRT compared with DA. For rectal mean dose, p-value was nonsignificant for IMRT in comparison to DA, while significant difference was observed for change in photon energies for both treatment modalities respectively, except for 10 MV versus 15 MV DA plans. Significant improvements in HI (except 6 MV vs. 10 MV DA), CI (except 6 MV vs. 10 MV IMRT and DA), MUs, NR, and FR were noted. Conclusion DA generates more conformal, homogenous plans, requires less numbers of MUs, and deposits fewer doses to NR and FR regions of nontarget tissues in comparison to conventional IMRT. Although increase in photon energy for IMRT and DA plans reduces numbers of MUs and dose deposited to NR and FR regions, yet the choice for treatment of carcinoma cervix remains 6 MV due to production of photo-neutrons at higher energies.

Applications of improved artificial fish swarm algorithm in microscopy autofocus

The selection of the focusing window is the key procedure in achieving precise automatic focus of the microscope. For the traditional focus window selection method, the limitation is that the target object cannot be accurately positioned. This paper proposes an improved artificial fish focusing window method. The method takes the area-of-interest of the whole image as the basis of the selection window. Through utilizing the global optimization ability of the artificial fish swarm algorithm, the best focusing window can be obtained. Adding the global optimal value to the behavior update of each artificial fish makes the artificial fish quickly move to the optimal position. Under the premise of ensuring accuracy, the elimination behavior is introduced to improve the convergence speed of the algorithm in the later period. Furthermore, according to the characteristics of the bulletin board in the algorithm, the interference area is identified with the trend comparison method, and the influence of the non-target area is effectively excluded. Experiment results show that the focusing window obtained by this algorithm can be well-suited for the target object, greatly improve the accuracy of autofocus, and make the efficiency improvement 1.65 times than the traditional method.