Shape Of Histogram Research Articles

Visual analysis of pulmonary blood flow in pulmonary circulation assessment: differences between two variant algorithms for processing dynamic images.

In the quantitative assessment of pulmonary blood flow, two different processing algorithms [cross-correlation calculation processing (CCC-pro) and reference frame subtraction processing (RFS-pro)] within dynamic imaging systems have been reported to exhibit high correlations with conventional measurement methods. However, reports still need to evaluate these two processing algorithms regarding the different aspects of pulmonary blood flow. This study aimed to analyze the differences in pulmonary circulation. We conducted a cross-sectional study to evaluate patients with lung cancer who underwent radical surgery, simultaneous dynamic chest radiography (DCR), and pulmonary perfusion scintigraphy (PPS). We assessed the correlation between PPS and two algorithms (CCC-pro and RFS-pro) regarding calculated blood flow ratio (BFR) using Pearson's correlation and linear regression analysis. Additionally, we evaluated consistency using the Bland-Altman analysis. We compared the pulmonary blood flow distributions across six-division lung fields and evaluated each method's blood flow images and histograms of pixel values. From May 2018 to December 2020, we consecutively enrolled 46 patients with lung cancer who met the inclusion criteria (40 male patients, with a mean age of 72.91 years). In these patients, CCC-pro and RFS-pro were correlated (R=0.718, P<0.01); however, CCC-pro was more strongly correlated with PPS than RFS-pro (R=0.859, P<0.01 vs. R=0.549, P<0.01). The Bland-Altman analysis showed high agreement, although systematic errors were observed in relationships other than RFS-pro to PPS. CCC-pro and RFS-pro showed similar blood flow distributions in the upper and lower lung fields, with RFS-pro being dominant in the middle. RFS-pro showed higher pixel values in the hilar region and a histogram shape similar to PPS; however, posture affected the right upper lung field gradient. RFS-pro showed no difference in the BFR when the pulmonary artery region was symmetric; however, potential inaccuracies existed when it overlapped with the cardiovascular shadow. The CCC-pro algorithm was useful for quantifying BFRs, whereas the RFS-pro algorithm accurately evaluated blood flow distribution in lung fields. Further algorithm development is required to enable versatile pulmonary blood flow analysis.

Bimodalność i długoterminowe trendy wartości ekstremalnych temperatury powietrza

Histograms of air temperature with a bimodal shape are commonly observed in many regions of the world. In this study, we investigate the causes of bimodality in the histograms of daily temperature series (minimum, average, and maximum) for selected climatological stations in Slovakia. Our findings suggest that in the Central European region, the bimodal shape of air temperature histograms is mainly due to the latent heat of freezing, as the surface of snow and ice and the air are thermally coupled. The asymmetry in the air temperature histograms is due to the lower mass heat capacity of ice compared to water and air. The energy-intensive latent heat of conversion of ice to water (and vice versa) results in the more frequent occurrence of ground-layer air temperatures around the freezing point, leading to the formation of the observed local maximum. This has far-reaching implications, such as the calculation of the annual mean air temperature at climatological stations. When calculating the average air temperature, negative temperatures should be given less weight than positive temperatures. Temperatures around 0-6°C should be given higher weight. This may also explain why Arctic regions are experiencing more significant warming than equatorial regions. In the second part of this paper, we analyze the long-term trends of selected temperature indices for the climatological station at Hurbanovo (Slovakia) from 1871 to 2020. Our results indicate statistically significant changes in all temperature indices, with indices related to cold temperatures increasing more significantly than those associated with high temperatures. Finally, study examines theoretical probability distributions to estimate T-year temperatures for temperature indices at the Hurbanovo climate station in Slovakia. The analysis includes three time periods (1901–1960, 1961–2020, and 1991–2020) and reveals significant changes in temperature indices at the Hurbanovo station. The 100-year temperature of TN,min was –35.75°C in 1901–1960, –28.69°C in 1961–2020, and –26.52°C in 1991–2020. The 100-year temperature of TX,max was 39.4°C in 1901–1960 and 39.63°C in 1961–2020. TN,min showed the most significant changes, with the 100-year temperature increasing by up to 7.06°C in 1961–2020 and up to 9.23°C in 1991–2020.

An automated technique for global noise level measurement in CT image with a conjunction of image gradient.

Automated assessment of noise level in clinical computed tomography (CT) images is a crucial technique for evaluating and ensuring the quality of these images. There are various factors that can impact CT image noise, such as statistical noise, electronic noise, structure noise, texture noise, artifact noise, etc. In this study, a method was developed to measure the global noise index (GNI) in clinical CT scans due to the fluctuation of x-ray quanta. Initially, a noise map is generated by sliding a 10 × 10 pixel for calculating Hounsfield unit (HU) standard deviation and the noise map is further combined with the gradient magnitude map. By employing Boolean operation, pixels with high gradients are excluded from the noise histogram generated with the noise map. By comparing the shape of the noise histogram from this method with Christianson's tissue-type global noise measurement algorithm, it was observed that the noise histogram computed in anthropomorphic phantoms had a similar shape with a close GNI value. In patient CT images, excluding the HU deviation due the structure change demonstrated to have consistent GNI values across the entire CT scan range with high heterogeneous tissue compared to the GNI values using Christianson's tissue-type method. The proposed GNI was evaluated in phantom scans and was found to be capable of comparing scan protocols between different scanners. The variation of GNI when using different reconstruction kernels in clinical CT images demonstrated a similar relationship between noise level and kernel sharpness as observed in uniform phantom: sharper kernel resulted in noisier images. This indicated that GNI was a suitable index for estimating the noise level in clinical CT images with either a smooth or grainy appearance. The study's results suggested that the algorithm can be effectively utilized to screen the noise level for a better CT image quality control.

Statistical Analysis of the Negative–Positive Transformation in Image Encryption

The negative–positive transformation (NPT) is a widely employed technique for encrypting images on pixel blocks, commonly integrated into cryptosystems compatible with compression algorithms. The existing literature on NPT analysis can be categorized into two types: theoretical analyses with results that apply to any image, primarily focused on compression compatibility, and numerical analyses that report empirical results from specific images, some without explaining the causes of the security results, while others are only related to the compression performance. Consequently, there is a significant gap in understanding the implications of applying the NPT for data protection. For that reason, this paper conducts a theoretical statistical analysis, presenting, demonstrating, and verifying six theorems to understand the security contributions of NPT. Two theorems examine the shape of the image histogram and the scatter plot of adjacent pixels after the NPT application. The subsequent four theorems explore the influence of NPT on the mean, variance, covariance, and correlation within each pixel block. The findings indicate that the NPT generates images with symmetrical histograms, the correlation of pixel blocks remains invariant, and distinct vertical and horizontal reflections manifest on the scatter plot. These theorems are verified by encrypting the Lena image with four pixel-block sizes. The histogram symmetry passed the goodness-of-fit test at a significance level of 5%, revealing consistent results. The correlation of pixel blocks remained unchanged, and the scatter plot exhibited an x-shaped pattern. Therefore, as the NPT alone does not achieve desirable encryption results, such as uniform histograms, scatter plots, and decreasing correlation, cryptosystems should complement it with additional techniques.

Octagonal lattice-based triangulated shape descriptor engaging second-order derivatives supplementing image retrieval

Erstwhile shape description schemes lack primarily in establishing trade-offs with accuracy and computational load. Accordingly, a lightweight shape descriptor offering precise definition and compaction of high-frequency features is contributed in this paper using a simple geometrical shape for localization and shape characterization. Initially, the input image is octagonally tessellated and triangularly decomposed into sub-regions whose side-wise differences are evaluated and subjected to second-order differentiation to produce three high-frequency values representing triangle corners. The resultant is processed by the law of sines to yield localized shape features exhibiting congruence and is reiterated on the residual regions, followed by a novel octal encoding scheme encompassing maximal variations in the localized regions. The resulting features are globally fabricated into shape histograms in a non-overlapping manner representing the shape vector. This scheme validated on widely popular benchmark shape datasets demonstrates superior retrieval and recognition accuracies greater than 93% which is lacking in its competitors.

Microscopic theory, analysis, and interpretation of conductance histograms in molecular junctions

Molecular electronics break-junction experiments are widely used to investigate fundamental physics and chemistry at the nanoscale. Reproducibility in these experiments relies on measuring conductance on thousands of freshly formed molecular junctions, yielding a broad histogram of conductance events. Experiments typically focus on the most probable conductance, while the information content of the conductance histogram has remained unclear. Here we develop a microscopic theory for the conductance histogram by merging the theory of force-spectroscopy with molecular conductance. The procedure yields analytical equations that accurately fit the conductance histogram of a wide range of molecular junctions and augments the information content that can be extracted from them. Our formulation captures contributions to the conductance dispersion due to conductance changes during the mechanical elongation inherent to the experiments. In turn, the histogram shape is determined by the non-equilibrium stochastic features of junction rupture and formation. The microscopic parameters in the theory capture the junction’s electromechanical properties and can be isolated from separate conductance and rupture force (or junction-lifetime) measurements. The predicted behavior can be used to test the range of validity of the theory, understand the conductance histograms, design molecular junction experiments with enhanced resolution and molecular devices with more reproducible conductance properties.

Classification Algorithm of 3D Pattern Film Using the Optimal Widths of a Histogram

A recent development in marketing tools aimed at boosting sales involves the use of three-dimensional pattern films. These films are designed to captivate customers’ interest by applying 3D patterns to the surface of products. However, ensuring the quality of these produced 3D films can be quite challenging due to factors such as low contrast and unclear layout. Furthermore, there has been a shortage of research on methods for evaluating the quality of 3D pattern films, and existing approaches have often failed to yield satisfactory results. To address this pressing issue, we propose an algorithm for classifying 3D pattern films into either ‘good’ or ‘bad’ categories. Unlike conventional segmentation algorithm or edge detection methods, our proposed algorithm leverages the width information at specific heights of the image histograms. The experimental results demonstrate a significant disparity in histogram shapes between good and bad patterns. Specifically, by comparing the widths of all images at the quintile of the histogram height, we show that it is possible to achieve a 100% accuracy in classifying patterns as either ‘good’ or ‘bad’. In comparative experiments, our proposed algorithm consistently outperformed other methods, achieving the highest classification accuracy.

Multilevel histogram shape‐based image watermarking invariant to geometric attacks

AbstractIn this paper, a geometrically invariant image watermarking scheme is proposed by exploiting multilevel histogram shapes. The embedding procedure starts by decomposing the host image with the first level Haar wavelet. After that, histograms are extracted from the approximation subband via several rounds, which are used to embed watermark bits. Each round of embedding first extracts a histogram at a specified level. Then the histogram is split into fragments, into which a number of watermark bits can be embedded. In this way, a considerable watermarking capacity is available. Besides, a histogram adjustment in the first embedding round is suggested to guarantee good population of histogram bins. Experimental results support its robustness against various common attacks and geometric attacks. Moreover, the scheme can embed multiple watermark sequences with various robustness and capacity profiles, which enriches its practical applications.

A field-wide assessment of differential expression profiling by high-throughput sequencing reveals widespread bias.

We assess inferential quality in the field of differential expression profiling by high-throughput sequencing (HT-seq) based on analysis of datasets submitted from 2008 to 2020 to the NCBI GEO data repository. We take advantage of the parallel differential expression testing over thousands of genes, whereby each experiment leads to a large set of p-values, the distribution of which can indicate the validity of assumptions behind the test. From a well-behaved p-value set π0, the fraction of genes that are not differentially expressed can be estimated. We found that only 25% of experiments resulted in theoretically expected p-value histogram shapes, although there is a marked improvement over time. Uniform p-value histogram shapes, indicative of <100 actual effects, were extremely few. Furthermore, although many HT-seq workflows assume that most genes are not differentially expressed, 37% of experiments have π0-s of less than 0.5, as if most genes changed their expression level. Most HT-seq experiments have very small sample sizes and are expected to be underpowered. Nevertheless, the estimated π0-s do not have the expected association with N, suggesting widespread problems of experiments with controlling false discovery rate (FDR). Both the fractions of different p-value histogram types and the π0 values are strongly associated with the differential expression analysis program used by the original authors. While we could double the proportion of theoretically expected p-value distributions by removing low-count features from the analysis, this treatment did not remove the association with the analysis program. Taken together, our results indicate widespread bias in the differential expression profiling field and the unreliability of statistical methods used to analyze HT-seq data.

A proof-of-concept of cross-luminescent metascintillators: testing results on a BGO:BaF2 metapixel

Objective: Time-of-flight positron emission tomography (PET) is the next frontier in improving the effective sensitivity. To achieve superior timing for time-of-flight PET, combined with high detection efficiency and cost-effectiveness, we have studied the applicability of BaF2 in metascintillators driven by the timing of cross-luminescence photon production. Approach: Based on previous simulation studies of energy sharing and analytic multi-exponential scintillation pulse, as well as sensitivity characteristics, we have experimentally tested a pixel of 3 × 3 × 15 mm3 based on 300 μm BGO and 300 μm BaF2 layers. To harness the deep ultraviolet cross-luminescent light component, which carries improved timing, we use the FBK VUV SiPM. Metascintillator energy sharing is addressed through a double integration approach. Main results: We reach an energy resolution of 22%, comparable to an 18% resolution of simple BGO pixels using the same readout, through the optimized use of the integrals of the metascintillator pulse in energy sharing calculation. We measure the energy sharing extent of each pulse with a resolution of 25% and demonstrate that experimental and simulation results agree well. Based on the energy sharing, a timewalk correction is applied, exhibiting significant improvements for both the coincidence time resolution (CTR) and the shape of the timing histogram. We reach 242 ps CTR for the entire photopeak, while for a subset of 13% of the most shared events, the CTR value improves to 108 ps, comparable to the 3 × 3 × 5 mm3 LYSO:Ce:Ca reference crystal. Significance: While we are considering different ways to improve further these results, this proof-of-concept demonstrates the applicability of cross-luminescence for metascintillator designs through the application of VUV compatible SiPM coupling, and easily implementable digital algorithms. This is the first test of BaF2-based metascintillators of sufficient stoppng power to be included in a PET scanner, demonstrating the industrial applicability of such cross-luminescent metascintillators.

A local-global shape characterization scheme using quadratic Bezier triangle aiding retrieval

Shape characterization plays a highly prominent role in retrieval and relies extremely upon descriptors inbuilt with lightweight operations and compaction qualities. However, realizing such a simple and robust shape descriptor capable of dealing with noise, variations in brightness, and deformations pose a significant challenge. In this regard, a simple and effective shape descriptor using the Quadratic Bezier Triangle (QBT) targeting shape matching and retrieval is presented in this work. The mechanism commences with triangular tessellation of each image followed by determining their side-wise intensity differences that are then mapped to QBT vertices to yield the order-wise control points. The maxima of the resulting points are transformed into the binary equivalent of the given shape that is compacted into octal values. Later, these localized values are globally transformed into shape histograms to yield the QBT-based Feature (QBTF) descriptor representing the given input. QBTFs performance is exhaustively analyzed in terms of Bulls Eye Retrieval (BER) score and classification accuracy using the public datasets namely Kimia-99, MPEG-7 CE-1 part B, PHOS, and Tari-1000. Relative BER investigations witnessed across diverse datasets reveal a consistent and improved score of 93% achieved by this scheme over its peers. Mathematical analysis of invariance, noise resilience, and the complexities involved in realizing QBTF, establish the robustness and suitability of this scheme for real-time shape description.

NATURAL REGENERATION CAPACITY OF VITELLARIA PARADOXA (SHEA TREE) ON THE HILLS OF BASSAR (NORTH-TOGO)

As the consequence of anthropic pressure along with climatic factors, the vegetation of the hills of Bassar is degrading day after day and a regression of many woody species is observed. Vitellaria paradoxa is a highly valued multi-use species, but it is threatened with extinction in the area. The "bell" and "L" shapes of the diameter class distribution histograms reflect aging and/or disturbed populations. Stand disturbance is illustrated by the low proportion of large diameter class individuals. The "L" shape reveals an advanced state of degradation characterized by a near absence of large diameter trees. The study of the natural regeneration of Vitellaria paradoxa on the hills of Bassar shows that the density of regeneration by seedlings, shoots and suckers depend on the diameter of the individual stem. The larger the diameter increases, the higher the density of seedlings becomes. However, plantlets stemming from seedlings are higher, and weaker from suckers and shoots. The density of Vitellaria paradoxa seedlings is lower in shrub savannahs than in tree savannahs. The analysis of variance showed that whether under the crown or outside the crown, there was a significant difference between individuals from regeneration in all the formations studied. In the inventory plots, the densities of young trees are equally variable under the crown and outside the crown as those of large trees.

Local Contrast-Based Pixel Ordering for Exact Histogram Specification.

Histogram equalization is one of the basic image processing tasks for contrast enhancement, and its generalized version is histogram specification, which accepts arbitrary shapes of target histograms including uniform distributions for histogram equalization. It is well known that strictly ordered pixels in an image can be voted to any target histogram to achieve exact histogram specification. This paper proposes a method for ordering pixels in an image on the basis of the local contrast of each pixel, where a Gaussian filter without approximation is used to avoid the duplication of pixel values that disturbs the strict pixel ordering. The main idea of the proposed method is that the problem of pixel ordering is divided into small subproblems which can be solved separately, and then the results are merged into one sequence of all ordered pixels. Moreover, the proposed method is extended from grayscale images to color ones in a consistent manner. Experimental results show that the state-of-the-art histogram specification method occasionally produces false patterns, which are alleviated by the proposed method. Those results demonstrate the effectiveness of the proposed method for exact histogram specification.

U-Net architecture variants for brain tumor segmentation of histogram corrected images

Abstract In this paper we propose to create an end-to-end brain tumor segmentation system that applies three variants of the well-known U-Net convolutional neural networks. In our results we obtain and analyse the detection performances of U-Net, VGG16-UNet and ResNet-UNet on the BraTS2020 training dataset. Further, we inspect the behavior of the ensemble model obtained as the weighted response of the three CNN models. We introduce essential preprocessing and post-processing steps so as to improve the detection performances. The original images were corrected and the different intensity ranges were transformed into the 8-bit grayscale domain to uniformize the tissue intensities, while preserving the original histogram shapes. For post-processing we apply region connectedness onto the whole tumor and conversion of background pixels into necrosis inside the whole tumor. As a result, we present the Dice scores of our system obtained for WT (whole tumor), TC (tumor core) and ET (enhanced tumor) on the BraTS2020 training dataset.

Local Energy Based Shape Histogram (LESH) and Adaboost Machine Learning Techniques to Detect Lung Cancer

Abstract: Numerous cancerous tissues develop in a manner that obscures the specific side effects, making it challenging to identify the effects of lung disease. This investigation's findings can be verified using high-resolution images. With this method, the LESH Algorithm's vital activity will be examined in images. In this study, the GLCM approach is used to pre-measure the previews and element extraction framework, as well as to verify a patient's infection rate at a period when it is abnormal to know it. The malignancy stage will be assessed with the help of the results. Patients with malignant growths whose endurance rates can be estimated using the informational index. The outcome is wholly determined by how accurately the tissue patterns were constructed or how incorrectly they were constructed.

Traditional Pattern Feature Extraction and Cultural Creative Design Application Based on Multilevel Histogram Shape Segmentation

Content-based stereo video and image retrieval relies on traditional pattern feature extraction and cultural creative design applications. In a 3D system, the 3D model is typically projected into a 2D point cloud, after which feature extraction takes place. A depth map is an image created by orthogonal projection of three-dimensional data or viewpoint matching, parallax calculation, and resampling into regular data based on depth value. In this paper, a multilevel histogram-based shape segmentation method is proposed. The threshold can be determined by analyzing the relationship between the peaks if there are several obvious peaks on the image histogram. This paper focuses on the use of traditional pattern feature extraction and cultural creative design based on the shape segmentation of multilevel histograms. Each algorithm can achieve good classification results in the experiment of rotation invariant similarity classification. The average accuracy of the HOD algorithm is 84.1%, the average accuracy of the RSDF algorithm is 86.2%, the average accuracy of the GIF algorithm is 81.3%, and the average accuracy of this paper’s algorithm is 89.9% when the rotation angle is 60–120°. The results show that this paper’s algorithm has a higher classification accuracy compared to the other algorithms and that it maintains a high average classification accuracy across all rotation angles, indicating that it is rotation invariant and robust. The histogram-based threshold method has the advantage of not requiring knowledge of the image’s prior information. The algorithm is easy to understand, straightforward, and quick. However, because the valley point in the histogram is not visible in images with low or high contrast between the target and the background area, it is difficult to select the threshold. The results of traditional pattern feature extraction can be improved using the retrieved pixels.

Audio-lossless robust watermarking against desynchronization attacks

Audio-lossless robust watermarking (ALRW) is a new research issue in the field of information hiding, which can resist those content-preserving manipulations while can restore the cover audio files completely in case of no attacks. Most ALRW countermeasures proposed in the literature usually focus on additive noise-like manipulations such as MP3 compression, low-pass filtering and Gaussian additive noise, but few are resistant to challenging desynchronization attacks such as random cropping and time-scale modification (TSM). The main reason is that the exploited robust features in the existing ALRW algorithms are related to the sample position. In this paper, we propose an ALRW method by efficiently embedding a watermark into the histogram shape of the low-frequency component after the discrete wavelet transform (DWT) and reversibly hiding the distortion due to the robust watermark embedding as the compensation information for restoration of the cover audio file. The use of the histogram shape in the DWT low-frequency component as the exploited robust feature is: 1) mathematically invariant to TSM; 2) robust to random cropping attacks since an audio clip before and after the cropping often has similar distribution, and 3) resistant to interpolation errors during time scaling and those common audio processing operations. To reduce the compensation information, the robust watermarking process is elaborately and luminously designed to represent the difference between the original and the robust watermarked audio. As a result, an ALRW system against desynchronization attacks is achieved with satisfactory performance. Experimental results show that the proposed ALRW scheme can effectively reduce the compensation information and provide strong robustness to those common manipulations including TSM, random cropping, MP3 lossy compression and other noise-like manipulations. In case of no attacks, the cover audio signal can be recovered without any loss.

Including Volume Effects in Biological Treatment Plan Optimization for Carbon Ion Therapy: Generalized Equivalent Uniform Dose-Based Objective in TRiP98.

We describe a way to include biologically based objectives in plan optimization specific for carbon ion therapy, beyond the standard voxel-dose-based criteria already implemented in TRiP98, research planning software for ion beams. The aim is to account for volume effects—tissue architecture-dependent response to damage—in the optimization procedure, using the concept of generalized equivalent uniform dose (gEUD), which is an expression to convert a heterogeneous dose distribution (e.g., in an organ at risk (OAR)) into a uniform dose associated with the same biological effect. Moreover, gEUD is closely related to normal tissue complication probability (NTCP). The multi-field optimization problem here takes also into account the relative biological effectiveness (RBE), which in the case of ion beams is not factorizable and introduces strong non-linearity. We implemented the gEUD-based optimization in TRiP98, allowing us to control the whole dose–volume histogram (DVH) shape of OAR with a single objective by adjusting the prescribed gEUD 0 and the volume effect parameter a, reducing the volume receiving dose levels close to mean dose when a = 1 (large volume effect) while close to maximum dose for a >> 1 (small volume effect), depending on the organ type considered. We studied the role of gEUD 0 and a in the optimization, and we compared voxel-dose-based and gEUD-based optimization in chordoma cases with different anatomies. In particular, for a plan containing multiple OARs, we obtained the same target coverage and similar DVHs for OARs with a small volume effect while decreasing the mean dose received by the proximal parotid, thus reducing its NTCP by a factor of 2.5. Further investigations are done for this plan, considering also the distal parotid gland, obtaining a NTCP reduction by a factor of 1.9 for the proximal and 2.9 for the distal one. In conclusion, this novel optimization method can be applied to different OARs, but it achieves the largest improvement for organs whose volume effect is larger. This allows TRiP98 to perform a double level of biologically driven optimization for ion beams, including at the same time RBE-weighted dose and volume effects in inverse planning. An outlook is presented on the possible extension of this method to the target.

Local directional coding-based scene change detection for frame rate up-conversion

This paper proposes a new scene change detection method that uses local directional coding for frame rate up-conversion. The directional coding technique converts the luminance value of the edge direction response for given frames to bit codes and calculates the bit code difference between the previous and current frames. Existing scene change detection methods usually calculate the difference between the histogram shapes or the luminance values between the previous and current frames to detect scene changes. Thus, they erroneously detect scene changes in a scene with moving objects or camera motion. The existing automatic thresholding-based method uses iterative operations to calculate the optimal threshold and determine the scene change using the difference between the calculated threshold values, and it therefore has a high computational cost. To solve these problems, the proposed method uses the difference between the bit codes from the eight edge response values between the previous and current frames to detect the initial scene change regions. In addition, the proposed method uses a refinement process on the detected initial scene change regions to enhance the scene change detection accuracy. The experimental results showed that the proposed method enhanced average F1 score to 0.5235 (a 126.36 % improvement) as compared with the benchmark methods. The average computation time per pixel of the proposed method also reduced to 13.3668 μs (an 87.39 % reduction) compared with the benchmark methods.

Maintaining dosimetric quality when switching to a Monte Carlo dose engine for head and neck volumetric-modulated arc therapy planning.

Head and neck cancers present challenges in radiation treatment planning due to the large number of critical structures near the target(s) and highly heterogeneous tissue composition. While Monte Carlo (MC) dose calculations currently offer the most accurate approximation of dose deposition in tissue, the switch to MC presents challenges in preserving the parameters of care. The differences in dose‐to‐tissue were widely discussed in the literature, but mostly in the context of recalculating the existing plans rather than reoptimizing with the MC dose engine. Also, the target dose homogeneity received less attention. We adhere to strict dose homogeneity objectives in clinical practice. In this study, we started with 21 clinical volumetric‐modulated arc therapy (VMAT) plans previously developed in Pinnacle treatment planning system. Those plans were recalculated “as is” with RayStation (RS) MC algorithm and then reoptimized in RS with both collapsed cone (CC) and MC algorithms. MC statistical uncertainty (0.3%) was selected carefully to balance the dose computation time (1–2 min) with the planning target volume (PTV) dose‐volume histogram (DVH) shape approaching that of a “noise‐free” calculation. When the hot spot in head and neck MC‐based treatment planning is defined as dose to 0.03 cc, it is exceedingly difficult to limit it to 105% of the prescription dose, as we were used to with the CC algorithm. The average hot spot after optimization and calculation with RS MC was statistically significantly higher compared to Pinnacle and RS CC algorithms by 1.2 and 1.0 %, respectively. The 95% confidence interval (CI) observed in this study suggests that in most cases a hot spot of ≤107% is achievable. Compared to the 95% CI for the previous clinical plans recalculated with RS MC “as is” (upper limit 108%), in real terms this result is at least as good or better than the historic plans.