Utilizing cone beam computed tomography (CBCT) to monitor minimally invasive surgery (MIS) is essential for ensuring surgical safety, particularly in high-precision procedures such as cochlear implantation (CI). Existing CBCT systems-including whole-body units and those tailored for ear diagnostics, dentistry, or head imaging-are typically bulky and poorly integrated, limiting their utility in intraoperative environments. To address this gap, we propose a highly integrated, high-resolution CBCT system specifically designed for intraoperative cochlear MIS. The system incorporates a dual-joint robotic structure, enabling both compact integration and mechanical flexibility by allowing the imaging chain to be folded to accommodate other surgical instruments. From a measurement perspective, the system is engineered to optimize key imaging performance parameters through a balance between field of view (FOV), spatial resolution, and mechanical stability. Geometric parameters were quantitatively optimized to minimize rotational bending moments, ensuring consistent and precise scanning. Imaging performance was systematically assessed using four types of phantoms. Quantitative measurements demonstrated a spatial resolution of 150 mu m (3.3 lp/mm), a contrast-to-noise ratio (CNR) of 8.63 at a 2.65% contrast level, and a 3-D uniformity deviation (UD) of 1.8%. In addition, qualitative validation was conducted using a live pig and a human cadaver head to simulate real clinical conditions. These findings highlight the system's capability to deliver accurate, repeatable, and measurement-driven intraoperative imaging, offering enhanced safety and precision for cochlear MIS. The work contributes not only to imaging system integration but also to quantitative measurement methodology aligned with the focus of instrumentation and measurement research.

BACKGROUND:Respiratory motion of tumors during radiotherapy can compromise dose delivery and further may impact tumor control efficacy. Developing a predictive model that integrates tumor heterogeneity with respiratory motion may enable the identification of high-risk patients, thereby guiding adaptive radiotherapy strategies and refining follow-up protocols. PURPOSE:To propose a novel prognosis prediction method utilizing intra-fractional temporal signatures and validate preliminarily on the prediction of local recurrence (LR) for patients with non-small cell lung cancer (NSCLC). METHODS:A cohort of 100 NSCLC patients were retrospectively analyzed. The intra-fractional temporal changes of 1321 radiomics features between phase 0% and i were calculated from 4DCT (Delta_RFi, i = 10%,.90%) and stacked as accumulative delta-radiomics features (Delta_RFacc). The delta-radiomics signatures (DRSi, i = 10%,.90%) and the novel accumulative delta-radiomics signature (ADRS) were developed by applying principal component analysis (PCA) to Delta_RFi and Delta_RFacc. The conventional radiomics signature (RS) was built from 3DCT. The predicting performance using RS, DRSs, and ADRS were compared through univariate Logistic regression. LR risk stratification using ADRS and RS were also evaluated. RESULTS:Based on the mean performance from 7:3 random-split cross-validations with 50 random seeds, the best area under the receiver operating characteristic curve (AUC) and area under the precision-recall curve (AUPRC) were observed in ADRS model (AUC = 0.8391, AUPRC = 0.8043), which were significantly (all p < 0.05) higher than that of RS model (AUC = 0.7021, AUPRC = 0.5260) and the models based on DRS of any single phase (AUC ranging 0.6470∼0.7693, AUPRC ranging 0.4571∼0.6047). Significantly worse local progression-free survival was associated with higher signature values of both ADRS and RS, with hazard ratios (HR) of 6.74 for ADRS (95% confidence interval (CI): 2.79-16.24, p < 0.0001) and 3.28 for RS (95% CI: 1.39-7.75, p = 0.0043). The motion-sensitive feature was found contributive to the superior performance of ADRS. CONCLUSIONS:The incorporation of temporal information from 4DCT significantly improved the model performance than that of the conventional method based on 3DCT. A novel ADRS approach based on intra-fractional delta-radiomics was proposed and validated on the LR risk prediction for patients with NSCLC. The improved accuracy of risk management can be beneficial to assist the personalized optimization of treatment and follow-up protocols, achieving a balance among cost, efficiency, and outcome.

The rotation effect on the QCD properties is an open question. We study the dynamic gluon mass in a dense QCD matter, the rotation is introduced by taking a covariant transformation between the flat and curved spaces. The law of causality which restricts the rotation strength of the system is carefully considered in the calculation. We find that the rotation effect is not monotonous. Overall, it behaves like an anti-screening effect, reflecting in the decreasing gluon mass, but the strength changes with the rotation. For a QCD matter with low baryon density, the screening effect in the flat space can be completely canceled by the rotation, and gluons are confined in a strongly rotating matter. When the rotation is extremely high, the matter approaches to a weakly interacting gas.

In this work a non-abelian gauge theory is reformulated in a rotating frame. With this new formalism, the influence of the background rotation on the color deconfinement transition for a pure SU(2) gluon system has been studied. The KvBLL caloron, which is a color neutral and topologically nontrivial solution of Yang-Mills equation at finite temperature, is reexamined under rotation and adopted as the confined vacuum of such a system. With rotation-modified solutions of the caloron's constituent solitons, i.e. dyons, the non-perturbative part of effective potential of the rotating system has been obtained. Combining with the perturbative potential by Gaussian fluctuations, the critical temperature of confinement-deconfinement phase transition is investigated. It is found that neither the rotational semi-classical potential nor Gaussian fluctuations can confine color charges more tightly when the rotation becomes faster. While only a stronger coupling constant is able to make the critical temperature increase with angular velocity, as what indicated in lattice simulations. And it is found a non-monotonic dependence of the critical temperature on the angular velocity is established because of the competition between these two contrary contributions.

A semi-analytical solution to the unified Boltzmann equation is constructed to exactly describe the scatter distribution on a flat-panel detector for high-quality conebeam CT (CBCT) imaging. The solver consists of three parts, including the phase space distribution estimator, the effective source constructor and the detector signal extractor. Instead of the tedious Monte Carlo solution, the derived Boltzmann equation solver achieves ultrafast computational capability for scatter signal estimation by combining direct analytical derivation and time-efficient one-dimensional numerical integration over the trajectory along each momentum of the photon phase space distribution. The execution of scatter estimation using the proposed ultrafast Boltzmann equation solver (UBES) for a single projection is finalized in around 0.4 seconds. We compare the performance of the proposed method with the state-of-the-art schemes, including a time-expensive Monte Carlo (MC) method and a conventional kernel-based algorithm using the same dataset, which is acquired from the CBCT scans of a head phantom and an abdominal patient. The evaluation results demonstrate that the proposed UBES method achieves comparable correction accuracy compared with the MC method, while exhibits significant improvements in image quality over learning and kernel-based methods. With the advantages of MC equivalent quality and superfast computational efficiency, the UBES method has the potential to become a standard solution to scatter correction in high-quality CBCT reconstruction.

In this paper, we introduce a novel approach in quantum field theories to estimate actions using artificial neural networks (ANNs). The actions are estimated by learning system configurations governed by the Boltzmann factor, e(-S ), at different temperatures within the imaginary time formalism of thermal field theory. Specifically, we focus on the 0+1 dimensional quantum field with kink/anti-kink configurations to demonstrate the feasibility of the method. Continuous-mixture autoregressive networks (CANs) enable the construction of accurate effective actions with tractable probability density estimation. Our numerical results demonstrate that this methodology not only facilitates the construction of effective actions at specified temperatures but also adeptly estimates the action at intermediate temperatures using data from both lower and higher temperature ensembles. This capability is especially valuable for detailed exploration of phase diagrams.

In this work a rotating SU(2) gluon system has been studied with the framework of a dyon ensemble. By solving the rotation-modified Yang-Mills equation we have obtained rotational corrections to the so-called dyon solutions with arbitrary centers to O(omega(2)) order and the corresponding semiclassical potential. In the dyon dilute limit, the radial position-dependent deconfinement temperature has been obtained by minimizing the semiclassical potential in both real and imaginary angular velocity cases. Although without the rotation-dependent coupling constant the critical temperature, as a function of omega, behaves differently from the lattice simulation at each radial position, its radial dependence is qualitatively the same as the recent lattice result. That is, in the real velocity case the outer layer will deconfine in a more difficult manner while the reverse is true in the imaginary velocity case.

强作用物质相结构的研究是核物理里一个非常活跃的前沿领域,对于理解相对论重离子碰撞实验和中子星观测有重要意义.传统的相结构研究集中于高温度和高密度所带来的物性变化,特别是相关的相变过程,比如手征恢复相变、色超导相变等.近年的实验和理论研究表明,非对心重离子碰撞系统携带很大的初始角动量从而产生极强的流体涡旋场.这带来了涡旋场中的强作用物质性质的一系列重要问题,对它们的探索产生了许多新颖结果.特别是在强作用物质相变方面,基于转动参考系的热场计算得到了发展,并结合平均场近似对相关的手征相变、色超导相变、高同位旋下的超流相变等现象作了深入研究,发现了涡旋场对相边界的影响以及其所带来的丰富相结构.

In this work the non-abelian gauge theory is reformulated in a local inertial frame with the presence of a background rotation. With this new formalism the influence of the background rotation on the color deconfinement transition for a SU(2) pure gluon system. The KvBLL caloron, which is a color neutral and asymptotically nontrivial solution of Yang-Mills equation at finite temperature, is adopted to confine the color charges. With new solutions of the caloron's constituent particles, i.e. dyons, the semi-classical potential, which confines color charges, and the perturbative potential, induced by the Gaussian fluctuation, have been obtained for this system under rotation. By solving the critical temperature of confinement-deconfinement phase transition in different computation schemes, it is found that neither the rotational semi-classical potential nor Gaussian fluctuations can confine color charges more tightly when the rotation becomes faster. While only a stronger coupling constant is able to make the critical temperature increasing with angular velocity, as that indicated in lattice simulations. And it is also found with some particular sets of parameters, a non-monotonic dependence of the critical temperature will be obtained in the most physically realistic case, in which all the three factors are taken into account.

Background Rhinoplasty is one of the most challenging plastic surgeries because it lacks a uniform standard for preoperative design or implementation. For a long time, rhinoplasties were done without an accurate consensus of aesthetic design between surgeons and patients before surgery and consequently brought unsatisfactory appearance for patients. In recent years, three-dimensional (3D) simulation has been used to visualize the preoperative design of rhinoplasty, and good results have been achieved. However, it still relied on individual aesthetics and experience. The preoperative design remained a huge challenge for inexperienced surgeons and could be time-consuming to perform manually. Therefore, we adopted artificial intelligence (AI) in this work to provide a new idea for automated and efficient preoperative nasal contour design. Methods We collected a dataset of 3D facial images from 209 patients. For each patient, both the original face and the manually designed face using 3D simulation software were included. The 3D images were transformed into point clouds, based on which we used the modified FoldingNet model for deep neural network training (by pytorch 1.12). Results The trained AI model gained the ability to perform aesthetic design automatically and achieved similar results to manual design. We analysed the 1027 facial features captured by the AI model and concluded two of its possible cognitive modes. One is to resemble the human aesthetic considerations while the other is to fulfil the given task in a special way of the machine. Conclusion We presented the first AI model for automated preoperative 3D simulation of rhinoplasty in this study. It provided a new idea for the automated, individual and efficient preoperative design, which was expected to bring a new paradigm for rhinoplasty and even the whole field of plastic surgery. Level of Evidence IV This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

BACKGROUND:Adaptive radiotherapy (ART) can incorporate anatomical variations in a reoptimized treatment plan for fractionated radiotherapy. An automatic solution to objectively determine whether ART should be performed immediately after the daily image acquisition is highly desirable. PURPOSE:We investigate a quantitative criterion for whether ART should be performed in prostate cancer radiotherapy by synthesizing pseudo-CT (sCT) images and evaluating dosimetric impact on treatment planning using deep learning approaches. METHOD AND MATERIALS:Planning CT (pCT) and daily cone-beam CT (CBCT) data sets of 74 patients are used to train (60 patients) and evaluate (14 patients) a cycle adversarial generative network (CycleGAN) that performs the task of synthesizing high-quality sCT from daily CBCT. Automatic delineation (AD) of the bladder is performed on the sCT using the U-net. The combination of sCT and AD allows us to perform dose calculations based on the up-to-date bladder anatomy to determine whether the original treatment plan (ori-plan) is still applicable. For positive cases that the patients' anatomical changes and the associated dose calculations warrant re-planning, we made rapid plan revisions (re-plan) based on the ori-plan. RESULTS:The mean absolute error within the region-of-interests (i.e., body, bladder, fat, muscle) between the sCT and pCT are 41.2, 25.1, 26.5, and 29.0HU, respectively. Taking the calculated results of pCT doses as the standard, for PTV, the gamma passing rates of sCT doses at 1 mm/1%, 2 mm/2% are 87.92%, 98.78%, respectively. The Dice coefficients of the AD-contours are 0.93 on pCT and 0.91 on sCT. According to the result of dose calculation, we found when the bladder volume underwent a substantial change (79.7%), the bladder dose is still within the safe limit, suggesting it is insufficient to solely use the bladder volume change as a criterion to determine whether adaptive treatment needs to be done. After AD-contours of the bladder using sCT, there are two cases whose bladder dose D mean > 4000 cGy ${{\mathrm{D}}}_{{\mathrm{mean}}} &gt; 4000{\mathrm{\ cGy}}$ . For the two cases, we perform re-planning to reduce the bladder dose to D mean = 3841 cGy ${{\mathrm{D}}}_{{\mathrm{mean}}} = 3841{\mathrm{\ cGy}}$ , D mean = 3580 cGy ${{\mathrm{D}}}_{{\mathrm{mean}}} = 3580{\mathrm{\ cGy\ }}$ under the condition that the PTV meets the prescribed dose. CONCLUSION:We provide a dose accurate adaptive workflow for prostate cancer patients by using deep learning approaches, and implement ART that adapts to bladder dose. Of note, the specific replanning criterion for whether ART needs to be performed can adapt to different centers' choices based on their experience and daily observations.

We develop deep autoregressive networks with multi channels to compute many-body systems with continuous spin degrees of freedom directly. As a concrete example, we demonstrate the two-dimensional XY model with the continuous-mixture networks and rediscover the Kosterlitz–Thouless (KT) phase transition on a periodic square lattice. Vortices characterizing the quasi-long range order are accurately detected by the generative model. By learning the microscopic probability distributions from the macroscopic thermal distribution, the networks are trained as an efficient physical sampler which can approximate the free energy and estimate thermodynamic observables unbiasedly with importance sampling. As a more precise evaluation, we compute the helicity modulus to determine the KT transition temperature. Although the training process becomes more time-consuming with larger lattice sizes, the training time remains unchanged around the KT transition temperature. The continuous-mixture autoregressive networks we developed thus can be potentially used to study other many-body systems with continuous degrees of freedom.

随着信息技术的快速发展和学科间不断交叉渗透,作为与理论物理和实验物理并列的第三种研究物理现象和规律的方法,计算物理变得越来越重要.然而,由于学科发展相对较晚以及学科自身的一些特点,本科生和研究生的计算物理课程教学尚未形成较成熟的体系.本文主要介绍北京航空航天大学本科生和研究生计算物理课程协同建设的探索和实践情况.本科生课程定位于掌握计算物理的基础理论知识和解决物理问题的案例实践,课程教学小班化;研究生课程定位于构建计算物理坚实的专业基础和宽广的知识结构,突出专业性和前沿性,重视计算实践能力,并开设计算物理二级学科平行课.本文主要介绍了从课程内容、教学方式和考核手段等方面进行的协同建设的探索与实践,这项探索使得本科生和研究生课程在内容、结构和方式上具有连续性和统一性.

Background and purpose: To determine the neck management of tongue cancer, this study attempted to construct an artificial neural network (ANN)-assisted model based on computed tomography (CT) radiomics of primary tumors to predict neck lymph node (LN) status in patients with tongue squamous cell carcinoma (SCC). Materials and methods: Three hundred thirteen patients with tongue SCC were retrospectively included and randomly divided into training (60%), validation (20%) and internally independent test (20%) sets. In total, 1673 feature values were extracted after the semiautomatic segmentation of primary tumors and set as input layers of a classical 3-layer ANN incorporated with or without clinical LN (cN) status after dimension reduction. The receiver operating characteristic (ROC) curve, accuracy (ACC), sensitivity (SEN), specificity (SPE), area under curve (AUC) and Net Reclassification Index (NRI), were used to evaluate and compare the models. Results: Four models with different settings were constructed. The ACC, SEN, SPE and AUC reached 84.1%, 93.1%, 76.5% and 0.943 (95% confidence interval: 0.891-0.996, p<.001), respectively, in the test set. The NRI of models compared with radiologists reached 40% (p<.001). The occult nodal metastasis rate was reduced from 30.9% to a minimum of 12.7% in the T1-2 group. Conclusion: ANN-based models that incorporated CT radiomics of primary tumors with traditional LN evaluation were constructed and validated to more precisely predict neck LN metastasis in patients with tongue SCC than with naked eyes, especially in early-stage cancer. (C) 2021 Elsevier Masson SAS. All rights reserved.

In the present paper, we study the effect of the rotation on the masses of scalar meson as well as vector meson in the framework of 2-flavor Nambu--Jona-Lasinio model. The existence of rotation causes a tedious quark propagator and corresponding polarization function. Applying the random phase approximation, the meson mass is calculated numerically. It is found that the behavior of scalar and pseudoscalar meson masses under the angular velocity $\omega$ is similar to that at finite chemical potential, both rely on the behavior of constituent quark mass and reflect the property related to the chiral symmetry. However, masses of vector meson $\rho$ have more profound relation with rotation. After tedious calculation, it turns out that at low temperature and small chemical potenial, the mass for spin component $s_z=0,\pm 1$ of vector meson under rotation shows very simple mass splitting relation $m_{\rho}^{s_z}(\omega)=m_\rho(\omega=0)-\omega s_z$, similar to the Zeeman splitting of charged meson under magnetic fields. Especially it is noticed that the mass of spin component $s_z=1$ vector meson $\rho$ decreases linearly with $\omega$ and reaches zero at $\omega_c=m_\rho(\omega=0)$, this indicates the system will develop $s_z=1$ vector meson condensation and the system will be spontaneously spin polarized under rotation.

Gluon interaction introduces remarkable corrections to the magnetic polarization effects on the chiral fermions, which is known as the inverse magnetic catalysis. It is a natural speculation that the vorticity, which has many similar properties as magnetic field, would bring non-negligible contribution to the chiral rotational suppression. Using an intuitive semi-classical background field method we studied the rotation dependence of the effective strong interaction coupling constant. Contrary to the magnetic field case the rotation increases the effective coupling which would slow down the condensate melting with temperature. This could be named as the chiral vortical catalysis or inverse rotation suppression. Imposing such dependence on the 4-fermion coupling in the NJL model, we numerically checked this analysis qualitatively. The pseudo critical temperature is shown to rise with the rotation and approach saturation eventually which may be induced by the model cutoff.

In this work, the existence of Borromean states is discussed for bosonic and fermionic cases in both the relativistic and non-relativistic limits from the 3-momentum shell renormalization. With the linear bosonic model, we check the existence of Efimov-like states in the bosonic system. In both limits a geometric series of singularities is found in the 3-boson interaction vertex, while the energy ratio is reduced by around 70% in the relativistic limit because of the anti-particle contribution. Motivated by the quark-diquark model in heavy baryon studies, we have carefully examined the p -wave quark-diquark interaction and found an isolated Borromean pole at finite energy scale. This may indicate a special baryonic state of light quarks in high energy quark matter. In other cases, trivial results are obtained as expected. In the relativistic limit, for both bosonic and fermionic cases, potential Borromean states are independent of the mass, which means the results would also be valid even in the zero-mass limit.

We develop deep autoregressive networks with multi channels to compute many-body systems with \emph{continuous} spin degrees of freedom directly. As a concrete example, we embed the two-dimensional XY model into the continuous-mixture networks and rediscover the Kosterlitz-Thouless (KT) phase transition on a periodic square lattice. Vortices characterizing the quasi-long range order are accurately detected by the autoregressive neural networks. By learning the microscopic probability distributions from the macroscopic thermal distribution, the neural networks compute the free energy directly and find that free vortices and anti-vortices emerge in the high-temperature regime. As a more precise evaluation, we compute the helicity modulus to determine the KT transition temperature. Although the training process becomes more time-consuming with larger lattice sizes, the training time remains unchanged around the KT transition temperature. The continuous-mixture autoregressive networks we developed thus can be potentially used to study other many-body systems with continuous degrees of freedom.