Fast Mode Decision for 3D-HEVC Depth Intracoding

The emerging international standard of high efficiency video coding based 3D video coding (3D-HEVC) is a successor to multiview video coding (MVC). In 3D-HEVC depth intracoding, depth modeling mode (DMM) and high efficiency video coding (HEVC) intraprediction mode are both employed to select the best coding mode for each coding unit (CU). This technique achieves the highest possible coding efficiency, but it results in extremely large encoding time which obstructs the 3D-HEVC from practical application. In this paper, a fast mode decision algorithm based on the correlation between texture video and depth map is proposed to reduce 3D-HEVC depth intracoding computational complexity. Since the texture video and its associated depth map represent the same scene, there is a high correlation among the prediction mode from texture video and depth map. Therefore, we can skip some specific depth intraprediction modes rarely used in related texture CU. Experimental results show that the proposed algorithm can significantly reduce computational complexity of 3D-HEVC depth intracoding while maintaining coding efficiency

A reinforcement learning agent for head and neck intensity-modulated radiation therapy

Head and neck (HN) cancers pose a difficult problem in the planning of intensity-modulated radiation therapy (IMRT) treatment. The primary tumor can be large and asymmetrical, and multiple organs at risk (OARs) with varying dose-sparing goals lie close to the target volume. Currently, there is no systematic way of automating the generation of IMRT plans, and the manual options face planning quality and long planning time challenges. In this article, we present a reinforcement learning (RL) model for the purposes of providing automated treatment planning to reduce clinical workflow time as well as providing a better starting point for human planners to modify and build upon. Several models with progressing complexity are presented, including the relevant plan dosimetry analysis and model interpretations of the resulting strategies learned by the auto-planning agent. Models were trained on a set of 40 patients and validated on a set of 20 patients. The presented models are shown to be consistent with the requirements of an RL model to be underpinned by a Markov decision process (MDP). In-depth interpretability of the models is presented by examination of the decision space using action hyperplanes. The auto-planning agent was able to generate plans with superior reduction in the mean dose of the left and right parotid glands by approximately 7 Gy ± 2.5 Gy (p < 0.01) over a starting, static template plan with only pre-defined general prescription information. RL plans were comparable to a human expert’s clinical plans for the primary (44 Gy), boost (26 Gy) , and the summed plans (70 Gy) with p-values of 0.43, 0.72, and 0.67, respectively, for the dosimetric endpoints and uniform target coverage normalization. The RL planning agent was able to produce the plans used in validation in an average of 13.58 min, with a minimum and a maximum planning time of 2.27 and 44.82 min, respectively

On-Line Adaptive Radiation Therapy: Feasibility and Clinical Study

The purpose of this paper is to evaluate the feasibility and clinical dosimetric benefit of an on-line, that is, with the patient in the treatment position, Adaptive Radiation Therapy (ART) system for prostate cancer treatment based on daily cone-beam CT imaging and fast volumetric reoptimization of treatment plans. A fast intensity-modulated radiotherapy (IMRT) plan reoptimization algorithm is implemented and evaluated with clinical cases. The quality of these adapted plans is compared to the corresponding new plans generated by an experienced planner using a commercial treatment planning system and also evaluated by an in-house developed tool estimating achievable dose-volume histograms (DVHs) based on a database of existing treatment plans. In addition, a clinical implementation scheme for ART is designed and evaluated using clinical cases for its dosimetric qualities and efficiency

Job burnout and associated influencing factors in employees of 7 research and development enterprises in Minhang District of Shanghai

BackgroundJob burnout is an early mental health condition caused by job stress and contributes to many negative effects on work and life. Employees of research and development (R&D) enterprises are exposed to constant pressure from innovation, production speed and sales expansion, and they are prone to burnout symptoms if such factors are not under effective control. ObjectiveTo evaluate the current situation of job burnout among employees of R&D enterprises in Minhang District of Shanghai and explore its influencing factors. MethodsDuring November to December 2021, a cross-sectional study was developed and a convenient sampling method was used to enroll employees from 7 R&D enterprises in Minhang District of Shanghai. On the basis of voluntary participation with informed consent, a survey was conducted by using a self-made questionnaire (collecting data about general demographic characteristics, occupational characteristics, behavior and lifestyle), the Chinese version of the Concise Occupational Stress Questionnaire, and the Chinese version of the Maslach Burnout Inventory-General Survey. Occupational stress and its dimensions (job demand, job control, and social support) were divided into high, medium, and low levels according to tertiles. The positive rate of job burnout was reported according to score categorization (<1.5 refers to no job burnout, ≥1.5 refers to job burnout, where ≥1.5 and <3.5 refer to mild and moderate job burnout, and ≥3.5 refers to severe job burnout). Potential influencing factors of job burnout were evaluated by using one-way ANOVA, chi-square test, forward stepwise regression, and non-conditional binary logistic regression (α=0.05, two-sided test). ResultsA total of 3153 subjects were enrolled and 3014 samples were included in the analysis, with a valid response rate of 95.6%. Among the included subjects, 888 (29.46%) reported no job burnout, 1775 (58.89%) reported mild to moderate job burnout, and 351 (11.64%) reported severe job burnout. The mean of total job burnout score was 2.17±1.12, and the dimentional mean scores were 2.78±1.61 for emotional exhaustion, 1.60±1.60 for cynicism, and 4.05±1.57 for diminished personal accomplishment. Varied categories of sex, age, marital status, working position, sleep status, job demand, job control, and social support groups of workers resulted in significant differences in job burnout score. Compared with the low job demand group, the positive rate of job burnout was elevated in the medium and high job demand groups; the risk of job burnout in the medium job demand group was 1.42 (95%CI: 1.04-1.94) times higher, and that in the high job demand group was 2.64 (95% CI : 2.17-3.22) times higher versus the low job demand group. The risk of job burnout in the medium job control group was 1.35 (95%CI: 1.06-1.72) times higher versus the low job control group. Compared with the low social support group, job burnout was less reported in the other groups, and the OR (95%CI) values of the medium and high social support groups were 0.41 (0.31-0.53) and 0.15 (0.12-0.19) respectively. ConclusionThe rate of reporting positive job burnout in R&D enterprises is high, which deserves sufficient attention. Relieving work pressure, increasing job control and social support, and maintaining adequate sleep are helpful to reduce job burnout

Algorithm and performance of a clinical IMRT beam-angle optimization system

This paper describes the algorithm and examines the performance of an IMRT beam-angle optimization (BAO) system. In this algorithm successive sets of beam angles are selected from a set of predefined directions using a fast simulated annealing (FSA) algorithm. An IMRT beam-profile optimization is performed on each generated set of beams. The IMRT optimization is accelerated by using a fast dose calculation method that utilizes a precomputed dose kernel. A compact kernel is constructed for each of the predefined beams prior to starting the FSA algorithm. The IMRT optimizations during the BAO are then performed using these kernels in a fast dose calculation engine. This technique allows the IMRT optimization to be performed more than two orders of magnitude faster than a similar optimization that uses a convolution dose calculation engine.Comment: Final version that appeared in Phys. Med. Biol. 48 (2003) 3191-3212. Original EPS figures have been converted to PNG files due to size limi

Continuous photon energy modulation in IMRT of pancreatic cancer

Purpose: To develop a novel IMRT optimization method based on the principle of photon energy synthesis that simultaneously optimizes fluence map and beamlet energy. The method was validated on pancreatic cancers to demonstrate the benefits of the additional degree of freedom of photon energy in IMRT.Methods: Previous work has demonstrated that the effect of a photon beam of known energy can be achieved by the combination of two existing energy photons in the proper ratio. It further implied that any energy photon can be synthesized. Based on this, we propose the concept of continuous beamlet energy modulation in IMRT, or IMRT-BEM. The IMRT-BEM was modeled as the simultaneous optimization of two fluence maps, one for the low energy beam and one for the high energy beam, and it was implemented in an in-house inverse planning system. The IMRT-BEM was applied on 10 pancreatic cancer cases, where the IMRT-BEM plan was compared with single-energy IMRT plans of 6 MV (IMRT-6MV) and 15 MV photons (IMRT-15MV).Results: The IMRT-BEM plan provides a noticeable reduction to the volume irradiated at the high dose level (PTV105%) for PTV, at least 24.7% (6.4 ± 6.8 vs. 31.1 ± 18.7 (p = 0.005) and 43.8 ± 19.8 (p = 0.005) for IMRT-BEM, IMRT-6MV, and IMRT-15MV respectively). For target dose coverage, there were statistically significant improvements between the IMRT-BEM plans and the other two plans in terms of CI and HI. Compared to the IMRT-6MV plan, there were significant reductions in the Dmean of the spinal cord, liver, bowel, duodenum, and stomach. The irradiation volumes of the medium dose (V20Gy, and V40Gy) for the duodenum and bowel were reduced significantly. There were no significant differences between the IMRT-BEM and IMRT-15MV plans except for the Dmean of the spinal cord and the duodenum, the V20Gy, and V40Gy for the duodenum, and the V20Gy of the stomach.Conclusion: IMRT-BEM has certain dosimetric advantages for PTV and improves OAR sparing in pancreatic cancer, and can be effectively used in radiation treatment planning, providing another degree of freedom for planners to improve treatment plan quality