Physician workforce planning in Ontario must move from short-term reactivity to long-term proactivity

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An Automated Treatment Plan Quality Control Tool for Intensity-Modulated Radiation Therapy Using a Voxel-Weighting Factor-Based Re-Optimization Algorithm.

Intensity-modulated radiation therapy (IMRT) currently plays an important role in radiotherapy, but its treatment plan quality can vary significantly among institutions and planners. Treatment plan quality control (QC) is a necessary component for individual clinics to ensure that patients receive treatments with high therapeutic gain ratios. The voxel-weighting factor-based plan re-optimization mechanism has been proved able to explore a larger Pareto surface (solution domain) and therefore increase the possibility of finding an optimal treatment plan. In this study, we incorporated additional modules into an in-house developed voxel weighting factor-based re-optimization algorithm, which was enhanced as a highly automated and accurate IMRT plan QC tool (TPS-QC tool). After importing an under-assessment plan, the TPS-QC tool was able to generate a QC report within 2 minutes. This QC report contains the plan quality determination as well as information supporting the determination. Finally, the IMRT plan quality can be controlled by approving quality-passed plans and replacing quality-failed plans using the TPS-QC tool. The feasibility and accuracy of the proposed TPS-QC tool were evaluated using 25 clinically approved cervical cancer patient IMRT plans and 5 manually created poor-quality IMRT plans. The results showed high consistency between the QC report quality determinations and the actual plan quality. In the 25 clinically approved cases that the TPS-QC tool identified as passed, a greater difference could be observed for dosimetric endpoints for organs at risk (OAR) than for planning target volume (PTV), implying that better dose sparing could be achieved in OAR than in PTV. In addition, the dose-volume histogram (DVH) curves of the TPS-QC tool re-optimized plans satisfied the dosimetric criteria more frequently than did the under-assessment plans. In addition, the criteria for unsatisfied dosimetric endpoints in the 5 poor-quality plans could typically be satisfied when the TPS-QC tool generated re-optimized plans without sacrificing other dosimetric endpoints. In addition to its feasibility and accuracy, the proposed TPS-QC tool is also user-friendly and easy to operate, both of which are necessary characteristics for clinical use

PND-Net: Physics based Non-local Dual-domain Network for Metal Artifact Reduction

Metal artifacts caused by the presence of metallic implants tremendously degrade the reconstructed computed tomography (CT) image quality, affecting clinical diagnosis or reducing the accuracy of organ delineation and dose calculation in radiotherapy. Recently, deep learning methods in sinogram and image domains have been rapidly applied on metal artifact reduction (MAR) task. The supervised dual-domain methods perform well on synthesized data, while unsupervised methods with unpaired data are more generalized on clinical data. However, most existing methods intend to restore the corrupted sinogram within metal trace, which essentially remove beam hardening artifacts but ignore other components of metal artifacts, such as scatter, non-linear partial volume effect and noise. In this paper, we mathematically derive a physical property of metal artifacts which is verified via Monte Carlo (MC) simulation and propose a novel physics based non-local dual-domain network (PND-Net) for MAR in CT imaging. Specifically, we design a novel non-local sinogram decomposition network (NSD-Net) to acquire the weighted artifact component, and an image restoration network (IR-Net) is proposed to reduce the residual and secondary artifacts in the image domain. To facilitate the generalization and robustness of our method on clinical CT images, we employ a trainable fusion network (F-Net) in the artifact synthesis path to achieve unpaired learning. Furthermore, we design an internal consistency loss to ensure the integrity of anatomical structures in the image domain, and introduce the linear interpolation sinogram as prior knowledge to guide sinogram decomposition. Extensive experiments on simulation and clinical data demonstrate that our method outperforms the state-of-the-art MAR methods.Comment: 19 pages, 8 figure

A plan quality classifier derived with overlap-wall-histogram of hollow organs for automatic IMRT plan quality control of prostate cancer cases

Purpose: We developed a plan quality classification model to assess IMRT plan quality of prostate cancer patients for automatic plan quality control. Methods: For hollow organs such as rectum and bladder, dose-wall-histogram (DWH) was used to evaluate OAR dose sparing in our institution. Correspondingly, we proposed a new descriptor called overlap-wall-histogram (OWH) to describe the complex spatial relationship between PTV and a hollow organ. Two metrics calculated from the OWH and DWH are introduced to quantitatively evaluate the difficulty of patient geometry for planning and plan quality in terms of OAR sparing, respectively. A linear correlation between these two metrics was observed after plotting plan quality metric as a function of geometry difficulty metric studied from a database of prostate cases treated in our institution with acceptable plan quality. Thus, a fitting line was built acting as the boundary of high quality and poor quality plans. A query plan falling above the boundary is assessed as high quality, vice versa poor quality. Results: 15 prostate IMRT plans were used to test our model. One was identified as poor quality and the others were common-level. After re-planning all plans, the dose constraints for bladder wall W75 (percentage of wall receiving more than 75Gy), W70, W65 and W60 can be reduced by 3.34%, 3%, 6.99%, 6.54% for that poor quality plan and 1.11%, 0.95%, 1.45% and 1.81% averagely for the common-level quality group, without sacrificing PTV coverage and rectum dose sparing. Conclusion: An effective model was built to provide automatic IMRT plan quality control by evaluating hollow OAR dose sparing for prostate cancer patients. Furthermore, for the query plan with poor quality, potential improvement of plan quality can be estimated and a good reference plan with similar or harder geometry can be automatically chosen from our database to help guide the re-planning if necessary.---------------------------Cite this article as: Song T, Tian Z, Jia X, Zhou L, Jiang SB, Gu X. A plan quality classifier derived with overlap-wall-histogram of hollow organs for automatic IMRT plan quality control of prostate cancer cases. Int J Cancer Ther Oncol 2014; 2(2):020241. DOI: 10.14319/ijcto.0202.4

Iterative Image Reconstruction for Limited-Angle CT Using Optimized Initial Image

Limited-angle computed tomography (CT) has great impact in some clinical applications. Existing iterative reconstruction algorithms could not reconstruct high-quality images, leading to severe artifacts nearby edges. Optimal selection of initial image would influence the iterative reconstruction performance but has not been studied deeply yet. In this work, we proposed to generate optimized initial image followed by total variation (TV) based iterative reconstruction considering the feature of image symmetry. The simulated data and real data reconstruction results indicate that the proposed method effectively removes the artifacts nearby edges

Ultrafast cone-beam CT scatter correction with GPU-based Monte Carlo simulation

Purpose: Scatter artifacts severely degrade image quality of cone-beam CT (CBCT). We present an ultrafast scatter correction framework by using GPU-based Monte Carlo (MC) simulation and prior patient CT image, aiming at automatically finish the whole process including both scatter correction and reconstruction within 30 seconds.Methods: The method consists of six steps: 1) FDK reconstruction using raw projection data; 2) Rigid Registration of planning CT to the FDK results; 3) MC scatter calculation at sparse view angles using the planning CT; 4) Interpolation of the calculated scatter signals to other angles; 5) Removal of scatter from the raw projections; 6) FDK reconstruction using the scatter-corrected projections. In addition to using GPU to accelerate MC photon simulations, we also use a small number of photons and a down-sampled CT image in simulation to further reduce computation time. A novel denoising algorithm is used to eliminate MC noise from the simulated scatter images caused by low photon numbers. The method is validated on one simulated head-and-neck case with 364 projection angles.Results: We have examined variation of the scatter signal among projection angles using Fourier analysis. It is found that scatter images at 31 angles are sufficient to restore those at all angles with < 0.1% error. For the simulated patient case with a resolution of 512 × 512 × 100, we simulated 5 × 106 photons per angle. The total computation time is 20.52 seconds on a Nvidia GTX Titan GPU, and the time at each step is 2.53, 0.64, 14.78, 0.13, 0.19, and 2.25 seconds, respectively. The scatter-induced shading/cupping artifacts are substantially reduced, and the average HU error of a region-of-interest is reduced from 75.9 to 19.0 HU.Conclusion: A practical ultrafast MC-based CBCT scatter correction scheme is developed. It accomplished the whole procedure of scatter correction and reconstruction within 30 seconds.----------------------------Cite this article as: Xu Y, Bai T, Yan H, Ouyang L, Wang J, Pompos A, Zhou L, Jiang SB, Jia X. Ultrafast cone-beam CT scatter correction with GPU-based Monte Carlo simulation. Int J Cancer Ther Oncol 2014; 2(2):020245. DOI: 10.14319/ijcto.0202.4

Study on Resistance Switching Properties of Na0.5Bi0.5TiO3Thin Films Using Impedance Spectroscopy

The Na0.5Bi0.5TiO3(NBT) thin films sandwiched between Au electrodes and fluorine-doped tin oxide (FTO) conducting glass were deposited using a sol–gel method. Based on electrochemical workstation measurements, reproducible resistance switching characteristics and negative differential resistances were obtained at room temperature. A local impedance spectroscopy measurement of Au/NBT was performed to reveal the interface-related electrical characteristics. The DC-bias-dependent impedance spectra suggested the occurrence of charge and mass transfer at the interface of the Au/NBT/FTO device. It was proposed that the first and the second ionization of oxygen vacancies are responsible for the conduction in the low- and high-resistance states, respectively. The experimental results showed high potential for nonvolatile memory applications in NBT thin films