59 research outputs found

    A client-server framework for 3D remote visualization of radiotherapy treatment space.

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    Radiotherapy is safely employed for treating wide variety of cancers. The radiotherapy workflow includes a precise positioning of the patient in the intended treatment position. While trained radiation therapists conduct patient positioning, consultation is occasionally required from other experts, including the radiation oncologist, dosimetrist, or medical physicist. In many circumstances, including rural clinics and developing countries, this expertise is not immediately available, so the patient positioning concerns of the treating therapists may not get addressed. In this paper, we present a framework to enable remotely located experts to virtually collaborate and be present inside the 3D treatment room when necessary. A multi-3D camera framework was used for acquiring the 3D treatment space. A client-server framework enabled the acquired 3D treatment room to be visualized in real-time. The computational tasks that would normally occur on the client side were offloaded to the server side to enable hardware flexibility on the client side. On the server side, a client specific real-time stereo rendering of the 3D treatment room was employed using a scalable multi graphics processing units (GPU) system. The rendered 3D images were then encoded using a GPU-based H.264 encoding for streaming. Results showed that for a stereo image size of 1280 × 960 pixels, experts with high-speed gigabit Ethernet connectivity were able to visualize the treatment space at approximately 81 frames per second. For experts remotely located and using a 100 Mbps network, the treatment space visualization occurred at 8-40 frames per second depending upon the network bandwidth. This work demonstrated the feasibility of remote real-time stereoscopic patient setup visualization, enabling expansion of high quality radiation therapy into challenging environments

    Modeling Simulation And Visualization Of Conformal 3D Lung Tumor Dosimetry

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    Lung tumors move during breathing depending on the patient\u27s patho-physiological condition and orientation, thereby compromising the accurate deposition of the radiation dose during radiotherapy. In this paper, we present and validate a computer-based simulation framework to calculate the delivered dose to a 3D moving tumor and its surrounding normal tissues. The computer-based simulation framework models a 3D volumetric lung tumor and its surrounding tissues, simulates the tumor motion during a simulated dose delivery both as a self-reproducible motion and a random motion using the dose extracted from a treatment plan, and predicts the amount and location of radiation doses deposited. A radiation treatment plan of a small lung tumor (1-3 cm diameter) was developed in a commercial planning system (iPlan software, BrainLab, Munich, Germany) to simulate the radiation dose delivered. The dose for each radiation field was extracted from the software. The tumor motion was simulated for varying values of its rate, amplitude and direction within a single breath as well as from one breath to another. Such variations represent the variations in tumor motion induced by breathing variations. During the simulation of dose delivery, the dose on the target was summed to generate the real-time dose to the tumor for each beam independently. The simulation results show that the dose accumulated on the tumor varies significantly with both the tumor size and the tumor\u27s motion rate, amplitude and direction. For a given tumor motion rate, amplitude and direction, the smaller the tumor size the smaller is the percentage of the radiation dose accumulated. The simulation results are validated by comparing the center plane of the 3D tumor with 2D film dosimetry measurements using a programmable 4D motion phantom moving in a self-reproducible pattern. The results also show the real-time capability of the framework at 40 discrete tumor motion steps per breath, which is higher than the number of four-dimensional computed tomography (CT) steps (approximately 20) during a single breath. The real-time capability enables the framework to be coupled with real-time tumor monitoring systems such as implanted fiducials for computing the dose delivered in real time during the treatment. © 2009 Institute of Physics and Engineering in Medicine

    Aggressiveness of Familial Prostate Cancer

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