Publications by authors named "Zun Piao"
Article Synopsis
- The study aimed to assess organ motion and the benefits of MRI guided adaptive radiotherapy (ART) for cervical cancer, using 150 treatment sessions as a basis.
- Significant changes in organ positions were found both before (interfractional) and during (intrafractional) treatments, affecting radiation dose delivery, especially to the bladder and rectum.
- ART plans showed improved coverage of the target area (CTV) with less radiation exposure to surrounding organs compared to non-adaptive plans, highlighting the importance of real-time imaging in treatment effectiveness.
Intensity-modulated radiotherapy (IMRT) is currently the most important treatment method for nasopharyngeal carcinoma (NPC). This study aimed to enhance prediction accuracy by incorporating dose information into a deep convolutional neural network (CNN) using a multichannel input method. A target conformal plan (TCP) was created based on the maximum planning target volume (PTV).
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- Scattering photons can negatively impact the quality of cone-beam CT (CBCT) images, leading to artifacts and inaccurate CT values, which hampers its use in medicine.
- To address this issue, the authors propose an intelligent scatter correction framework that combines the scatter kernel deconvolution (SKD) method with deep reinforcement learning (DRL) to enhance scatter estimation accuracy.
- Simulation results show that the new method outperforms traditional SKD in terms of mean absolute percentage error (MAPE) and peak signal-to-noise ratio (PSNR), indicating it could significantly improve CBCT image quality.
Background: The energy spectrum is the property of the X-ray tube that describes the energy fluence per unit interval of photon energy. The existing indirect methods for estimating the spectrum ignore the influence caused by the voltage fluctuation of the X-ray tube.
Methods: In this work, we propose a method for estimating the X-ray energy spectrum more accurately by including the voltage fluctuation of the X-ray tube.
Article Synopsis
- CBCT imaging is essential in medicine, but its effectiveness is hindered by severe scatter contamination, which traditional Monte Carlo (MC) methods struggle to correct due to high computational costs.
- This study introduces a new MC simulation that uses a variance reduction technique called correlated sampling to enhance the speed and accuracy of scatter correction in CBCT images.
- Results indicate that the proposed method significantly reduces scatter artifacts, improving the accuracy of the scatter estimation and enhancing the quality of corrected CBCT images while maintaining efficiency.