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Guidance document on delivery, treatment planning, and clinical implementation of IMRT: report of the IMRT Subcommittee of the AAPM Radiation Therapy Committee.
TLDR
This report provides the framework and guidance to allow clinical radiation oncology physicists to make judicious decisions in implementing a safe and efficient IMRT program in their clinics.
Segmentation of organs‐at‐risks in head and neck CT images using convolutional neural networks
TLDR
This work proposed the first deep learning‐based algorithm, for segmentation of OARs in HaN CT images, and compared its performance against state‐of‐the‐art automated segmentation algorithms, commercial software, and interobserver variability.
Evaluation of on-board kV cone beam CT (CBCT)-based dose calculation.
TLDR
The CBCT can be employed directly for dose calculation for a disease site such as the prostate, where there is little motion artefact and a large discrepancy between the original treatment plan and the CBCT (or mCBCT)-based calculation is noted.
Overview of image-guided radiation therapy.
Deep Generative Adversarial Neural Networks for Compressive Sensing MRI
TLDR
A novel CS framework that uses generative adversarial networks (GAN) to model the (low-dimensional) manifold of high-quality MR images that retrieves higher quality images with improved fine texture details compared with conventional Wavelet-based and dictionary- learning-based CS schemes as well as with deep-learning-based schemes using pixel-wise training.
Stereotactic body radiation therapy in multiple organ sites.
INTRODUCTION Stereotactic body radiation therapy (SBRT) uses advanced technology to deliver a potent ablative dose to deep-seated tumors in the lung, liver, spine, pancreas, kidney, and prostate.
Model-based image reconstruction for four-dimensional PET.
TLDR
A method to enhance the performance of 4D PET by developing a new technique of4D PET reconstruction with incorporation of an organ motion model derived from 4D-CT images based on the well-known maximum-likelihood expectation-maximization (ML-EM) algorithm is proposed.
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