A machine learning approach to the accurate prediction of multi-leaf collimator positional errors-Reference-Cited by-同舟云学术

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A machine learning approach to the accurate prediction of multi-leaf collimator positional errors

Published:2016-03-07 Issue:6 Volume:61 Page:2514-2531
ISSN:0031-9155
Container-title:Physics in Medicine and Biology
language:
Short-container-title:Phys. Med. Biol.

Author:

Carlson Joel N K,Park Jong Min,Park So-Yeon,Park Jong In,Choi Yunseok,Ye Sung-Joon

Publisher

IOP Publishing

Subject

Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Link

http://stacks.iop.org/0031-9155/61/i=6/a=2514/pdf

Reference32 articles.

1. AAPM's TG-51 protocol for clinical reference dosimetry of high-energy photon and electron beams

2. Effect of MLC leaf position, collimator rotation angle, and gantry rotation angle errors on intensity-modulated radiotherapy plans for nasopharyngeal carcinoma

3. Impact of leaf motion constraints on IMAT plan quality, deliver accuracy, and efficiency

4. On the sensitivity of common gamma-index evaluation methods to MLC misalignments in Rapidarc quality assurance

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