MO-D-105-05: A Novel Web-Based Tool for Quantification of VMAT/IMRT Treatment Plan Quality

2013 ◽  
Vol 40 (6Part23) ◽  
pp. 395-395
Author(s):  
M Fan ◽  
F DeBlois ◽  
K Sultanem ◽  
G Stroian
Keyword(s):  
Treatment Plan ◽  
Plan Quality ◽  
Web Based ◽  
Imrt Treatment Plan

Evaluation of MLC leaf transmission on IMRT treatment plan quality of patients with advanced lung cancer

2018 ◽  
Vol 43 (4) ◽  
pp. 313-318 ◽  
Author(s):  
Jiayun Chen ◽  
Guishan Fu ◽  
Minghui Li ◽  
Yixin Song ◽  
Jianrong Dai ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Treatment Plan ◽  
Advanced Lung Cancer ◽  
Plan Quality ◽  
Imrt Treatment Plan

SU-GG-T-131: A Linear Metric of Knowledge-Based IMRT Treatment Plan Quality for the Prostate

2010 ◽  
Vol 37 (6Part17) ◽  
pp. 3214-3214
Author(s):  
M S Freeman ◽  
V Chanyavanich ◽  
S K Das ◽  
J Y Lo
Keyword(s):  
Treatment Plan ◽  
Plan Quality ◽  
Knowledge Based ◽  
Imrt Treatment Plan

SU-E-T-38: An Overlap-Wall-Histogram Plan Quality Classifier for Automatic Prostate Cancer IMRT Treatment Plan Quality Control

2014 ◽  
Vol 41 (6Part11) ◽  
pp. 230-230
Author(s):  
T Song ◽  
Z Tian ◽  
X Jia ◽  
L Zhou ◽  
S Jiang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Quality Control ◽  
Treatment Plan ◽  
Plan Quality ◽  
Imrt Treatment Plan

Patient geometry-driven information retrieval for IMRT treatment plan quality control

2009 ◽  
Vol 36 (12) ◽  
pp. 5497-5505 ◽  
Author(s):  
Binbin Wu ◽  
Francesco Ricchetti ◽  
Giuseppe Sanguineti ◽  
Misha Kazhdan ◽  
Patricio Simari ◽  
...  
Keyword(s):  
Quality Control ◽  
Information Retrieval ◽  
Treatment Plan ◽  
Plan Quality ◽  
Imrt Treatment Plan

scholarly journals Multicriterial CNN based beam generation for robotic radiosurgery of the prostate

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Stefan Gerlach ◽  
Christoph Fürweger ◽  
Theresa Hofmann ◽  
Alexander Schlaefer
Keyword(s):  
Deep Learning ◽  
Treatment Planning ◽  
Treatment Plan ◽  
Plan Quality ◽  
New Approach ◽  
Beam Generation ◽  
Radiological Features ◽  
Improve Treatment ◽  
Robotic Radiosurgery ◽  
Treatment Plans

AbstractAlthough robotic radiosurgery offers a flexible arrangement of treatment beams, generating treatment plans is computationally challenging and a time consuming process for the planner. Furthermore, different clinical goals have to be considered during planning and generally different sets of beams correspond to different clinical goals. Typically, candidate beams sampled from a randomized heuristic form the basis for treatment planning. We propose a new approach to generate candidate beams based on deep learning using radiological features as well as the desired constraints. We demonstrate that candidate beams generated for specific clinical goals can improve treatment plan quality. Furthermore, we compare two approaches to include information about constraints in the prediction. Our results show that CNN generated beams can improve treatment plan quality for different clinical goals, increasing coverage from 91.2 to 96.8% for 3,000 candidate beams on average. When including the clinical goal in the training, coverage is improved by 1.1% points.

PO-1435: Large treatment plan quality enhancement in robotic radiotherapy

2020 ◽  
Vol 152 ◽  
pp. S762-S763
Author(s):  
M. Giżyńska ◽  
L. Rossi ◽  
W. Den Toom ◽  
M. Milder ◽  
L. Inrocci ◽  
...  
Keyword(s):  
Treatment Plan ◽  
Quality Enhancement ◽  
Plan Quality

SP-0452 Treatment plan quality assessment

2021 ◽  
Vol 161 ◽  
pp. S348-S349
Author(s):  
V. Hernandez ◽  
C. Rønn Hansen ◽  
L. Widesott ◽  
N. Jornet
Keyword(s):  
Quality Assessment ◽  
Treatment Plan ◽  
Plan Quality

Treatment plan quality and delivery accuracy assessments on 3 IMRT delivery methods of stereotactic body radiotherapy for spine tumors

2019 ◽  
Vol 44 (1) ◽  
pp. 11-14 ◽  
Author(s):  
Long Huang ◽  
Toufik Djemil ◽  
Tingliang Zhuang ◽  
Martin Andrews ◽  
Samuel T. Chao ◽  
...  
Keyword(s):  
Stereotactic Body Radiotherapy ◽  
Treatment Plan ◽  
Plan Quality ◽  
Delivery Methods ◽  
Spine Tumors ◽  
Delivery Accuracy ◽  
Accuracy Assessments
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