scholarly journals Knowledge‐based radiation treatment planning: A data‐driven method survey

2021 ◽  
Author(s):  
Shadab Momin ◽  
Yabo Fu ◽  
Yang Lei ◽  
Justin Roper ◽  
Jeffrey D. Bradley ◽  
...  
Keyword(s):  
Treatment Planning ◽  
Radiation Treatment ◽  
Data Driven ◽  
Radiation Treatment Planning ◽  
Knowledge Based

SU-E-J-04: A Data-Driven, Response-Based, Multi-Criteria Decision Support System for Personalized Lung Radiation Treatment Planning

2014 ◽  
Vol 41 (6Part6) ◽  
pp. 155-155
Author(s):  
Y Luo ◽  
D McShan ◽  
M Schipper ◽  
M Matuszak ◽  
F Kong ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Treatment Planning ◽  
Support System ◽  
Radiation Treatment ◽  
Data Driven ◽  
Radiation Treatment Planning

Stereotactic radiation treatment planning and follow-up studies involving fused multimodality imaging

2004 ◽  
Vol 101 (Supplement3) ◽  
pp. 326-333 ◽  
Author(s):  
Klaus D. Hamm ◽  
Gunnar Surber ◽  
Michael Schmücking ◽  
Reinhard E. Wurm ◽  
Rene Aschenbach ◽  
...  
Keyword(s):  
Image Fusion ◽  
Treatment Planning ◽  
Radiation Treatment ◽  
Data Sets ◽  
Slice Thickness ◽  
Radiation Treatment Planning ◽  
Content Type ◽  
Follow Up Studies ◽  
Fine Print

Object. Innovative new software solutions may enable image fusion to produce the desired data superposition for precise target definition and follow-up studies in radiosurgery/stereotactic radiotherapy in patients with intracranial lesions. The aim is to integrate the anatomical and functional information completely into the radiation treatment planning and to achieve an exact comparison for follow-up examinations. Special conditions and advantages of BrainLAB's fully automatic image fusion system are evaluated and described for this purpose. Methods. In 458 patients, the radiation treatment planning and some follow-up studies were performed using an automatic image fusion technique involving the use of different imaging modalities. Each fusion was visually checked and corrected as necessary. The computerized tomography (CT) scans for radiation treatment planning (slice thickness 1.25 mm), as well as stereotactic angiography for arteriovenous malformations, were acquired using head fixation with stereotactic arc or, in the case of stereotactic radiotherapy, with a relocatable stereotactic mask. Different magnetic resonance (MR) imaging sequences (T1, T2, and fluid-attenuated inversion-recovery images) and positron emission tomography (PET) scans were obtained without head fixation. Fusion results and the effects on radiation treatment planning and follow-up studies were analyzed. The precision level of the results of the automatic fusion depended primarily on the image quality, especially the slice thickness and the field homogeneity when using MR images, as well as on patient movement during data acquisition. Fully automated image fusion of different MR, CT, and PET studies was performed for each patient. Only in a few cases was it necessary to correct the fusion manually after visual evaluation. These corrections were minor and did not materially affect treatment planning. High-quality fusion of thin slices of a region of interest with a complete head data set could be performed easily. The target volume for radiation treatment planning could be accurately delineated using multimodal information provided by CT, MR, angiography, and PET studies. The fusion of follow-up image data sets yielded results that could be successfully compared and quantitatively evaluated. Conclusions. Depending on the quality of the originally acquired image, automated image fusion can be a very valuable tool, allowing for fast (∼ 1–2 minute) and precise fusion of all relevant data sets. Fused multimodality imaging improves the target volume definition for radiation treatment planning. High-quality follow-up image data sets should be acquired for image fusion to provide exactly comparable slices and volumetric results that will contribute to quality contol.

Implementation of simplified stochastic microdosimetric kinetic models into PHITS for application to radiation treatment planning

2021 ◽  
pp. 1-11
Author(s):  
Tatsuhiko Sato ◽  
Shintaro Hashimoto ◽  
Taku Inaniwa ◽  
Kenta Takada ◽  
Hiroaki Kumada
Keyword(s):  
Treatment Planning ◽  
Kinetic Models ◽  
Radiation Treatment ◽  
Radiation Treatment Planning

Automation of Radiation Treatment Planning. II. Calculation of Non-convergent Field Dose Distributions

1963 ◽  
Vol 36 (421) ◽  
pp. 63-67 ◽  
Author(s):  
Theodor D. Sterling ◽  
Harold Perry ◽  
James J. Weinkam
Keyword(s):  
Treatment Planning ◽  
Radiation Treatment ◽  
Radiation Treatment Planning ◽  
Dose Distributions
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