scholarly journals Boosting radiotherapy dose calculation accuracy with deep learning

2020 ◽  
Vol 21 (8) ◽  
pp. 149-159
Author(s):  
Yixun Xing ◽  
You Zhang ◽  
Dan Nguyen ◽  
Mu‐Han Lin ◽  
Weiguo Lu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dose Calculation ◽  
Radiotherapy Dose

scholarly journals Technical Note: A feasibility study on deep learning‐based radiotherapy dose calculation

2019 ◽  
Vol 47 (2) ◽  
pp. 753-758 ◽  
Author(s):  
Yixun Xing ◽  
Dan Nguyen ◽  
Weiguo Lu ◽  
Ming Yang ◽  
Steve Jiang
Keyword(s):  
Deep Learning ◽  
Feasibility Study ◽  
Dose Calculation ◽  
Technical Note ◽  
Radiotherapy Dose

scholarly journals GPU-based fast Monte Carlo simulation for radiotherapy dose calculation

2011 ◽  
Vol 56 (22) ◽  
pp. 7017-7031 ◽  
Author(s):  
Xun Jia ◽  
Xuejun Gu ◽  
Yan Jiang Graves ◽  
Michael Folkerts ◽  
Steve B Jiang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Dose Calculation ◽  
Radiotherapy Dose

scholarly journals A GPU-based finite-size pencil beam algorithm with 3D-density correction for radiotherapy dose calculation

2011 ◽  
Vol 56 (11) ◽  
pp. 3337-3350 ◽  
Author(s):  
Xuejun Gu ◽  
Urszula Jelen ◽  
Jinsheng Li ◽  
Xun Jia ◽  
Steve B Jiang
Keyword(s):  
Finite Size ◽  
Dose Calculation ◽  
Pencil Beam ◽  
Density Correction ◽  
Pencil Beam Algorithm ◽  
Radiotherapy Dose

Monte Carlo photon beam modeling and commissioning for radiotherapy dose calculation algorithm

2014 ◽  
Vol 30 (7) ◽  
pp. 833-837 ◽  
Author(s):  
A. Toutaoui ◽  
S. Ait chikh ◽  
N. Khelassi-Toutaoui ◽  
B. Hattali
Keyword(s):  
Monte Carlo ◽  
Dose Calculation ◽  
Photon Beam ◽  
Calculation Algorithm ◽  
Dose Calculation Algorithm ◽  
Radiotherapy Dose ◽  
Beam Modeling

scholarly journals Radiotherapy dose calculation on KV cone-beam CT image for lung tumor using the CIRS calibration

2014 ◽  
Vol 5 (1) ◽  
pp. 68-73 ◽  
Author(s):  
Changsheng Ma ◽  
Jianping Cao ◽  
Yong Yin ◽  
Jian Zhu
Keyword(s):  
Lung Tumor ◽  
Cone Beam Ct ◽  
Dose Calculation ◽  
Cone Beam ◽  
Ct Image ◽  
Radiotherapy Dose

scholarly journals Automated and Robust Organ Segmentation for 3D-based Internal dose Calculation

2021 ◽  
Author(s):  
Mahmood Nazari ◽  
Luis David Jimenez-Franco ◽  
Michael Schroeder ◽  
Andreas Kluge ◽  
Marcus Bronzel ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dose Calculation ◽  
Ct Images ◽  
Daily Routine ◽  
Segmentation Method ◽  
Spread Function ◽  
Adequate Accuracy ◽  
Segmen Tation ◽  
Medical Physicists ◽  
Kidney Segmentation

Abstract Purpose: In this work we address image segmentation within dosimetry using deep learning and make three main contributions: a) to extend and op- timize the architecture of an existing Convolutional Neural Network (CNN) in order to obtain a fast, robust and accurate Computed Tomography (CT) based organ segmentation method for kidneys and livers; b) to train the CNN with an inhomogeneous set of CT scans and validate the CNN for daily dosimetry; c) to evaluate dosimetry results obtained using automated organ segmentation in comparison to manual segmentation done by two independent experts. Methods: We adapted a performant deep learning approach using CT-images to calculate organ boundaries with sufficiently high and adequate accuracy and processing time. The segmented organs were consequently used as binary masks for further convolution with a point spread function to retrieve the ac- tivity values from quantitatively reconstructed SPECT images for ”volumet- ric”/3D dosimetry. The retrieved activities were used to perform dosimetry calculations considering the kidneys as source organ. Results: The computational expenses of the algorithm was adequate enough to be used in clinical daily routine, required minimum pre-processing and per- formed within an acceptable accuracy of 93 . 4% for liver segmentation and of 94 . 1% for kidney segmentation. Additionally, kidney self-absorbed doses calcu- lated using automated segmentation differed 6 . 3% from dosimetries performed by two medical physicists in 8 patients. Conclusion: The proposed approach may accelerate volumetric dosimetry of kidneys in molecular radiotherapy with 177Lu-labelled radio-pharmaceuticals such as 177Lu-DOTATOC. However, even though a fully automated segmen- tation methodology based on CT images accelerates the organ segmentation and performs with high accuracy, it does not remove the need for supervision and corrections by experts, mostly due to misalignments in the co-registration between SPECT and CT images.Trial registration: EudraCT, 2016-001897-13. Registered 26.04.2016, www.clinicaltrialsregister.eu/ctr-search/search?query=2016-001897-13

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