21.7.1 Radiotherapy, Radiation Therapy, Radiotherapy Planning, X-Ray Images

Chapter Contents (Back)
Radiotherapy. X Rays.

Tizhoosh, H.R., Krell, G., Michaelis, B.,
Knowledge-based enhancement of megavoltage images in radiation therapy using a hybrid neuro-fuzzy system,
IVC(19), No. 4, March 2001, pp. 217-233.
Elsevier DOI 0102
BibRef
Earlier:
Additive Fuzzy Enhancement and an Associative Memory for Feature Tracking in Radiation Therapy Images,
ICIP97(II: 398-401).
IEEE DOI BibRef

Calow, R., Gademann, G., Krell, G., Mecke, R., Michaelis, B., Riefenstahl, N., Walke, M.,
Photogrammetric measurement of patients in radiotherapy,
PandRS(56), No. 5-6, August 2002, pp. 347-359.
HTML Version. 0208
BibRef

Bevilacqua, V., Mastronardi, G., Piscopo, G.,
Evolutionary approach to inverse planning in coplanar radiotherapy,
IVC(25), No. 2, February 2007, pp. 196-203.
Elsevier DOI 0701
Genetic algorithms, Conformal, Aperture-based, Intensity modulated radiation therapy BibRef

He, B., Wahl, R.L., Du, Y., Sgouros, G., Jacene, H., Flinn, I., Frey, E.C.,
Comparison of Residence Time Estimation Methods for Radioimmunotherapy Dosimetry and Treatment Planning: Monte Carlo Simulation Studies,
MedImg(27), No. 4, April 2008, pp. 521-530.
IEEE DOI 0804
BibRef

Papadakis, A.E., Zacharakis, G., Maris, T.G., Ripoll, J., Damilakis, J.,
A New Optical-CT Apparatus for 3-D Radiotherapy Dosimetry: Is Free Space Scanning Feasible?,
MedImg(29), No. 5, May 2010, pp. 1204-1212.
IEEE DOI 1006
BibRef

Iyengar, S, Li, X.[Xin], Xu, H.[Huanhuan], Mukhopadhyay, S.[Supratik], Balakrishnan, N., Sawant, A.[Amit], Iyengar, P.[Puneeth],
Toward More Precise Radiotherapy Treatment of Lung Tumors,
Computer(45), No. 1, January 2012, pp. 59-65.
IEEE DOI 1201
Model respiratory motion of the tumors to guide radiotherapy. BibRef

McIntosh, C., Svistoun, I., Purdie, T.G.,
Groupwise Conditional Random Forests for Automatic Shape Classification and Contour Quality Assessment in Radiotherapy Planning,
MedImg(32), No. 6, 2013, pp. 1043-1057.
IEEE DOI 1307
Cancer; Heart; Lungs; radiation therapy BibRef

Juneja, P., Evans, P.M., Harris, E.J.,
The Validation Index: A New Metric for Validation of Segmentation Algorithms Using Two or More Expert Outlines With Application to Radiotherapy Planning,
MedImg(32), No. 8, 2013, pp. 1481-1489.
IEEE DOI 1308
Brain BibRef

Schlosser, J., Hristov, D.,
Radiolucent 4D Ultrasound Imaging: System Design and Application to Radiotherapy Guidance,
MedImg(35), No. 10, October 2016, pp. 2292-2300.
IEEE DOI 1610
Computed tomography BibRef

McIntosh, C., Purdie, T.G.,
Contextual Atlas Regression Forests: Multiple-Atlas-Based Automated Dose Prediction in Radiation Therapy,
MedImg(35), No. 4, April 2016, pp. 1000-1012.
IEEE DOI 1604
Breast BibRef

Lê, M., Delingette, H., Kalpathy-Cramer, J., Gerstner, E.R., Batchelor, T., Unkelbach, J., Ayache, N.,
Personalized Radiotherapy Planning Based on a Computational Tumor Growth Model,
MedImg(36), No. 3, March 2017, pp. 815-825.
IEEE DOI 1703
Brain modeling BibRef

Wang, Z.S.[Zhen-Song], Wei, L.F.[Li-Fang], Wang, L.[Li], Gao, Y.Z.[Yao-Zong], Chen, W.F.[Wu-Fan], Shen, D.G.[Ding-Gang],
Hierarchical Vertex Regression-Based Segmentation of Head and Neck CT Images for Radiotherapy Planning,
IP(27), No. 2, February 2018, pp. 923-937.
IEEE DOI 1712
Biomedical imaging, Computed tomography, Deformable models, Image segmentation, Shape, Testing, Training, Image segmentation, vertex regression BibRef

Shams, R., Xiao, Y., Hébert, F., Abramowitz, M., Brooks, R., Rivaz, H.,
Assessment of Rigid Registration Quality Measures in Ultrasound-Guided Radiotherapy,
MedImg(37), No. 2, February 2018, pp. 428-437.
IEEE DOI 1802
Bayes methods, Image registration, Supervised learning, Training, Ultrasonic imaging, supervised learning BibRef

Schipaanboord, B., Boukerroui, D., Peressutti, D., van Soest, J., Lustberg, T., Dekker, A., van Elmpt, W., Gooding, M.J.,
An Evaluation of Atlas Selection Methods for Atlas-Based Automatic Segmentation in Radiotherapy Treatment Planning,
MedImg(38), No. 11, November 2019, pp. 2654-2664.
IEEE DOI 1911
Image segmentation, Computed tomography, Planning, Databases, Neck, Strain, Measurement, Multi-atlas segmentation, atlas selection, radiotherapy BibRef

Lipková, J., Angelikopoulos, P., Wu, S., Alberts, E., Wiestler, B., Diehl, C., Preibisch, C., Pyka, T., Combs, S.E., Hadjidoukas, P., van Leemput, K., Koumoutsakos, P., Lowengrub, J., Menze, B.,
Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans, and Bayesian Inference,
MedImg(38), No. 8, August 2019, pp. 1875-1884.
IEEE DOI 1908
Tumors, Mathematical model, Bayes methods, Biomedical imaging, Magnetic resonance imaging, Predictive models, Glioblastoma, multimodal medical scans BibRef

Singh, D.[Deepika], Singh, A.K.[Ashutosh Kumar], Tiwari, S.[Sonia],
Breast Thermography as an Adjunct Tool to Monitor the Chemotherapy Response in a Triple Negative BIRADS V Cancer Patient: A Case Study,
MedImg(41), No. 3, March 2022, pp. 737-745.
IEEE DOI 2203
Breast, Chemotherapy, Breast cancer, Tumors, Tools, Monitoring, Thermal analysis, Breast thermogram, invasive ductal-carcinoma, statistical features BibRef

Qi, X.F.[Xiao-Feng], Hu, J.J.[Jun-Jie], Zhang, L.[Lei], Bai, S.[Sen], Yi, Z.[Zhang],
Automated Segmentation of the Clinical Target Volume in the Planning CT for Breast Cancer Using Deep Neural Networks,
Cyber(52), No. 5, May 2022, pp. 3446-3456.
IEEE DOI 2206
Computed tomography, Image segmentation, Breast cancer, Planning, Feature extraction, Breast cancer, clinical target volume (CTV), radiotherapy BibRef

Huttinga, N.R.F.[Niek R. F.], Bruijnen, T.[Tom], van den Berg, C.A.T.[Cornelis A. T.], Sbrizzi, A.[Alessandro],
Real-Time Non-Rigid 3D Respiratory Motion Estimation for MR-Guided Radiotherapy Using MR-MOTUS,
MedImg(41), No. 2, February 2022, pp. 332-346.
IEEE DOI 2202
Image reconstruction, Real-time systems, Dynamics, Motion estimation, Data models, Magnetic resonance imaging, iterative reconstruction BibRef

Chandran, L.P.[Lekshmy P.], Rahiman, A.N.K.P.A.[Abdul Nazeer Kochannan Parampil Abdul], Puzhakkal, N.[Niyas], Makuni, D.[Dinesh],
A 3D U-Net based two stage deep learning framework for predicting dose distributions in radiation treatment planning,
IJIST(34), No. 1, 2024, pp. e22939.
DOI Link 2401
deep learning, dose volume histogram (DVH), knowledge based planning, radiotherapy, transfer learning, U-Net BibRef


Gao, R.[Riqiang], Lou, B.[Bin], Xu, Z.[Zhoubing], Comaniciu, D.[Dorin], Kamen, A.[Ali],
Flexible-Cm GAN: Towards Precise 3D Dose Prediction in Radiotherapy,
CVPR23(715-725)
IEEE DOI 2309
BibRef

Xie, L.Q.[Lisi-Qi], He, K.J.[Kang-Jian], Xu, D.[Dan], Fu, Q.G.[Qing-Guo],
A Personalized Image-Guided-Radiotherapy Workflow Based on Subtypes of Diagnostic Site,
ICIVC22(488-497)
IEEE DOI 2301
Performance evaluation, Image registration, Head, Medical services, Oncology, Neck, Image reconstruction, IGRT, radiotherapy, CBCT, site-specific workflow BibRef

Kodym, O.[Oldrich], Španel, M.[Michal], Herout, A.[Adam],
Segmentation of Head and Neck Organs at Risk Using CNN with Batch Dice Loss,
GCPR18(105-114).
Springer DOI 1905
CT. Radiotherapy planning. BibRef

He, T.C.[Tian-Cheng], Pino, R.[Ramiro], Teh, B.[Bin], Wong, S.[Stephen], Xue, Z.[Zhong],
Dynamic Respiratory Motion Estimation Using Patch-Based Kernel-PCA Priors for Lung Cancer Radiotherapy,
RAMBO17(55-65).
Springer DOI 1711
BibRef

Wodzinski, M., Skalski, A., Kedzierawski, P., Kuszewski, T.,
Application of B-splines FFD image registration in breast cancer radiotherapy planning,
WSSIP17(1-5)
IEEE DOI 1707
Breast cancer, Image registration, Measurement, Splines (mathematics), Tumors, B-splines, breast cancer, free-form registration, image registration, radiotherapy BibRef

Zhao, X.[Xuan], Wang, Y.[Yao], Jozsef, G.[Gabor],
Robust shape-constrained active contour for whole heart segmentation in 3-D CT images for radiotherapy planning,
ICIP14(1-5)
IEEE DOI 1502
Active contours BibRef

Stefano, A.[Alessandro], Vitabile, S.[Salvatore],
A Graph-Based Method for PET Image Segmentation in Radiotherapy Planning: A Pilot Study,
CIAP13(II:711-720).
Springer DOI 1309
BibRef

Huang, W.[Wei], Li, J.[Jing], Zhang, P.[Peng], Wan, M.[Min],
A novel marker-less tumor tracking strategyonlow-rank fluoroscopic images for image-guided lung cancer radiotherapy,
ICIP13(1399-1403)
IEEE DOI 1402
Fluoroscopic image BibRef

Li, D.W.[Deng-Wang], Wang, X.Y.[Xiu-Ying], Wang, H.J.[Hong-Jun], Yin, Y.[Yong], Feng, D.D.[David Dagan],
Multiscale deformable registration using edge preserving scale space for adaptive radiation therapy,
ICIP10(4409-4412).
IEEE DOI 1009
BibRef

Slagmolen, P.[Pieter], Loeckx, D.[Dirk], Roels, S.[Sarah], Geets, X.[Xavier], Maes, F.[Frederik], Haustermans, K.[Karin], Suetens, P.[Paul],
Nonrigid Registration of Multitemporal CT and MR Images for Radiotherapy Treatment Planning,
WBIR06(297-305).
Springer DOI 0607
BibRef

Posada, R., Daul, C., Wolf, D., Aletti, P., Miranda, R.,
Towards a fractioned treatment in conformal radiotherapy using 3d-multimodal data registration,
ICIP04(III: 1911-1914).
IEEE DOI 0505
BibRef

Orkisz, M.[Maciej], Frery, A.[Anne], Chapet, O.[Olivier], Mornex, F.[Françoise], Magnin, I.E.[Isabelle E.],
Attempts to Bronchial Tumor Motion Tracking in Portal Images during Conformal Radiotherapy Treatment,
CAIP01(247 ff.).
Springer DOI 0210
BibRef

Bendl, R., Hoess, A., Schlegel, W.,
Virtual Simulation In Radiotherapy Planning,
CVRMed95(XX-YY) BibRef 9500

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Breast Cancer, Mammograms, Analysis, Mammography .


Last update:May 23, 2024 at 14:31:23