NDLI: Respiratory lung motion using an artificial neural network

Content Provider	Springer Nature Link
Author	Laurent, R. Henriet, J. Salomon, M. Sauget, M. Gschwind, R. Makovicka, L.
Copyright Year	2011
Abstract	One of the possibilities to enhance the accuracy of lung radiotherapy is to improve the understanding of the individual lung motion of each patient. Indeed, using this knowledge, it becomes possible to follow the evolution of the clinical target volume defined by a set of points according to the lung breathing phase. This paper presents an innovative method to simulate the positions of points in a person’s lungs for each breathing phase. Our method, based on an artificial neural network (ANN), allowed us to learn the lung motion of five different patients and then to simulate it accurately for three other patients using only beginning and end points. The training set for our ANN consisted of more than 1,100 points spread over ten breathing phases from the five patients on a specific area of the lungs. The points were defined by a medical expert. The first results are very promising: we obtain an average accuracy of 1.5 mm while the spatial resolution is 1 × 1 × 2.5 mm3. The accuracy of the method will be improved even more with additional data and providing complete lung coverage.
Starting Page	929
Ending Page	934
Page Count	6
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	21
Issue Number	5
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-08-19
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Lung motion Artificial neural network Simulation Simulation and Modeling
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Hybrid artificial neural network

Ability to forecast unsteady aerodynamic forces of flapping airfoils by artificial neural network

Scanned images resolution improvement using neural networks

Fingerprint minutiae filtering using ARTMAP

Evaluation of neural network performance and generalisation using thresholding functions

Neural network-based simulation for response identification of two-storey shear building subject to earthquake motion

Global optimisation in neural network training

Application of an emotional neural network to facial recognition

Prediction of phosphorus content of electroless nickel–phosphorous coatings using artificial neural network modeling

Respiratory lung motion using an artificial neural network

Similar Documents

Hybrid artificial neural network

Ability to forecast unsteady aerodynamic forces of flapping airfoils by artificial neural network

Scanned images resolution improvement using neural networks

Fingerprint minutiae filtering using ARTMAP

Evaluation of neural network performance and generalisation using thresholding functions

Neural network-based simulation for response identification of two-storey shear building subject to earthquake motion

Global optimisation in neural network training

Application of an emotional neural network to facial recognition

Prediction of phosphorus content of electroless nickel–phosphorous coatings using artificial neural network modeling

Respiratory lung motion using an artificial neural network