NDLI: Monte Carlo results from neural networks as an alternative to Compton photons LOR analysis

Content Provider	IEEE Xplore Digital Library
Author	Michaud, J.-B. Rechka, S. Brunet, C.-A. Lecomte, R. Fontaine, R.
Copyright Year	2009
Description	Author affiliation: Department of Electrical and Computer Engineering, Université de Sherbrooke, Sherbrooke, Québec, Canada, J1K 2R1 (Michaud, J.-B.) \|\| Department of Electrical and Computer Engineering, Université de Sherbrooke (Rechka, S.; Brunet, C.-A.; Fontaine, R.) \|\| Department of Nuclear Medicine and Radiobiology, Université de Sherbrooke (Lecomte, R.)
Abstract	Sensitivity is critical in small-animal PET, and including more of the discarded detections would increase it. However lowering the energy threshold compromises the spatial resolution. This paper is an update on a method to include triple coincidences in the image without significant image degradation. With the energy threshold set as low as 50 keV, the triple coincidences analyzed are the simplest inter-crystal Compton scatter scenario where one photoelectric 511-keV detection coincides with two detections whose energy sum is also 511-keV. The method uses neural networks instead of traditional Compton interaction mathematical models to compute the proper Line-of-Response (LOR) for that coincidence. The paper revisits the algorithm structure, and in particular the preprocessing steps required to simplify the data fed to the network, preprocessing which improves the LOR computation significantly. The paper then presents Monte-Carlo analysis of the method with various point and cylinder sources. The simulated scanner geometry is purposely made to encompass the very worst-case conditions seen in PET scanners today: small diameter, poor photoelectric fraction, poor 35% FWHM energy resolution. LOR identification error is around 20 to 25% and the sensitivity increase ranges from approximately 70 to 100%. Finally, images were recently obtained, with overall very good quality.
Starting Page	3909
Ending Page	3916
File Size	707162
Page Count	8
File Format	PDF
ISBN	9781424439614
ISSN	10957863
DOI	10.1109/NSSMIC.2009.5401932
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-24
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Monte Carlo methods Neural networks Positron emission tomography Computer networks Spatial resolution Degradation Electromagnetic scattering Particle scattering Mathematical model Computational modeling
Content Type	Text
Resource Type	Article

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

Sensitivity in PET: Neural networks as an alternative to compton photons LOR analysis

A Monte Carlo correction for Compton scattering effects in 3D PET brain imaging

Monte Carlo modelling of cone beam collimation with scattering in objects (SPECT application)

A Monte Carlo study of depth of interaction in PET

A PET Monte Carlo simulator from numerical phantom: validation against the EXACT ECAT HR+ scanner

A novel iterative image reconstruction method for high-resolution PET Imaging with a Monte Carlo based positron range model

Compression of a Monte-Carlo based system matrix for iterative reconstruction of PET images

Convolution-Based Forced Detection Monte Carlo Simulation Incorporating Septal Penetration Modeling

Assessment of the spatial resolution of PET scanners using a Geant4-based Monte Carlo tool

Monte Carlo results from neural networks as an alternative to Compton photons LOR analysis

Similar Documents

Sensitivity in PET: Neural networks as an alternative to compton photons LOR analysis

A Monte Carlo correction for Compton scattering effects in 3D PET brain imaging

Monte Carlo modelling of cone beam collimation with scattering in objects (SPECT application)

A Monte Carlo study of depth of interaction in PET

A PET Monte Carlo simulator from numerical phantom: validation against the EXACT ECAT HR+ scanner

A novel iterative image reconstruction method for high-resolution PET Imaging with a Monte Carlo based positron range model

Compression of a Monte-Carlo based system matrix for iterative reconstruction of PET images

Convolution-Based Forced Detection Monte Carlo Simulation Incorporating Septal Penetration Modeling

Assessment of the spatial resolution of PET scanners using a Geant4-based Monte Carlo tool

Monte Carlo results from neural networks as an alternative to Compton photons LOR analysis