Compton PET

A simulation study for a PET module with novel geometry and machine learning for position decoding

Peng Peng, Martin S. Judenhofer, Adam Q. Jones, Simon R Cherry

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This paper describes a simulation study of a positron emission tomography (PET) detector module that can reconstruct the kinematics of Compton scattering within the scintillator. We used a layer structure, with which we could recover the positions and energies for the multiple interactions of a gamma ray in the different layers. Using the Compton scattering formalism, the sequence of interactions can be estimated. The true first interaction position extracted in the Compton scattering will help minimize the degradation of the reconstructed image resolution caused by intercrystal scatter events. Because of the layer structure, this module also has readily available user-defined resolution for the depth of interaction. The semi-monolithic crystals enable high light collection efficiency and an energy resolution of ∼10% has been achieved in the simulation. We used machine learning to decode the gamma ray interaction locations, achieving an average spatial resolution of 0.40 mm. Our proposed detector design provides a pathway to increase the sensitivity of a system without affecting other key performance features.

Original languageEnglish (US)
Article number015018
JournalBiomedical Physics and Engineering Express
Volume5
Issue number1
DOIs
StatePublished - Jan 1 2019

Fingerprint

Gamma Rays
Positron-Emission Tomography
Biomechanical Phenomena
Light
Machine Learning

Keywords

  • Compton scattering
  • layer structure
  • neural network
  • PET
  • scintillating crystal
  • side readout

ASJC Scopus subject areas

  • Nursing(all)

Cite this

Compton PET : A simulation study for a PET module with novel geometry and machine learning for position decoding. / Peng, Peng; Judenhofer, Martin S.; Jones, Adam Q.; Cherry, Simon R.

In: Biomedical Physics and Engineering Express, Vol. 5, No. 1, 015018, 01.01.2019.

Research output: Contribution to journalArticle

@article{01c6fd1ec38b44db84334b7ce36710a0,
title = "Compton PET: A simulation study for a PET module with novel geometry and machine learning for position decoding",
abstract = "This paper describes a simulation study of a positron emission tomography (PET) detector module that can reconstruct the kinematics of Compton scattering within the scintillator. We used a layer structure, with which we could recover the positions and energies for the multiple interactions of a gamma ray in the different layers. Using the Compton scattering formalism, the sequence of interactions can be estimated. The true first interaction position extracted in the Compton scattering will help minimize the degradation of the reconstructed image resolution caused by intercrystal scatter events. Because of the layer structure, this module also has readily available user-defined resolution for the depth of interaction. The semi-monolithic crystals enable high light collection efficiency and an energy resolution of ∼10{\%} has been achieved in the simulation. We used machine learning to decode the gamma ray interaction locations, achieving an average spatial resolution of 0.40 mm. Our proposed detector design provides a pathway to increase the sensitivity of a system without affecting other key performance features.",
keywords = "Compton scattering, layer structure, neural network, PET, scintillating crystal, side readout",
author = "Peng Peng and Judenhofer, {Martin S.} and Jones, {Adam Q.} and Cherry, {Simon R}",
year = "2019",
month = "1",
day = "1",
doi = "10.1088/2057-1976/aaef03",
language = "English (US)",
volume = "5",
journal = "Biomedical Physics and Engineering Express",
issn = "2057-1976",
publisher = "IOP Publishing Ltd.",
number = "1",

}

TY - JOUR

T1 - Compton PET

T2 - A simulation study for a PET module with novel geometry and machine learning for position decoding

AU - Peng, Peng

AU - Judenhofer, Martin S.

AU - Jones, Adam Q.

AU - Cherry, Simon R

PY - 2019/1/1

Y1 - 2019/1/1

N2 - This paper describes a simulation study of a positron emission tomography (PET) detector module that can reconstruct the kinematics of Compton scattering within the scintillator. We used a layer structure, with which we could recover the positions and energies for the multiple interactions of a gamma ray in the different layers. Using the Compton scattering formalism, the sequence of interactions can be estimated. The true first interaction position extracted in the Compton scattering will help minimize the degradation of the reconstructed image resolution caused by intercrystal scatter events. Because of the layer structure, this module also has readily available user-defined resolution for the depth of interaction. The semi-monolithic crystals enable high light collection efficiency and an energy resolution of ∼10% has been achieved in the simulation. We used machine learning to decode the gamma ray interaction locations, achieving an average spatial resolution of 0.40 mm. Our proposed detector design provides a pathway to increase the sensitivity of a system without affecting other key performance features.

AB - This paper describes a simulation study of a positron emission tomography (PET) detector module that can reconstruct the kinematics of Compton scattering within the scintillator. We used a layer structure, with which we could recover the positions and energies for the multiple interactions of a gamma ray in the different layers. Using the Compton scattering formalism, the sequence of interactions can be estimated. The true first interaction position extracted in the Compton scattering will help minimize the degradation of the reconstructed image resolution caused by intercrystal scatter events. Because of the layer structure, this module also has readily available user-defined resolution for the depth of interaction. The semi-monolithic crystals enable high light collection efficiency and an energy resolution of ∼10% has been achieved in the simulation. We used machine learning to decode the gamma ray interaction locations, achieving an average spatial resolution of 0.40 mm. Our proposed detector design provides a pathway to increase the sensitivity of a system without affecting other key performance features.

KW - Compton scattering

KW - layer structure

KW - neural network

KW - PET

KW - scintillating crystal

KW - side readout

UR - http://www.scopus.com/inward/record.url?scp=85062891892&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062891892&partnerID=8YFLogxK

U2 - 10.1088/2057-1976/aaef03

DO - 10.1088/2057-1976/aaef03

M3 - Article

VL - 5

JO - Biomedical Physics and Engineering Express

JF - Biomedical Physics and Engineering Express

SN - 2057-1976

IS - 1

M1 - 015018

ER -