Characterisation of Components of a Scintillation-fiber-based Compton Camera

Wrońska, A.; Hetzel, R.; Kasper, J.; Lalik, R.; Magiera, A.; Rusiecka, K.; Stahl, A.

doi:10.5506/aphyspolb.51.17

Cited by 7 publications

(6 citation statements)

References 11 publications

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“…• The SiFi-CC project pursued by the Cracow-Aachen collaboration aims at building a Compton camera composed of fibers made of heavy scintillator read out by SiPMs. Laboratory tests and Monte-Carlo simulations indicated that the optimal building blocks for such a setup would be 1 × 1 × 100 mm 3 LYSO fibers [51]. High granularity of the detectors will allow to reduce the problem of pile-ups occurring at high count rates.…”

Section: Compton Camera (Cc)mentioning

confidence: 99%

Range Verification by Means of Prompt-Gamma Detection in Particle Therapy 1

Wrońska¹,

Dauvergne²

2021

Radiation Detection Systems

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Ion range verification in particle therapy, possibly in real time, is still an open issue to improve the treatment quality by reducing safety margins. Among the various methods that are under study or already brought to the clinics, prompt gamma offer a unique opportunity of real time control. In this chapter, we present the physical background and conditions specific for the operation environment in the clinics, followed by an overview of the various techniques relying on prompt-gamma detection that have been proposed and developed for more than a decade. Different approaches, based on imaging and non-imaging techniques, providing either integrated or differential information are described, and their maturity, limitations and clinical usefulness are discussed.

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Section: Compton Camera (Cc)mentioning

confidence: 99%

Range Verification by Means of Prompt-Gamma Detection in Particle Therapy 1

Wrońska¹,

Dauvergne²

2021

Radiation Detection Systems

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“…For the readout, silicon photomultipliers (SiPMs) are coupled to the both ends of each fiber. More details about the fiber units can be found in Wrońska et al (2020).…”

Section: Geant4 Simulationsmentioning

confidence: 99%

“…Moreover, the uncertainties of each interaction position along the x-and z-coordinates were given by the fiber width (1.3 mm). This approach was explained in more detail in Kasper et al (2020), Wrońska et al (2020).…”

Section: Geant4 Simulationsmentioning

confidence: 99%

Machine learning-based event recognition in SiFi Compton camera imaging for proton therapy monitoring

Kozani

Magiera

2022

Phys. Med. Biol.

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Objective. Online monitoring of dose distribution in proton therapy is currently being investigated with the detection of prompt gamma (PG) radiation emitted from a patient during irradiation. The SiPM and scintillation Fiber based Compton Camera (SiFi-CC) setup is being developed for this aim. Approach. A machine learning approach to recognize Compton events is proposed, reconstructing the PG emission profile during proton therapy. The proposed method was verified on pseudo-data generated by a Geant4 simulation for a single proton beam impinging on a polymethyl methacrylate (PMMA) phantom. Three different models including the boosted decision tree (BDT), multilayer perceptron (MLP) neural network, and k-nearest neighbour (k-NN) were trained using 10-fold cross-validation and then their performances were assessed using the receiver operating characteristic (ROC) curves. Subsequently, after event selection by the most robust model, a software based on the List-Mode Maximum Likelihood Estimation Maximization (LM-MLEM) algorithm was applied for the reconstruction of the PG emission distribution profile. Main results. It was demonstrated that the BDT model excels in signal/background separation compared to the other two. Furthermore, the reconstructed PG vertex distribution after event selection showed a significant improvement in distal falloff position determination. Significance. A highly satisfactory agreement between the reconstructed distal edge position and that of the simulated Compton events was achieved. It was also shown that a position resolution of 3.5 mm full width at half maximum (FWHM) in distal edge position determination is feasible with the proposed setup.

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“…Monte-Carlo simulations play a crucial role in the design and optimization phase o f detection setups, including those dedicated for prompt-gamma imaging (PG I) [8][9][10][11][12]. Not only the detector response is modelled this way, but also the characteristics o f gamma quanta induced in the irradiated object by a proton beam, which depend on the description o f the underlying processes in a given implementation o f a Monte-Carlo engine.…”

Section: Introductionmentioning

confidence: 99%

Prompt-gamma emission in GEANT4 revisited and confronted with experiment

et al. 2021

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The field o f online monitoring o f the beam range is one o f the most researched topics in proton therapy over the last decade. The development of detectors that can be used for beam range verification under clinical conditions is a challenging task. One promising possible solution are modalities that record prompt-gamma radiation produced by the interactions of the proton beam with the target tissue. A good understanding o f the energy spectra o f the prompt

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Characterisation of Components of a Scintillation-fiber-based Compton Camera

Cited by 7 publications

References 11 publications

Range Verification by Means of Prompt-Gamma Detection in Particle Therapy 1

Range Verification by Means of Prompt-Gamma Detection in Particle Therapy 1

Machine learning-based event recognition in SiFi Compton camera imaging for proton therapy monitoring

Prompt-gamma emission in GEANT4 revisited and confronted with experiment

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