Evaluation of Compton gamma camera prototype based on pixelated CdTe detectors

Calderon, Yonatan; Chmeissani, M.; Kolstein, M.; Lorenzo, G. De

doi:10.1088/1748-0221/9/06/c06003

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…Their first application in medical imaging was proposed in Todd et al (1974). Thenceforth, there has been an interest in their application in the field of nuclear medical imaging (Han et al 2008, Seo et al 2008, Harkness et al 2009, Karimian et al 2009, Alnaaimi et al 2011, Calderón et al 2014, Fontana et al 2017. In addition, there is also a growing interest in their use to image the prompt gamma emission produced during proton therapy for in vivo range assessment (Peterson et al 2010, Richard et al 2011, Kormoll et al 2011, Hilaire et al 2016, Solevi et al 2016, Rohling et al 2017.…”

Section: Introductionmentioning

confidence: 99%

CCMod: a GATE module for Compton camera imaging simulation

et al. 2020

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Compton cameras are gamma-ray imaging systems which have been proposed for a wide variety of applications such as medical imaging, nuclear decommissioning or homeland security. In the design and optimization of such a system Monte Carlo simulations play an essential role. In this work, we propose a generic module to perform Monte Carlo simulations and analyses of Compton Camera imaging which is included in the open-source GATE/Geant4 platform. Several digitization stages have been implemented within the module to mimic the performance of the most commonly employed detectors (e.g. monolithic blocks, pixelated scintillator crystals, strip detectors...). Time coincidence sorter and sequence coincidence reconstruction are also available in order to aim at providing modules to facilitate the comparison and reproduction of the data taken with different prototypes. All processing steps may be performed during the simulation (on-the-fly mode) or as a post-process of the output files (offline mode). The predictions of the module have been compared with experimental data in terms of energy spectra, angular resolution, efficiency and back-projection image reconstruction. Consistent results within a 3-sigma interval were obtained for the energy spectra except for low energies where small differences arise. The angular resolution measure for incident photons of 1275 keV was also in good agreement between both data sets with a value close to 13 • . Moreover, with the aim of demonstrating the versatility of such a tool the performance of two different Compton camera designs was evaluated and compared.

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Section: Introductionmentioning

confidence: 99%

CCMod: a GATE module for Compton camera imaging simulation

et al. 2020

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“…A width of 1-4 keV (25-100 channels) yields similar accuracy and starts dropping at > 5 keV (20 channels). Here we are using a width of 4 keV which is aligned with state-of-the art detector resolutions [18,21].…”

Section: Resultsmentioning

confidence: 99%

Depth resolved pencil beam radiography using AI — a proof of principle study

Häggström¹,

Carter²,

Fuchs³

et al. 2022

J. Inst.

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Aims: clinical radiographic imaging is seated upon the principle of differential keV photon transmission through an object. At clinical x-ray energies the scattering of photons causes signal noise and is utilized solely for transmission measurements. However, scatter — particularly Compton scatter, is characterizable. In this work we hypothesized that modern radiation sources and detectors paired with deep learning techniques can use scattered photon information constructively to resolve superimposed attenuators in planar x-ray imaging. Methods: we simulated a monoenergetic x-ray imaging system consisting of a pencil beam x-ray source directed at an imaging target positioned in front of a high spatial- and energy-resolution detector array. The setup maximizes information capture of transmitted photons by measuring off-axis scatter location and energy. The signal was analyzed by a convolutional neural network, and a description of scattering material along the axis of the beam was derived. The system was virtually designed/tested using Monte Carlo processing of simple phantoms consisting of 10 pseudo-randomly stacked air/bone/water materials, and the network was trained by solving a classification problem. RESULTS: from our simulations we were able to resolve traversed material depth information to a high degree, within our simple imaging task. The average accuracy of the material identification along the beam was 0.91 ± 0.01, with slightly higher accuracy towards the entrance/exit peripheral surfaces of the object. The average sensitivity and specificity was 0.91 and 0.95, respectively. Conclusions: our work provides proof of principle that deep learning techniques can be used to analyze scattered photon patterns which can constructively contribute to the information content in radiography, here used to infer depth information in a traditional 2D planar setup. This principle, and our results, demonstrate that there is information in Compton scattered photons, and this may provide a basis for further development. The work was limited by simple testing scenarios and without yet integrating complexities or optimizations. The ability to scale performance to the clinic remains unexplored and requires further study.

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“…This is possible because the charge collection at the electrodes would not be affected by a trapezoidal geometry of the CdTe detectors. The reason that for this prototype we opt for a standard rectangular shape of the CdTe detectors instead of a trapezoidal shape, is because of our future plans to reuse the 18 detector modules to construct a gamma Compton camera (Calderón 2014) and a positron emission mammography (PEM) device similar to NaviScan (MacDonald et al 2009). Although the NaviScan PEM system has shown a good detection sensitivity for small cancerous lesions (Shkumat et al 2011), an even better result could be achieved with a VIP detector module as detailed in De Lorenzo et al (2013).…”

Section: Detector Designmentioning

confidence: 99%

“…Additionally, in the future we aim to reuse the 18 detector modules to construct a gamma Compton camera (Calderón 2014) and a positron emission mammography (PEM) device similar to NaviScan (MacDonald et al 2009). Although the NaviScan PEM system has shown a good detection sensitivity for small cancerous lesions (Shkumat et al 2011), an even better result could be achieved with a VIP detector module as detailed in De Lorenzo et al (2013).…”

Section: Summary and Future Workmentioning

confidence: 99%

First results of a highly granulated 3D CdTe detector module for PET

et al. 2018

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We present the performance of a highly granulated 3D detector module for PET, consisting of a stack of pixelated CdTe detectors. Each detector module has 2 cm × 2 cm × 2 cm of CdTe material, subdivided into 4000 voxels, where each voxel has size 1 mm × 1 mm × 2 mm and is connected to its own read-out electronics via a BiSn solder ball. Each read-out channel consists of a preamp, a discriminator, a shaper, a peak-and-hold circuit and a 10 bits SAR ADC. The preamp has variable gain where at the maximum gain the ADC resolution is equivalent to 0.7 keV. Each ASIC chip reads 100 CdTe pixel channels and has one TDC to measure the time stamp of the triggered events, with a time resolution of less than 1 ns. With the bias voltage set at -250 V mm and for 17838 working channels out of a total of 20 000, we have obtained an average energy resolution of 2.2% FWHM for 511 keV photons. For 511 keV photons that have undergone Compton scattering, we measured an energy resolution of 3.2% FWHM. A timing resolution for PET coincidence events of 60 ns FWHM was found.

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Evaluation of Compton gamma camera prototype based on pixelated CdTe detectors

Cited by 6 publications

References 18 publications

CCMod: a GATE module for Compton camera imaging simulation

CCMod: a GATE module for Compton camera imaging simulation

Depth resolved pencil beam radiography using AI — a proof of principle study

First results of a highly granulated 3D CdTe detector module for PET

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