Processing of Compton events in the PETALO readout system

Renner, J.; Benlloch-Rodríguez, J.M.; Carrión, J.V.; Gadea, R.; Bosch, Vicente; Kekic, M.; Romo-Luque, C.; Ferrario, P.; Gómez-Cadenas, J.J.

doi:10.1109/nss/mic42101.2019.9059979

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…This way an excellent performance could be achieved, at the expenses of only a reduced loss of efficiency. The possibility of identifying good versus bad reconstructed events have been studied recently using Deep Neural Networks [5] and work is in progress to apply this kind of algorithms to image reconstruction.…”

Section: Jinst 17 P05044mentioning

confidence: 99%

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Monte Carlo characterization of PETALO, a full-body liquid xenon-based PET detector

et al. 2022

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New detector approaches in Positron Emission Tomography imaging will play an important role in reducing costs, lowering administered radiation doses, and improving overall performance. PETALO employs liquid xenon as the active scintillating medium and UV-sensitive silicon photomultipliers for scintillation readout. The scintillation time in liquid xenon is fast enough to register time-of-flight information for each detected coincidence, and sufficient scintillation is produced with low enough fluctuations to obtain good energy resolution. The present simulation study examines a full-body-sized PETALO detector and evaluates its potential performance in PET image reconstruction.

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Section: Jinst 17 P05044mentioning

confidence: 99%

“…In addition to an excellent time resolution, this can lead to a boost in the quality of reconstructed images. PETALO is a novel detector concept for PET imaging based on LXe and a silicon photomultiplier (SiPM) read-out, which is currently exploring these potential advantages [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo characterization of PETALO, a full-body liquid xenon-based PET detector

et al. 2022

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“…In this paper, the HGC's current blob detection method is compared with five other techniques (a Laplacian of Gaussian (LoG) algorithm [14], a Difference of Gaussian (DoG) algorithm [15] and a Hessian of Determinant (DoH) algorithm [16] and two methods derived from Faster-Region-Based Convolutional Neural Networks (Faster-RCNNs) [17,18]). Although there are some similar works on scintillation light splash localisation by machine learning algorithms, this has been limited to position information [19][20][21][22]. This is the first paper that adopts deep learning techniques for scintillation light splash localisation and scaling.…”

Section: Jinst 17 P06021mentioning

confidence: 99%

Automatic detection of scintillation light splashes using conventional and deep learning methods

Jiang¹,

Bugby²,

Cosma³

2022

J. Inst.

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Six methods for the automatic detection of scintillation light splashes in a portable gamma camera are compared. Each imaging frame might contain any number of light splashes (including none), and the location and size of each light splash must be identified. For real-time imaging, splashes must be identified and characterised quickly and with minimal processing overhead. The techniques are compared on their ability to accurately determine the number, position, and size of light splashes, and to reconstruct the deposited energy within each splash for a simulated data set with known ground-truths. The speed of each technique and the ease of implementation are also discussed. For accuracy in blob (light splash) localisation, a Laplacian of Gaussian approach was found to provide the most accurate estimation. However, its performance greatly relies on the appropriate tuning of preprocessing parameters prior to image analysis and the number of blobs in each frame. Deep learning approaches (Faster-RCNNs) performed significantly better than traditional algorithms in terms of predicting the size of each light splash, did not require image preprocessing and were also more stable over a range of frame occupancies. Moreover, the paper fine-tuned a VGG16 based Faster-RCNN model with the simulated data set for the scintillation light splash detection, called DeepSplashSpotter (DSS).

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TOF-PET Image Reconstruction With Multiple Timing Kernels Applied on Cherenkov Radiation in BGO

Efthimiou

Kratochwil

Gundacker

et al. 2021

IEEE Trans. Radiat. Plasma Med. Sci.

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Today Time-of-Flight (TOF), in PET scanners, assumes a single, well-defined timing resolution for all events. However, recent BGO–Cherenkov detectors, combining prompt Cherenkov emission and the typical BGO scintillation, can sort events into multiple timing kernels, best described by the Gaussian mixture models. The number of Cherenkov photons detected per event impacts directly the detector time resolution and signal rise time, which can later be used to improve the coincidence timing resolution. This work presents a simulation toolkit which applies multiple timing spreads on the coincident events and an image reconstruction that incorporates this information. A full cylindrical BGO–Cherenkov PET model was compared, in terms of contrast recovery and contrast-to-noise ratio, against an LYSO model with a time resolution of 213 ps. Two reconstruction approaches for the mixture kernels were tested: 1) mixture Gaussian and 2) decomposed simple Gaussian kernels. The decomposed model used the exact mixture component applied during the simulation. Images reconstructed using mixture kernels provided similar mean value and less noise than the decomposed. However, typically, more iterations were needed. Similarly, the LYSO model, with a single TOF kernel, converged faster than the BGO–Cherenkov with multiple kernels. The results indicate that the model complexity slows down convergence. However, due to the higher sensitivity, the contrast-to-noise ratio was 26.4% better for the BGO model.

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Processing of Compton events in the PETALO readout system

Cited by 3 publications

References 5 publications

Monte Carlo characterization of PETALO, a full-body liquid xenon-based PET detector

Monte Carlo characterization of PETALO, a full-body liquid xenon-based PET detector

Automatic detection of scintillation light splashes using conventional and deep learning methods

TOF-PET Image Reconstruction With Multiple Timing Kernels Applied on Cherenkov Radiation in BGO

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