Asymmetric Data Acquisition System for an Endoscopic PET-US Detector

Zorraquino, Carlos; Bugalho, R.; Rolo, Manuel; Silva, José C.; Vecklans, Viesturs; Silva, Rui; Ortigao, Catarina; Neves, Joana; Tavernier, S.; Guerra, P.; Santos, A.; Varela, J.

doi:10.1109/tns.2016.2514600

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…Since the SIPM pixels and ASIC channels of the system do not all achieve exactly the same performance, the response of the full system is nonuniform. The solution to this problem lays in the calibration process [11]. For each system channel, an energy calibration procedure obtains a table mapping the measured TOT and the corresponding gamma event energy in keV units.…”

Section: Filter Implementationsmentioning

confidence: 99%

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Online energy discrimination at DAQ front-end level on pixelated TOF-PET systems

Zorraquino,

Ferramacho,

Bugalho

et al. 2018

Preprint

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Pixelated positron emission tomography (PET) systems produce higher count rates than traditional block detectors because they integrate several detecting channels per detector module. An increased data flow from the detectors leads to higher bandwidth requirements. We aim to optimize the bandwidth usage efficiency by on-the-fly filtering of detected events for non-valid energies. PET systems with a SiPM-ASIC readout scheme are being extensively used to obtain enhanced images on time-of-flight PET scanners. Such digital readout systems are especially interesting for the application of online processing techniques given the ease of access to the digital information of each detected event. This study analyzes online processing techniques at the DAQ front-end level (on-detector electronics) for pixelated PET systems with SiPM-ASIC readout. In particular, we worked with a tunable online energy discriminating stage. For the optimization of its hardwired internal limits, we analyzed the system energy space. We explored various solutions, some of which are dependent on the system's energy calibration, whereas others were not. Results obtained through the application of various filter versions confirm the minimal resources consumption of such processing techniques implemented at the DAQ front-end level. Our experiments demonstrate how the filtering process reduces the bandwidth needs by excluding all non-valid energy events from the data stream, thus improving the system sensitivity under saturation conditions. Additionally, these experiments highlight how setting proper energy limits can ensure preservation of the system performance, which maintains its original energy and time resolution. In light of these findings, we see great potential for the application of online processing techniques for time-of-flight PET at the DAQ font-end level (on-detector electronics); therefore, we envisage the development of more complex processing methods.

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Section: Filter Implementationsmentioning

confidence: 99%

“…The DAQ could drop these events with no detriment to the resulting system sensitivity because their energy is too low. Hence, the DAQ of this system [10,11] would benefit from the online discrimination of events. The use of the filter would optimize the usage of the available BW, limiting the transmission to useful data.…”

Section: Introductionmentioning

confidence: 99%

Online energy discrimination at DAQ front-end level on pixelated TOF-PET systems

Zorraquino,

Ferramacho,

Bugalho

et al. 2018

Preprint

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Semantic SLAM Based on Deep Learning in Endocavity Environment

Zhao

et al. 2022

Symmetry

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Traditional endoscopic treatment methods restrict the surgeon’s field of view. New approaches to laparoscopic visualization have emerged due to the advent of robot-assisted surgical techniques. Lumen simultaneous localization and mapping (SLAM) technology can use the image sequence taken by the endoscope to estimate the pose of the endoscope and reconstruct the lumen scene in minimally invasive surgery. This technology gives the surgeon better visual perception and is the basis for the development of surgical navigation systems as well as medical augmented reality. However, the movement of surgical instruments in the internal cavity can interfere with the SLAM algorithm, and the feature points extracted from the surgical instruments may cause errors. Therefore, we propose a modified endocavity SLAM method combined with deep learning semantic segmentation that introduces a convolution neural network based on U-Net architecture with a symmetric encoder–decoder structure in the visual odometry with the goals of solving the binary segmentation problem between surgical instruments and the lumen background and distinguishing dynamic feature points. Its segmentation performance is improved by using pretrained encoders on the network model to obtain more accurate pixel-level instrument segmentation. In this setting, the semantic segmentation is used to reject the feature points on the surgical instruments and reduce the impact caused by dynamic surgical instruments. This can provide more stable and accurate mapping results compared to ordinary SLAM systems.

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Asymmetric Data Acquisition System for an Endoscopic PET-US Detector

Cited by 2 publications

References 15 publications

Online energy discrimination at DAQ front-end level on pixelated TOF-PET systems

Online energy discrimination at DAQ front-end level on pixelated TOF-PET systems

Semantic SLAM Based on Deep Learning in Endocavity Environment

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