Initial Measurements with the PETsys TOFPET2 ASIC Evaluation Kit and a Characterization of the ASIC TDC

Schug, David; Nadig, Vanessa; Weissler, Bjöern; Gebhardt, Pierre; Schulz, Volkmar

doi:10.1109/trpms.2018.2884564

Cited by 47 publications

(54 citation statements)

References 46 publications

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“…The satellite peak is directly related to the overvoltage of the SiPMs as well as with the value of the vth_t1 discriminator. Detailed analysis of this effect can be found in [28].…”

Section: Monolithic Detectors Time Analysismentioning

confidence: 99%

Exploring TOF capabilities of PET detector blocks based on large monolithic crystals and analog SiPMs

et al. 2020

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A B S T R A C TMonolithic scintillators are more frequently used in PET instrumentation due to their advantages in terms of accurate position estimation of the impinging gamma rays both planar and depth of interaction, their increased efficiency, and expected timing capabilities. Such timing performance has been studied when those blocks are coupled to digital photosensors showing an excellent timing resolution.In this work we study the timing behaviour of detectors composed by monolithic crystals and analog SiPMs read out by an ASIC. The scintillation light spreads across the crystal towards the photosensors, resulting in a high number of SiPMs and ASIC channels fired. This has been studied in relation with the Coincidence Timing Resolution (CTR). We have used LYSO monolithic blocks with dimensions of 50 × 50 × 15 mm 3 coupled to SiPM arrays (8 × 8 elements with 6 × 6 mm 2 area) which compose detectors suitable for clinical applications.While a CTR as good as 186 ps FWHM was achieved for a pair of 3 × 3 × 5 mm 3 LYSO crystals, when using the monolithic block and the SiPM arrays, a raw CTR over 1 ns was observed. An optimal timestamp assignment was studied as well as compensation methods for the time-skew and time-walk errors. This work describes all steps followed to improve the CTR. Eventually, an average detector time resolution of 497 ps FWHM was measured for the whole thick monolithic block. This improves to 380 ps FWHM for a central volume of interest near the photosensors. The timing dependency with the photon depth of interaction and planar position are also included.

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“…The satellite peak is directly related to the overvoltage of the SiPMs as well as with the value of the vth_t1 discriminator. Detailed analysis of this effect can be found in [28].…”

Section: Monolithic Detectors Time Analysismentioning

confidence: 99%

Exploring TOF capabilities of PET detector blocks based on large monolithic crystals and analog SiPMs

et al. 2020

View full text Add to dashboard Cite

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“…These parameters operate on different scales (approx. 2.5 mV, 15 mV, and 20 mV per DAC step [44]) and adjust the voltage threshold over a channel-specific baseline determined in the calibration routine. If an incident event only triggers D T1, it is rejected.…”

Section: B Tofpet2 Asicmentioning

confidence: 99%

“…It is possible that small pulses occurring close to coincidence events cause the timestamp to be generated by the output of D T1, which results in the appearance of satellite peaks in the time difference spectra of matched coincidence events. A detailed description of the operation of the trigger circuit and the appearance of satellite peaks can be found in [44]. Three software configuration parameters influence the input stage impedance R IN and the discriminator noise of the TOFPET2 ASIC channel circuit, which both can be used to adjust the power consumption of the TOFPET2 ASIC.…”

Section: B Tofpet2 Asicmentioning

confidence: 99%

“…Performance data were evaluated in the same manner as described in [44]: Data were prepared applying the PETsys routine convert raw to singles to convert raw data into single hit information. The obtained table containing a timestamp, an energy value, and a channel id for each single hit registered was used for further processing.…”

Section: Data Processingmentioning

confidence: 99%

“…6c). Since prior studies showed that the satellite peak fraction depends on the configured trigger threshold of the first discriminator [44], the increased fraction calls for an adjustment of the trigger thresholds during performance experiments. Additionally, the dark count rate was acquired for settings in the full range of possible thresholds vth t1.…”

Section: Impact On Performancementioning

confidence: 99%

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Investigation of the Power Consumption of the PETsys TOFPET2 ASIC

Nadig

Weissler

Radermacher

et al. 2020

IEEE Trans. Radiat. Plasma Med. Sci.

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In state-of-the-art positron emission (PET) tomography systems, application-specific integrated circuits (ASICs) are commonly used to precisely digitize the signals of analog silicon photo-multipliers (SiPMs). However, when operating PET electronics in a magnetic resonance (MR) system, one faces the challenge of mutual interference of these imaging techniques. To prevent signal deterioration along long analog signal lines, PET electronics with a low power consumption digitizing the signals close to the SiPMs are preferred. In this study, we evaluate the power consumption of the TOFPET2 ASIC. Its power consumption ranges from 3.6 mW/channel to 7.2 mW/channel as a function of the input stage impedance and discriminator noise settings. We present an analytical model allowing to compute the power consumption of a given ASIC configuration. The configured input stage impedance and discriminator noise have an impact on the coincidence resolution time, energy resolution, and dark count rate. Since TOFPET2 ASICs delivers state-ofthe art performance with a power consumption similar or even lower than other ASICs typically used for PET applications, it is a favorable candidate to digitize the signals of SiPMs in future simultaneous PET/MR systems.

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Sub‐2 mm depth of interaction localization in PET detectors with prismatoid light guide arrays and single‐ended readout using convolutional neural networks

et al. 2021

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Purpose Depth of interaction (DOI) readout in PET imaging has been researched in efforts to mitigate parallax error, which would enable the development of small diameter, high‐resolution PET scanners. However, DOI PET has not yet been commercialized due to the lack of practical, cost‐effective, and data efficient DOI readout methods. The rationale for this study was to develop a supervised machine learning algorithm for DOI estimation in PET that can be trained and deployed on unique sets of crystals. Methods Depth collimated flood data was experimentally acquired using a Na‐22 source with a depth‐encoding single‐ended readout Prism‐PET module consisting of lutetium yttrium orthosilicate (LYSO) crystals coupled 4‐to‐1 to 3×3 mm2 silicon photomultiplier (SiPM) pixels on one end and a segmented prismatoid light guide array on the other end. A convolutional neural network (CNN) was trained to perform DOI estimation on data from center, edge and corner crystals in the Prism‐PET module using (a) all non‐zero readout pixels and (b) only the 4 highest readout signals per event. CNN testing was performed on data from crystals not included in CNN training. Results An average DOI resolution of 1.84 mm full width at half maximum (FWHM) across all crystals was achieved when using all readout signals per event with the CNN compared to 3.04 mm FWHM DOI resolution using classical estimation. When using only the 4 highest signals per event, an average DOI resolution of 1.92 mm FWHM was achieved, representing only a 4% dropoff in CNN performance compared to using all non‐zero pixels per event. Conclusions Our CNN‐based DOI estimation algorithm provides the best reported DOI resolution in a single‐ended readout module and can be readily deployed on crystals not used for model training.

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Initial Measurements with the PETsys TOFPET2 ASIC Evaluation Kit and a Characterization of the ASIC TDC

Cited by 47 publications

References 46 publications

Exploring TOF capabilities of PET detector blocks based on large monolithic crystals and analog SiPMs

Exploring TOF capabilities of PET detector blocks based on large monolithic crystals and analog SiPMs

Investigation of the Power Consumption of the PETsys TOFPET2 ASIC

Sub‐2 mm depth of interaction localization in PET detectors with prismatoid light guide arrays and single‐ended readout using convolutional neural networks

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