Signal Processing for Radiation Detectors

Nakhostin, M.

doi:10.1002/9781119410225

Cited by 27 publications

(31 citation statements)

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“…For digital signal processing in high-resolution X-ray spectroscopy, a common type of filter is finite impulse response (FIR) filter. 6 This type of filter is inherently stable, and the finite duration of their pulse response leads to a well-defined length of the filter output for the steplike signals of semiconductor X-ray detectors. During application, often a trade-off between noise reduction and dead-time due to pile-up effects has to be found.…”

Section: Filter Descriptionmentioning

confidence: 99%

“…For state-of-the-art signal processing, the output voltage signals of silicon drift detectors are converted into digital values by an analogto-digital converter (ADC), and pulse height analysis is done in a digital signal processing unit like a field programmable gate array (FPGA). 6 Statistical disturbances and in particular electronic noise superimpose the desired X-ray signals. Since the disturbances lead to inaccuracies in the signal height determination, energy resolution worsens and the quality of the material analysis suffers.…”

Section: Introductionmentioning

confidence: 99%

“…Since the disturbances lead to inaccuracies in the signal height determination, energy resolution worsens and the quality of the material analysis suffers. 6,7 In order to reduce the influence of noise, digital filters are applied during signal processing. Beside the optimization of the signal-to-noise ratio, the filters need to fulfill several requirements regarding their time-domain transfer function.…”

Section: Introductionmentioning

confidence: 99%

“…7 The most common type of filter is the trapezoid filter which provides good reduction of white noise, fulfills the requirements, and can be implemented very efficiently in digital electronics. 6 However, the trapezoid filter provides nonoptimal noise reduction in certain cases, like, for example, in the presence of flicker noise and cannot take into account unknown experimental disturbances, like, for example, pickup noise caused by electromagnetic interference. 8 In this work, a method for the calculation of alternative filters providing optimum signal-to-noise ratio will be demonstrated, targeting the improvement of the energy resolution achieved by silicon drift detector system.…”

Section: Introductionmentioning

confidence: 99%

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Design of optimum filters for signal processing with silicon drift detectors

Rettenmeier

Maurer

2021

X-Ray Spectrometry

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In order to improve noise filtering for high-resolution X-ray spectroscopy using silicon drift detectors, optimum finite impulse response filters are calculated and tested experimentally. Common matched filter theory cannot be applied for this problem since the filters need to fulfill several requirements regarding their time domain transfer function, like, for example, the presence of a flat-top, zero-filter area. Therefore, the utilization of digital penalized least mean square method with silicon drift detectors is presented. Adaptations to the method are applied in order to suit the considered application targeting low dead-time at high count rates using modern silicon drift detectors with fast charge amplifiers. The workflow for filter calculation is presented: Signal data are acquired using a spectroscopy setup in order to obtain pulse shape and noise information. Desired constraints regarding the time-domain transfer function of the filters are written out, and limits for the precision of their fulfillment are derived. Optimization is carried out, and resulting filters are presented. In order to experimentally test the calculated filters, implementation in hardware is done. 55 Fe spectra are acquired and evaluated. Noise reduction is rated by calculation of residual electronic noise in the recorded 55 Fe spectra. Comparison to the noise reduction of trapezoid filters is done. Improvement up to (5.2 ± 0.3)% was found using the calculated optimum filter, proving the successful utilization of digital penalized least mean square method for signal processing with modern silicon drift detectors.

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Section: Filter Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design of optimum filters for signal processing with silicon drift detectors

Rettenmeier

Maurer

2021

X-Ray Spectrometry

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“…Noise amplitude lies within a limit of 6σ peak to peak from a determined source, whereby the total noise is the root-mean-square (rms) value of all mean-square voltages of the individual noise sources. Low energy noise from other sources can trigger the digitizer [29]. With these considerations, the threshold level value was set to baseline +210 LSB, which corresponds to approximately the baseline +6σ, which is a value low enough to acquire most of the gamma pulses, but high enough to reduce the noise rate.…”

Section: Timing Analysismentioning

confidence: 99%

Evaluation of digital methods for energy calculation and timing pick-off in positron emission tomography

2018

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Traditionally, pulse processing in Positron Emission Tomography (PET) has been based on analog or discrete circuits forming a decentralized processing system. However, there is a convergence for digital and integrated implementations due to the characteristics of the modern electronic devices which are real-time processing capable, such as Application-Specific Integrated Circuit (ASIC) and Field Programmable Gate Array (FPGA) with fast Analog to Digital Converters (ADC). However, FPGA can provide fast implementation at relatively low cost and also enables the development of sophisticated digital pulse processing algorithms to improve energy, position and time resolutions in PET systems. Our group has developed and evaluated one energy calculation and three timing pick-off methods for implementation onto an FPGA-based system. For a typical PET detector setup, our charge integration method presents energy resolution similar to previously designed PET detectors. The best performance for timing pick-off was achieved by the Initial Rise Interpolation (IRI) method, where a coincidence time resolution of around 440 ps is suitable for Time of Flight (TOF) PET. Future works include embedding the proposed algorithms in a FPGA-based data acquisition system under development by our group which will be employed in a PET prototype.

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Electronics for Gamma‐Ray Spectrometry

2024

Practical Gamma‐ray Spectrometry

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Signal Processing for Radiation Detectors

Cited by 27 publications

References 0 publications

Design of optimum filters for signal processing with silicon drift detectors

Design of optimum filters for signal processing with silicon drift detectors

Evaluation of digital methods for energy calculation and timing pick-off in positron emission tomography

Electronics for Gamma‐Ray Spectrometry

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