Impact on the Spatial Resolution Performance of a Monolithic Crystal PET Detector Due to Different Sensor Parameters

Abstract- The performance characteristics of a monolithic crystal PET detector utilizing a novel sensor on the entrance surface (SES) design is reported. To facilitate this design, we propose to utilize a 2D silicon photomultiplier (SiPM) array device. SiPMs are a form of Geiger-Muller mode avalanche photodiodes (GMAPD) that can provide signal gain similar to a photomultiplier tube (PMT). Since these devices are still under active development, their performance parameters are changing. Using a multi-step simulation process, we investigated how different SiPM parameters affect the performance of a monolithic crystal PET detector. These parameters include gain variability between different channels; gain instability; and dark count noise. The detector simulated was a 49.6 mm by 49.6 mm by 15 mm LYSO crystal detector readout by a 16 by 16 array of 2.8 mm by 2.8 mm SiPM elements. To reduce the number of signal channels that need to be collected, the detector utilizes row-column summing. A statistics based positioning method is used for event positioning and depth of interaction (DOI) decoding. Of the variables investigated, the dark count noise had the largest impact on the intrinsic spatial resolution. Gain differences of 5-10% between detector calibration and detector testing had a modest impact on the intrinsic spatial resolution performance and led to a slight bias in positioning. There was no measurable difference with a gain variability of up to 25% between the individual SiPM channels. Based upon these results we are planning to cool our detectors below room temperature to reduce dark count noise and to actively control the temperature of the SiPMs to reduce drifts in gain over time

A step towards new irrigation scheduling strategies using plant-based measurements and mathematical modelling

Because of the increasing worldwide shortage of freshwater and costs of irrigation, a new plant-based irrigation scheduling method is proposed. In this method, two real-time plant-based measurements (sap flow and stem diameter variations) are used in combination with a mathematical water flow and storage model in order to predict the stem water potential. The amount of required irrigation water is derived from a time integration of the sap flow profile, while the timing of the irrigation is controlled based on a reference value for the predicted stem water potential. This reference value is derived from the relationship between midday values of maximum photosynthesis rates and stem water potential. Since modelling is an important part of the proposed methodology, a thorough mathematical analysis (identifiability analysis) of the model was performed. This analysis showed that an initial (offline) model calibration was needed based on measurements of sap flow, stem diameter variation and stem water potential. Regarding irrigation scheduling, however, only sap flow and stem diameter variation measurements are needed for online simulation and daily model calibration. Model calibration is performed using a moving window of 4 days of past data of stem diameter variations. The research tool STACI (Software Tool for Automatic Control of Irrigation) was used to optimally combine the continuous measurements, the mathematical modelling and the real-time irrigation scheduling. The new methodology was successfully tested in a pilot-scale setup with young potted apple trees (Malus domestica Borkh) and its performance was critically evaluated