Feasibility study of photon-counting CT for material identification based on YSO/SiPM detector: A proof of concept

BACKGROUND

Current photon-counting computed tomography (CT) systems utilize semiconductor detectors, such as cadmium telluride (CdTe), cadmium zinc telluride (CZT), and silicon (Si), which convert x-ray photons directly into charge pulses. An alternative approach is indirect detection, which involves Yttrium Orthosilicate (YSO) scintillators coupled with silicon photomultipliers (SiPMs). This presents an attractive and cost-effective option due to its low cost, high detection efficiency, low dark count rate, and high sensor gain.

OBJECTIVE

This study aims to establish a comprehensive quantitative imaging framework for three-energy-bin proof-of-concept photon-counting CT based on YSO/SiPM detectors developed in our group using multi-voltage threshold (MVT) digitizers and assess the feasibility of this spectral CT for material identification.

METHODS

We developed a proof-of-concept YSO/SiPM-based benchtop spectral CT system and established a pipeline for three-energy-bin photon-counting CT projection-domain processing. The empirical A-table method was employed for basis material decomposition, and the quantitative imaging performance of the spectral CT system was assessed. This evaluation included the synthesis errors of virtual monoenergetic images, electron density images, effective atomic number images, and linear attenuation coefficient curves. The validity of employing A-table methods for material identification in three-energy-bin spectral CT was confirmed through both simulations and experimental studies.

RESULTS

In both noise-free and noisy simulations, the thickness estimation experiments and quantitative imaging results demonstrated high accuracy. In the thickness estimation experiment using the practical spectral CT system, the mean absolute error for the estimated thickness of the decomposed Al basis material was 0.014 ± 0.010 mm, with a mean relative error of 0.66% ± 0.42%. Similarly, for the decomposed polymethyl methacrylate (PMMA) basis material, the mean absolute error in thickness estimation was 0.064 ± 0.058 mm, with a mean relative error of 0.70% ± 0.38%. Additionally, employing the equivalent thickness of the basis material allowed for accurate synthesis of 70 keV virtual monoenergetic images (relative error 1.85% ± 1.26%), electron density (relative error 1.81% ± 0.97%), and effective atomic number (relative error 2.64% ± 1.26%) of the tested materials. In addition, the average synthesis error of the linear attenuation coefficient curves in the energy range from 40 to 150 keV was 1.89% ± 1.07%.

CONCLUSIONS

Both simulation and experimental results demonstrate the accurate generation of 70 keV virtual monoenergetic images, electron density, and effective atomic number images using the A-table method. Quantitative imaging results indicate that the YSO/SiPM-based photon-counting detector is capable of accurately reconstructing virtual monoenergetic images, electron density images, effective atomic number images, and linear attenuation coefficient curves, thereby achieving precise material identification.

Setup and characterisation according to NEMA NU 4 of thephenoPET scanner, a PET system dedicated for plant sciences

Objective.ThephenoPET system is a plant dedicated positron emission tomography (PET) scanner consisting of fully digital photo multipliers with lutetium-yttrium oxyorthosilicate crystals and located inside a custom climate chamber. Here, we present the setup ofphenoPET, its data processing and image reconstruction together with its performance.Approach.The performance characterization follows the national electrical manufacturers association (NEMA) standard for small animal PET systems with a number of adoptions due to the vertical oriented bore of a PET for plant sciences. In addition temperature stability and spatial resolution with a hot rod phantom are addressed.Main results.The spatial resolution for a22Na point source at a radial distance of 5 mm to the center of the field-of-view (FOV) is 1.45 mm, 0.82 mm and 1.88 mm with filtered back projection in radial, tangential and axial direction, respectively. A hot rod phantom with18F gives a spatial resolution of up to 1.6 mm. The peak noise-equivalent count rates are 550 kcps @ 35.08 MBq, 308 kcps @ 33 MBq and 45 kcps @ 40.60 MBq for the mouse, rat and monkey size scatter phantoms, respectively. The scatter fractions for these phantoms are 12.63%, 22.64% and 55.90%. We observe a peak sensitivity of up to 3.6% and a total sensitivity of up toSA,tot= 2.17%. For the NEMA image quality phantom we observe a uniformity of %STD= 4.22% with ordinary Poisson maximum likelihood expectation-maximization with 52 iterations. Here, recovery coefficients of 0.12, 0.64, 0.89, 0.93 and 0.91 for 1 mm, 2 mm, 3 mm, 4 mm and 5 mm rods are obtained and spill-over ratios of 0.08 and 0.14 for the water-filled and air-filled inserts, respectively.Significance.ThephenoPET and its laboratory are now in routine operation for the administration of [11C]CO2and non-invasive measurement of transport and allocation of11C-labelled photoassimilates in plants.

Kinetically Consistent Data Assimilation for Plant PET Sparse Time Activity Curve Signals

Time activity curve (TAC) signal processing in plant positron emission tomography (PET) is a frontier nuclear science technique to bring out the quantitative fluid dynamic (FD) flow parameters of the plant vascular system and generate knowledge on crops and their sustainable management, facing the accelerating global climate change. The sparse space-time sampling of the TAC signal impairs the extraction of the FD variables, which can be determined only as averaged values with existing techniques. A data-driven approach based on a reliable FD model has never been formulated. A novel sparse data assimilation digital signal processing method is proposed, with the unique capability of a direct computation of the dynamic evolution of noise correlations between estimated and measured variables, by taking into explicit account the numerical diffusion due to the sparse sampling. The sequential time-stepping procedure estimates the spatial profile of the velocity, the diffusion coefficient and the compartmental exchange rates along the plant stem from the TAC signals. To illustrate the performance of the method, we report an example of the measurement of transport mechanisms in zucchini sprouts.

Physical and Imaging Performance of SIAT aPET under Different Energy Windows and Timing Windows

PURPOSE

The performance of small animal PET scanners depends on the energy window (EW) and timing window (TW). In NEMA Standards Publication NU 4-2008, detailed procedures of the performance measurements are defined, but the EW and TW are not specified. In this work, the effects of EW and TW on the physical and imaging performance of SIAT aPET will be evaluated.

METHODS

First, the flood histogram, energy resolution and timing resolution were measured for a detector of SIAT aPET. Second, the spatial resolutions were measured with different EWs. Third, the sensitivities, the scatter fractions (SFs), and noise equivalent count rates (NECRs) of a mouse-sized phantom and a rat-sized phantom, the recovery coefficients (RCs) of rods of different sizes, and the percentage standard deviation (%STD) of the NEMA image quality phantom were measured for different EWs and TWs. Last, images of a hot rod phantom, a mouse heart and a rat brain were acquired from the scanner with different EWs.

RESULTS

The SIAT aPET detectors provided good flood histograms such that all but the corner crystals can be resolved even with lower energies of 250-350 keV, an average energy resolution of 21.1±1.9 % and an average timing resolution of 2.63±0.69 ns. The average spatial resolutions obtained with EWs of 250-350 keV and 450-550 keV are 0.68 mm and 0.75 mm. For EWs of 250-750 keV, 350-750 keV, and 450-750 keV with a fixed TW of 12 ns, the sensitivities at center of field of view are 16.0%, 11.9%, and 8.2%, the peak NECRs of a mouse-sized phantom are 355.6 kcps, 324.4 kcps, and 249.4 kcps, and the peak NECRs of a rat-sized phantom are 148.5 kcps, 144.3 kcps, and 117.7 kcps, respectively. For the TWs of 4 ns, 8 ns,12 ns, and 20 ns with a fixed EW of 350-750 keV, the sensitivities at center of field of view are 9.6%, 11.4%, 11.9%, and 12.2%, the peak NECRs of a mouse-sized phantom are 260.1 kcps, 311.5 kcps, 324.4 kcps and 324.9 kcps, and the peak NECRs of a rat-sized phantom are 110.5 kcps, 137.3 kcps,144.3 kcps and 142.6 kcps, respectively. Narrowing the EW and TW improves the RCs of rods of all sizes, and the %STD of images obtained with different EWs and TWs are similar. Rods with diameter down to 0.8 mm can be visually resolved from images of the hot rod phantom obtained with different EWs. Images of mouse heart with high spatial resolution and rat brain with detail brain structure were obtained with different EWs. Images of both phantom and in-vivo animals obtained with different EWs only showed subtle difference.

CONCLUSION

The performance of SIAT aPET under different EWs and TWs was compared. The EW and TW affect the sensitivity, SF, and NECR, but not the spatial resolution and animal images of SIAT aPET, which imply that careful optimization of the EW and TW is not required. This article is protected by copyright. All rights reserved.

Design Study of a Novel Positron Emission Tomography System for Plant Imaging

Positron Emission Tomography is a non-disruptive and high-sensitive digital imaging technique which allows to measure in-vivo and non invasively the changes of metabolic and transport mechanisms in plants. When it comes to the early assessment of stress-induced alterations of plant functions, plant PET has the potential of a major breakthrough. The development of dedicated plant PET systems faces a series of technological and experimental difficulties, which make conventional clinical and preclinical PET systems not fully suitable to agronomy. First, the functional and metabolic mechanisms of plants depend on environmental conditions, which can be controlled during the experiment if the scanner is transported into the growing chamber. Second, plants need to be imaged vertically, thus requiring a proper Field Of View. Third, the transverse Field of View needs to adapt to the different plant shapes, according to the species and the experimental protocols. In this paper, we perform a simulation study, proposing a novel design of dedicated plant PET scanners specifically conceived to address these agronomic issues. We estimate their expected sensitivity, count rate performance and spatial resolution, and we identify these specific features, which need to be investigated when realizing a plant PET scanner. Finally, we propose a novel approach to the measurement and verification of the performance of plant PET systems, including the design of dedicated plant phantoms, in order to provide a standard evaluation procedure for this emerging digital imaging agronomic technology.

Past and Future of Plant Stress Detection: An Overview From Remote Sensing to Positron Emission Tomography

Plant stress detection is considered one of the most critical areas for the improvement of crop yield in the compelling worldwide scenario, dictated by both the climate change and the geopolitical consequences of the Covid-19 epidemics. A complicated interconnection of biotic and abiotic stressors affect plant growth, including water, salt, temperature, light exposure, nutrients availability, agrochemicals, air and soil pollutants, pests and diseases. In facing this extended panorama, the technology choice is manifold. On the one hand, quantitative methods, such as metabolomics, provide very sensitive indicators of most of the stressors, with the drawback of a disruptive approach, which prevents follow up and dynamical studies. On the other hand qualitative methods, such as fluorescence, thermography and VIS/NIR reflectance, provide a non-disruptive view of the action of the stressors in plants, even across large fields, with the drawback of a poor accuracy. When looking at the spatial scale, the effect of stress may imply modifications from DNA level (nanometers) up to cell (micrometers), full plant (millimeters to meters), and entire field (kilometers). While quantitative techniques are sensitive to the smallest scales, only qualitative approaches can be used for the larger ones. Emerging technologies from nuclear and medical physics, such as computed tomography, magnetic resonance imaging and positron emission tomography, are expected to bridge the gap of quantitative non-disruptive morphologic and functional measurements at larger scale. In this review we analyze the landscape of the different technologies nowadays available, showing the benefits of each approach in plant stress detection, with a particular focus on the gaps, which will be filled in the nearby future by the emerging nuclear physics approaches to agriculture.