Noise Power Characteristics of a Micro-Computed Tomography System

A novel 3D volumetric method for directly quantifying porosity and pore space morphology in flocculated suspended sediments

Flocculated suspended sediments (flocs) are found in a variety of environments globally, and their transport and behavior bear substantial importance to several industries including fisheries, aquaculture, and shipping. Additionally, the modelling of their behavior is important for estuarine and coastal flood prediction and defence, and the process of flocculation occurs in other unrelated industries such as paper and chemical production. Floc porosity is conventionally assessed using inferential indirect or proxy data approaches. These methods underestimate floc porosity % by c. 30% and cannot measure the micro-scale complexity of these pore spaces and networks, rendering inputs to models sub-optimal. This study introduces a novel 3D porosity and pore space quantification protocol, that produces directly quantified porosity % and pore space data.•3D floc data from micro-CT scanning is segmented volumetrically•This segmented volume is quantified to extract porosity and several pore space parameters from the floc structure.

Bridging nano- and microscale X-ray tomography for battery research by leveraging artificial intelligence

X-ray computed tomography (CT) is a non-destructive imaging technique in which contrast originates from the materials' absorption coefficient. The recent development of laboratory nanoscale CT (nano-CT) systems has pushed the spatial resolution for battery material imaging to voxel sizes of 50 nm, a limit previously achievable only with synchrotron facilities. Given the non-destructive nature of CT, in situ and operando studies have emerged as powerful methods to quantify morphological parameters, such as tortuosity factor, porosity, surface area and volume expansion, during battery operation or cycling. Combined with artificial intelligence and machine learning analysis techniques, nano-CT has enabled the development of predictive models to analyse the impact of the electrode microstructure on cell performances or the influence of material heterogeneities on electrochemical responses. In this Review, we discuss the role of X-ray CT and nano-CT experimentation in the battery field, discuss the incorporation of artificial intelligence and machine learning analyses and provide a perspective on how the combination of multiscale CT imaging techniques can expand the development of predictive multiscale battery behavioural models.

Image quality assessment of a photon counting detector in x-ray projection imaging

The recent advancements in the photon counting detection have created a significant growing research interest in the x-ray imaging. It is essential to objectively understand the image quality parameters of a photon counting detector before developing imaging applications. In this work, we have assessed the imaging quality of a cadmium telluride (CdTe) based PCD in projection imaging mode. The detector is 70.4 mm × 6.6 mm dimensions. The detector has a pixel array of 64×4 with a pixel pitch of 1.1 mm×1.65 mm. With each pixel having 4 channels in its corresponding ASIC, this PCD can create three bin images from a single projection. With a microfocus x-ray source, the imaging quality in each bin image was measured in terms of the spatial resolution, noise, and contrast to noise ratio (CNR). We used 70 kV, 50μA, 10 s (0.5mAs) with 0.5mm thick aluminum (Al) filter for the acquisition of each image. The MTF curves indicated that the spatial resolution for the bin-1, bin-2, and bin-3 was almost identical. The NNPS curves indicated that the noise in bin 1 and bin 2 images was almost the same for all frequencies while bin 3 image had relatively less noise. The CNR analyses showed that the bin-1 image had the highest CNR. As the flux was increased from 0.5 to 1 mAs, the number of detected counts also increased that resulted in the CNR increase. Beyond this flux, the pulse pileup occurred due to which multiple counts were read as single that resulted in few detected counts and lower CNR. The knowledge of the spatial resolution, noise, and CNR in terms of energy binning allows the determination and optimization of imaging techniques necessary for various applications.

Tutorial on X-ray photon counting detector characterization

BACKGROUND

Recent advances in photon counting detection technology have led to significant research interest in X-ray imaging.

OBJECTIVE

As a tutorial level review, this paper covers a wide range of aspects related to X-ray photon counting detector characterization.

METHODS

The tutorial begins with a detailed description of the working principle and operating modes of a pixelated X-ray photon counting detector with basic architecture and detection mechanism. Currently available methods and techniques for charactering major aspects including energy response, noise floor, energy resolution, count rate performance (detector efficiency), and charge sharing effect of photon counting detectors are comprehensively reviewed. Other characterization aspects such as point spread function (PSF), line spread function (LSF), contrast transfer function (CTF), modulation transfer function (MTF), noise power spectrum (NPS), detective quantum efficiency (DQE), bias voltage, radiation damage, and polarization effect are also remarked.

RESULTS

A cadmium telluride (CdTe) pixelated photon counting detector is employed for part of the characterization demonstration and the results are presented.

CONCLUSIONS

This review can serve as a tutorial for X-ray imaging researchers and investigators to understand, operate, characterize, and optimize photon counting detectors for a variety of applications.