Digital radiography detectors - A technical overview: Part 1

During the last two decades screen-film (SF) systems have been replaced by digital X-ray systems. The advent of digital technologies brought a number of digital solutions based on different detector and readout technologies. Improvements in technology allowed the development of new digital technologies for projection radiography such as computed radiography (CR) and digital radiography (DR). The large number of scientific papers concerning digital X-ray systems that have been published over the last 25 years indicates the relevance of these technologies in healthcare. There are important differences among different detector technologies that may affect system performance and image quality for diagnostic purposes. Radiographers are expected to have an effective understanding of digital X-ray technologies and a high level of knowledge and awareness concerning the capabilities of these systems. Patient safety and reliable diagnostic information are intrinsically linked to these factors. In this review article - which is the first of two parts - a global overview of the digital radiography systems (both CR and DR) currently available for clinical practice is provided.

Digital radiography detectors - A technical overview: Part 2

Digital X-ray detector technologies provide several advantages when compared with screen-film (SF) systems: better diagnostic quality of the radiographic image, increased dose efficiency, better dynamic range and possible reduction of radiation exposure to the patient. The transition from traditional SF systems to digital technology-based systems highlights the importance of the discussion around technical factors such as image acquisition, themanagement of patient dose and diagnostic image quality. Radiographers should be aware of these aspects concerning their clinical practice regarding the advantages and limitations of digital detectors. Newdigital technologies require an up-to-date of scientific knowledge concerning their use in projection radiography. This is the second of a two-part review article focused on a technical overview of digital radiography detectors. This article provides a discussion about the issues related to the image acquisition requirements and advantages of digital technologies, the management of patient dose and the diagnostic image quality.

A 3-phase model for VIS/NIR mu C-Si : H p-i-n detectors

The spectral response and the photocurrent delivered by entirely microcrystalline p-i-n-Si:H detectors an analysed under different applied bias and light illumination conditions. The spectral response and the internal collection depend not only on the energy range but also on the illumination side. Under [p]- and [n]-side irradiation, the internal collection characteristics have an atypical shape. It is high for applied bias and lower than the open circuit voltage, shows a steep decrease near the open circuit voltage (higher under [n]-side illumination) and levels off for higher voltages. Additionally, the numerical modeling of the VIS/NIR detector, based on the band discontinuities near the grain boundaries and interfaces, complements the study and gives insight into the internal physical process.

Digital radiography detectors: a technical overview

Developments in digital detector technologies have been taking place and new digital technologies are available for clinical practice. This chapter is intended to give a technical state-of-the-art overview about computed radiography (CR) and digital radiography (DR) detectors. CR systems use storage-phosphor image plates with a separate image readout process and DR technology converts X-rays into electrical charges by means of a readout process using TFT arrays. Digital detectors offer several advantages when compared to analogue detectors. The knowledge about digital detector technology for use in plain radiograph examinations is thus a fundamental topic to be acquired by radiology professionals and students. In this chapter an overview of digital radiography systems (both CR and DR) currently available for clinical practice is provided.

Tunnig optical A-Sic/A-Si active filters by UV bias light in the visible and infrared spectral ranges

Visible range to telecom band spectral translation is accomplished using an amorphous SiC pi'n/pin wavelength selector under appropriate front and back optical light bias. Results show that background intensity works as selectors in the infrared region, shifting the sensor sensitivity. Low intensities select the near-infrared range while high intensities select the visible part according to its wavelength. Here, the optical gain is very high in the infrared/red range, decreases in the green range, stays close to one in the blue region and strongly decreases in the near-UV range. The transfer characteristics effects due to changes in steady state light intensity and wavelength backgrounds are presented. The relationship between the optical inputs and the output signal is established. A capacitive optoelectronic model is presented and tested using the experimental results. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

In Situ Near-Infrared (NIR) Versus High-Throughput Mid- Infrared (MIR) Spectroscopy to Monitor Biopharmaceutical Production

The development of biopharmaceutical manufacturing processes presents critical constraints, with the major constraint being that living cells synthesize these molecules, presenting inherent behavior variability due to their high sensitivity to small fluctuations in the cultivation environment. To speed up the development process and to control this critical manufacturing step, it is relevant to develop high-throughput and in situ monitoring techniques, respectively. Here, high-throughput mid-infrared (MIR) spectral analysis of dehydrated cell pellets and in situ near-infrared (NIR) spectral analysis of the whole culture broth were compared to monitor plasmid production in recombinant Escherichia coil cultures. Good partial least squares (PLS) regression models were built, either based on MIR or NIR spectral data, yielding high coefficients of determination (R-2) and low predictive errors (root mean square error, or RMSE) to estimate host cell growth, plasmid production, carbon source consumption (glucose and glycerol), and by-product acetate production and consumption. The predictive errors for biomass, plasmid, glucose, glycerol, and acetate based on MIR data were 0.7 g/L, 9 mg/L, 0.3 g/L, 0.4 g/L, and 0.4 g/L, respectively, whereas for NIR data the predictive errors obtained were 0.4 g/L, 8 mg/L, 0.3 g/L, 0.2 g/L, and 0.4 g/L, respectively. The models obtained are robust as they are valid for cultivations conducted with different media compositions and with different cultivation strategies (batch and fed-batch). Besides being conducted in situ with a sterilized fiber optic probe, NIR spectroscopy allows building PLS models for estimating plasmid, glucose, and acetate that are as accurate as those obtained from the high-throughput MIR setup, and better models for estimating biomass and glycerol, yielding a decrease in 57 and 50% of the RMSE, respectively, compared to the MIR setup. However, MIR spectroscopy could be a valid alternative in the case of optimization protocols, due to possible space constraints or high costs associated with the use of multi-fiber optic probes for multi-bioreactors. In this case, MIR could be conducted in a high-throughput manner, analyzing hundreds of culture samples in a rapid and automatic mode.

Comparative analysis of near and mid-infrared spectroscopy to monitor recombinant cyprosin production

Infrared spectroscopy, either in the near and mid (NIR/MIR) region of the spectra, has gained great acceptance in the industry for bioprocess monitoring according to Process Analytical Technology, due to its rapid, economic, high sensitivity mode of application and versatility. Due to the relevance of cyprosin (mostly for dairy industry), and as NIR and MIR spectroscopy presents specific characteristics that ultimately may complement each other, in the present work these techniques were compared to monitor and characterize by in situ and by at-line high-throughput analysis, respectively, recombinant cyprosin production by Saccharomyces cerevisiae. Partial least-square regression models, relating NIR and MIR-spectral features with biomass, cyprosin activity, specific activity, glucose, galactose, ethanol and acetate concentration were developed, all presenting, in general, high regression coefficients and low prediction errors. In the case of biomass and glucose slight better models were achieved by in situ NIR spectroscopic analysis, while for cyprosin activity and specific activity slight better models were achieved by at-line MIR spectroscopic analysis. Therefore both techniques enabled to monitor the highly dynamic cyprosin production bioprocess, promoting by this way more efficient platforms for the bioprocess optimization and control.