X-ray and gamma imaging with Medipix and Timepix detectors in medical research

Procz, S.; Ávila, C.; Fey, Julian; Roque, Gerardo; Schuetz, M.; Hamann, Elias

doi:10.1016/j.radmeas.2019.04.007

Cited by 30 publications

(15 citation statements)

References 97 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, some large-area HPCDs have been developed, with a high spatial resolution and fully spectroscopic capabilities [136] or with spectral discrimination achieved using two/four energy thresholds to be opportunely set [137,138]. Moreover, different sensor configurations and materials have been developed, from a more standard thickness of the Si sensor for low-medium X-ray energies to thicker Si or CdTe sensors, for higher X-ray energies, such as those required by XCT for CH investigations.…”

Section: X-ray Detectors-xctmentioning

confidence: 99%

Detectors and Cultural Heritage: The INFN-CHNet Experience

et al. 2021

View full text Add to dashboard Cite

Detectors are a key feature of the contemporary scientific approach to cultural heritage (CH), both for diagnostics and conservation. INFN-CHNet is the network of the Italian National Institute of Nuclear Physics that develops and applies new instrumentation for the study of CH. This process results in both optimized traditional state-of-the-art and highly innovative detection setups for spectrometric techniques. Examples of the former are X-rays, gamma-rays, visible-light and particles spectrometers tailored for CH applications, with optimized performances, reliability, weight, transportability, cost, absorbed power, and complementarity with other techniques. Regarding the latter, examples are ARDESIA, the array of detectors at the DAΦNE-Light facility, the MAXRS detection setup at the Riken-RAL muon beamline and the imaging facilities at the LENA Laboratory. Paths for next-generation instruments have been suggested, as in the case of the X-ray Superconductive Detectors and X-ray Microcalorimeter Spectrometers, allowing astonishing improvement in energy resolution. Many issues in CH can now be addressed thanks to scientific techniques exploiting the existing detectors, while many others are still to be addressed and require the development of new approaches and detectors.

show abstract

Section: X-ray Detectors-xctmentioning

confidence: 99%

Detectors and Cultural Heritage: The INFN-CHNet Experience

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Computed tomography (CT) scan, for example, is an X-ray procedure that displays the cross-sectional image of the body, which now also helps to assess brain or head-related injuries [1]. Another circumstance in which X-rays have been influential for further progress in research and application is Mammography, where a low energy x-ray photon beam is implemented to diagnose breast cancer, which is presently a common medical problem worldwide [2]. These implementations of x-rays are more commonly known as computer-aided diagnosis (CAD) [3].…”

Section: Introductionmentioning

confidence: 99%

Trends in Deep Learning for Medical Hyperspectral Image Analysis

et al. 2021

View full text Add to dashboard Cite

Deep learning algorithms have seen acute growth of interest in their applications throughout several fields of interest in the last decade, with medical hyperspectral imaging being a particularly promising domain. So far, to the best of our knowledge, there is no review paper that discusses the implementation of deep learning for medical hyperspectral imaging, which is what this work aims to accomplish by examining publications that currently utilize deep learning to perform effective analysis of medical hyperspectral imagery. This paper discusses deep learning concepts that are relevant and applicable to medical hyperspectral imaging analysis, several of which have been implemented since the boom in deep learning. This will comprise of reviewing the use of deep learning for classification, segmentation, and detection in order to investigate the analysis of medical hyperspectral imaging. Lastly, we discuss the current and future challenges pertaining to this discipline and the possible efforts to overcome such trials.INDEX TERMS Deep learning, neural networks, machine learning, medical image analysis, medical hyperspectral imaging.

show abstract

“…The development of direct pixelated detectors has revolutionized the imaging capabilities of multiple scientific fields, from particle tracking to X-ray and electron microscopy [1,2]. In electron microscopy (EM), the efficiency of direct detectors has provided a leap forward in the dose efficiency possible in transmission electron microscopy (TEM).…”

Section: Introductionmentioning

confidence: 99%

Event driven 4D STEM acquisition with a Timepix3 detector: microsecond dwell time and faster scans for high precision and low dose applications

Jannis,

Hofer,

Gao

et al. 2021

Preprint

View full text Add to dashboard Cite

Four dimensional scanning transmission electron microscopy (4D STEM) records the scattering of electrons in a material in great detail. The benefits offered by 4D STEM are substantial, with the wealth of data it provides facilitating for instance high precision, high electron dose efficiency phase imaging via center of mass or ptychography based analysis. However the requirement for a 2D image of the scattering to be recorded at each probe position has long placed a severe bottleneck on the speed at which 4D STEM can be performed. Recent advances in camera technology have greatly reduced this bottleneck, with the detection efficiency of direct electron detectors being especially well suited to the technique. However even the fastest frame driven pixelated detectors still significantly limit the scan speed which can be used in 4D STEM, making the resulting data susceptible to drift and hampering its use for low dose beam sensitive applications. Here we report the development of the use of an event driven Timepix3 direct electron camera that allows us to overcome this bottleneck and achieve 4D STEM dwell times down to 100 ns; orders of magnitude faster than what has been possible with frame based readout. We characterise the detector for different acceleration voltages and show that the method is especially well suited for low dose imaging and promises rich datasets without compromising dwell time when compared to conventional STEM imaging.

show abstract

X-ray and gamma imaging with Medipix and Timepix detectors in medical research

Cited by 30 publications

References 97 publications

Detectors and Cultural Heritage: The INFN-CHNet Experience

Detectors and Cultural Heritage: The INFN-CHNet Experience

Trends in Deep Learning for Medical Hyperspectral Image Analysis

Event driven 4D STEM acquisition with a Timepix3 detector: microsecond dwell time and faster scans for high precision and low dose applications

Contact Info

Product

Resources

About