November 11th, 2020

лошадь, диаграмма, Фейнман

Основы фазоконтрастной рентгенографии

Шикарный обзор на 103 страницы, тут тебе и оптика, и матфизика, и уравнение эйконала, и Фоккера-Планка...

X-ray phase-contrast imaging: a broad overview of some fundamentals:
David M. Paganin, Daniele Pelliccia
We outline some basics of imaging using both fully-coherent and partially-coherent X-ray beams, with an emphasis on phase-contrast imaging. We open with some of the basic notions of X-ray imaging, including the vacuum wave equations and the physical meaning of the intensity and phase of complex scalar fields. The projection approximation is introduced, together with the concepts of attenuation contrast and phase contrast. We also outline the multi-slice approach to X-ray propagation through thick samples or optical elements, together with the Fresnel scaling theorem. Having introduced the fundamentals, we then consider several aspects of the forward problem, of modelling the formation of phase-contrast X-ray images. Several topics related to this forward problem are considered, including the transport-of-intensity equation, arbitrary linear imaging systems, shift-invariant linear imaging systems, the transfer-function formalism, blurring induced by finite source size, the space-frequency model for partially-coherent fields, and the Fokker-Planck equation for paraxial X-ray imaging. Having considered these means for modelling the formation of X-ray phase-contrast images, we then consider aspects of the associated inverse problem of phase retrieval. This concerns how one may decode phase-contrast images to gain information regarding the sample-induced attenuation and phase shift.
Comments: Submitted to Advances in Imaging and Electron Physics
лошадь, диаграмма, Фейнман

Моделирование быстрой конической КТ с открытым кодом

FastCAT: Fast Cone Beam CT (CBCT) Simulation:
Jericho O'Connell, Magdalena Bazalova-Carter
FastCAT, a fast cone-beam computed tomography (CBCT) simulator is demonstrated. The fastCAT application uses pre-calculated Monte Carlo (MC) CBCT scatter and detector response functions to reduce simulation time for kV and MV CBCT. Pre-calculated x-ray beam energy spectra, detector optical spread functions and energy deposition, and phantom scatter kernels are combined with beam filtration and GPU raytracing to produce CBCT volumes. Source spectra are either simulated in EGSnrc using 2.5 and 6 MeV electron beams incident on a variety of target materials or analytical spectra from a tungsten x-ray tube. Detectors were modelled in Geant4 extended by Topas including optical transport in the scintillators. Two MV detectors were modelled, a standard Varian AS1200 GOS detector and a novel CWO high quantum efficiency detector. Energy dependent scatter kernels were created in Topas for a two 16-cm diameter water phantoms: a Catphan 515 contrast phantom and a spatial resolution phantom. The Catphan phantom contained inserts of 1-5 mm in diameter of six different tissue types. These pre-calculated MC datasets allow the user to create CT volumes with custom phantom inserts, imaging beam, focal spots and detector combinations. FastCAT simulations retain high fidelity to measurements and MC simulations: MTF curves were within 1.1% and 4.8% of measured values for the CWO and GOS detectors, respectively. Contrast in a fastCAT Catphan 515 simulation was seen to be within 1% of an equivalent MC simulation for all of the tissues. A fastCAT simulation of the Catphan 515 module at a resolution of 1024x1024x10 took 61 seconds on a GPU while the equivalent Topas MC was estimated to take approximately 5.6 CPU years. We present an open source fast CBCT simulation with high fidelity to MC simulations. The fastCAT application can be found at this https URL
Comments: 17 Pages
лошадь, диаграмма, Фейнман

Дешевая рентгеновская система

X-ray imaging detector for radiological applications in the harsh environments of low-income countries:

This paper describes the development of a novel medical Xray imaging system adapted to the needs and constraints of low and middle income countries. The developed system is based on an indirect conversion chain: a scintillator plate produces visible light when excited by the Xrays, then a calibrated multi camera architecture converts the visible light from the scintillator into a set of digital images. The partial images are then unwarped, enhanced and stitched through parallel processing units and a specialized software. All the detector components were carefully selected focusing on optimizing the system s image quality, robustness, cost, effectiveness and capability to work in harsh tropical environments. With this aim, different customized and commercial components were characterized. The resulting detector can generate high quality medical diagnostic images with DQE levels up to 60 percent, at 2.34 micro Gray, even under harsh environments i.e. 60 degrees Celsius and 98 percent humidity.
квадратная птица Владислав

Любителям пешего туризма: фрактальная размерность хайка

The fractal dimension of the Appalachian Trail:
Brian Skinner
The Appalachian Trail (AT) is a 2193-mile-long hiking trail in the eastern United States. The trail has many bends and turns at different length scales, which gives it a nontrivial fractal dimension. Here I use GPS data from the Appalachian Trail Conservancy to estimate the fractal dimension of the AT. I find that, at length scales between ∼20 m and ∼100 km, the trail has a well-defined "divider dimension" of ≈1.08. This dimension can be used to estimate the true hiking distance between two points, given the distance as estimated from a map with finite spatial resolution (e.g., Google Maps).
Comments: 2 pages, 2 figures