X-ray computed tomography based on sparse information
Since the first commercially available Computed Tomography (CT) scanners in the early 1970th, CT technology improved enormously. Most of these improvements focussed on acquisition speed, slice count, or image quality. The latest CT scanner can even capture a beating heart in brilliant quality. An important issue of X-ray based computed tomography which recently is more addressed, is radiation dose. The typical radiation dose of a CT is about two orders of magnitude higher than the dose of a corresponding plain film X-ray.

A variant of the classical computed tomography is Cone Beam Computed Tomography (CBCT) where the X-rays form a cone. CBCT scanners are typically used in dentistry. Here, the scanner rotates around the patient's head and acquires a number (typically several hundred) of plain X-ray images. While the total radiation dose from CBCT scanners in general is lower than from classical CT scanners it still delivers more radiation than dental plain film X-ray. The radiation dose of a CBCT exam can be cut by reducing the number of images acquired. Thereby, in order to retain reconstructions of satisfactory quality it is important to carefully calibrate the CBCT scanner, suppress images artifacts, and adjust reconstruction.

In this presentation, I will give an overview of the development of X-ray based computed tomography as well as actual developments in the field of CBCT technologies. Especially, I will discuss actual algebraic reconstruction techniques as well as the suppression of image artifacts and present a method to automatically determine the geometry of a CBCT system with high precision.
Lecturer:
Ulrich Schwanecke (Hochschule RheinMain)
Dates:
Fr. 24.01.2014, 13:00 c.t. , G29-R335
Additional Information:
> Lecture website <
Recent news:
12.06.2017

Research Seminar Announcement

In the course of the Visual Computing research seminar Prof. Dr. Nils Thuerey from the Technical University of Munich will give a talk with subsequent discussion about the topic Fluid Simulations and Neural Networks.

We would like to invite everyone interested to join us. The research seminar will take place in on Friday 30.06.2017, 13:15 in G29-R335.

To the overview…