𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Motion artifacts in the X-ray CT image of a revolving point object

✍ Scribed by Takashi Sakamoto; Shunsuke Sato


Publisher
John Wiley and Sons
Year
1994
Tongue
English
Weight
753 KB
Volume
25
Category
Article
ISSN
0882-1666

No coin nor oath required. For personal study only.

✦ Synopsis


Abstract

To obtain the cross section of an object by using an X‐ray CT scanner, many projections of the object with various angles are required. This also takes time for scanning. If the object moves during this period, the projections would be disturbed causing motion artifact in the CT image. Understanding of their characteristics in relation to the movements of the objects will be useful for analyzing CT images and will supply new knowledge to medical diagnosis.

This paper investigates a reconstructed image of a point object which revolves around the center of a scanning area. The results show that the reconstructed image of the point object forms an epicycloid or hypocycloid (a curve on a plane) and this reduces with time. The same type of artifact is simulated by a computer, and this justifies the analysis.


πŸ“œ SIMILAR VOLUMES


Image degradation and stroboscopic image
✍ Takashi Sakamoto; Shunsuke Sato πŸ“‚ Article πŸ“… 1993 πŸ› John Wiley and Sons 🌐 English βš– 635 KB

## Abstract In an X‐ray computed tomography (CT), time to scan an object must be allowed to obtain its projection data. If the object is not steady during the scanning, its reconstructed image would contain errors called β€œmotion artifacts.” Recently, two kinds of motion artifact, β€œblurred image” an

Evaluation of osteoporosis in X-ray CT e
✍ Sadamitsu Nishihara; Hiroshi Fujita; Tadayuki Iida; Atsushi Takigawa; Takeshi Ha πŸ“‚ Article πŸ“… 2005 πŸ› Elsevier Science 🌐 English βš– 191 KB

We have developed an algorithm that can distinguish the central part of the vertebral body from abdominal X-ray CT images to determine whether it is possible to aid a diagnosis of osteoporosis. We classified three measures for the principal component analysis and linear discriminant function. When w