Spatio-temporal filtering of digital angiography image data.
As welfare diseases become more common all over the world the demand for angiography examinations is increasing rapidly. The development of advanced medical signal processing methods has with few exceptions been concentrated towards CT and MR while traditional contrast based radiology depend on methods developed for ancient photography techniques despite the fact that angiography sequences are generally recorded in digital form. This article presents a new approach for processing of angiography sequences based on advanced image processing methods. The developed algorithm automatically processes angiography sequences containing motion artifacts that cannot be processed by conventional methods like digital subtraction angiography (DSA) and pixel shift due to non uniform motions. The algorithm can in simple terms be described as an ideal pixelshift filter carrying out shifts of different directions and magnitude according to the local motions in the image. In difference to conventional methods it is fully automatic, no mask image needs to be defined and the manual pixelshift operations, which are extremely time consuming, are eliminated. The algorithm is efficient and robust and is designed to run on standard hardware of a powerful workstation which excludes the need for expensive dedicated angiography platforms. Since there is no need to make additional recordings if the patient moves, the patient is exposed to less amount of radiation and contrast fluid. The most exciting benefits by this method are, however, that it opens up new areas for contrast based angiography that are not possible to process with conventional methods e.g. nonuniform motions and multiple layers of moving tissue. Advanced image processing methods provide significantly better image quality and noise suppression but do also provide the means to compute flow velocity and visualize the flow dynamics in the arterial trees by e.g. using color. Initial tests have proven that it is possible to discriminate capillary blood flow from angiography data which opens up interesting possibilities for estimating the blood flow in the heart muscle without use of nuclear methods.
Knutsson
H
H
Computer Vision Laboratory, Linköping University, Sweden. knutte@isy.liu.se
Andersson
M T
MT
Kronander
T
T
Hemmendorff
M
M
D000465:Algorithms; D003287:Contrast Media; D006801:Humans; D011856:Radiographic Image Enhancement / Q000379:methods*
D003287:Contrast Media