1. Field of the Invention
This invention relates generally to imaging systems. More specifically, the present invention is directed to systems and methods of image processing in which images from different detectors or different detector configurations are resized to a standard matrix size.
2. Background Discussion
Radiography is the use of certain spectra of electromagnetic radiation, usually x-rays, to image a human body. Angiography, a particular radiographic method, is the study of blood vessels using x-rays. An angiogram uses a radiopaque substance, or contrast medium, to make the blood vessels visible under x-ray. Angiography is used to detect abnormalities, including narrowing (stenosis) or blockages (occlusions), in the blood vessels throughout the circulatory system and in certain organs.
Cardiac angiography, also known as coronary angiography, is a type of angiographic procedure in which the contrast medium is injected into one of the arteries of the heart, in order to view blood flow through the heart, and to detect obstruction in the coronary arteries, which can lead to a heart attack.
Peripheral angiography, in contrast, is an examination of the peripheral arteries in the body; that is, arteries other than the coronary arteries. The peripheral arteries typically supply blood to the brain, the kidneys, and the legs. Peripheral angiograms are most often performed in order to examine the arteries which supply blood to the head and neck, or the abdomen and legs.
Recent advances in x-ray imaging have allowed the acquisition of digital radiographic images. Different x-ray applications, such as cardiac angiography, interventional angiography, and general radiography will apply different, specifically designed, x-ray flat-panel detectors. Differences typically include the size, image formats, pixel size, frame rates, dose sensitivity, and other parameters of the x-ray detectors. Differences between detectors can arise because the pixels of the x-ray detector are hardwired to the input screen. For example, for flat-panel detectors, the x-ray conversion layer is directly coupled to the underlying pixel matrix.
Conventional image processing typically involves both frequency-domain and time-domain techniques. Frequency-dependent processing may include such techniques as edge enhancement, dynamic range compression, and other advanced multi-frequency filter techniques. Temporal filter techniques may include motion-detection algorithms and time-domain filters. Most of these algorithms depend on the actual pixel size of the detector (including combined super-pixels generated by binning techniques) and the local noise level or signal-to-noise ratio. The pixel size influences the object-sizes on which the algorithms operate, while the signal-to-noise-ratio, which is governed by the dose as well as the pixel size, influences the thresholds or distinction capabilities between noise and structure in those algorithms which involve local or global noise analysis.
Recently, increased use of combinations of applications, such as combination labs for combined cardiovascular as well as peripheral angiography applications, has lead to mixed detector types being used, with each detecting having different characteristics and specific designs. For example, different planes of a single combination lab may utilize different detectors, thereby introducing the problem of different image parameters in a single application.
In another example, a user may use two different labs utilizing two different detector configurations, and may later wish to reconcile the different images taken by the two different detectors, again introducing the problem of different image parameters in a single application.
Conventional techniques to attempt to overcome image quality differences in such combined detector applications typically include attempts for individual parameter optimization. A similar approach that has been attempted involves individual parameter optimization of the image processing pipeline for each detector type so that the image characteristics are as close as possible. This approaches has the disadvantages that (1) it requires independent parameter handling; and (2) it may not be feasible.
Furthermore, independent parameter handling is becoming increasingly complex with the increasing complexity of the algorithms being used.
For example, independent parameter handling is unacceptable when used, for example, with edge-enhancements, which require small, odd-sized kernels, such as 3×3, 5×5, or 7×7 pixel kernels. In such instances, the parameters are discrete, and no fine-matching is possible. Also, in an instance in which two detectors are used, one with a small 150 μm pixel size, and the other with a larger 200 μm pixel size; in the first detector, a 5×5 pixel kernel will act on 750 μm size object structures, while the second detector will act on 1000 μm size object structures. Using a 5×5 kernel for the small pixel detector and a 3×3 kernel for the larger pixel detector will not resolve the problem, and in fact no other kernel size will be an appropriate alternative.
Clearly, this conventional approach has disadvantages for parameter complexity, and inconsistency of image processing from one detector type to another.
Therefore, it would be an advancement in the state of the art to provide a system and method of image processing of images in which images from different detectors are resized to a standard matrix size.