Bush__The_Essential_Physics_for_Medical_Imaging - Biomedical ...

(1.4 MB/disk)(l,024 2 bytes/MB)/[(64 x 64 pixels/image) (2 bytes/pixel)] =179 images/disk(if no other information is stored on the disk).ProcessingAn important use of computers in medical imaging is to process digital images todisplay the information contained in the images in more useful for~s. Digitalimage processing cannot add information to images. For example, if an imagingmodality cannot resolve objects less that a particular size, image processing cannotcause such objects to be seen.In some cases, the information of interest is visible in the unprocessed imagesand is merely made more conspicuous. In other cases, information of interest maynot be visible at all in the unprocessed image data. X-ray computed tomography isan example of the latter case; observation of the raw projection data fails to revealmany of the structures visible in the processed cross-sectional images. The followingexamples of image processing are described only superficially and will be discussedin detail in later chapters.The addition or subtraction of digital images is used in several imaging modalities.Both of these operations require the images being added or subtracted to be ofthe same format (same number of pixels along each axis of the images) and producea resultant image (sum image or difference image) in that format. In image subtraction,each pixel in one image is subtracted from the corresponding image in asecond image to yield the corresponding pixel value in the difference image. Imageaddition is similar, but with pixel-by-pixel addition instead of subtraction. Imagesubtraction is used in digital subtraction angiography to remove the effects ofanatomic structures not of interest from the images of contrast-enhanced blood vessels(see Chapter 12) and in nuclear gated cardiac blood pool imaging to yield differenceimages depicting ventricular stroke-volume and ventricular wall dyskinesis(Chapter 21).Spatial filtering is used in many types of medical imaging. Medical images oftenhave a grainy appearance, called quantum mottle, caused by the statistical nature ofthe acquisition process. The visibility of quantum mottle can be reduced by a spatialfiltering operation called smoothing. In most spatial smoothing algorithms, eachpixel value in the smoothed image is obtained by a weighted averaging of the correspondingpixel in the unprocesssed image with its neighbors. Although smoothingreduces quantum mottle, it also blurs the image. Images must not be smoothedto the extent that clinically significant detail is lost. Spatial filtering can also enhancethe edges of structures in an image. Edge-enhancement increases the statistical noisein the image. Spatial filtering is discussed in detail in Chapter 11.A computer can calculate physiologic performance indices from image data.For example, the estimation of the left ventricular ejection fraction from nucleargated cardiac blood pool image sequences is described in Chapter 21. In some cases,these data may be displayed graphically, such at the time-activity curves of a bolusof a radiopharmaceutical passing through the kidneys.In x-ray and nuclear CT, sets of projection images are acquired from differentangles about the long axes of patients. From these sets of projection images, computerscalculate cross-sectional images depicting tissue linear attenuation coefficients(x-ray CT) or radionuclide concentration (SPECT and PET). The methods