By Jerry L. Prince
For classes in scientific imaging platforms. With sign processing as its beginning, this article covers an important imaging modalities in radiology: projection radiography, x-ray computed tomography, nuclear medication, ultrasound imaging, and magnetic resonance imaging. prepared into components to stress key total conceptual divisions, scientific Imaging is wonderful for engineering scholars who've taken the prerequisite signs and structures classes in addition to basic chance.
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Additional info for Medical Imaging: Signals and Systems
Sample text
In addition, many signals will have properties that distinguish them from other signals and make their use in analysis easier. Separability and periodicity are two such common properties. We now study these special signals and properties. 1 Point Impulse It is very useful in medical imaging to mathematically model the concept of a point source, which is used in the characterization of imaging system resolution. 1 x x (a) (b) Two alternative signal visualizations: (a) a functional plot and (b) an image display.
Based on the previous signal classification, systems can also be organized into categories. For example, we may consider a continuous-to-continuous system that responds to a continuous signal by producing a continuous signal, or a continuous-to-discrete system that responds to a continuous signal by producing a discrete signal. For example, an analog-to-digital converter takes a continuous signal as input and produces a discrete signal as output through a process called sampling (see Chapter 3).
52) in which case h(x, y) = −(x2 + y2 ) 1 exp 2π (σ12 + σ22 ) 2(σ12 + σ22 ) . 52), we have substituted the variable ξ − [σ22 /(σ12 + σ22 )] x with τ ; whereas, to obtain the third equality, we have used the fact that ∞ −∞ 2τ 2 e−a dτ = The resulting PSF is Gaussian as well. √ π , a for a = 0 . 5 Separable Systems Separable systems form an important class of LSI systems. As with separable signals, a 2-D LSI system with PSF h(x, y) is a separable system if there are two 1-D systems with PSFs h1 (x) and h2 (y), such that h(x, y) = h1 (x)h2 (y) .