CT Image Quality and Dose Management
Now consider a SPECT image with 64 images of poor quality. Those 64 images SPECT images. First consider the matrix size and its relationship to resolution. Image size is what if often called resolution, basically the number of pixels stored in the image file. So on a 12 megapixel camera, you can. In MRI, spatial resolution is defined by the size of the imaging voxels. The matrix size is the number of frequency encoding steps, in one direction; and the.
Each has its characteristics that must be considered. The simplest, and the only method available for many years is the "scan and step" method illustrated here. With the body not moving the x-ray beam is rotated completely around the body and a data set for a slice is collected.
With this method the data set is "locked" to a specific slice determining its thickness and position. After each beam rotation the body is then moved or "stepped" to the next slice position where it is scanned. The two major limitations of this method are that it is relatively slow in covering a body section and the slices position and thickness are fixed at the time of the scan and cannot be changed later. Spiral or Helical Scanning The preferred alternative for many procedures is the helical or spiral scanning method shown here.
With this method there are two continuous motions occurring at the same time. The x-ray tube and beam is rotating around the body continuously and at the same time the body is being moved through the scanner. If we can imagine the path of the x-ray beam on the patient's body it would form a spiral or helical pattern.
Either name is an appropriate description of this method. There is one very important adjustable protocol factor associated with this method that can have an effect on both image quality and dose to the patient. To do so we must record a representation of all possible sound. You could say that we are attempting to record the entire band width of sound.
In essence, Nyquist represents the entire spectrum in the frequency domain. By setting the correct NF we can reconstruct the entire spectrum of spatial data in the frequency domain Furthermore, one must appreciate that the sum note the above image of all the frequencies means that there are a lot of different wave patters within NF.
In general frequencies can be broken down into three categories Low - background and large objects Middle - variation of smaller objects. As the object continue to get smaller the frequency continues to get higher High frequencies - small to very small objects plus noise. One cannot differentiate between very small objects and noise because it goes beyond the resolution of the imaging system FWHM An appropriate sampling for NF is two cycles as noted by the formula below Fn D Size of Pixel The formula identifies the NF to pixel size and the number determines what the actual size the frequency should be in cycles per mm.
Let us apply some numbers to see how this works. This means that in order capture the best spatial frequency the NF must be set at 0. To go beyond this point smaller object would not improve our resolution Note that the pixel value, D, must be twice per cycle it is for that reason that the value is multiplied by 2 If a matrix is used with a pixel size of 3. This is because the maximum NF will always be 0.
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The reason for this is that it takes two pixels to capture the entire cycle. Failure to not include all of it would result in not capturing all information that is available for processing.
The images above show the correct NF at 0. An example of over sampling is also noted when the NF is set to 1. What happens if over sampling occurs?
Or try to image higher frequencies Look below! Notice the "squares" that can be seen. This Moire type defect is seen more clearly in the background of the transverse slice, but also within the brain phantom.
This is known as aliasing. The generation of this artifact is not specifically from over sampling, however, the end result is the same, aliasing B - Shows a set of bars with variation in size.
Aliasing, in the center of the image it noted, specifically were the bars just don't line up.
This occurred because the NF was set at 1. Limited counts in nuclear medicine images always reduces the amplitude of the original image frequency. However, the more the counts the less noise there is. Noise and small objects can become lost in the data which can potential miss disease Low end frequency issues might normally be considered background and scatter issues, however, Poisson statistical noise "blurs" or "smooth's" critical data in the low frequency domain, that occurs when reconstruction via backprojection is applied.
Also classified in the realm of low frequencies is star defect as discussed in your previous lecture The above graph is similar to the previous one, however, there are several differences The frequency curves that are displayed include: MTF of the gamma camera black with two separately acquired images in red From left to right activity on the left contains low frequencies, while that on the right contains high frequencies Noise level 1 represents a low count image where noise breaks away midway from the MTF Noise 2 represents more counts acquired, when compared to Noise 1.
Here the noise level breaks away from the MTF curve further into the higher frequency range One should conclude several points: The more counts you acquire the better your resolution via the higher the frequency being image Once the red curve breaks from the MTF it is impossible to tell the difference between small objects and noise If more counts you acquire the closer the frequency will match the camera's MTF giving you the best possible resolution Filters Pre-filtering the raw data Many places actually pre-filter the raw rata, which mans that each 2-D image within the or degree rotation is filtered prior to reconstruction.
The rational for this is simple - If you are dealing with low count images then you're going to have a lot of high frequency noise. By completing a 9-point smooth on each 2-D slice you removing excessive fluctuation in the high frequency range Filtering during imaging reconstruction When filtering during backprojection and image reconstruction there are several aspects that filters can accomplish Removing low frequency BKG Removing high frequency noise.
Low pass filters only allow low to mid frequencies to be processed.
Digital Image Characteristics
This means the exclusion of high frequency data during reconstruction. Example of this might be a Butterworth or Hanning filter High pass filters do the opposite. An example of this would include a Ramp filter Band pass filter eliminate low and high frequencies and only let mid-range frequencies to be processed Restoration filters attempt to restore data and improve the quality of the image.
Generally, not considered very useful because it does not allow you to look inside the structure. However, analyzing the external anatomy of the brain might lead to finding large "hole" on the brain's exterior surface.
This could be due to a lesions or an infarct that affected the brain and included the surface anatomy Post-filtering after reconstruction is also sometime done, especially if the end results from reconstruction contain a grainy or statistically noisy image. Smoothing the image may be recommended Filter the reconstruction with high and low pass filters Ramp filter high pass - Two issues to consider The main role of the linear ramp filter is to amplify the frequency to better resolve the data.
However, if the entire frequency was amplified then true counts, bkg, and noise would all be amplified.
MR Image Quality - FRCR Physics Notes
This would serve no purpose Hence, the ramp filter cuts off the low frequency background, which removes unwanted data. What do you do with the high frequency noise? Adding a low pass filter to the to reconstruction process will can cutoff the noise Keep this thought in mind Filters and orders Some filters allow you to adjust its order. Essentially what that does is change the slope on the filter.
In the case above, when you increase the order number on a butterworth filter you increase the negative component of the slope, making it steeper This manipulation allows the user to remove or add difference frequencies to the reconstructed image Note that an order of 8 increases the amount of lower frequencies, but doesn't accept as many frequencies at the higher end.
The steeper the slope of the curve the greater the response is to contrast, while a less negative slope has less sensitivity. Filters and cut-offs Another angle on filters has to do with its cut-off. The examples above show the cut-off frequency set to three different points.