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Chapter 9

09-01
Volume and Picture Elements

09-02
Image Matrix and Field-of-View

09-03
Spatial Resolution and Partial Volume Effects

09-04
Definition of Contrast

09-05
Signal-to-Noise

... and Data Averaging
... and Field Strength
09-06
Contrast-to-Noise Ratio
09-07
Age

09-08
Temperature

09-09
Image Windowing


Chapter Nine
Fundamentals of Image Characteristics: The MR Image

n diagnostic imaging, the contents depicted on an image should reflect the essence of the original information as objectively as possible. However, there are limitations, as we have seen earlier: for instance, hardware and software of the MR equipment and possibly other outside forces influence image content (Figure 09-01).


Figure 09-01:
Object and "mirror" image – excitation, in this case with a broom, will change picture elements soon.


09-01 Volume and Picture Elements

In computerized imaging, be it nuclear medicine imaging, x-ray CT or an­giog­ra­phy, or magnetic resonance imaging, pictures are composed of elements, called picture elements or pixels, which, in turn, reflect the content of volume ele­ments or voxels. Figure 09-02 explains this.


Figure 09-02:
Voxel and pixel.
We want to image an entire person who for this purpose is mathematically divided into volume elements. In each volume element, the sig­nals are averaged and turned into a number which represents a certain level on a gray scale. These numbers are used to create a picture consisting of pi­xels.


In principle, voxels could be as small as a single cell. In reality, however, voxel size depends on a number of limiting factors, with computer capacity and the sig­nal obtained from an individual voxel being the main obstacles. Therefore, for instance, 256×256×1 voxels are created of a slice of an object and turned into pi­xels. These 256×256 picture elements are called the image matrix.


09-02 Image Matrix and Field-of-View

The image matrix is characterized by the number of pixels in the x- and y-di­rec­tions. It is defined by the steepness of the x-gra­dient (the frequency-encoding gradient) and the number of phase-encoding steps in the y-gradient. Both com­bi­ned represent the field-of-view (FOV), as shown in Figure 09-03.


Figure 09-03:
Image matrix and field-of-view.
In this case, we have an image matrix of 6×6, i.e., a grid of 6 rows and 6 columns with a total number of 36 pixels. Usually in MR imaging, the field-of-view is at least 256×256. Commonly, individual voxels and pixels are larger in body imaging than in head imaging.


If the FOV is the whole head with an edge length of 25.6 cm and a matrix size of 256×256 is used, then a single pixel represents 1 mm. If the FOV is smaller (e.g., 12.8 cm) and the same matrix size is used, the spatial resolution is 0.5 mm.


09-03 Spatial Resolution and Partial Volume Effects

As in other digitized imaging methods, voxel and pixel size influence spatial re­so­lu­tion and thus contrast.

All anatomical structures within one voxel add to its averaged signal intensity in the final image. If the voxel has a large volume, it can contain many different structures and tissue types. In the final image pixel, they will be in­dis­tin­gui­shable. If the voxel can be kept smaller, less structures will be represented by one single pixel, and therefore spatial resolution and contrast will be better.


Data acquisition and reconstruction methods define different voxel shapes.

Isotropic reconstructions use cubes, while in anisotropic methods one side is longer than the two others. Although they may look the same in the picture plane, the content and thus the calculated number for the gray level representation in the pixel can be different (Figure 09-04).


Figure 09-04:
Different slice thickness (a) can lead to iso­tro­pic or anisotropic volume elements (b) and different signal intensities.


The sometimes blurry features of these images are caused by the averaging of different structures. This is known as partial volume effect. The smaller the pixel size, the better the suppression of partial volume effects (Figure 09-05).

However, the bigger the voxel size, the better will be the signal (and signal-to- noise). In general, the signal-to-noise is the determining factor for the final voxel-versus-pixel size. Increasing the matrix size from 128×128 to 256×256, while keeping field-of-view, slice thickness and imaging constant, will reduce the signal-to-noise ratio by a factor of 4. Thus, the signal-to-noise has to be high enough to permit the increase in resolution.


Figure 09-05:

Spatial resolution and partial volume effects: matrix size (a) 256×256, (b) 128×128, (c) 64×64, and (d) 32×32. Due to the partial volume effects, anatomic details disappear.

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