Hello everyone. In this video, we will cover an overview of some basic principles of Computed
Tomography, such as image reconstruction and back projection.
The X-ray radiation produced by an X-ray source go through the patient body.
The X-ray tube and the X-ray detectors rotate around the patient bed.
The detector here plays the same role as a photographic film in a conventional X-ray
radiography.
You might remember that every part of the body attenuates X-ray radiation differently,
depending on the density of the organs and tissues.
The role of the detector is to measure the amount of X-ray radiation passing through
the different organs and convert the radiation intensities into electrical signals.
Those signals are then process by a computer using different algorithms, to create CT images.
If we use a simple X-ray system, only a 2 dimensional image can be created.
However, with CT technology, by measuring X-ray radiation going through the body at
different angles, we can now make three dimensional pictures.
The computer here plays a key role, splitting up organs in the body into small volumes called
voxels.
A voxel is a three dimensional equivalent of a pixel.
Now that the data have been acquired, the next step is to produce the images.
The electrical signals measured by the detectors are re-assembled together with a computer
software creating images.
For example, in this figure each voxel from a scanned body part is allocated a number
also called an attenuation coefficient number, mu.
The mu value represents the level of attenuation of the X-ray going through the body parts.
The X-ray transmission measurement (Ni) represents the sum of all attenuation values from all
voxels in each tissue slice.
For example, the voxels from bones will have high mu values due to their high attenuation
of X-ray radiation.
But, the voxels from skin will have low mu value as the skin has very low X-ray attenuation.
Let's take an example of voxels from a tissue slice.
I have assembled them in 2-rows and 2-columns.
I have also assigned some random attenuation value of 3, 5, 1 and 8 to each voxel.
In this case, a value of 1 will represent no reduction of radiation intensity meaning
a low attenuation of the X-ray by the tissue.
A value of 8 here will represent here a maximum attenuation of the X-ray.
The X-ray pass through the tissue slices at different angles: 0 degree will come from
left to right, 45 angle diagonal, then 90 degree from the
top to the bottom, and finally 135 degree angle will be diagonal on the other side.
Each attenuation value will be expressed as the sum of the voxel attenuation value along
the radiation path.
So in this example, the new attenuation values after X-ray radiation from the 0 degree angle
will be then 8 and 9. From 45 degrees the new value will be 11 and then from the top
at 90 degree the values will change to 4 and 13.
And finally at 135 degrees the new value will be 6.
An agreement from the earliest days of CT is to replace the name attenuation value by
CT numbers or Hounsfield unit.
The Hounsfield unit is a concept that we actually will discuss a bit more during this course.
To understand back-projection re-construction, let's try with another example, here, showing
a CT scan of an object.
This object has 3 different attenuation materials. The attenuation values from each view taken
at different angles are divided evenly along the path of the ray shown here.
This process is called back projection.
In the Figure C, as you can see, after back projection, the 4 views are summed together
and here it is a different direction. And an image of the test object is beginning to
appear.
It is quite blurry at first, but the quality of the image can be improved by using a mathematic
process such as filtering.
To understand the important of filtering, let's compare two pictures.
This first picture is a CT scan image of an object before mathematical filtering, and
here is the same picture after filtering. As you can see the image becomes much sharper.
To summarise, in this section we have covered some of the basic principles of image reconstruction
and filtered back projection for Computed Tomography.
If you want to learn more about these topics, you can view Dr Andrew Janke's presentation
in the advanced topic section.