Methodology and analysis for the recognition of tumour tissue based on FDG-PET

For diagnosing the cause of a patient’s illness medical images are used more and more. Each medical problem can be visualized with its own specific image. For the detection of tumour tissue the main medical image is a Positron Emission Tomography (PET) image. A patient with suspicious tissue is injected with a radio-active substance. This active substance will decline and a positron is formed. This element will bump into an electron and two gamma photons appear due to this collision. When two opposite detectors in the scanner detect an arrival of a gamma photon within a certain time interval, the computer receives a signal. The intensity of these signals gives an indication of the density and location of the radio-active substance. Active organs and tumour tissue attract this radioactive substance since it is combined with glucose, therefore these parts are visible on a PET scan. So by making a PET scan the location and amount of the tumour tissue can be determined. Not all photons are detected correctly, so noise arises. This noise affects the accuracy of the image. It is important to get an indication of the effect and consequences of the noise. Besides the noise an extra difficulty of a PET image is the difference between two various PET images, each patient has its own PET image with specific intensity values. The reason is that each patient is different by gender, age or level of metabolism. Another difficulty is the origin of a high intensity pixel, a pixel can have a high intensity for many reasons. For example, a high intensity pixel describes a part of an active organ, but it can also be caused by an infection. Therefore it is very difficult to determine whether or not a pixel is suspicious based on its intensity only. A literature study on PET images and the effect of noise is performed. A PET image looks like a photo, so perhaps it is possible to use algorithms used in the photo-industry, unfortunately this does not seem possible. There are big differences between pictures and PET images, the main difference is the number of pixels. One slice of a PET image exists of 168 × 168 pixels, this slice is a vertical cross-cut of your body. Note that a PET scan is three-dimensional, therefore it consists of multiple two-dimensional slices. A normal photo has at least twice as much pixels as a PET image, therefore a PET image is not accurate enough and that is why it is so important to distinguish tumour tissue from the noise. To illustrate this, one strange pixel can be a tumour of 0.5cm2. In a photo it is easy to detect a strange pixel, because of the precision. This strange pixel can be determined based on its neighbors, a white pixel stands out from a group of black pixels. In a PET image it is unknown whether a pixel should be white or black. Other algorithms for clustering pixels into groups based on their gradation are not useful but still it is important to understand the effects of the noise. Therefore the distribution of the noise is investigated and the data from a PET scan is estimated with a distribution. This results in a difficult distribution which can not be implemented properly, therefore instead a normal distribution is used to perform the simulations. The methodology of the simulations and the performed steps are described extensively. From the data set of a phantom study a normal distribution is estimated. With this distribution a data set with correlated random values is generated. All pixels with a high intensity, the outliers, are determined for a certain percentage (1% or 5%) and one pixel is increased to a certain value. The hypothesis from the derivation of the noise model is that there exists a certain correlation between pixels. A pixel with a high intensity due to noise, will have neighbors with a high intensity as well. However a suspicious pixel is high because of itself. Therefore a suspicious pixel has less correlation (similarity) with its neighbors. This knowledge is used to distinguish a strange pixel from the outliers.