MRI is one of the most widely used imaging techniques within radiology. The theory behind MRI is well documented in various sources. This section provides a summary of the information presented in [30], which has also been briefly summarised in [17]. A more detailed and illustrated explanation for MRI is presented in [31].
MRI uses the natural magnetic properties of the body to produce images. The hydrogen nucleus (a single proton) is used for imaging purposes because it is found in abundance in water and fat. These hydrogen nuclei have an axial spin, with their axes randomly aligned.
When the body is placed in a strong magnetic field, such as an MRI scanner, the protons’
axes all align. This uniform alignment creates a magnetic vector oriented along the axis of the MRI scanner.
This magnetic vector is then deflected by adding additional energy (in the form of a radio wave) to the magnetic field. The radio wave frequency (RF) is determined by the sought element (usually hydrogen) and the strength of the magnetic field generated by the MRI scanner. The strength of the magnetic field can be altered electronically from head to toe using a series of gradient electric coils. Thus, by altering the local magnetic field by these small increments, different slices of the body will resonate as different frequencies are applied.
When the RF source is switched off, the magnetic vector returns to its resting state.
This causes a signal (in the form of a radio wave) to be emitted from the affected nuclei. It is this resulting radio wave signal which is used to create MRI images. Receiver coils are placed around the body part that is imaged to improve the detection of the emitted signal.
The intensity of the received signal is then plotted on a grey scale, and cross-sectional images are generated.
Additionally, there is a difference in how quickly different tissue relax once the RF pulse is switched off. These times are measured in the following two ways. T1 relaxation is the time taken for the magnetic vector to return to its resting state. And T2 relaxation is the time needed for the axial spin to return to its resting state.
”There are no known biological hazards of MRI because, unlike x-ray and computed tomography, MRI uses radiation in the radiofrequency range which is found all around us and does not damage tissue as it passes through.” [30]
2.2.1 Image types
MRI can produce different images depending on the weighting of T1 and T2 relaxation times. Because different tissues have different relaxation times, the weighing can be used to create differences in signal intensities and by extension tissue grey levels. The datasets used in this thesis has three differently weighted images for each patient. These are T1, PD, and FS weighted images.
T1 images
T1 images present the difference in T1 relaxation times. T1 images are useful for identi-fying fluid filled spaces in the body. Fat appears very bright in these images, while fluid is dark.
Figure 2.2:Example of an T1 weighted image
PD images
In a PD weighted MR image, it is the tissues with a higher concentration/density of protons (hydrogen nuclei) which produce the strongest signals, and thus appears the brightest. [18]
Figure 2.3:Example of an PD weighted image
FS images
FS images are used to suppress the signal from normal adipose tissue. The result is that adipose tissue appears darker, while any other tissue appears brighter by contrast. [32]
Figure 2.4:Example of an FS weighted image
2.2.2 Image formats
There are a lot of different MRI file formats. The four most commonly used are Analyze, Nifti, Minc, and Dicom. Dicom is designed to standardize the generated images by
diag-nostic modalities. While the other 3 aim at facilitating and strengthening post-processing analysis. [33]
Nifti
In this thesis, the datasets were supplied as Nifti files. This format can be seen as a revised Analyze format. The notable improvements include updated header information such as rotation and orientation. Nifti also includes support for additional data types, such as unsigned 16-bit. [33]
2.3 Segmentation
Segmentation is a large subfield within digital image processing. Segmentation is a task that aims to divide an image into regions with similar properties, such as colour or texture [34]. Segmentation techniques range from the simple threshold method to the more ad-vanced edge detection and clustering techniques, and also includes various ML algorithms.
Conventional segmentation algorithms often rely on a critical selection of parameters, for instance, to derive an accurate membership function in the case of clustering. This requires a considerable amount of user expertise [35]. These aspects are simply not prac-tical when it comes to more advanced segmentation tasks such as segmenting multiple structures, especially when dealing with complex 3-dimensional structures such as those created by MRI [36] [17].
There are three main difficulties when it comes to segmentation tasks:
• Noise:Noise during the data generation process can potentially alter the intensity of either a singular pixel or a group of pixels, resulting in the classification becoming uncertain.
• Low variety of pixel intensity between classes:When segmenting multiple classes within the same image, the different classes need to be distinguished somehow. If the variety of pixel intensity between different classes is very low, then they become almost indistinguishable.
• Class imbalance: When an image contains classes of varying sizes, the smaller classes are easily ignored during training due to the low impact they have on the overall accuracy of the segmentation task.
The first two of these difficulties are related to the data generation. Some amount of noise is always going to be present during an MRI scan.
The intensity of pixels can have increased variety by utilizing different weightings for the MR image generation. However, if multiple classes are made up of the same or similar tissue, such as tendons and ligaments, then this problem becomes unavoidable.
This problem can then only be solved by considering the spatial information of the image, as opposed to strictly the image intensity. This is something that CNNs are especially well suited for, due to their local receptive field which will be discussed more in detail in section 2.5.6.
The class imbalance is also unavoidable during data generation, but can be handled by ANNs through the choice of the loss function, which is briefly mentioned in section 2.4.2 and discussed in detail in section 2.6
Semantic segmentationis the main focus for this thesis, and it is a specific type of segmentation where each pixel (or voxel for 3D images) is given a class label. This task is also often called dense prediction or dense semantic segmentation.