The aim of this research is to build a machine learning model in order to predict the success of invasive surgical treatment on a degenerated cervical spine, based on the baseline X-ray images. The purpose of the results of this research is an application in computer-aided diagnostics and treatment planning in the field of neurosurgery. Spinal degeneration can be described as the gradual loss of spinal structure and a decreased functioning of the spine over time. Before the diagnosis of spine degeneration can be made, a sagittal X-ray analysis of the spine is very important. With the current ageing population and the relatively high prevalence of neck pain and spinal complaints of approximately 30%, there is a great demand on MRI and X-ray analysis in healthcare. Machine learning techniques, and especially convolutional neural networks, seem promising for the application of X-ray image analysis. This research was preceded by a literature study about cervical degeneration and the implementation of ML in spine research. Of the available machine learning techniques, artificial neural networks show the best classification accuracy when it comes to image classification. Convolutional Neural Networks (CNNs) in particular are applicable for the computer vision classification task of this study. That is why, in consultation with the neurosurgery department of the Leiden University Medical Center (LUMC), it was decided to focus on the development of a Convolutional Neural Network (CNN) to perform the binary classification task. The final configuration of the convolutional neural network (CNN) consists of four convolutional layers with ReLU activation function and a maximal pooling function. Batch normalization was applied after the first convolutional layer of the model in order to create a more stable training environment. The false positive rate (FPR) is 19% on average and 15% during the best performing run. The ROC curve shows an AUC during the best performing run of 0.91 and 0.86 on average, whereby 1.0 would be a perfect classifier. It can be concluded that this classification task could be performed by a convolutional neural network (CNN), adapted to the specific classification task. The lowest achieved false positive rate of the model was 15%, which shows a major improvement compared to the current clinical situation at the department of neurosurgery in the Leiden University Medical Center, where about 25% of invasive spinal surgical operations, involving cervical degeneration, are of no benefit to the patient.

Low-field Magnetic Resonance Imaging (LF MRI) is a cheap and safe technique to visualise the internal structure of the human body. Unlike other imaging techniques, Magnetic Resonance Imaging does not use ionising radiation to generate the images. Instead, it uses magnetic fields and radio waves which are nonthreatening to the health. The LF MRI scanners are constructed out of inexpensive materials and their maintenance is affordable. Therefore, these scanners are a promising alternative for developing countries that present economic limitations. Nonetheless, since Magnetic Resonance scanners use a weak magnetic field, the process of image reconstruction requires complex algorithms that need time. This thesis will examine the way in which the computational time of the image reconstruction from a low-field Magnetic Resonance Imaging can be reduced, using an algorithm based on the fast Fourier transform.

Delft University of Technology (TU Delft), Leiden University Medical Center (LUMC), Pennsylvania State University (PSU) and Mbarara University of Science and Technology (MUST) have an ongoing collaboration to create an affordable, portable and simplified version of the magnetic resonance imaging (MRI) scan for the CURE children’s hospital to diagnose children with hydrocephalus (water on the brain). As opposed to the conventional MRI scan, the low-field MRI prototype uses permanent magnets to create a magnetic field in the order of Milliteslas (mT). A downside of the low-field MRI application is the difficulty with spatial encoding due to small variations in the strength of magnetic field. This is a major problem for image reconstruction. The purpose of this research was to implement a deep learning (DL) network to overcome two of the major bottlenecks in image reconstruction for low-field MRI. These are the lack of real measured data for DL purposes, and the signal model associated with the low-field MRI. For DL purposes we generated synthetic data and acquired measured data. Each dataset consists of samples and each sample consist of an image and the corresponding signal. Due to technical limitations the measured dataset is small, 53 samples. To partially circumvent the problem, the data set was augmented to a total of 1908 samples. In addition, we used Transfer learning, which is a powerful method that applies knowledge gained from one problem to a different but related problem. We present three image reconstruction techniques, Model I, II, and III, based on convolutional and feedforward neural networks, which take MR signal data as input and directly and quickly outputs an image. We demonstrated that DL generates high quality images using synthetic data. In addition, we showed that Model III needs less training to reconstructs good quality images compared to Models I and III, respectively. Finally, Models I and III were unsuccessfully applied to real measured data. However, this study shows that neural networks are able to find a mapping between signal and image, therefore this idea can be extended to work on real measured data.