Electrical property tomography (EPT) reconstructs the human body’s conductivity and relative permittivity using the radio frequency field from a magnetic resonance machine. Since conductivity and relative permittivity can be biomarkers for many illnesses, it is necessary to reconstruct them accurately. To this end, this thesis focuses on the two-dimensional tranceive phase corrected contrast source inversion algorithm (2D-CSI).

This 2D-CSI algorithm is significantly faster than its three-dimensional (3D) counterpart. However, reconstruction artefacts may appear since the 2D field description is not always applicable. In this thesis, these artefacts are identified and it is investigated if the 2D-CSI algorithm can be improved to handle them. In particular, the proposed updates are, first, implementing the early stopping principle, then adding a positivity constraint to the conductivity and relative permittivity, and lastly, initialising with Helmholtz electrical property tomography (HEPT). The first two enhancements create a more robust algorithm, while the third update could be useful only in specific cases (with no fine structures and low noise levels).

After achieving a more robust algorithm, the 2D-CSI algorithm is applied to the realistic 3D dataset ”A Database for MR-based Electrical Properties Tomography” (ADEPT). The first and foremost challenge is accurately simulating the incident field, as the reconstructions depend a lot on the incident fields. If the incident fields do not match perfectly, the algorithm can only produce satisfactory reconstruction results in a part of the reconstruction domain. The reconstructions are only partly accurate because the reconstructed electric fields converge to a line creating line artefacts. It is shown that small changes in the incident fields produce small changes in the position of the artefacts. Based on this observation, a reconstruction strategy has been developed in which a reconstruction without artefacts is produced by combining reconstruction results of multiple incident fields. Integrating all modifications of the 2D-CSI
algorithm in one single framework shows that the conductivity and relative permittivity of the brain in the middle of the birdcage coil can be reconstructed accurately

The tissue electrical properties of conductivity and permittivity affect the interactions of electromagnetic fields in the body. These properties vary throughout the different tissues as the tissue structure and composition varies. In this thesis, medical imaging and diagnosis is used as primary example to motivate exploration of a novel regularization approach to an MRI-based electrical properties tomography (EPT) method.

Total variation (TV) regularization has been shown to perform noise reduction in the iterative Contrast Source Inversion EPT (CSI-EPT) method. The Jacobi matrix inversion regularization, an alternative to the known conjugate gradient formulation, is elaborated and applied to an E-polarized MRI fields scenario such that this thesis presents the Jacobi step regularized CSI-EPT.

The alternative regularization method outperforms the known regularization method in the reconstruction qualities of noise-suppression and edge-preservation in the simulated MRI experiments using a virtual body model. Further advancements are also described, such as multiple inner-iterations Jacobi regularization and an anatomical prior initialization of the contrast function. Important future research topics are the incorporation and evaluation of the Jacobi step regularization into more advanced CSI-EPT versions, which are the three-dimensional and transceive phase based algorithms to correct realistic MRI data.

The development of neural prostheses, especially those directly targeting the brain, requires extensive research and modelling before clinical trials can be performed. Currently, the resolution of artificial vision is not sufficient for everyday tasks. By studying the expected spatial extent of stimulation, we aim to provide insights to researchers that can be used to improve the resolution of artificial vision. The goal of this MSc thesis was to visualise the shape and intensity of electric fields in the cortex as a response to intracortical microelectrode stimulation to observe the expected regions of neuronal activation considering electrode design parameters. To do this, parameters that can influence the generated electric field and the regions of activated tissue have been defined. Implementing these parameters in a Finite Element Model (FEM) allows the computation of the generated electric field in 3D of a stimulating electrode to observe the spatial extent of activated tissue. The spatial extent of activated tissue can be estimated using simplified methods such as the activating function (AF) or current density threshold. The result is a parameterised framework that creates a Visualisation of Neuronal Activation (VoNA) that can be used to assess activated tissue regions for varying scenarios by defining material properties and dimensions of the model, and allows for the adjustment of the stimulation configuration, electrode contact spacing, customisation of electrode size and modulation of stimulation current. It enables the user to tune the model settings towards their specific needs and explore the possibilities by visualising the results from different angles by defining subsets of the entire solution. In line with expectations, the presented models show that the model parameters can influence factors such as the generated electric field, the current density and electric potential, which are indicators of neuronal activation. The findings support the hypothesis that these parameters should be considered during electrode design to achieve accurate stimulation.

Atrial Fibrillation (AF) is a cardiac arrhythmia that occurs due to the initiation of electrical impulses in the heart in an uncoordinated manner, giving rise to a disorganized and rapid heartbeat. The progression of AF over time can lead to complications such as stroke and heart failure. Catheter ablation is an established treatment protocol adopted to eliminate AF triggers and restore normal sinus rhythm in patients. To improve the success rates of ablation procedures, mapping and localizing the regions in the heart that give rise to uncoordinated impulses is crucial. This work focuses on obtaining a visual estimate of the location of irregular electrical activity within a volume domain of cardiac tissue using epicardial electrogram measurements.
A straightforward, effective, and computationally fast backprojection imaging method is proposed that provides an indication of the regions in a given volume of cardiac tissue that exhibit prominent irregular atrial activity. The chosen volume is subdivided into non-overlapping voxels, and the standard electrogram signal model is discretized to a matrix multiplication form. The inverse problem of reconstructing the transmembrane current distribution in the volume is solved using the epicardial electrogram readings obtained from the surface and the inverse distances matrix containing the inverse of the distances from each electrode to each voxel within the volume domain. The generated backprojection images are visualized to estimate the location (x and y coordinate information) and depth of irregular atrial activity in the volume. To improve the accuracy of localization while maintaining the computationally fast nature of the proposed solution, Singular Value decomposition (SVD) of the inverse distances matrix is proposed for the transmembrane current reconstruction. The obtained visual estimates can guide ablation procedures, making them more targeted, and reducing the need for repeat ablation procedures due to AF recurrence.

The driver’s safety is one of the problems that is considered by modern vehicle technologies. Many accidents occur due to extreme weather conditions, such as snow or freezing rain. Such weather causes decreases in the friction of the road surface, which cause danger for drivers in this kind of area. To improve the driver’s safety, ADAS (Advanced Driver Assistance Systems) can be used to recognize different road surface conditions and warn the driver to avoid them. To do this, ADAS needs to perform classifications on the surface, and radars operating above 77 GHz have shown potential for this kind of task. The polarimetric information of the surface is needed as the prior knowledge for radar to perform the classification properly, which can be obtained from the scattering of the surface. The scattering properties of the asphalt surface at 76 GHz is a complex problem and it is difficult to have a comprehensive solution. In the existing research, there is no research that used a full-wave numerical approach with a simulation model for the kind of problem in the 76 GHz frequency band. In this thesis, a numerical model is developed in the electromagnetic software suite FEKO to simulate asphalt surface scattering. The model considers surface roughness, dielectric medium, source polarization, and incidence angles for scattering from the asphalt surface. Based on the model simulations, statistical analysis is performed using the Monte Carlo method. The results of the model in the case of an RMS height of 2 mm and correlation length of 2mm show that for a very rough surface, the RCS of the surface decreases as the incidence angle is larger, and the value of the two polarization components is close to each other. By verifying the numerical model with SPM theory, it has been shown that the designed model can give accurate results in the designed scenarios, which proves that the designed model can be used for investigate rough surface scattering properties.

Estimating the transmembrane currents travelling through the epicardium and local activation times based on atrial epicardial electrograms can greatly help in the study of cardiac arrhythmias such as atrial fibrillation. This work focuses on the accurate estimation of the aforementioned signals and features. To do this, two least squares-based regression methods were used to estimate transmembrane currents from electrograms and then find their local activation times by searching for the maximum negative slope. The first least squares optimization method consists of using standard least squares, while the second consists of regularized least squares, by combining both lasso and ridge regression, to deal with signal sparsity and multicollinearity, respectively. Furthermore, to improve estimation results, multiresolution analyses based on wavelet decompositions and principal components analysis were used to filter out parasitic components that were present in the estimated transmembrane currents by separating them from the main activation complex of the decomposed signals.
Using these algorithms on simulated data, it was shown that promising results can be achieved for both transmembrane current estimations and LAT estimations. Several wavelet support sizes were tested on the simulated data to observe performance changes. These were compared to an already existing LAT estimation algorithm. The results mainly confirm the efficiency of the proposed methods on severely diseased tissue corrupted by conduction blocks and noise.

Magnetic resonance electrical properties tomography is a type of quantitative magnetic resonance imaging that aims to reconstruct the conductivity and permittivity of biological tissue. These electrical properties of the tissue can be used to compute the specific absorption rate, to differentiate tumours from healthy tissue and for hyperthermia treatment planning. Several methods to reconstruct these electrical properties exist with different degrees of success. Combining analytical reconstruction methods with deep learning methods is left relatively unexplored in the field of magnetic resonance electrical properties tomography. Hence, this work explores such hybrid methods in which deep learning is embedded in an analytical reconstruction method. A recurrent inference machine is integrated into the iterative reconstruction scheme called Contrast Source Inversion, in an attempt to decrease its high computational load. Additionally, a U-net is trained to correct reconstructed conductivity maps using discrepancies in measured- and reconstructed phase data, which is based on the relation between conductivity and phase in the Helmholtz equation. The recurrent inference machine embedded version of contrast source inversion failed to achieve a desirable reconstruction quality with its current implementation. However, the large amount of potential improvements to its implementation motivates further research into its application before discarding it. The conductivity correction U-net is able to correct conductivity errors as small as 0.13 S/m when used iteratively or 0.05 S/m when used a single time when noiseless data is used. Further research in its capabilities of handling noisy data is required to assess practical usage.

This thesis research focuses on MRI technology and more specifically the evaluation of the generalized signal model (full-wave signal). The research aims to investigate how dielectric properties influence the magnetic fields in MRI scans, by using a more complex signal model that takes into account scattered fields caused by dielectric properties of human tissue. Optimizing the Signal-to-Noise ratio (SNR) has been a main research focus since the invention of MRI machines. Higher-quality images, result in better diagnosis and therefore help the investigations of patient conditions. One way to improve the SNR is to operate MRIs in higher frequencies. Operating an MRI in higher frequencies, affects the measured signal at the receiving coil, as dielectric properties of biological tissue, induce currents due to magnetization. By having a full wave signal model, one can model the effect of the dielectric properties and thus take them into account when reconstructing an MRI image. The first step was to investigate whether a magnetic dipole can be simulated as a single emitting loop. The results from the simulation and the analytical solution have been compared. There’s a high correlation be-
tween the two and thus it is concluded that it can be used. Using a single emitting loop, the effect of varying conductivity was simulated for a dielectric sphere placed in free space, with the single emitting loop submerged into the solution. The simulations showed that by increasing the conductivity, the measured voltage at the receiver coil reduces. Note that this simulation was performed twice, for relative
permittivity values εr = 1 and εr = 80. The reason for choosing these two values was to first eliminate the effect of relative permittivity εr = 1 and to simulate a water-based solution εr = 80. Simulations verified that for εr = 1, the measured voltage at the receiver location is lower than εr = 80. After verifying the full-wave model and observing the effect of varying the electrical conductivity, two more simulations were performed to prepare for the experiment. In the first simulation, instead of a single loop, a series of loops was used to simulate the beam excitation of an MRI. As it is shown in the results, there is not a linear correlation between increasing the conductivity and the induced voltage at the receiver coil. Secondly, the experimental setup was simulated. In addition to the previous simulation, a plastic layer was added to the outside of the ball, to model the plastic sphere used during the experiment. In chapter 4, the results of the experiment are introduced. The results from the experiment could not justify the simulations due to reasons that are explained in the conclusions.

Magnetic resonance imaging (MRI) is a non-invasive tool to image the body’s anatomy and physiology, but suffers from long scan times. Compressed Sensing (CS) is used to accelerate MRI scans by incoherently taking fewer measurements and using a nonlinear optimization algorithm to image the undersampled data. Convex optimization techniques are generally used for image reconstruction. Minimizing a data fidelity term along with two regularization terms, which are a total variation (TV) based and a wavelet transform based function, is a standard procedure in CS-MRI. Regularization parameters are needed to balance the different terms, but it is impossible to know upfront what the optimal regularization parameters are to get the desired output. A consequence is that the algorithm of choice needs to be executed many times for many different values of the regularization parameters, which is a time-consuming process requiring knowledge of the algorithm.

In this work we rewrite and implement the regularization functions in a multiplicative manner by multiplying the data fidelity term with the regularization terms, thereby eliminating the need to tune the regularization parameters. Moreover, we include a region of support (ROS) mask to further accelerate reconstruction. The performance of different combinations of regularization functions and reconstruction algorithms are validated on a simulation study and various experiments on a low-field MRI scanner. This also shows the capability of CS applied to low-field MRI, which has lower signal-to-noise ratio compared to conventional MRI. Of all proposed methods, a nonlinear conjugate gradient method applied to the fully multiplicatively regularized objective function shows the most robust performance.

Low field magnetic resonance imaging (MRI) scanners provide a unique low-cost alternative to conventional MRI scanners. Nevertheless, low-field scanners come with drawbacks such as reduced signal-to-noise ratio and resolution, and also distorted images caused by magnetic field inhomogeneity and non-linear gradient fields. Despite this, it still provides a more accessible way to provide MRI in resource-limited areas. The main goal of this thesis is to develop an algorithm that can reconstruct 3D data from the low-field scanner efficiently and without distortion to the image. To do this, conjugate phase reconstruction (CPR) is employed, particularly frequency segmented reconstruction (Noll, 1991) and multifrequency interpolation (Man et al., 1997).

MRI has been used worldwide as an imaging modality for decades due to its ability to distinguish between soft tissues. However, MRIs cannot be used in all situations due to their extensive hard- and software. For this reason, research in the field of low-field MRI has been conducted. Low-field MRI systems do not use superconducting magnets but restive, or in this research, permanent magnets. This reduces the costs of these scanners, but these same changes result in a poorer signal-to-noise ratio (SNR) than high-field scanners. Based on this hardware, research in single-sided scanners has emerged, and various research groups have been able to reconstruct images with these scanners. They are based on conventional MRI hardware including a radiofrequency (RF) coil, permanent magnets and gradients coils for encoding. In collaboration with the Technical University (TU) Delft, a handheld scanner has been constructed in the Leiden University Medical Center (LUMC). This scanner consists of an RF coil and a permanent magnet and does not have a gradient coil. The inhomogeneity of the 50 can be used for spatially encoding the signal based on translations. The SNR in images is improved with model-based image reconstruction. Although, in theory, this set-up should work, the image results so far have been somewhat disappointing. Therefore, in this thesis, we have investigated a different approach, using a neural network. In this research, the correlation between the received signal of a measurement and a simulation of the same shape is found to be greater than the correlation between other simulations and the received signal. Due to this proven correlation, an image reconstruction deep learning algorithm is constructed based on the AUTOMAP model. Simulated signals are reconstructed into images with promising results; however, the algorithm cannot reconstruct mea- sured data. Therefore, suggestions for future improvement include improving the simulated data set and adding more real measurements that would allow for training on a measured data set.

Ultrasound imaging is an important modality in ocular oncology, allowing for fast examination of the eye by the ophthalmologist themselves. It is clinically used to measure tumour sizes for treatment planning. However, ocular ultrasound is limited to two-dimensional imaging, and suffers from poor contrast between tumour and sclera, which negatively impacts the accuracy of tumour measurements. In this work, low field MRI is investigated as a possible alternative for ultrasound imaging.
Design requirements are a scan time of less than 4 minutes; resolution of 1.0 mm isotropic; Field of View (FOV) large enough to contain the eye and the orbit; contrast sufficient to distinguish the sclera, vitreous, tumour, lens and lipid. The experimental setup consists of a 46 mT Halbach-array based scanner, a volume coil as transmit coil and a custom-built surface coil as receive coil. Images are made of a water phantom to characterise the FOV, and a porcine eye to characterise the contrast.
The FOV is found to meet the requirements, and the contrast is sufficient to distinguish the sclera, vitreous, lens and lipid in porcine eyes. The resolution is too low and the scans take too long (about 5 minutes at a resolution of 1.0 × 1.0 × 7.5 mm). Increasing the resolution and decreasing the scan time will result in a low Contrast to Noise Ratio (CNR), causing the contrast requirement to be violated. Fast, high-resolution three-dimensional imaging is therefore not feasible on the current system.
The CNR can be improved by using a higher field strength, which requires the development of new hardware. Furthermore, in order to develop a clinically useable system, it is necessary to determine tumour contrast, design optimised pulse sequences, and test the method on human subjects.

This research addresses the following question: Is it possible to detect vascular obstructions and oedemas or monitor the decellularization process in perfused ex-vivo porcine kidneys using electrical impedance spectroscopy? Patients on the waiting list for kidney transplantation die because of a kidney shortage. A part of this problem can be solved by prolonging the period of preservation between the removal of the kidney from the body and the transplantation. However, oedemas and vascular obstructions are more likely to occur with longer perfusions. The kidney donor risk index doesn’t take this into account so a new technique needs to be established to assess the development of oedema and vascular obstruction. Another approach for increasing the supply of donor kidneys is de- and recellularization of the kidney. Electrical impedance spectroscopy shows promising results in the pancreas and this technique can probably be transferred to the kidney. EIS is a non-invasive, low cost, portable measuring technique that shows the results in real-time. A Spectra Bioimpedance and EIT Complete Kit with the 4-electrode attachment have been used at low frequencies (500 Hz) and high frequencies (70.000 Hz) for the evaluation of the porcine kidneys. Two different measurement techniques will be used, measurements through the renal artery and measurements on the kidney. These techniques are tested during the decellularization process, oedema formation, and vascular obstruction simulation to determine if impedance changes can signal these defects in the kidney. Robust changes have been made to the porcine kidneys in this regard. The sensitivity of the methods is less than expected. Only the decellularization results showed a notable change over time. The conclusion of this research is therefore: The results cannot prove a clear connection between vascular obstruction and oedema detection and electrical impedance changes. There is a probable correlation between electrical impedance and the phase of decellularization in which the kidney is in. Follow-up research is needed to substantiate this further.

Each year, hundreds of thousands of infants develop hydrocephalus ("water on the brain"). This is a disease that, if untreated, leads to brain damage and ultimately death. The prevalence of hydrocephalus is relatively high in children living in the Global South (in sub-Saharan countries, for example), but access to advanced imaging technology is usually limited in countries belonging to the Global South. This is especially problematic for hydrocephalus, since magnetic resonance imaging often is the diagnostic tool of choice for this disease, but MRI scanners are essentially out of reach due to their cost, size, and stringent infrastructure demands. Therefore, the introduction of an inexpensive, portable, low-field MRI scanner is clinically relevant. An interdisciplinary team of researchers at the Leiden University Medical Center, Pennsylvania State University, Mbarara University of Science and Technology and Delft University of Technology has been working on the development of such low-fieldMRI scanners, with the first goal being to aid in the diagnosis of hydrocephalus in infants in sub-Saharan Africa. Within this project, several prototypes and various dedicated image reconstruction techniques have been developed. This dissertation focuses on the latter. High-field MRI scanners have very strong and homogeneous static magnetic background fields, due to the superconducting magnets they are equipped with. To significantly reduce production costs, the low-field scanners considered in this work use permanent magnets to realize their static background fields. Obviously, such background fields are much weaker than in a high-field MRI scanner, leading to measured signals with a significantly lower signal-to-noise ratio, since this ratio scales with the magnitude of the background field. For spatial encoding (i.e., to distinguish what part of the signal originates from what part of the body or object inside the scanner), high-field scanners depend on gradient coils which superimpose a linearly varying magnetic field on the background field. The first prototype we consider does not have any gradient coils. Instead, spatial encoding is carried out by making use of the inhomogeneities in the static magnetic background field. Due to the nonbijective nature of the field, a single measurement does not yield enough information for a reconstruction. However, by carrying out several measurements and rotating the field between subsequent measurements, image reconstruction should be possible. The second prototype follows the design of high-field scannersmore closely: it was designed such that the static magnetic field is as homogeneous as possible and the scanner is equipped with three gradient coils to allow for spatial encoding in three directions. In this case, the relationship between signal and image can be described by a Fourier Transform...

Magnetic Resonance Imaging (MRI) is a non-invasive, non-ionizing imaging modality that is commonly used in the clinic today. However, it is an expensive technique. The high purchase, operational and maintenance costs, as well as the need for trained staff with technical expertise, put this technique out of reach for a large part of the world population. To combat this, low-cost MRI systems are being developed. The use of lower magnetic fields allows for reductions in cost and size, increasing the accessibility and portability of the device in developing countries. Nevertheless, the signal-to-noise ratio is proportional to the magnetic field, and so low-field MR images are of a significantly lower quality. As such, a reconstruction algorithm is necessary in order to denoise the image, while preserving the details. Besides the cost, MRI is a relatively slow modality, leading to decreased patient comfort and increased chance of motion artifacts. The data acquisitions can be sped up through Parallel Imaging, which requires the use of a receiver coil array rather than a single RF coil. This leads to incomplete data as well as spatial variations in the coil sensitivity profiles that must be accounted for. Furthermore, while the Field-of-View of the image is generally larger than the spatial support of the object, information on this support is not used in MR image reconstructions. In this work, a reconstruction algorithm is developed to reconstruct MR images using incomplete multicoil data and spatial support information in a low-field setting. This algorithm is based on the single coil algorithm of De Leeuw Den Bouter et al. (2021), which will be extended to the multicoil case using the Contrast Source Inversion method. Three adaptations of the algorithm have been considered. Their performance is characterized in terms of denoising ability, edge-preservation and execution time, using both simulated and real low-field data. A mask S is introduced to include information on the spatial support. The use of this mask is shown to have a positive effect on the quality of the reconstruction, improving the preservation of edges and details. Furthermore, a mask R is introduced to deal with incomplete data in case of accelerated acquisitions. In conjuction with a Parallel Imaging technique, the reconstruction algorithm can yield de-aliased and denoised reconstructions, combatting the inherent drop in SNR caused by the acceleration. Chances for improving the algorithms remain, for instance by the exploring the possibilities for a multiplicative implementation of the Total Generalized Variation regularization term, as well as the automatic detection of the spatial support and the inclusion of compressed sensing.

Magnetic resonance imaging (MRI) scanners are a crucial diagnostic tool for radiologists. They are able to render two­ and three­dimensional images of the body without exposure to harmful radiation. MRI systems are, however, costly to build and maintain. This adversely impacts access to these scanners in developing regions. In an effort to combat this problem, a low­field MRI scanner is being developed. Conventional MRI scanners utilize a superconducting solenoid to generate the main magnetic field. The low­field scanner, on the other hand, induces the main magnetic field through a Hallbach array of permanent neodymium magnets. While beneficial for production and maintenance costs, as well as portability, the Hallbach array is not able to generate a perfectly homogeneous magnetic field. The inhomogeneities present in the main magnetic field result in distortion of the images when reconstructed using conventional fast Fourier transform (FFT) methods. To counteract this, a reconstruction method that utilizes field information needs to be employed. In this thesis, existing methods to determine and utilize the field information to correct image distortion are explored. From this analysis, it is evident that model­based (MB) methods are most suitable for reconstruction of data from the low­field scanner. Current MB methods are only implemented for two­dimensional reconstruction. The goal of this thesis is to expand these methods to three­dimensional reconstruction. A novel MB method for three­dimensional reconstruction is presented. This new method is able to circumvent memory constraints that arise from reconstruction of large data sets. Though the new method requires several hours to reconstruct a 128 × 128 × 30 data set, visual inspection indicates that an accurate result is achieved.

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.