Introduction: Deep brain stimulation (DBS) is an important therapeutic option for various neurological diseases. For certain indications the optimal target cannot be identified on structural magnetic resonance imaging (MRI), but can be visualized with tractography. A recent improvement for clinical practice is the probabilistic tractography algorithm, providing superior results to the deterministic counterpart. However, it is suggested that clinical users are unfamiliar with the complex technology of this new approach, which might lead to missed potential of tractography in DBS-care. The overall aims of this thesis were threefold: to create an overview of the national landscape on the application of tractography, to narrow the gap between medicine and advanced technology in the application of tractography in DBS surgery planning, and to report potentialities and pitfalls of the implementation of tractography in a general hospital. Methods: In the first part, a survey was conducted among Dutch DBS clinicians on the deployment of tractography. The survey consisted of 25 questions about the provision of DBS care, the use of tractography, the expert’s opinion and the respondent’s demographic characteristics. A comprehensive literature study is conducted in the second part. In the third part, an economic evaluation is performed by analyzing the costs and benefits of tractography. Guidelines are suggested based on literature and expert experience. Results: Tractography is considered valuable for essential tremor (p<0.001) and not valuable for epilepsy (p=0.002) and chronic cluster headache (p=0.016). The majority uses deterministic approaches like DTI. Probabilistic users consider tractography more valuable than deterministic users (p=0.036). There is a heterogeneity in used image acquisition parameters and a lack of knowledge exist on technical background of tractography. The key elements of the technical principles of tractography are summarized, including diffusion weighted MRI, the concept of estimating the diffusion tensors, image acquisition parameters and tract reconstruction with the deterministic and probabilistic approach and quantitative measures. Conclusion: Tractography is used by DBS clinicians with limited knowledge on the technical aspects. Generally the inferior deterministic approach is used without an (inter)nationally standardized protocol for image acquisition and tract reconstruction. A comprehensive explanation of the technical concepts and suggested practical guidelines should contribute to better application of this promising technology in DBS-care.

In this report, the conversion from spin-echo signals, obtained with a low-field hand-held MRI scanner that was designed and built at the Leiden University, to images of the proton density within the sample is considered. This scanner does not make use of switchable gradient coils, but instead relies solely on the natural inhomogeneity of the field and on translations of the sample over this field for its spatial encoding. Specifically, an attempt is made to answer the question of how we can reconstruct a phantom using this kind of scanner. This is done by deriving a signal model, discretising it and writing it as a linear least squares problem. Then, we can make use of the techniques of Cojugate Gradient for Least Squares (CGLS) wih `2-regularization and Generalized Conjugate Gradient Minimal Error (GCGME) with `1-regularization for the difference between neighbouring pixels in order to solve this inverse problem. Firstly, we theoretically consider combinations for magnetic field geometry and measurement strategy for their usability for image reconstruction. After this, the obtained strategies are tested in three experiments, with two magnets and two samples. We start by doing this numerically, using a simulated phantom in combination with a measured magnetic field and the translation strategy. By doing this, we can determine if reconstruction is possible using that combination of field and strategy. Finally, the strategy is tested on real samples. Using numerical phantoms in combination with the magnetic field and translation strategy used in the measurements, we were able to correctly reconstruct the phantoms. However, the reconstruction broke down when data from real samples was considered. A variety of possible improvements is discussed. The improvement that would have the most impact would be to design a magnet that has a less uniform gradient in the z-direction, and instead has some locations in the xy-plane where the field falls slowly as function of z, but quickly in other locations.

We present application and developments of qMRI. We reconstructed data from the Prediva Ouderen Extentie (POE) database and calculated quantitative maps. The POE database consists of 29 7 Tesla (T) whole-brain MRI scans, including both male and female participants covering the age group between 80 and 91 years old. We used this data to extend the currently existing Amsterdam Ultra-high field adult lifespan database (AHEAD), covering the entire adult life span (18-80 years). Data was acquired at a submillimeter resolution using a the MP2RAGEME sequence, resulting in complete anatomical alignment of quantitative $R_1$-maps, $R_2^*$-maps, $T_1$-maps, $T_2^*$-maps, and quantitative susceptibility mapping (QSM). As qMRI acquisitions and reconstructions are slow, we also investigated the potential of deep learning to speed up this process using the same dataset. Conventional MRI acquisitions are already time consuming, scanning the multiple images necessary for qMRI may take even longer. Computing a quantitative susceptibility map requires several processing steps involving phase unwrapping, background phase removal, and solving an ill-posed inverse problem, which all together is computationally expensive. COSMOS is currently seen as gold standard for solving the dipole inversion, although sampling from different orientations makes it a lengthy procedure and not feasible for clinical practice. Iterative methods were developed to make high quality QSM maps out of a single orientation measurement, however they suffer from artifacts and can be computationally expensive. Deep learning may improve the QSM pipeline in terms of time, convenience and quality. Neural networks were developed to learn processing steps like phase unwrapping and dipole inversion. Especially for the dipole inversion step several methods are proposed to calculate a high quality QSM map out of single orientation measurements which could speed up the QSM pipeline. We propose a method which can be useful in accelerated end-to-end QSM calculation and highlight advances and possible points for improvement. We will conclude with a discussion of the challenges that need to be overcome to establish a fast, easy and high quality QSM pipeline.

Biological vision adopts a coarse-to-fine information processing pathway, from initial visual detection and binding of salient features of a visual scene, to the enhanced and preferential processing given relevant stimuli. On the contrary, CNNs employ a fine-to-coarse processing, moving from local, edge-detecting filters to more global ones extracting abstract representations of the input. In the current paper we propose the extraction of top-down networks, by reversing the feature extraction part of the baseline, bottom-up architecture. This coarse-to-fine pathway, by blurring out higher frequency information and restoring it only at later stages, offers a line of defence against attacks introducing high frequency noise. High resolution of the final convolutional layer's feature map can contribute to the transparency of the network's decision making process, as well as favor more object-driven decisions over context driven ones and thus provide better localized class activation maps. The paper offers empirical evidence for the applicability of the method to various existing architectures, but also on multiple visual recognition tasks.

A study has been conducted on the application of Spherical Harmonics based Statistical Shape Modelling for Image Segmentation. This study is focused on the segmentation of Wrist bones using the above mentioned technique.

Low-field MRI is susceptible to temperature changes in magnet temperature, resulting in time-dependent inhomogeneities. This causes time-dependent phase shifts in the measured signal, leading to image distortions. In this project, a method is proposed to interleave short magnetic field (B0) mapping sequences in the target image acquisition for Turbo Spin Echo (TSE) and Magnetic Resonance Fingerprinting (MRF). Simulations show that the proposed correction method works in 2D. Experiment results show that interleaved B0 mapping can be done at relatively low spatial and temporal resolution, shortening the B0 acquisition time. Also, the distortion correction works for both TSE and MRF, though its advantages over static inhomogeneity correction are limited to acquisitions with a small bandwidth per voxel and large or very inhomogeneous frequency drifts.

Magnetic resonance imaging (MRI) is a clinical imaging technique that allows for non-invasive visualization inside the human body with excellent soft tissue contrast with a sub-millimeter resolution. Qualitative MRI is used to visually highlight normal or pathological components by exploiting the physical properties of different tissues. However, these acquisitions provide minimal consistency between scans, patients, and scanners. To address this issue, quantitative MRI (qMRI) provides absolute measures that give meaningful physical information about tissues, enabling objective comparisons. Relaxometry, a branch of qMRI that characterizes tissues through their magnetic relaxation properties, has been employed to quantitatively assess various diseases with different biomarkers in the past. However, certain radiofrequency (RF) pulses used to induce relaxation times weighting in the MRI signal are sensitive to field inhomogeneities, which makes consistent quantification of relaxation times difficult. In order to improve sensitivity and detect more diseases, better contrast mechanisms and biomarkers are crucial. One promising technique is Relaxation Along a Fictitious Field (RAFF), which may serve as a biomarker for a wide range of diseases due to its sensitivity to slow molecular motion in tissue. Currently, it has the downside of being sensitive to off-resonance and B1+ artifacts, which hampers clinical application. This project aims to develop novel contrasts for quantitative MRI by investigating the performance of adapted RF pulses. Ultimately, the goal is to reduce the susceptibility to off-resonance and B1+ artifacts for the RF pulses.

The gap between predicted brain age and chronological age could serve as biomarker for early-stage neurodegeneration and as potentially as a risk indicator for dementia. We assess the utility of this age gap as a risk biomarker for incident dementia in a general elderly population. The brain age is estimated from longitudinal brain magnetic resonance imaging (MRI) data using deep learning models. From the population-based Rotterdam Study, 5656 dementia-free and stroke-free participants (mean age 64.67±9.82, 54.73% women) underwent brain MRI at 1.5T, including three-dimensional (3D) T1-weighted sequence, between 2006 and 2015. All participants were followed for incident dementia until 2016. During 6.66±2.46 years of follow-up, 159 subjects developed dementia. The entire dataset was split into control (N=5497) and incident dementia (N=159) groups. We then built a convolutional neural network (CNN) model trained on the control group to predict brain age based on brain MRI. Model prediction performance was measured in mean absolute error MAE=4.45±3.59 years of brain age prediction. Reproducibility of prediction was tested using the intra-class correlation coefficient ICC=0.97 (95% confidence interval CI=0.96-0.98), computed on a subset of 80 subjects. Hereafter, we investigated the gap between model predicted age and chronological age of the incident dementia group data, compared to control group. Logistic regressions and Cox proportional hazards models were used to assess the association of the age gap with incident dementia, adjusted for years of education, ApoE4 allele carriership, GM and intracranial volume. These models showed that the age gap was significantly related to incident dementia (odds ratio OR=1.11 and 95% confidence intervals CI=1.05-1.16; hazard ratio HR=1.11 and 95% CI=1.06-1.15, respectively). Additionally, we computed the attention maps of CNN, which shows the importance of brain regions for age prediction. These were particularly focused on the amygdalae and hippocampi. We show that the gap between predicted and chronological brain age is a biomarker, associated with a risk of dementia development. This suggests that it can potentially be used as a complimentary biomarker for early-stage dementia risk screening.

In clinical practice, as a first approximation, the severity of an abnormality on an image is often determined by measuring its volume. Researchers often first segment this abnormality with a neural network trained by voxel-wise labels and thereafter extract the volume. Instead of this indirect two steps approach, we propose to train neural networks directly using the volumes as image-level label and predict the volume directly. Using image-level labels to train automatic abnormality prediction could decrease the labeling burden for clinical experts, which is both expensive and time consuming. In this report, a neural network that consisted of a segmentation part and an appended regression part was compared with the indirect segmentation approach. It was investigated if networks trained with image-level labels have the same performance of image-level prediction as networks trained with voxel-wise labels. The neural networks were trained on a large local dataset to quantify white matter hyperintensity (WMH) burden from brain MRI, and their performance was evaluated on a held-out test set. Furthermore, generalization properties were compared by applying the trained networks on four independent public datasets. The networks trained with image-level labels achieved volume quantification that was slightly better than their counterpart on the held-out test set. The attention maps of these networks showed that the networks were able to focus on the surroundings of the WMH, and hence learned meaningful image features. Nevertheless, the attention maps were not suitable to achieve a compatible segmentation. In terms of generalization towards external datasets, the advantage of weak labels for volume quantification did not hold as there was no significant difference between the performance of the label types. The results suggest that neural networks optimized with image-level labels were able to directly predict WMH volume as well as neural networks trained with voxel-wise labels. Subsequently, we also studied networks that were optimized on both image-level and voxel-wise labels. Those networks reached a lower performance, which suggested that the tasks and their image features learned were not similar enough.

We focused on analysis of long-term medication influenced white matter tracts based on diffusion-weighted MRI brain images of patients with ADHD disorder. We applied a framework of consistent model selection with Tract-based spatial statistics (TBSS) to give proper and consistent modelling of fiber-crossing in white matter. An orientation atlas was constructed to give an `orientation prior' during the ball-and-2sticks model estimation. Consistent metrics of fiber properties were obtained for each subject and thus statistical power in crossing-fiber region was improved. Besides, we improved this pipeline by optimising orientation prior and by integrating a high-dimensional image registration.

A technique that can be used in the study of the magnetic properties of ferritin is electron paramagnetic resonance (EPR), which is sensitive to the magnetic moments of unpaired electrons. The magnetic properties of human liver ferritin were studied using 9 GHz EPR, with which temperature dependent spectra were acquired in the following range: 5-150 K. A novel approach, typically used in magnetic resonance spectroscopy, was employed to pre-process the baseline-corrected spectra and remove features for which it was unlikely to originate from the ferritin core. This allowed for the isolation and preservation of the general lineshape of the signal belonging to the ferritin core. The spectra were further analysed using both a phenomenological approach and by employing the Spin Hamiltonian. The phenomenological analysis showed that in the 20-70 K temperature range, the amplitude peak-to-peak of the ferritin-core signal decreases linearly with decreasing temperature, while its lineshape changes from Lorentzian to Gaussian between 150 and 70 K. The blocking temperature was suggested to occur between 10 and 20 K, where the signal amplitude was lost. Whether and how the lineshape of the ferritin core signal shifts or broadens below 70 K could not be determined due to a six-line signal contamination, most likely caused by manganese impurities, obscuring the ferritin-core signal in the field range where its resonance field is positioned. In order to study the magnetic properties of the ferritin core, and therefore gain insight on the magnetic and electronic structure of the ferritin core, a Spin Hamiltonian approach was employed. The simplified Giant Spin Hamiltonian model featured a single spin system with total spin S = 10. This analysis suggests that the ferritin-core signal is centred at g’=2.0154, hinting at a core composition of magnetite or maghemite, rather than of ferrihydrite. An almost fully automated procedure to pre-process a set of human liver ferritin EPR spectra obtained at different temperatures is described in this thesis.

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.

Human brain consists of grey and white matter. Grey matter largely forms the outer layer of the brain, and is responsible for decision making and functioning of the human body. Complementary, the white matter (WM) contains the communication pathways between the grey matter areas. Diffusion-weighted magnetic resonance imaging (dMRI) is an imaging modality allowing to model the brain’s white matter structures by making the MRI acquisition sensitive to diffusion processes. When brain structure is altered due to pathology, it can be assessed and quantified by analysing dMRI data...@en