Introduction and purpose: Long-term xerostomia and dysphagia affect the quality of life (QoL) of head and neck (HN) cancer patients treated with radiotherapy (RT). However, xerostomia is subjectively measured with questionnaires, which has several limitations. It is of interest to investigate whether quantitative magnetic resonance imaging (qMRI) techniques can capture radiation-induced damage to organs in the HN area. This study uses diffusion-weighted imaging (DWI) to measure the apparent diffusion coefficient (ADC), mDIXON Quant to measure the fat fraction (FF), and T2-mapping to map T2 values. All three qMRI parameters are expected to increase with dose and, therefore, differ for different patient-reported xerostomia scores. In addition, recent literature emphasizes the relevance of the stem cell rich (SCR) region in the parotid glands, but the delineation methods are not patient-specific and incorporate a large margin to account for differences between patients.

Methods: 26 healthy controls (HC) and 54 HNC patients treated with RT have been included. MRI acquisition was performed 6 months to 3 years after the last RT fraction. 19 patients were scanned in a test-retest fashion to calculate the repeatability coefficient (RC). In addition, planning CT with dose distributions were available for all but two patients. Pearson’s correlation with a simple linear regression was performed to investigate the relationship between qMRI values and dose. A linear mixed-effects model (LMM) analysis was performed to assess the effect of subject characteristics on qMRI values. The fixed-effects estimates of this LMM were used to correct for subject characteristics in the correlation analysis. Cohorts were defined based on patient-reported scores for xerostomia (score 1 or score ≥ 2) and sticky saliva (score 1 or score ≥ 2). A one-way ANOVA with Tukey’s HSD post-hoc test was performed to test for differences in qMRI histogram parameters between HC and xerostomia cohorts, and between HC and sticky saliva cohorts. The hybrid and biomechanical deformable image registration (DIR) were performed for one patient to map the parotid glands from T2-weighted TSE to planning CT and similarity metrics were calculated. The best performing DIR algorithm was used in the delineation process of the SCR region, to delineate the SCR region on MR sialography imaging and map the delineations from MRI to planning CT. Volume and qMRI values were extracted.

Results: The difference in RC between HCs as reported in literature and patients ranged from -0.10 to 0.02 · 103 mm2/s for ADC, -0.4 to 1.6 % for FF, and -2.6 to 2.0 ms for T2, showing sufficient and comparable repeatability. Overall, ADC positively correlated with the dose for several relevant organs, while T2 only positively correlated with the dose of the submandibular glands and the superior pharyngeal constrictor muscle. FF did not show a positive correlation with dose. The LMM analysis showed that age affects the FF and T2 values of several organs related to xerostomia or dysphagia. BMI was also shown to affect the FF and T2 values of the submandibular glands. The fixed-effects estimates were not sufficient to correct for the effect of subject characteristics on qMRI values. Comparison of HC and patient cohorts showed differences in ADC, FF, and T2 for the parotid and submandibular glands, which can partly be explained by the effect of age. For the submandibular glands, the xerostomia cohorts also differed in ADC and T2 values, which can be due to the effects of dose, since the mean dose of the glands differed for the two patient cohorts. This effect was not observed for the sticky saliva cohorts. The hybrid DIR outperformed the biomechanical DIR and was therefore used in the delineation process of the SCR region. It was feasible to delineate the SCR region based on MR sialography imaging and map the delineation to qMRI maps and planning CT.

Conclusion: The findings of this study show the promise of ADC and T2 values in quantifying radiation-induced toxicity long-term after treatment, providing a basis for an improved understanding of long-term toxicities in head and neck cancer patients treated with RT.

Fat fraction (FF) and apparent diffusion coefficient (ADC) values estimated by Dixon MRI and diffusion weighted MRI (DWI) techniques respectively, are relatively new quantitative imaging parameters and increasingly accepted as imaging biomakers for all sorts of purposes. The aim of the BOCASEcA study is to research whether these techniques can be used as biomarkers for patient-reported xerostomia and dysphagia post-radiotherapy. In this project, steps have been taken to validate the use of certain fat quantification and ADC mapping protocols in the BOCASEcA study. mDIXON Quant is a Philips product designed for MR fat quantification. We performed phantom studies and a healthy volunteer study to evaluate the accuracy and repeatability of a standard mDIXON Quant protocol with default parameters and an mDIXON Quant protocol that is used in LUMC on muscles throughout the whole body. Another phantom study was done to evaluate the (geometrical) accuracy of DWI-SPLICE, a technique that can be used for ADC mapping. This DWI technique is known to have less susceptibility issues than conventional EPI-DWI. We tested both the accuracy and deforming artifacts for an EPI sequence and a clinically used DWI-SPLICE protocol from LUMC. Adequate accuracy and robustness were observed for the standard Philips mDIXON Quant protocol. The LUMC muscle protocol, however, yielded incorrect measurements that were underestimations of the real FF values. The DWI-SPLICE protocol showed better geometrical accuracy than the EPI protocol. Accuracy of the ADC measurements was sufficient for ADC values higher than 0.6 x 10−3 mm2/s which is a clinically relevant range. Since the accuracy of both the standard Philips mDIXON Quant protocol and DWI-SPLICE protocol was validated, it is recommended that they are used and further optimized for the BOCASEcA study.

Demyelination is described as loss of myelin sheath in neurons which could lead to disruption in signal transmission in nervous system. Inhomogeneous Magnetization Transfer(ihMT) is a novel MRI technique used to image myelin. It uses the dipolar coupling between methyl chains in lipid layers of myelin to acquire myelin specific information. In this study, we investigated the feasibility of ihMT at 7T. Based on phantom experiments and Bloch simulations, the influence of off-resonance frequency $\Delta$, RF field strength $B_{1}$ and RF pulse proprieties on ihMTR are studied and optimized. For in-vivo imaging, the experiments are conducted on healthy volunteers. Since $B_{1}$ inhomogeneties are prevalent at higher fields, an optimized protocol is devised to achieve maximum ihMT effect at 7T. A comparison of 3T and 7T for the off-resonance frequency, RF field strength and pulse proprieties is presented to emphasize the impact of parameters at both the field strengths.

Magnetic Resonance Imaging is a popular modality for brain imaging in present times. The quality of the images depends on the strength of the magnetic field. An MRI scanner with a magnetic field strength of 3 Tesla(T) is pre-dominantly used for clinical purposes. However, with the advancement in technology, and the need to image finer image finer structures, we are gradually shifting to higher magnetic field strengths like 7T and above. One of the major bottlenecks in these systems is the bias induced in the images due to field inhomogeneities of higher field strengths. Yet another drawback of these systems is the increase in power dissipation in the tissues. This is measured by a quantity called Specific Absorption Rate(SAR). The goal of this thesis is to accurately predict SAR values for various volunteers as they may differ from subject to subject. Many homogeneous models have been created earlier to estimate the value of SAR, however, these estimates are often over-conservative and safe which compromises with image quality. A personalised numerical body model is created for all volunteers using relevant information derived from simulations performed on generalized models. Various tissues have to be segmented to create these 3D numerical body models. However, there has to be a trade-off between ease of segmentation and SAR accuracy. Keeping that in mind, it was found that the optimum number of tissues to get reliable SAR estimates is six. A deep learning method was then used for segmentation. A numerical body model was derived for all the volunteers using the deep learning segmentation. An adapted ForkNet, which is similar to U-net in architecture is used to segment these images. The SAR values derived from the predicted numerical body model and the original body model are similar, hence speeding up the process for SAR prediction. However, there are certain limitations of the thesis that can be addressed in the future. Inadequate data remains a major bottleneck for the project, increasing the data should result in improved segmentation, this can be addressed by acquiring more data. Another major drawback of the thesis is the segmentation accuracy, the ground truth segmentation is performed to the best of our knowledge, however, some errors are still present in the ground truth segmentation. The next steps for this project would be to acquire more data, train the data on multiple input sequences, use a 3D network and using localizer images that are acquired at the start of the MRI scan. Nonetheless, the principles established in this thesis confirm that a deep learning approach can be used to create numerical body models for SAR estimates. It also establishes the fact that these SAR estimates are comparable to the SAR estimates generated from the ground truth numerical body models.

Perivascular spaces (PVS) visible on MRI are currently emerging as an important potential neuroimaging marker for several pathologies in the brain like Alzheimer’s disease and cerebral small vessel disease. PVS are fluid-filled spaces surrounding vessels as they enter the brain. Although PVS are normally not noticeable on MRI scans acquired at clinical field strengths, when these spaces increase in size they become increasingly visible and quantifiable. To study these spaces it is important to have a robust method for quantifying PVS. Manual quantification of PVS is challenging, time-consuming and subject to observer bias due to the difficulty of distinguishing PVS from mimics and the large number of PVS that can occur in MRI scans. Many promising (semi-)automated methods have been proposed recently to decrease annotation time and intra- and inter-observer variability while providing more information about EPVS. However there are still various limitations in the current methods that need to be overcome.
An important limitation is that most of the methods are based on elaborate preprocessing steps, feature extraction and heuristic fine-tuning of parameters, making the use of these methods on new datasets cumbersome. Furthermore the majority of the currently proposed methods have been evaluated on small sets of barely 30 images, as most of these methods aim to segment PVS and require voxel-wise annotations for evaluation. In this thesis we propose a method for automated detection of perivascular spaces that combines a convolutional neural network and geodesic distance transform (GDT). We propose to use dot annotations instead of voxel-wise segmentations as this is less time-consuming than fully segmenting PVS while still providing the location of PVS. This enables us to use a considerably larger dataset with ground truth locations than is used in all previously proposed (semi-)automatic methods that provide the location of PVS. We investigated two approaches of using geodesic distance transform to optimize the CNN to detect PVS. The first approach focuses on optimizing the CNN for voxel-wise regression of the geodesic distance map (GDM) computed from the dots and the intensity image. The second approach aims to predict segmentations of the PVS using a CNN that is trained on approximated segmentations obtained by thresholding GDMs. We use 1202 proton density-weighted (PDw) MRI scans to develop our methods and 1000 other scans are used to evaluate the performance of the methods. We show that our methods match human intra-rater performance on detecting PVS without the need for any user interaction. Additionally we show that GDMs are extremely useful for capturing complex morphologies when computed from dot annotations. Our experiments indicate that GDMs can be used to provide valuable additional information to CNNs during training.