During a brain tumour resection, a neurosurgeon is constantly navigating a delicate balance between resecting as much of the tumour as possible, while avoiding any damage to healthy brain tissue. This challenge is particularly difficult when the tumour is located in a critical functional area, involved in for example language or motor function. For these types of tumours, the awake craniotomy was developed. During this surgery the patient wakes up to perform language and motor tasks, to enable the surgeon to localize these functions inside the brain. In this thesis, we investigate and develop a new quantitative method to monitoring motor function that could potentially improve intraoperative decision making and enables neuroscientific and neurosurgical research. Chapter 1 provides a background about surgical strategies and technologies that have been developed to aid surgeons’ decisions during complex brain tumour resections. We will explain the complexity of robust research in the neurosurgical environment and the need for a dedicated Research Operating Room to create an environment to improve neurosurgical and neuroscientific research. In Chapter 2 we make an overview of the possible solutions to quantify motor function before, during and after awake craniotomies and discuss the best solution for the Erasmus MC. In Chapter 3 we present a new frame to create a standardized environment inside the operating room for good quality data collection of patient functionality. To design this frame, we identified and interviewed all the important stakeholders and designed three prototypes. The two most promising prototypes were developed. The final prototype was implemented during three awake craniotomies. This newly developed frame was used in Chapter 4 to explore video tracking as a new tool to quantify hand motor function. Three patients were followed one day prior to the surgery, during the awake craniotomy, and one day postoperatively. During these three cases, we identified several prerequisites for a reliable recording set-up and explored the potential to detect clinically relevant events during fingertapping and direct electrical stimulation (DES). This showed promising results and underscores the potential for video tracking to be further investigated for quantification of hand motor function. In Chapter 5 we put the discussed work into context, discussing it’s clinical and scientific relevance and future perspectives. In this thesis, we have demonstrated that it is possible to implement a new quantitative measurement method to monitor hand function in the challenging environment of an operating room. Quantification of visual observations has shown to be low-cost, easily available and implementable in clinical context, because of the fast technological advancements in this field. Video tracking can be used for future research to investigate the relation between intraoperative findings and long-term outcomes, and has the potential to add valuable information for neurosurgical and neuroscientific research.

In this thesis, we investigate a sparse basis for ultrasound images, so that we can use sparse regularization in imaging. Actually, there are few previous researches explicitly demonstrating that medical ultrasound images can be sparsified for some dictionary. We consider various orthogonal transforms such as wavelet transforms, cosine transforms and wave atom transforms. Then, we perform those transforms on various ultrasound images and analyzes their sparsity. These ultrasound images include the images of two computer ultrasound phantoms and beamformed ultrasound images with good quality from real people. We looked at sparsity of the true pre-beamformed images, as well as beamformed images. We also consider constructing a specific ultrasound image dictionary using the K-SVD algorithm. We observed that, the pre-beamformed images hardly haVe no sparse basis, and the sparsity of beamformed images will only increase slightly if we use different 1D-DWT in each direction. We also found that the wide overdetermined dictionary generated by K-SVD significantly increases sparsity. After this, we simulate the ultrasound image reconstruction from the ultrasound RF measurements, and we analyze the effects of the different sparse spaces on the reconstruction performance. We observed that, the L1-regularization can work for ultrasound imaging better than L2-regularization, but the orthogonal transforms as well as the dictionary do not improve the reconstruction image quality much.

Background context: Intraoperative neuromonitoring (IONM) has proven effective in reducing postoperative neurological complications. However, current understanding of IONM is limited and its precise meaning in relation to neurological outcomes remains unclear. Machine learning (ML) is a promising solution to analyze the excessive amount of IONM data quickly, objectively and in real-time. Purpose: The goal is to develop a ML algorithm that can effectively predict neurological outcomes after spinal surgery using IONM data that include both motor evoked potentials (MEPs) and somatosensory evoked potentials (SSEPs), and analyze its key predicting features. To more effectively determine the specific independent contribution of both separate modalities, a separate ML model will be created for both MEP and SSEP in addition to a combined MEP-SSEP model. Study setting: Retrospective study. Patient sample: A total of 67 patients were analyzed. Outcome measures: The neurological status three months postoperatively compared to the preoperative status, categorized into three classes: 'Neurological stable deficits', ‘Neurologically intact’ and 'Neurological improvement'. Methods: 260 features were obtained from patients who underwent spinal surgery monitored by IONM. During nested cross-validation, the data was split into five folds, for both the inner and the outer loop. The four ML classifiers developed were support vector machine, K-nearest neighbors, random forest and extreme gradient boosting, and tested along the three modalities MEP, SSEP, and MEP-SSEP combination. Results: Extreme gradient boosting outperformed the other classifiers on all performance metrics. The combined MEP-SSEP model exhibited the highest scores for sensitivity: 70.4%, specificity: 88.3% and accuracy: 87.1%, while the MEP model exhibited the highest performance for precision: 75.6%. Highest predicting scores per individual class were also obtained by this XGBoost classifier on the combined MEP-SSEP model. Key predicting features were the presence or absence of preoperative neurological deficits and last measured signal latency compared to baseline, with a contribution of 29% and 13.5% in the best performing model, respectively. Conclusion: A reliable prediction of neurological outcomes three months postoperatively can be made combining MEP and SSEP IONM features, provided that the patient's preoperative status is accurately documented and included in the prediction. Though either MEP or SSEP features alone offer predictive value, MEP features show superior predictive values compared to SSEP features when both modalities are accessible, with latency emerging as a prominent predictive IONM feature.

To better understand how brain signals are processed and even how the human mind works, analyzing the hemodynamic signal model is one of the most essential steps. In the CUBE group of Erasmus MC, functional ultrasound (fUS) data of a mouse’s brain is recorded. By using this fUS dataset, this thesis will solve the problem regarding the joint estimation of hemodynamic response function (HRF) and the underlying stimulus. Usually, hemodynamic responses are investigated in the time domain, while this thesis provides another perspective from frequency domain signal processing. We consider the hemodynamic response as a convolutive signal mixture, then try to transform it into an instantaneous mixing model by converting the context into the frequency domain. By applying independent vector analysis (IVA), this estimation problem can be solved without facing permutation ambiguity which is a well-unknown problem regarding independent component analysis (ICA). Additional steps before and after IVA are also discussed so that a whole estimation road map is formed. Both simulation and experimental analysis are provided to validate this estimation algorithm. Results show that by using this method, both stimulus and HRF estimation can be achieved satisfyingly in a suitable experimental setting. This thesis provides insights and future potentials for IVA to be further investigated in neural signal processing problems.