The complexity and interdependency of hospital activities make it challenging to seamlessly integrate new technologies into clinical routines, especially with various stakeholders involved. Considering contextual factors when implementing innovations has been recognized as critical to success. This highlights the significance of an approach that focuses on integrating technology with the existing healthcare culture and motivating the context to change, rather than focusing on technology alone. Therefore, the aim of this thesis is to provide a method to ensure the clinical context is considered when developing a new technology. This approach is put in practice by utilizing it to introduce augmented reality (AR) at the oral and maxillofacial surgery (OMFS) department of the University Medical Center (UMC) Utrecht, to provide new insights on how to better align the technology with clinical needs and to familiarize and motivate the medical team with the technology. This is done in close collaboration with the UMC Groningen. Currently, the UMC Groningen, UMC Utrecht, and the Center for Information Technology (CIT) of the University of Groningen are developing an AR surgical navigation system. The aim of the introduction is to provide valuable information for the further development of the system and to prepare and involve the clinical environment.
The approach used in this thesis is based on human-centered design and design-thinking. Furthermore, key factors for successful adoption found from previous research are considered. The approach consists of four main components. The first is to assign a med-tech innovation coordinator; someone with both a medical and technical background who will be responsible for driving adoption. Furthermore, it is an iterative three-step process: 1) Gain understanding; 2) Share understanding; 3) Actively engage stakeholders.
The application of this approach to introduce AR at the OMFS department of the UMC Utrecht has resulted in multiple positive effects. In clinical context it resulted in an understanding of the technology and its value, motivations to invest time in the system, and the establishment of a shared future perspective on the implementation of AR. Furthermore, valuable input from a clinical perspective is obtained, including confirmation of valuable system elements and new ideas/insights for the further development of the AR navigation system.
In conclusion, this thesis provides guidance for departments seeking to adopt new technologies based on a practical example. The methodology provides activities that facilitate the early introduction of technologies, ensuring the active involvement of the clinical context.

Objective: The two-fold aim of this phantom study was first to evaluate the accuracy of pulmonary nodule detection for lung cancer screening at progressively lower dose levels of photon-counting computed tomography (PC-CT) and, second, to objectively compare the image quality across different acquisition and reconstruction settings, through a comparison of PC-CT and conventional energy-integrating detector (EID) CT.
Methods: Thirty-six artificial lung nodules with 6 diameters (2.5, 3, 4, 5, 6 and 10mm), 3 shapes (spherical, lobulated, spiculated) and two densities (-300 HU and +100 HU) were placed in an anthropomorphic chest phantom. The phantom was scanned using a standard lung cancer screening protocol (Sn 100 kV) with dose-matched EID-CT and PC-CT (CTDIvol of 0.8 mGy), in addition to 75%, 50%, 25% and 10% doses on the PC-CT (CTDIvol 0.6-0.07mGy). Nodule detection
was performed by one experienced reader, and denoted as the sensitivity, specificity, precision and false positive (FP) nodules. The acquisitions for assessing image quality were performed on a Quality Assurance (QA) phantom at full dose, with varying levels of iterative reconstruction (IR), virtual monoenergetic image (VMI) keV levels, slice thicknesses and increments, kernel strengths and scan modes. The noise power spectrum (NPS) and the task-based transfer function (TTF) were computed. The detectability index (d’) was computed to model the detection of a 4 mm solid and 5 mm subsolid pulmonary nodule.
Results: Sensitivity of 51-62% and specificity of 50-95% were obtained at 100%, 75%, 50%, 25% and 10% doses on the PC-CT, respectively, compared to the 59% sensitivity and 100% specificity on the EID-CT. The precision was 100% on the EID-CT and 94-99% on the PC-CT down to 25% dose, whereas at 10% dose the precision dropped to 88% and the FP nodules tripled from 5 to 15. Increasing the IR level, VMI keV and slice thickness decreased noise magnitude, with only minimal changes in noise texture. In contrast, higher kernel strength increased the noise magnitude, but created a finer noise texture. All settings showed a minimal impact on the spatial resolution. Noise magnitude was 40-60% higher on the EID-CT compared to PC-CT for all scan modes, although no difference in spatial resolution was found. Furthermore, the PC-CT attained 30-50% higher d’ values independent of scanmode or tin filter, in comparison to the EID-CT at a similar dose.
Conclusion: PC-CT with similar acquisition and reconstruction settings demonstrated comparable sensitivity for lung nodule detection despite lower radiation dose, when compared with EID-CT for low-dose lung cancer CT screening. However, attention has to be paid to FP findings at ultra-low dose levels, since the specificity decreased with every dose reduction. Furthermore, PC-CT achieved higher d’ values compared to EID-CT at an equivalent dose, through enhanced image quality and less noise. Additionally, the study shows that optimizing the image acquisition and reconstruction parameters can further enhance the image quality of PC-CT. These findings show that PC-CT holds significant promise as an alternative method for low-dose lung cancer screening. Consequently, future research should focus on evaluating the performance of PC-CT further on a more representative anthropomorphic phantom and in a clinical setting.

Introduction: Breast reconstruction after a mastectomy is crucial to improve a patient’s quality of life. The Deep Inferior Epigastric artery Perforator (DIEP) flap procedure is considered the golden standard for breast reconstruction. Three-dimensional (3D) visualization methods have shown promise in providing a better understanding of Computed Tomography Angiography (CTA) information and potentially reducing operative time. However, for these methods manual segmentation of the perforators is needed, which is time-consuming and prone to variability. Automated segmentation using Deep learning (DL) can potentially overcome these limitations and provide accurate and efficient segmentation of the DIEP flap perforators. This study aimed to evaluate the application of DL for automated perforator segmentation in DIEP flap breast reconstruction, improving the efficiency and objectivity of DIEP flap perforator segmentation.

Materials and methods: The dataset comprised 25 CTA scans for training and validation, and 5 CTA scans for testing. DL was employed for automated segmentation, and quantitative evaluation included metrics such as Dice coefficient score, recall, precision, surface distance, and centerline overlap. The qualitative evaluation involved grading the clinical acceptability of segmentations by four experienced plastic reconstructive surgeons.

Results: On the training set, a Dice score of 0.58 (± 0.08) and a true positive centerline overlap of 0.66 (± 0.10) were achieved for perforator segmentation. The DL model successfully segmented the intramuscular main branch, but some perforators were missed in the subcutaneous fat tissue. Combined grading by all surgeons showed no statistical difference between manual and automated segmentations and both segmentations were evaluated as clinically acceptable.

Conclusion: Automated DL segmentation holds promise for enhancing the efficiency and objectivity of identifying DIEP flap perforators in CTA images, providing an alternative to manual segmentation. Nonetheless, further research is needed to refine the automated segmentation results and to validate the generalizability and clinical applicability of the DL segmentation approach in larger patient cohorts and different clinical settings.

The thesis is composed of two parts. The first part is the scientific article
that describes the main findings for our objective to align cerebral DSA series.
The second part of the thesis includes more extensive information regarding
the clinical and technical background of the project. Additionally, it contains
information on deep-learning

The efficacy of endovascular therapy in large vessel occlusion (LVO) of the anterior circulation is dependent to a high degree on the selection of patients who are likely to benefit from this procedure. To this end, functional outcome prediction based on clinical parameters is an active area of research. In the preoperative screening of LVO patients, CT-Angiography (CTA) imaging is commonly acquired. We compare the functional outcome prediction performance of multiple deep learning based classifiers with multiple conventional methods, including the clinically validated MR PREDICTS decision tool. Using a dataset composed of 1929 preprocedural CTA images combined with clinical data, we compare a clinical baseline model with an imaging based pipeline and a combined pipeline. For the imaging model backbone we train various state-of-the-art architectures (Med3D, Vision Transformer, Voxel Transformer). These models are used to predict dichotomized modified Rankin Scale score 90 days after mechanical thrombectomy. Binary classifier outcomes are quantified using Area-Under the receiver operating characteristic Curve (AUC). The activation maps of the best performing image based model are further investigated using the GradCAM++ posthoc visualization method. Combining clinical features with information extracted from CTA images does not significantly improve the performance of functional outcome prediction methods compared to the baseline model. The information extracted from the images does not seem to
be complementary to the clinical features. This multimodal technique can however replace radiologically derived biomarkers, as its performance is non-inferior.

Introduction The current gold standard for preoperative planning of deep inferior epigastric artery perforator (DIEAP) flap breast reconstruction is assessment of perforator vessels on computed tomography angiography (CTA) or magnetic resonance angiography (MRA) images in two-dimensional (2D) planes on a 2D screen. This research investigates the potential benefit of a 3D model of blood vessels on a 2D screen or Virtual Reality (VR) environment in preoperative planning for DIEAP flap breast reconstruction, compared to conventional CTA on a 2D screen. Materials & Methods A qualitative study was conducted among six reconstructive plastic surgeons and four residents. Participants were asked to select the ideal perforator for unilateral DIEAP flap reconstruction in three viewing environments: conventional CTA in a 2D view, a 3D model in a 2D view and a 3D model in an immersive virtual reality (VR) view. Subsequently, a questionnaire on anatomical structure visibility assessing six perforator characteristics (perforator location in the flap, calibre, intramuscular course through the rectus abdominis, perforator origin, subcutaneous branching and connection to other vessels) was completed. Second, an adjusted Usefulness, Satisfaction and Ease of Use (USE) questionnaire on system usability was filled out.Results Ten participants completed the experiments. The overall score distribution for perforator visibility characteristics was equal or higher except for perforator calibre in both 3D model views compared to conventional CTA. In the USE questionnaire, the 3D model in 2D view reported the highest scores for satisfaction and ease of learning. 90% of participants considered a 3D model in VR view to be of added value to preoperative planning, 60% considered a 3D model in 2D to be of added value. Overall, 40% would currently opt for the 3D model in a 2D view and 10% for the 3D model in a VR view. Conclusion This study presents a perspective on the application of 3D models for preoperative planning in DIEAP flap breast reconstructions. There is an interest in use of a 3D model in a 2D view or an immersive VR environment to optimize preoperative planning. The visibility of important perforator characteristics received overall higher scores for the viewing environments with a 3D model, highlighting the potential benefit for 3D models and VR application for selection of ideal perforators in preoperative planning for DIEAP flap breast reconstructions. 

Purpose. Distal radius fractures are common fractures of the wrist. These fractures are often displaced and need reduction, after adequate reduction, the patients will have follow-up X-rays to check if the fracture stays stable. This is important because surgery might be required if the fracture becomes unstable. This can lead to delayed surgery which can worsen the treatment outcome.
Therefore, it would be valuable to predict which distal radius fractures are likely to become unstable. Machine learning could help predict the stability of distal radius fractures based on CT. In addition, because there is only a small dataset available, this research also studies the effect of different machine learning methods.
Method. Two different methods were evaluated for the stability prediction of distal radius fractures: traditional machine learning (radiomics method) and a residual network with and without transfer learning (deep learning method). For the radiomics method, a python package called WORC was used, which automatically extracts radiomic features and optimizes machine learning models. For the deep learning method, the backbone of a residual network called Med3D with added layers for classification was used.
Results. The radiomics method combined with augmentation, gave the best results (AUC: 0.64± 0.01). Also, it was found that using an augmented data set in the radiomics method resulted in improved performance. This gives a slight indication that the radiomic method can learn to predict DRF when there would be more data available.
Conclusion. The radiomic method is the most promising method for predicting the stability of distal radius fractures, despite the small difference in performance compared to random guessing and the deep learning method. However, for further research, it is highly recommended to acquire a larger dataset.