Stroke remains a leading cause of morbidity and mortality worldwide, despite advances in treatment modalities. Endovascular thrombectomy (EVT), a revolutionary intervention for ischemic stroke, is limited by its reliance on 2D fluoroscopic imaging, which lacks depth and comprehensive vascular detail. We propose a novel AI-driven pipeline for 3D CTA to 2D DSA cross-modality registration, termed DeepIterReg. The proposed pipeline integrates neural network-based initialization with iterative optimization to align pre-intervention and peri-intervention data. Our approach addresses the challenges of cross-modality alignment, particularly in scenarios involving limited shared vascular structures, by leveraging synthetic data, vein-centric anchoring, and differentiable rendering techniques. We assess the efficacy of DeepIterReg through quantitative analysis of capture ranges and registration accuracy. Results indicate that our method is able to accurately register 70% of a testset of 20 patients, and is able to improve capture ranges when performing an initial pose estimation using a convolutional neural network.

Ischemic stroke, a leading cause of death and disability worldwide, occurs when a blood vessel is occluded by a thrombus. Current therapies for ischemic stroke, include Intravenous thrombolysis (IVT) and Endovascular thrombectomy (EVT). EVT relies on a Thrombolysis in Cerebral Infarction (TICI) score for assessing treatment effectiveness. This score, based on the visual evaluation of medical images by physicians, suffers from inter- and intra-observer variability, as it is influenced by the individual rater’s judgment. Digital Subtraction Angiography (DSA) imaging is commonly utilized both before therapy to identify the occluded vessel and after therapy to evaluate the treatment outcome. Accurate vessel correspondence before and after treatment is crucial for a reliable assessment. To enhance current evaluation methods and address this challenging task, we propose two automated approaches for determining vessel correspondence in pre- and post-EVT DSA imaging. The proposed methods
utilize graphical representations of the cerebral vascular network and distinct matching procedures. We refer to the methods as registration-based vessel matching (RB-VM) and graph based vessel matching (GB-VM). The methods were evaluated using manually annotated data with the RB-VM and GB-VM methods achieving a recall of 82.7% (78.2; 85.7) and 51.3% (47.4; 54.4) respectively. This work marks a significant step towards automatic stroke therapy assessment and showcases the potential benefits of graph based algorithms for this task, paving the way for more reliable and objective treatment assessments.

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.

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.

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.

Predicting functional outcome after intra-arterial treatment (IAT) in acute ischemic stroke (AIS) patients is an important aspect of treatment decision making and prognostics. Standard methods for functional outcome prediction after stroke combine baseline clinical (and radiological) parameters. In this study, we investigated to what extent baseline CTA images can be used for the prediction of functional outcome and how this relates to standard scoring methods. Furthermore, it was investigated whether combining baseline CTA images with clinical parameters improved the predictive accuracy compared to outcome prediction based on clinical parameters. We proposed two network architectures, a convolutional neural network (CNN) for the processing of image data and a multilayer perceptron for the processing of clinical (and radiological) parameters. Various training strategies were applied for the fusion of image and clinical data. The CNN processing CTA images achieved an average cross-validated area under the curve (AUC) score of 0.67, which was lower than for models processing clinical (and radiological) parameters. The best performing model combining CTA images and clinical parameters was trained end-to-end and applied weight initialization of the pre-trained CNN (AUC = 0.78). The DeLong test showed that the combined model performed significantly better than the model processing clinical parameters (AUC = 0.75). However, the difference is small and might not be clinically relevant. Compared to scoring methods processing clinical and radiological parameters the combined model achieved similar performance.