Sameer A. Ansari

Chaochao Zhou, Syed Hasib Akhter Faruqui, Abhinav Patel et al.

Many tasks performed in image-guided procedures can be cast as pose estimation problems, where specific projections are chosen to reach a target in 3D space. In this study, we first develop a differentiable projection (DiffProj) rendering framework for the efficient computation of Digitally Reconstructed Radiographs (DRRs) with automatic differentiability from either Cone-Beam Computerized Tomography (CBCT) or neural scene representations, including two newly proposed methods, Neural Tuned Tomography (NeTT) and masked Neural Radiance Fields (mNeRF). We then perform pose estimation by iterative gradient descent using various candidate loss functions, that quantify the image discrepancy of the synthesized DRR with respect to the ground-truth fluoroscopic X-ray image. Compared to alternative loss functions, the Mutual Information loss function can significantly improve pose estimation accuracy, as it can effectively prevent entrapment in local optima. Using the Mutual Information loss, a comprehensive evaluation of pose estimation performed on a tomographic X-ray dataset of 50 patients$'$ skulls shows that utilizing either discretized (CBCT) or neural (NeTT/mNeRF) scene representations in DiffProj leads to comparable performance in DRR appearance and pose estimation (3D angle errors: mean $\leq$ 3.2° and 90% quantile $\leq$ 3.4°), despite the latter often incurring considerable training expenses and time. These findings could be instrumental for selecting appropriate approaches to improve the efficiency and effectiveness of fluoroscopic X-ray pose estimation in widespread image-guided interventions.

Javier Bisbal, Patrick Winter, Sebastian Jofre et al.

Accurate anatomical labeling of intracranial arteries is essential for cerebrovascular diagnosis and hemodynamic analysis but remains time-consuming and subject to interoperator variability. We present a deep learning-based framework for automated artery labeling from 3D Time-of-Flight Magnetic Resonance Angiography (3D ToF-MRA) segmentations (n=35), incorporating uncertainty quantification to enhance interpretability and reliability. We evaluated three convolutional neural network architectures: (1) a UNet with residual encoder blocks, reflecting commonly used baselines in vascular labeling; (2) CS-Net, an attention-augmented UNet incorporating channel and spatial attention mechanisms for enhanced curvilinear structure recognition; and (3) nnUNet, a self-configuring framework that automates preprocessing, training, and architectural adaptation based on dataset characteristics. Among these, nnUNet achieved the highest labeling performance (average Dice score: 0.922; average surface distance: 0.387 mm), with improved robustness in anatomically complex vessels. To assess predictive confidence, we implemented test-time augmentation (TTA) and introduced a novel coordinate-guided strategy to reduce interpolation errors during augmented inference. The resulting uncertainty maps reliably indicated regions of anatomical ambiguity, pathological variation, or manual labeling inconsistency. We further validated clinical utility by comparing flow velocities derived from automated and manual labels in co-registered 4D Flow MRI datasets, observing close agreement with no statistically significant differences. Our framework offers a scalable, accurate, and uncertainty-aware solution for automated cerebrovascular labeling, supporting downstream hemodynamic analysis and facilitating clinical integration.