20 citations · 45 across the 23 of their papers we have counts for
26 papers
Deformable CT-US Registration via Anatomy-Aware Implicit Neural Representations
Agnieszka Lach, Magdalena Wysocki, Feng Li +7
Slice-to-volume registration between ultrasound (US) and preoperative computed tomography (CT) imaging would enhance many minimally invasive interventions, for example by locating…
UltraDiff: Differentiable Ray Tracing in Ultrasound for Shape Optimization
Felix Duelmer, Magdalena Wysocki, Nassir Navab +1
Physically-based differentiable rendering enables gradient-based optimization of scene parameters by matching rendered images to measurements, but has so far mainly focused on ligh…
ZODIAC: Zero-shot Octree-based Diffusion for Anatomical Completion
Miruna-Alexandra Gafencu, Vlad Bratulescu, Yordanka Velikova +2
Recovering the full 3D spine anatomy from intraoperative ultrasound is an ill-posed inverse problem, as the complete structure must be inferred from incomplete and noisy observatio…
Multivariate Gaussian NeRF for Wide Field-of-View Ultrasound Reconstruction
Patris Valera, Magdalena Wysocki, Felix Duelmer +4
Wide Field-of-View (WFoV) reconstruction enhances 3D ultrasound imaging by providing valuable anatomical context for segmentation models and visualization. Clinical ultrasound volu…
UltraG-Ray: Physics-Based Gaussian Ray Casting for Novel Ultrasound View Synthesis
Felix Duelmer, Jakob Klaushofer, Magdalena Wysocki +2
Novel view synthesis (NVS) in ultrasound has gained attention as a technique for generating anatomically plausible views beyond the acquired frames, offering new capabilities for t…
OSCAR: Occupancy-based Shape Completion via Acoustic Neural Implicit Representations
Magdalena Wysocki, Kadir Burak Buldu, Miruna-Alexandra Gafencu +2
Accurate 3D reconstruction of vertebral anatomy from ultrasound is important for guiding minimally invasive spine interventions, but it remains challenging due to acoustic shadowin…
US-X Complete: A Multi-Modal Approach to Anatomical 3D Shape Recovery
Miruna-Alexandra Gafencu, Yordanka Velikova, Nassir Navab +1
Ultrasound offers a radiation-free, cost-effective solution for real-time visualization of spinal landmarks, paraspinal soft tissues and neurovascular structures, making it valuabl…
UltraScatter: Ray-Based Simulation of Ultrasound Scattering
Felix Duelmer, Mohammad Farid Azampour, Nassir Navab
Traditional ultrasound simulation methods solve wave equations numerically, achieving high accuracy but at substantial computational cost. Faster alternatives based on convolution…
UltrON: Ultrasound Occupancy Networks
Magdalena Wysocki, Felix Duelmer, Ananya Bal +2
In free-hand ultrasound imaging, sonographers rely on expertise to mentally integrate partial 2D views into 3D anatomical shapes. Shape reconstruction can assist clinicians in this…
Shape Completion and Real-Time Visualization in Robotic Ultrasound Spine Acquisitions
Miruna-Alexandra Gafencu, Reem Shaban, Yordanka Velikova +2
Ultrasound (US) imaging is increasingly used in spinal procedures due to its real-time, radiation-free capabilities; however, its effectiveness is hindered by shadowing artifacts t…
HyperSORT: Self-Organising Robust Training with hyper-networks
Samuel Joutard, Marijn Stollenga, Marc Balle Sanchez +2
Medical imaging datasets often contain heterogeneous biases ranging from erroneous labels to inconsistent labeling styles. Such biases can negatively impact deep segmentation netwo…
Skelite: Compact Neural Networks for Efficient Iterative Skeletonization
Luis D. Reyes Vargas, Martin J. Menten, Johannes C. Paetzold +2
Skeletonization extracts thin representations from images that compactly encode their geometry and topology. These representations have become an important topological prior for pr…
UltraRay: Introducing Full-Path Ray Tracing in Physics-Based Ultrasound Simulation
Felix Duelmer, Mohammad Farid Azampour, Magdalena Wysocki +1
Traditional ultrasound simulators solve the wave equation to model pressure distribution fields, achieving high accuracy but requiring significant computational time and resources.…
Intraoperative Registration by Cross-Modal Inverse Neural Rendering
Maximilian Fehrentz, Mohammad Farid Azampour, Reuben Dorent +7
We present in this paper a novel approach for 3D/2D intraoperative registration during neurosurgery via cross-modal inverse neural rendering. Our approach separates implicit neural…
PHOCUS: Physics-Based Deconvolution for Ultrasound Resolution Enhancement
Felix Duelmer, Walter Simson, Mohammad Farid Azampour +3
Ultrasound is widely used in medical diagnostics allowing for accessible and powerful imaging but suffers from resolution limitations due to diffraction and the finite aperture of…
Diffusion as Sound Propagation: Physics-inspired Model for Ultrasound Image Generation
Marina Domínguez, Yordanka Velikova, Nassir Navab +1
Deep learning (DL) methods typically require large datasets to effectively learn data distributions. However, in the medical field, data is often limited in quantity, and acquiring…
Shape Completion in the Dark: Completing Vertebrae Morphology from 3D Ultrasound
Miruna-Alexandra Gafencu, Yordanka Velikova, Mahdi Saleh +4
Purpose: Ultrasound (US) imaging, while advantageous for its radiation-free nature, is challenging to interpret due to only partially visible organs and a lack of complete 3D infor…
Implicit Neural Representations for Breathing-compensated Volume Reconstruction in Robotic Ultrasound
Yordanka Velikova, Mohammad Farid Azampour, Walter Simson +2
Ultrasound (US) imaging is widely used in diagnosing and staging abdominal diseases due to its lack of non-ionizing radiation and prevalent availability. However, significant inter…
LOTUS: Learning to Optimize Task-based US representations
Yordanka Velikova, Mohammad Farid Azampour, Walter Simson +2
Anatomical segmentation of organs in ultrasound images is essential to many clinical applications, particularly for diagnosis and monitoring. Existing deep neural networks require…
Ultra-NeRF: Neural Radiance Fields for Ultrasound Imaging
Magdalena Wysocki, Mohammad Farid Azampour, Christine Eilers +3
We present a physics-enhanced implicit neural representation (INR) for ultrasound (US) imaging that learns tissue properties from overlapping US sweeps. Our proposed method leverag…
CACTUSS: Common Anatomical CT-US Space for US examinations
Yordanka Velikova, Walter Simson, Mehrdad Salehi +3
Abdominal aortic aneurysm (AAA) is a vascular disease in which a section of the aorta enlarges, weakening its walls and potentially rupturing the vessel. Abdominal ultrasound has b…
Weakly-supervised Biomechanically-constrained CT/MRI Registration of the Spine
Bailiang Jian, Mohammad Farid Azampour, Francesca De Benetti +9
CT and MRI are two of the most informative modalities in spinal diagnostics and treatment planning. CT is useful when analysing bony structures, while MRI gives information about t…
Uncertainty quantification in non-rigid image registration via stochastic gradient Markov chain Monte Carlo
Daniel Grzech, Mohammad Farid Azampour, Huaqi Qiu +3
We develop a new Bayesian model for non-rigid registration of three-dimensional medical images, with a focus on uncertainty quantification. Probabilistic registration of large imag…
Position-based Dynamics Simulator of Brain Deformations for Path Planning and Intra-Operative Control in Keyhole Neurosurgery
Alice Segato, Chiara Di Vece, Sara Zucchelli +6
Many tasks in robot-assisted surgery require planning and controlling manipulators' motions that interact with highly deformable objects. This study proposes a realistic, time-boun…