Search Results for author:
Found 13 papers, 9 papers with code
UltraRay: Full-Path Ray Tracing for Enhancing Realism in Ultrasound Simulation
We propose a novel ultrasound simulation pipeline that utilizes a ray tracing algorithm to generate echo data, tracing each ray from the transducer through the scene and back to the sensor.
Intraoperative Registration by Cross-Modal Inverse Neural Rendering
We present in this paper a novel approach for 3D/2D intraoperative registration during neurosurgery via cross-modal inverse neural rendering.
PHOCUS: Physics-Based Deconvolution for Ultrasound Resolution Enhancement
Conventionally, deconvolution techniques attempt to rectify the imaging system's dependent PSF, working directly on the radio-frequency (RF) data.
Diffusion as Sound Propagation: Physics-inspired Model for Ultrasound Image Generation
However, when it comes to ultrasound (US) imaging, the authenticity of generated data often diminishes due to the oversight of ultrasound physics.
Shape Completion in the Dark: Completing Vertebrae Morphology from 3D Ultrasound
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 information.
LOTUS: Learning to Optimize Task-based US representations
Anatomical segmentation of organs in ultrasound images is essential to many clinical applications, particularly for diagnosis and monitoring.
Ultra-NeRF: Neural Radiance Fields for Ultrasound Imaging
In our work, we discuss direction-dependent changes in the scene and show that a physics-inspired rendering improves the fidelity of US image synthesis.
CACTUSS: Common Anatomical CT-US Space for US examinations
Abdominal aortic aneurysm (AAA) is a vascular disease in which a section of the aorta enlarges, weakening its walls and potentially rupturing the vessel.
Weakly-supervised Biomechanically-constrained CT/MRI Registration of the Spine
We specifically design these losses to depend only on the CT label maps since automatic vertebra segmentation in CT gives more accurate results contrary to MRI.
A Variational Bayesian Method for Similarity Learning in Non-Rigid Image Registration
We propose a novel variational Bayesian formulation for diffeomorphic non-rigid registration of medical images, which learns in an unsupervised way a data-specific similarity metric.
Uncertainty quantification in non-rigid image registration via stochastic gradient Markov chain Monte Carlo
We develop a new Bayesian model for non-rigid registration of three-dimensional medical images, with a focus on uncertainty quantification.
Rethinking Ultrasound Augmentation: A Physics-Inspired Approach
Medical Ultrasound (US), despite its wide use, is characterized by artifacts and operator dependency.
Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning
In this paper we introduce the first reinforcement learning (RL) based robotic navigation method which utilizes ultrasound (US) images as an input.
