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Mohammad Farid Azampour

5 accepted papers

2025

Shape Completion and Real-Time Visualization in Robotic Ultrasound Spine Acquisitions

IROS 2025

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 that obscure deeper tissue structures. Traditional approaches, such as CT-to-US registration, incorporate anatomical info

Cited by 4SourceScholar
2024

Implicit Neural Representations for Breathing-compensated Volume Reconstruction in Robotic Ultrasound

ICRA 2024poster

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-operator variability and inconsistent image acquisition hinder the widespread adoption of extensive screening programs.…

Cited by 4SourceScholar
2022

A Variational Bayesian Method for Similarity Learning in Non-Rigid Image Registration

CVPR 2022poster

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. The proposed framework is general and may be used together with many existing image registration models. We evaluate…

Cited by 12PDFcodeScholar
2021

Position-Based Dynamics Simulator of Brain Deformations for Path Planning and Intra-Operative Control in Keyhole Neurosurgery

RA-L 2021

Many tasks in robot-assisted surgery require planning and controlling manipulators' motions that interact with highly deformable objects. This study proposes a realistic, time-bounded simulator based on Position-based Dynamics (PBD) simulation that mocks brain deformations due to catheter insertion

Cited by 16SourceScholar
2020

Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning

IROS 2020poster

In this paper we introduce the first reinforcement learning (RL) based robotic navigation method which utilizes ultrasound (US) images as an input. Our approach combines state-of-the-art RL techniques, specifically deep Q-networks (DQN) with memory buffers and a binary classifier for deciding when t…

Cited by 57SourceScholar