5 research outputs found
Ultra-NeRF: Neural Radiance Fields for Ultrasound Imaging
We present a physics-enhanced implicit neural representation (INR) for
ultrasound (US) imaging that learns tissue properties from overlapping US
sweeps. Our proposed method leverages a ray-tracing-based neural rendering for
novel view US synthesis. Recent publications demonstrated that INR models could
encode a representation of a three-dimensional scene from a set of
two-dimensional US frames. However, these models fail to consider the
view-dependent changes in appearance and geometry intrinsic to US 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. In
particular, we demonstrate experimentally that our proposed method generates
geometrically accurate B-mode images for regions with ambiguous representation
owing to view-dependent differences of the US images. We conduct our
experiments using simulated B-mode US sweeps of the liver and acquired US
sweeps of a spine phantom tracked with a robotic arm. The experiments
corroborate that our method generates US frames that enable consistent volume
compounding from previously unseen views. To the best of our knowledge, the
presented work is the first to address view-dependent US image synthesis using
INR.Comment: submitted to MID
Position-based Dynamics Simulator of Brain Deformations for Path Planning and Intra-Operative Control in Keyhole Neurosurgery
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 for
pre-operative path planning and intra-operative guidance in keyhole surgical
procedures. It maximizes the probability of success by accounting for
uncertainty in deformation models, noisy sensing, and unpredictable actuation.
The PBD deformation parameters were initialized on a parallelepiped-shaped
simulated phantom to obtain a reasonable starting guess for the brain white
matter. They were calibrated by comparing the obtained displacements with
deformation data for catheter insertion in a composite hydrogel phantom.
Knowing the gray matter brain structures' different behaviors, the parameters
were fine-tuned to obtain a generalized human brain model. The brain
structures' average displacement was compared with values in the literature.
The simulator's numerical model uses a novel approach with respect to the
literature, and it has proved to be a close match with real brain deformations
through validation using recorded deformation data of in-vivo animal trials
with a mean mismatch of 4.732.15%. The stability, accuracy, and real-time
performance make this model suitable for creating a dynamic environment for KN
path planning, pre-operative path planning, and intra-operative guidance.Comment: 8 pages, 8 figures. This article has been accepted for publication in
a future issue of IEEE Robotics and Automation Letters, but has not been
fully edited. Content may change prior to final publication. 2377-3766 (c)
2021 IEEE. Personal use is permitted, but republication/redistribution
requires IEEE permission. A. Segato and C. Di Vece equally contribute
A lightweight neural network with multiscale feature enhancement for liver CT segmentation
Segmentation of abdominal Computed Tomography (CT) scan is essential for analyzing, diagnosing, and treating visceral organ diseases (e.g., hepatocellular carcinoma). This paper proposes a novel neural network (Res-PAC-UNet) that employs a fixed-width residual UNet backbone and Pyramid Atrous Convolutions, providing a low disk utilization method for precise liver CT segmentation. The proposed network is trained on medical segmentation decathlon dataset using a modified surface loss function. Additionally, we evaluate its quantitative and qualitative performance; the Res16-PAC-UNet achieves a Dice coefficient of 0.950 ± 0.019 with less than half a million parameters. Alternatively, the Res32-PAC-UNet obtains a Dice coefficient of 0.958 ± 0.015 with an acceptable parameter count of approximately 1.2 million.This publication was made possible by NPRP-11S-1219-170106 from the Qatar National Research Fund (a member of Qatar Foundation). The findings herein reflect the work, and are solely the responsibility of the authors