28 research outputs found
Trackerless freehand ultrasound with sequence modelling and auxiliary transformation over past and future frames
Three-dimensional (3D) freehand ultrasound (US) reconstruction without a
tracker can be advantageous over its two-dimensional or tracked counterparts in
many clinical applications. In this paper, we propose to estimate 3D spatial
transformation between US frames from both past and future 2D images, using
feed-forward and recurrent neural networks (RNNs). With the temporally
available frames, a further multi-task learning algorithm is proposed to
utilise a large number of auxiliary transformation-predicting tasks between
them. Using more than 40,000 US frames acquired from 228 scans on 38 forearms
of 19 volunteers in a volunteer study, the hold-out test performance is
quantified by frame prediction accuracy, volume reconstruction overlap,
accumulated tracking error and final drift, based on ground-truth from an
optical tracker. The results show the importance of modelling the
temporal-spatially correlated input frames as well as output transformations,
with further improvement owing to additional past and/or future frames. The
best performing model was associated with predicting transformation between
moderately-spaced frames, with an interval of less than ten frames at 20 frames
per second (fps). Little benefit was observed by adding frames more than one
second away from the predicted transformation, with or without LSTM-based RNNs.
Interestingly, with the proposed approach, explicit within-sequence loss that
encourages consistency in composing transformations or minimises accumulated
error may no longer be required. The implementation code and volunteer data
will be made publicly available ensuring reproducibility and further research.Comment: 10 pages, 4 figures, Paper submitted to IEEE International Symposium
on Biomedical Imaging (ISBI
Towards Non-contact 3D Ultrasound for Wrist Imaging
Objective: The objective of this work is an attempt towards non-contact
freehand 3D ultrasound imaging with minimal complexity added to the existing
point of care ultrasound (POCUS) systems. Methods: This study proposes a novel
approach of using a mechanical track for non-contact ultrasound (US) scanning.
The approach thus restricts the probe motion to a linear plane, to simplify the
acquisition and 3D reconstruction process. A pipeline for US 3D volume
reconstruction employing an US research platform and a GPU-based edge device is
developed. Results: The efficacy of the proposed approach is demonstrated
through ex-vivo and in-vivo experiments. Conclusion: The proposed approach with
the adjustable field of view capability, non-contact design, and low cost of
deployment without significantly altering the existing setup would open doors
for up gradation of traditional systems to a wide range of 3D US imaging
applications. Significance: Ultrasound (US) imaging is a popular clinical
imaging modality for the point-of-care bedside imaging, particularly of the
wrist/knee in the pediatric population due to its non-invasive and radiation
free nature. However, the limited views of tissue structures obtained with 2D
US in such scenarios make the diagnosis challenging. To overcome this, 3D US
imaging which uses 2D US images and their orientation/position to reconstruct
3D volumes was developed. The accurate position estimation of the US probe at
low cost has always stood as a challenging task in 3D reconstruction.
Additionally, US imaging involves contact, which causes difficulty to pediatric
subjects while monitoring live fractures or open wounds. Towards overcoming
these challenges, a novel framework is attempted in this work.Comment: 9 Pages, 11 figure
Privileged Anatomical and Protocol Discrimination in Trackerless 3D Ultrasound Reconstruction
Three-dimensional (3D) freehand ultrasound (US) reconstruction without using
any additional external tracking device has seen recent advances with deep
neural networks (DNNs). In this paper, we first investigated two identified
contributing factors of the learned inter-frame correlation that enable the
DNN-based reconstruction: anatomy and protocol. We propose to incorporate the
ability to represent these two factors - readily available during training - as
the privileged information to improve existing DNN-based methods. This is
implemented in a new multi-task method, where the anatomical and protocol
discrimination are used as auxiliary tasks. We further develop a differentiable
network architecture to optimise the branching location of these auxiliary
tasks, which controls the ratio between shared and task-specific network
parameters, for maximising the benefits from the two auxiliary tasks.
Experimental results, on a dataset with 38 forearms of 19 volunteers acquired
with 6 different scanning protocols, show that 1) both anatomical and protocol
variances are enabling factors for DNN-based US reconstruction; 2) learning how
to discriminate different subjects (anatomical variance) and predefined types
of scanning paths (protocol variance) both significantly improve frame
prediction accuracy, volume reconstruction overlap, accumulated tracking error
and final drift, using the proposed algorithm.Comment: Accepted to Advances in Simplifying Medical UltraSound (ASMUS)
workshop at MICCAI 202
A Simplified 3D Ultrasound Freehand Imaging Framework Using 1D Linear Probe and Low-Cost Mechanical Track
Ultrasound imaging is the most popular medical imaging modality for
point-of-care bedside imaging. However, 2D ultrasound imaging provides only
limited views of the organ of interest, making diagnosis challenging. To
overcome this, 3D ultrasound imaging was developed, which uses 2D ultrasound
images and their orientation/position to reconstruct 3D volumes. The accurate
position estimation of the ultrasound probe at low cost has always stood as a
challenging task in 3D reconstruction. In this study, we propose a novel
approach of using a mechanical track for ultrasound scanning, which restricts
the probe motion to a linear plane, simplifying the acquisition and hence the
reconstruction process. We also present an end-to-end pipeline for 3D
ultrasound volume reconstruction and demonstrate its efficacy with an in-vitro
tube phantom study and an ex-vivo bone experiment. The comparison between a
sensorless freehand and the proposed mechanical track based acquisition is
available online (shorturl.at/jqvX0).Comment: 4 pages, 4 figure
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
Recent Advances in Artificial Intelligence-Assisted Ultrasound Scanning
Funded by the Spanish Ministry of Economic Affairs and Digital Transformation (Project MIA.2021.M02.0005 TARTAGLIA, from the Recovery, Resilience, and Transformation Plan financed by the European Union through Next Generation EU funds). TARTAGLIA takes place under the R&D Missions in Artificial Intelligence program, which is part of the Spain Digital 2025 Agenda and the Spanish National Artificial Intelligence Strategy.Ultrasound (US) is a flexible imaging modality used globally as a first-line medical exam procedure in many different clinical cases. It benefits from the continued evolution of ultrasonic technologies and a well-established US-based digital health system. Nevertheless, its diagnostic performance still presents challenges due to the inherent characteristics of US imaging, such as manual operation and significant operator dependence. Artificial intelligence (AI) has proven to recognize complicated scan patterns and provide quantitative assessments for imaging data. Therefore, AI technology has the potential to help physicians get more accurate and repeatable outcomes in the US. In this article, we review the recent advances in AI-assisted US scanning. We have identified the main areas where AI is being used to facilitate US scanning, such as standard plane recognition and organ identification, the extraction of standard clinical planes from 3D US volumes, and the scanning guidance of US acquisitions performed by humans or robots. In general, the lack of standardization and reference datasets in this field makes it difficult to perform comparative studies among the different proposed methods. More open-access repositories of large US datasets with detailed information about the acquisition are needed to facilitate the development of this very active research field, which is expected to have a very positive impact on US imaging.Depto. de Estructura de la Materia, Física Térmica y ElectrónicaFac. de Ciencias FísicasTRUEMinistry of Economic Affairs and Digital Transformation from the Recovery, Resilience, and Transformation PlanNext Generation EU fundspu