Geometric Ultrasound Localization Microscopy
- URL: http://arxiv.org/abs/2306.15548v3
- Date: Tue, 18 Jul 2023 10:26:58 GMT
- Title: Geometric Ultrasound Localization Microscopy
- Authors: Christopher Hahne and Raphael Sznitman
- Abstract summary: Ultrasound Localization Microscopy (ULM) has enabled a revolutionary breakthrough by offering ten times higher resolution.
This study questions whether beamforming is the most effective processing step for ULM.
A novel geometric framework for micro bubble localization via ellipse intersections is proposed to overcome existing beamforming limitations.
- Score: 6.602729062703117
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Contrast-Enhanced Ultra-Sound (CEUS) has become a viable method for
non-invasive, dynamic visualization in medical diagnostics, yet Ultrasound
Localization Microscopy (ULM) has enabled a revolutionary breakthrough by
offering ten times higher resolution. To date, Delay-And-Sum (DAS) beamformers
are used to render ULM frames, ultimately determining the image resolution
capability. To take full advantage of ULM, this study questions whether
beamforming is the most effective processing step for ULM, suggesting an
alternative approach that relies solely on Time-Difference-of-Arrival (TDoA)
information. To this end, a novel geometric framework for micro bubble
localization via ellipse intersections is proposed to overcome existing
beamforming limitations. We present a benchmark comparison based on a public
dataset for which our geometric ULM outperforms existing baseline methods in
terms of accuracy and robustness while only utilizing a portion of the
available transducer data.
Related papers
- TEAM PILOT -- Learned Feasible Extendable Set of Dynamic MRI Acquisition Trajectories [2.7719338074999547]
We introduce a novel deep-compressed sensing approach that uses 3D window attention and flexible, temporally extendable acquisition trajectories.
Our method significantly reduces both training and inference times compared to existing approaches.
Tests with real data show that our approach outperforms current state-of-theart techniques.
arXiv Detail & Related papers (2024-09-19T13:45:13Z) - PHOCUS: Physics-Based Deconvolution for Ultrasound Resolution Enhancement [36.20701982473809]
The impulse function of an ultrasound imaging system is called the point spread function (PSF), which is convolved with the spatial distribution of reflectors in the image formation process.
We introduce a physics-based deconvolution process using a modeled PSF, working directly on the more commonly available B-mode images.
By leveraging Implicit Neural Representations (INRs), we learn a continuous mapping from spatial locations to their respective echogenicity values, effectively compensating for the discretized image space.
arXiv Detail & Related papers (2024-08-07T09:52:30Z) - Inter-slice Super-resolution of Magnetic Resonance Images by Pre-training and Self-supervised Fine-tuning [49.197385954021456]
In clinical practice, 2D magnetic resonance (MR) sequences are widely adopted. While individual 2D slices can be stacked to form a 3D volume, the relatively large slice spacing can pose challenges for visualization and subsequent analysis tasks.
To reduce slice spacing, deep-learning-based super-resolution techniques are widely investigated.
Most current solutions require a substantial number of paired high-resolution and low-resolution images for supervised training, which are typically unavailable in real-world scenarios.
arXiv Detail & Related papers (2024-06-10T02:20:26Z) - Learning to sample in Cartesian MRI [1.2432046687586285]
Shortening scanning times is crucial in clinical settings, as it increases patient comfort, decreases examination costs and improves throughput.
Recent advances in compressed sensing (CS) and deep learning allow accelerated MRI acquisition by reconstructing high-quality images from undersampled data.
This thesis explores two approaches to address this gap in the context of Cartesian MRI.
arXiv Detail & Related papers (2023-12-07T14:38:07Z) - Learning Super-Resolution Ultrasound Localization Microscopy from
Radio-Frequency Data [8.312810360920107]
We propose to feed Radio-Frequency (RF) data into a super-resolution network while bypassing DAS beamforming and its limitations.
Results from our RF-trained network suggest that excluding DAS beamforming offers a great potential to optimize on the ULM resolution performance.
arXiv Detail & Related papers (2023-11-07T15:47:38Z) - OADAT: Experimental and Synthetic Clinical Optoacoustic Data for
Standardized Image Processing [62.993663757843464]
Optoacoustic (OA) imaging is based on excitation of biological tissues with nanosecond-duration laser pulses followed by detection of ultrasound waves generated via light-absorption-mediated thermoelastic expansion.
OA imaging features a powerful combination between rich optical contrast and high resolution in deep tissues.
No standardized datasets generated with different types of experimental set-up and associated processing methods are available to facilitate advances in broader applications of OA in clinical settings.
arXiv Detail & Related papers (2022-06-17T08:11:26Z) - Ultrasound Signal Processing: From Models to Deep Learning [64.56774869055826]
Medical ultrasound imaging relies heavily on high-quality signal processing to provide reliable and interpretable image reconstructions.
Deep learning based methods, which are optimized in a data-driven fashion, have gained popularity.
A relatively new paradigm combines the power of the two: leveraging data-driven deep learning, as well as exploiting domain knowledge.
arXiv Detail & Related papers (2022-04-09T13:04:36Z) - Optical-Flow-Reuse-Based Bidirectional Recurrent Network for Space-Time
Video Super-Resolution [52.899234731501075]
Space-time video super-resolution (ST-VSR) simultaneously increases the spatial resolution and frame rate for a given video.
Existing methods typically suffer from difficulties in how to efficiently leverage information from a large range of neighboring frames.
We propose a coarse-to-fine bidirectional recurrent neural network instead of using ConvLSTM to leverage knowledge between adjacent frames.
arXiv Detail & Related papers (2021-10-13T15:21:30Z) - Deep MRI Reconstruction with Radial Subsampling [2.7998963147546148]
Retrospectively applying a subsampling mask onto the k-space data is a way of simulating the accelerated acquisition of k-space data in real clinical setting.
We compare and provide a review for the effect of applying either rectilinear or radial retrospective subsampling on the quality of the reconstructions outputted by trained deep neural networks.
arXiv Detail & Related papers (2021-08-17T17:45:51Z) - A parameter refinement method for Ptychography based on Deep Learning
concepts [55.41644538483948]
coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability.
A modern Deep Learning framework is used to correct autonomously the setup incoherences, thus improving the quality of a ptychography reconstruction.
We tested our system on both synthetic datasets and also on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.
arXiv Detail & Related papers (2021-05-18T10:15:17Z) - Hyperspectral-Multispectral Image Fusion with Weighted LASSO [68.04032419397677]
We propose an approach for fusing hyperspectral and multispectral images to provide high-quality hyperspectral output.
We demonstrate that the proposed sparse fusion and reconstruction provides quantitatively superior results when compared to existing methods on publicly available images.
arXiv Detail & Related papers (2020-03-15T23:07:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.