Learning Super-Resolution Ultrasound Localization Microscopy from Radio-Frequency Data

• URL: http://arxiv.org/abs/2311.04081v1
• Date: Tue, 7 Nov 2023 15:47:38 GMT
• Title: Learning Super-Resolution Ultrasound Localization Microscopy from Radio-Frequency Data
• Authors: Christopher Hahne, Georges Chabouh, Olivier Couture, Raphael Sznitman
• Abstract summary: 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.
• Score: 8.312810360920107
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ultrasound Localization Microscopy (ULM) enables imaging of vascular structures in the micrometer range by accumulating contrast agent particle locations over time. Precise and efficient target localization accuracy remains an active research topic in the ULM field to further push the boundaries of this promising medical imaging technology. Existing work incorporates Delay-And-Sum (DAS) beamforming into particle localization pipelines, which ultimately determines the ULM image resolution capability. In this paper we propose to feed unprocessed Radio-Frequency (RF) data into a super-resolution network while bypassing DAS beamforming and its limitations. To facilitate this, we demonstrate label projection and inverse point transformation between B-mode and RF coordinate space as required by our approach. We assess our method against state-of-the-art techniques based on a public dataset featuring in silico and in vivo data. Results from our RF-trained network suggest that excluding DAS beamforming offers a great potential to optimize on the ULM resolution performance.

Related papers

• 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)
• CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers [66.15847237150909]
  We introduce a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images. The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism. We validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms.
  arXiv  Detail & Related papers  (2024-03-21T15:13:36Z)
• Speeding up Photoacoustic Imaging using Diffusion Models [0.0]
  Photoacoustic Microscopy (PAM) integrates optical and acoustic imaging, offering enhanced penetration depth for detecting optical-absorbing components in tissues. With speed limitations imposed by laser pulse repetition rates, the potential role of computational methods is highlighted in accelerating PAM imaging. We are proposing a novel and highly adaptable DiffPam algorithm that utilizes diffusion models for speeding up the PAM imaging process.
  arXiv  Detail & Related papers  (2023-12-14T11:34:27Z)
• RF-ULM: Ultrasound Localization Microscopy Learned from Radio-Frequency Wavefronts [7.652037892439504]
  Delay-and-sum beamforming leads to irreversible reduction of Radio-Frequency (RF) channel data. rich contextual information embedded within RF wavefronts offers great promise for guiding Deep Neural Networks (DNNs) in challenging localization scenarios. We propose to directly localize scatterers in RF channel data using learned feature channel shuffling, non-maximum suppression, and a semi-global convolutional block.
  arXiv  Detail & Related papers  (2023-10-02T18:41:23Z)
• ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic Diffusion Models [69.9178140563928]
  Colonoscopy analysis is essential for assisting clinical diagnosis and treatment. The scarcity of annotated data limits the effectiveness and generalization of existing methods. We propose an Adaptive Refinement Semantic Diffusion Model (ArSDM) to generate colonoscopy images that benefit the downstream tasks.
  arXiv  Detail & Related papers  (2023-09-03T07:55:46Z)
• Geometric Ultrasound Localization Microscopy [6.602729062703117]
  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.
  arXiv  Detail & Related papers  (2023-06-27T15:18:52Z)
• 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)
• ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning [47.68307909984442]
  Single Image Super-Resolution (SISR) is a technique aimed to obtain high-resolution (HR) details from one single low-resolution input image. Deep learning extracts prior knowledge from big datasets and produces superior MRI images from the low-resolution counterparts.
  arXiv  Detail & Related papers  (2021-02-25T14:52:23Z)
• 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)
• Multifold Acceleration of Diffusion MRI via Slice-Interleaved Diffusion Encoding (SIDE) [50.65891535040752]
  We propose a diffusion encoding scheme, called Slice-Interleaved Diffusion. SIDE, that interleaves each diffusion-weighted (DW) image volume with slices encoded with different diffusion gradients. We also present a method based on deep learning for effective reconstruction of DW images from the highly slice-undersampled data.
  arXiv  Detail & Related papers  (2020-02-25T14:48:17Z)

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.