Fast Sampling generative model for Ultrasound image reconstruction

• URL: http://arxiv.org/abs/2312.09510v1
• Date: Fri, 15 Dec 2023 03:28:17 GMT
• Title: Fast Sampling generative model for Ultrasound image reconstruction
• Authors: Hengrong Lan, Zhiqiang Li, Qiong He, Jianwen Luo
• Abstract summary: We propose a novel sampling framework that concurrently enforces data consistency of ultrasound signals and data-driven priors. By leveraging the advanced diffusion model, the generation of high-quality images is substantially expedited.
• Score: 3.3545464959630578
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image reconstruction from radio-frequency data is pivotal in ultrafast plane wave ultrasound imaging. Unlike the conventional delay-and-sum (DAS) technique, which relies on somewhat imprecise assumptions, deep learning-based methods perform image reconstruction by training on paired data, leading to a notable enhancement in image quality. Nevertheless, these strategies often exhibit limited generalization capabilities. Recently, denoising diffusion models have become the preferred paradigm for image reconstruction tasks. However, their reliance on an iterative sampling procedure results in prolonged generation time. In this paper, we propose a novel sampling framework that concurrently enforces data consistency of ultrasound signals and data-driven priors. By leveraging the advanced diffusion model, the generation of high-quality images is substantially expedited. Experimental evaluations on an in-vivo dataset indicate that our approach with a single plane wave surpasses DAS with spatial coherent compounding of 75 plane waves.

Related papers

• Denoising Plane Wave Ultrasound Images Using Diffusion Probabilistic Models [3.3463490716514177]
  High frame-rate ultrasound imaging is a cutting-edge technique that enables high frame-rate imaging. One challenge associated with high frame-rate ultrasound imaging is the high noise associated with them, hindering their wider adoption. Our proposed solution aims to enhance plane wave image quality. Specifically, the method considers the distinction between low-angle and high-angle compounding plane waves as noise. In addition, our approach employs natural image segmentation masks as intensity maps for the generated images, resulting in accurate denoising for various anatomy shapes.
  arXiv  Detail & Related papers  (2024-08-20T16:31:31Z)
• Ultrasound Imaging based on the Variance of a Diffusion Restoration Model [7.360352432782388]
  We propose a hybrid reconstruction method combining an ultrasound linear direct model with a learning-based prior coming from a generative Denoising Diffusion model. We conduct experiments on synthetic, in-vitro, and in-vivo data, demonstrating the efficacy of our variance imaging approach in achieving high-quality image reconstructions.
  arXiv  Detail & Related papers  (2024-03-22T16:10:38Z)
• ReNoise: Real Image Inversion Through Iterative Noising [62.96073631599749]
  We introduce an inversion method with a high quality-to-operation ratio, enhancing reconstruction accuracy without increasing the number of operations. We evaluate the performance of our ReNoise technique using various sampling algorithms and models, including recent accelerated diffusion models.
  arXiv  Detail & Related papers  (2024-03-21T17:52:08Z)
• Diffusion Reconstruction of Ultrasound Images with Informative Uncertainty [5.375425938215277]
  Enhancing ultrasound image quality involves balancing concurrent factors like contrast, resolution, and speckle preservation. We propose a hybrid approach leveraging advances in diffusion models. We conduct comprehensive experiments on simulated, in-vitro, and in-vivo data, demonstrating the efficacy of our approach.
  arXiv  Detail & Related papers  (2023-10-31T16:51:40Z)
• Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models [75.52575380824051]
  We present a learning method to optimize sub-sampling patterns for compressed sensing multi-coil MRI. We use a single-step reconstruction based on the posterior mean estimate given by the diffusion model and the MRI measurement process. Our method requires as few as five training images to learn effective sampling patterns.
  arXiv  Detail & Related papers  (2023-06-05T22:09:06Z)
• WaveDM: Wavelet-Based Diffusion Models for Image Restoration [43.254438752311714]
  Wavelet-Based Diffusion Model (WaveDM) learns the distribution of clean images in the wavelet domain conditioned on the wavelet spectrum of degraded images after wavelet transform. WaveDM achieves state-of-the-art performance with the efficiency that is comparable to traditional one-pass methods.
  arXiv  Detail & Related papers  (2023-05-23T08:41:04Z)
• 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)
• Deep Learning for Ultrasound Beamforming [120.12255978513912]
  Beamforming, the process of mapping received ultrasound echoes to the spatial image domain, lies at the heart of the ultrasound image formation chain. Modern ultrasound imaging leans heavily on innovations in powerful digital receive channel processing. Deep learning methods can play a compelling role in the digital beamforming pipeline.
  arXiv  Detail & Related papers  (2021-09-23T15:15:21Z)
• Deep Unfolded Recovery of Sub-Nyquist Sampled Ultrasound Image [94.42139459221784]
  We propose a reconstruction method from sub-Nyquist samples in the time and spatial domain, that is based on unfolding the ISTA algorithm. Our method allows reducing the number of array elements, sampling rate, and computational time while ensuring high quality imaging performance.
  arXiv  Detail & Related papers  (2021-03-01T19:19:38Z)
• CNN-Based Image Reconstruction Method for Ultrafast Ultrasound Imaging [9.659642285903418]
  Ultrafast ultrasound (US) revolutionized biomedical imaging with its capability of acquiring full-view frames at over 1 kHz. It suffers from strong diffraction artifacts, mainly caused by grating lobes, side lobes, or edge waves. We propose a two-step convolutional neural network (CNN)-based image reconstruction method, compatible with real-time imaging.
  arXiv  Detail & Related papers  (2020-08-28T17:15:37Z)

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.