Optimization of array encoding for ultrasound imaging

• URL: http://arxiv.org/abs/2403.00289v2
• Date: Thu, 20 Jun 2024 06:13:18 GMT
• Title: Optimization of array encoding for ultrasound imaging
• Authors: Jacob Spainhour, Korben Smart, Stephen Becker, Nick Bottenus,
• Abstract summary: We use machine learning (ML) to construct scanning sequences, parameterized by time delays and apodization weights, that produce high-quality B-mode images. We demonstrate these results experimentally on both wire targets and a tissue-mimicking phantom.
• Score: 2.357055571094446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Objective: The transmit encoding model for synthetic aperture imaging is a robust and flexible framework for understanding the effects of acoustic transmission on ultrasound image reconstruction. Our objective is to use machine learning (ML) to construct scanning sequences, parameterized by time delays and apodization weights, that produce high-quality B-mode images. Approach: We use a custom ML model in PyTorch with simulated RF data from Field II to probe the space of possible encoding sequences for those that minimize a loss function that describes image quality. This approach is made computationally feasible by a novel formulation of the derivative for delay-and-sum beamforming. Main Results: When trained for a specified experimental setting (imaging domain, hardware restrictions, etc.), our ML model produces optimized encoding sequences that, when deployed in the REFoCUS imaging framework, improve a number of standard quality metrics over conventional sequences including resolution, field of view, and contrast. We demonstrate these results experimentally on both wire targets and a tissue-mimicking phantom. Significance: This work demonstrates that the set of commonly used encoding schemes represent only a narrow subset of those available. Additionally, it demonstrates the value for ML tasks in synthetic transmit aperture imaging to consider the beamformer within the model, instead of purely as a post-processing step.

Related papers

• Enhanced Confocal Laser Scanning Microscopy with Adaptive Physics Informed Deep Autoencoders [0.0]
  We present a physics-informed deep learning framework to address limitations in Confocal Laser Scanning Microscopy. The model reconstructs high fidelity images from heavily noisy inputs by using convolutional and transposed convolutional layers.
  arXiv  Detail & Related papers  (2025-01-24T18:32:34Z)
• Enhancing Fluorescence Lifetime Parameter Estimation Accuracy with Differential Transformer Based Deep Learning Model Incorporating Pixelwise Instrument Response Function [0.3441582801949978]
  Fluorescence Lifetime Imaging (FLI) provides unique information about the tissue microenvironment. Recent advancements in Deep Learning have enabled improved fluorescence lifetime parameter estimation. We present MFliNet, a novel DL architecture that integrates the Instrument Response Function (IRF) as an additional input alongside experimental photon time-of-arrival histograms.
  arXiv  Detail & Related papers  (2024-11-25T20:03:41Z)
• Cross-Scan Mamba with Masked Training for Robust Spectral Imaging [51.557804095896174]
  We propose the Cross-Scanning Mamba, named CS-Mamba, that employs a Spatial-Spectral SSM for global-local balanced context encoding. Experiment results show that our CS-Mamba achieves state-of-the-art performance and the masked training method can better reconstruct smooth features to improve the visual quality.
  arXiv  Detail & Related papers  (2024-08-01T15:14:10Z)
• NeuroPictor: Refining fMRI-to-Image Reconstruction via Multi-individual Pretraining and Multi-level Modulation [55.51412454263856]
  This paper proposes to directly modulate the generation process of diffusion models using fMRI signals. By training with about 67,000 fMRI-image pairs from various individuals, our model enjoys superior fMRI-to-image decoding capacity.
  arXiv  Detail & Related papers  (2024-03-27T02:42:52Z)
• Semantic Image Synthesis via Diffusion Models [159.4285444680301]
  Denoising Diffusion Probabilistic Models (DDPMs) have achieved remarkable success in various image generation tasks. Recent work on semantic image synthesis mainly follows the emphde facto Generative Adversarial Nets (GANs)
  arXiv  Detail & Related papers  (2022-06-30T18:31:51Z)
• Active Phase-Encode Selection for Slice-Specific Fast MR Scanning Using a Transformer-Based Deep Reinforcement Learning Framework [34.540525533018666]
  We propose a light-weight transformer based deep reinforcement learning framework for generating high-quality slice-specific trajectory. The proposed method is roughly 150 times faster and achieves significant improvement in reconstruction accuracy.
  arXiv  Detail & Related papers  (2022-03-11T05:05:09Z)
• Regularization by Denoising Sub-sampled Newton Method for Spectral CT Multi-Material Decomposition [78.37855832568569]
  We propose to solve a model-based maximum-a-posterior problem to reconstruct multi-materials images with application to spectral CT. In particular, we propose to solve a regularized optimization problem based on a plug-in image-denoising function. We show numerical and experimental results for spectral CT materials decomposition.
  arXiv  Detail & Related papers  (2021-03-25T15:20:10Z)
• 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)
• Learning Ultrasound Rendering from Cross-Sectional Model Slices for Simulated Training [13.640630434743837]
  Computational simulations can facilitate the training of such skills in virtual reality. We propose herein to bypass any rendering and simulation process at interactive time. We use a generative adversarial framework with a dedicated generator architecture and input feeding scheme.
  arXiv  Detail & Related papers  (2021-01-20T21:58:19Z)
• 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)
• A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding [57.1077544780653]
  We introduce a general framework for designing and training neural network layers whose forward passes can be interpreted as solving non-smooth convex optimization problems. We focus on convex games, solved by local agents represented by the nodes of a graph and interacting through regularization functions. This approach is appealing for solving imaging problems, as it allows the use of classical image priors within deep models that are trainable end to end.
  arXiv  Detail & Related papers  (2020-06-26T08:34:54Z)

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.