Joint Blind Deconvolution and Robust Principal Component Analysis for Blood Flow Estimation in Medical Ultrasound Imaging

• URL: http://arxiv.org/abs/2007.05428v1
• Date: Fri, 10 Jul 2020 15:03:33 GMT
• Title: Joint Blind Deconvolution and Robust Principal Component Analysis for Blood Flow Estimation in Medical Ultrasound Imaging
• Authors: Duong-Hung Pham, Adrian Basarab, Ilyess Zemmoura, Jean-Pierre Remenieras and Denis Kouame
• Abstract summary: This paper addresses the problem of high-resolution Doppler blood flow estimation from an ultrafast sequence of ultrasound images. We propose a blind deconvolution method able to estimate both the blood component and the PSF from Doppler data.
• Score: 8.922669577341225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper addresses the problem of high-resolution Doppler blood flow estimation from an ultrafast sequence of ultrasound images. Formulating the separation of clutter and blood components as an inverse problem has been shown in the literature to be a good alternative to spatio-temporal singular value decomposition (SVD)-based clutter filtering. In particular, a deconvolution step has recently been embedded in such a problem to mitigate the influence of the experimentally measured point spread function (PSF) of the imaging system. Deconvolution was shown in this context to improve the accuracy of the blood flow reconstruction. However, measuring the PSF requires non-trivial experimental setups. To overcome this limitation, we propose herein a blind deconvolution method able to estimate both the blood component and the PSF from Doppler data. Numerical experiments conducted on simulated and in vivo data demonstrate qualitatively and quantitatively the effectiveness of the proposed approach in comparison with the previous method based on experimentally measured PSF and two other state-of-the-art approaches.

Related papers

• Resolution Enhancement of Under-sampled Photoacoustic Microscopy Images using Implicit Neural Representations [4.672315172406826]
  Acoustic-Resolution Photoacoustic Microscopy (AR-PAM) is promising for subcutaneous vascular imaging. Traditional deconvolution methods use the Point Spreadvolution (PSF) to improve resolution. We propose an approach based on Implicit Neural Representations (INR) Our method learns a continuous mapping from spatial coordinates to initial acoustic pressure, overcoming the limitations of discrete imaging.
  arXiv  Detail & Related papers  (2024-10-15T00:44:57Z)
• 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)
• Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models trained on Corrupted Data [56.81246107125692]
  Ambient Diffusion Posterior Sampling (A-DPS) is a generative model pre-trained on one type of corruption. We show that A-DPS can sometimes outperform models trained on clean data for several image restoration tasks in both speed and performance. We extend the Ambient Diffusion framework to train MRI models with access only to Fourier subsampled multi-coil MRI measurements.
  arXiv  Detail & Related papers  (2024-03-13T17:28:20Z)
• UNICORN: Ultrasound Nakagami Imaging via Score Matching and Adaptation [59.91293113930909]
  Nakagami imaging holds promise for visualizing and quantifying tissue scattering in ultrasound waves. Existing methods struggle with optimal window size selection and suffer from estimator instability. We propose a novel method called UNICORN that offers an accurate, closed-form estimator for Nakagami parameter estimation.
  arXiv  Detail & Related papers  (2024-03-10T18:05:41Z)
• Enhancing Blood Flow Assessment in Diffuse Correlation Spectroscopy: A Transfer Learning Approach with Noise Robustness Analysis [5.16677999056239]
  This study aims to assess the influence of Signal-to-Noise Ratios (SNRs) on the generalization ability of learned features. A synthetic dataset with varying levels of added noise is utilized to simulate different SNRs. The proposed model demonstrates excellent performance across different SNRs, exhibiting enhanced fitting accuracy.
  arXiv  Detail & Related papers  (2024-01-10T23:10:51Z)
• Rotational Augmented Noise2Inverse for Low-dose Computed Tomography Reconstruction [83.73429628413773]
  Supervised deep learning methods have shown the ability to remove noise in images but require accurate ground truth. We propose a novel self-supervised framework for LDCT, in which ground truth is not required for training the convolutional neural network (CNN) Numerical and experimental results show that the reconstruction accuracy of N2I with sparse views is degrading while the proposed rotational augmented Noise2Inverse (RAN2I) method keeps better image quality over a different range of sampling angles.
  arXiv  Detail & Related papers  (2023-12-19T22:40:51Z)
• Moving beyond simulation: data-driven quantitative photoacoustic imaging using tissue-mimicking phantoms [1.5006258585503878]
  We introduce a collection of experimentally well-characterised imaging phantoms and their digital twins. This first-of-a-kind phantom data set enables supervised training of a U-Net on experimental data for pixel-wise estimation of absorption coefficients. We show that training on simulated data results in artefacts and biases in the estimates, reinforcing the existence of a domain gap between simulation and experiment.
  arXiv  Detail & Related papers  (2023-06-11T19:12:30Z)
• 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)
• Perfusion Imaging: A Data Assimilation Approach [18.418067945495203]
  Perfusion imaging (PI) is clinically used to assess strokes and brain tumors. Contrast-agent free approaches, for example, based on intravoxel incoherent motion, also exist, but are so far not routinely used clinically. These methods rely on estimating on the arterial input function (AIF) to approximately model tissue perfusion, neglecting spatial dependencies, and reliably estimating the AIF is also non-trivial. In this work we propose a data-assimilation approach (PIANO) which estimates the velocity and diffusion fields of an advection-diffusion model that best explains the contrast dynamics.
  arXiv  Detail & Related papers  (2020-09-06T18:44:44Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)
• 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.