A Conditional Normalizing Flow for Accelerated Multi-Coil MR Imaging

• URL: http://arxiv.org/abs/2306.01630v1
• Date: Fri, 2 Jun 2023 15:49:26 GMT
• Title: A Conditional Normalizing Flow for Accelerated Multi-Coil MR Imaging
• Authors: Jeffrey Wen, Rizwan Ahmad, and Philip Schniter
• Abstract summary: We develop a novel conditional normalizing flow (CNF) that infers the signal component in the measurement operator's nullspace, which is later combined with measured data to form complete images. Using fastMRI brain and knee data, we demonstrate fast inference and accuracy that surpasses recent posterior sampling techniques for MRI.
• Score: 12.31503281925152
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accelerated magnetic resonance (MR) imaging attempts to reduce acquisition time by collecting data below the Nyquist rate. As an ill-posed inverse problem, many plausible solutions exist, yet the majority of deep learning approaches generate only a single solution. We instead focus on sampling from the posterior distribution, which provides more comprehensive information for downstream inference tasks. To do this, we design a novel conditional normalizing flow (CNF) that infers the signal component in the measurement operator's nullspace, which is later combined with measured data to form complete images. Using fastMRI brain and knee data, we demonstrate fast inference and accuracy that surpasses recent posterior sampling techniques for MRI. Code is available at https://github.com/jwen307/mri_cnf/

Related papers

• Robust Simultaneous Multislice MRI Reconstruction Using Deep Generative Priors [4.23798859509348]
  Simultaneous multislice (SMS) imaging is a powerful technique for accelerating magnetic resonance imaging (MRI) acquisitions. This study presents a robust SMS MRI reconstruction method using deep generative priors.
  arXiv  Detail & Related papers  (2024-07-31T13:34:14Z)
• Autoregressive Image Diffusion: Generation of Image Sequence and Application in MRI [2.0318411357438086]
  Generative models learn image distributions and can be used to reconstruct high-quality images from undersampled k-space data. We present the autoregressive image diffusion (AID) model for image sequences and use it to sample the posterior for accelerated MRI reconstruction. The results show that the AID model can robustly generate sequentially coherent image sequences.
  arXiv  Detail & Related papers  (2024-05-23T08:57:10Z)
• 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)
• CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI? [1.523157765626545]
  We propose a Cold Diffusion-based MRI reconstruction method called CDiffMR. We show that CDiffMR can achieve comparable or even superior reconstruction results than state-of-the-art models.
  arXiv  Detail & Related papers  (2023-06-25T21:53:50Z)
• CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super Resolution [78.734927709231]
  Medical image arbitrary-scale super-resolution (MIASSR) has recently gained widespread attention, aiming to super sample medical volumes at arbitrary scales via a single model. We propose Cube-based Neural Radiance Field (CuNeRF), a zero-shot MIASSR framework that can yield medical images at arbitrary scales and viewpoints in a continuous domain.
  arXiv  Detail & Related papers  (2023-03-28T18:36:19Z)
• A Regularized Conditional GAN for Posterior Sampling in Image Recovery Problems [11.393603788068777]
  In image recovery problems, one seeks to infer an image from distorted, incomplete, and/or noise-corrupted measurements. We propose a regularized conditional Wasserstein GAN that generates dozens of high-quality posterior samples per second. Our method produces state-of-the-art posterior samples in both multicoil MRI and large-scale inpainting applications.
  arXiv  Detail & Related papers  (2022-10-24T16:43:00Z)
• Physics-informed compressed sensing for PC-MRI: an inverse Navier-Stokes problem [78.20667552233989]
  We formulate a physics-informed compressed sensing (PICS) method for the reconstruction of velocity fields from noisy and sparse magnetic resonance signals. We find that the method is capable of reconstructing and segmenting the velocity fields from sparsely-sampled signals.
  arXiv  Detail & Related papers  (2022-07-04T14:51:59Z)
• Robust Compressed Sensing MRI with Deep Generative Priors [84.69062247243953]
  We present the first successful application of the CSGM framework on clinical MRI data. We train a generative prior on brain scans from the fastMRI dataset, and show that posterior sampling via Langevin dynamics achieves high quality reconstructions.
  arXiv  Detail & Related papers  (2021-08-03T08:52:06Z)
• 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)
• 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)
• Deep Residual Dense U-Net for Resolution Enhancement in Accelerated MRI Acquisition [19.422926534305837]
  We propose a deep-learning approach, aiming at reconstructing high-quality images from accelerated MRI acquisition. Specifically, we use Convolutional Neural Network (CNN) to learn the differences between the aliased images and the original images. Considering the peculiarity of the down-sampled k-space data, we introduce a new term to the loss function in learning, which effectively employs the given k-space data.
  arXiv  Detail & Related papers  (2020-01-13T19:01:17Z)

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.