Non-Cartesian Self-Supervised Physics-Driven Deep Learning Reconstruction for Highly-Accelerated Multi-Echo Spiral fMRI

• URL: http://arxiv.org/abs/2312.05707v1
• Date: Sat, 9 Dec 2023 23:33:12 GMT
• Title: Non-Cartesian Self-Supervised Physics-Driven Deep Learning Reconstruction for Highly-Accelerated Multi-Echo Spiral fMRI
• Authors: Hongyi Gu, Chi Zhang, Zidan Yu, Christoph Rettenmeier, V. Andrew Stenger, Mehmet Ak\c{c}akaya
• Abstract summary: We propose to use a physics-driven deep learning (PD-DL) reconstruction to accelerate multi-echo spiral fMRI by 10-fold. We achieves a self-supervised learning algorithm for optimized training with non-Cartesian trajectories and use it to train the PD-DL network.
• Score: 2.213603089873724
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Functional MRI (fMRI) is an important tool for non-invasive studies of brain function. Over the past decade, multi-echo fMRI methods that sample multiple echo times has become popular with potential to improve quantification. While these acquisitions are typically performed with Cartesian trajectories, non-Cartesian trajectories, in particular spiral acquisitions, hold promise for denser sampling of echo times. However, such acquisitions require very high acceleration rates for sufficient spatiotemporal resolutions. In this work, we propose to use a physics-driven deep learning (PD-DL) reconstruction to accelerate multi-echo spiral fMRI by 10-fold. We modify a self-supervised learning algorithm for optimized training with non-Cartesian trajectories and use it to train the PD-DL network. Results show that the proposed self-supervised PD-DL reconstruction achieves high spatio-temporal resolution with meaningful BOLD analysis.

Related papers

• Learning the Domain Specific Inverse NUFFT for Accelerated Spiral MRI using Diffusion Models [0.0]
  We create a generative diffusion model-based reconstruction algorithm for multi-coil highly undersampled spiral MRI. We show high quality (structural similarity > 0.87) in reconstructed images with ultrafast scan times (0.02 seconds for a 2D image).
  arXiv  Detail & Related papers  (2024-04-18T17:40:23Z)
• Self-STORM: Deep Unrolled Self-Supervised Learning for Super-Resolution Microscopy [55.2480439325792]
  We introduce deep unrolled self-supervised learning, which alleviates the need for such data by training a sequence-specific, model-based autoencoder. Our proposed method exceeds the performance of its supervised counterparts.
  arXiv  Detail & Related papers  (2024-03-25T17:40:32Z)
• Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI [23.142320453773696]
  Dynamic magnetic resonance imaging (MRI) plays an indispensable role in cardiac diagnosis. To enable fast imaging, the k-space data can be undersampled but the image reconstruction poses a great challenge of high-dimensional processing. This work proposes a novel and efficient approach, leveraging a reduced dimension-reduced separable learning scheme that excels even with highly limited training data.
  arXiv  Detail & Related papers  (2024-02-24T23:56:15Z)
• Multi PILOT: Learned Feasible Multiple Acquisition Trajectories for Dynamic MRI [0.7843343739054056]
  In this study, we consider acquisition learning in the dynamic imaging setting. We design an end-to-end pipeline for the joint optimization of multiple per-frame acquisition trajectories. We demonstrate improved image reconstruction quality in shorter acquisition times.
  arXiv  Detail & Related papers  (2023-03-13T14:23:39Z)
• Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers [72.047680167969]
  This article aims to introduce the deep learning based data driven techniques for fast MRI including convolutional neural network and generative adversarial network based methods. We will detail the research in coupling physics and data driven models for MRI acceleration. Finally, we will demonstrate through a few clinical applications, explain the importance of data harmonisation and explainable models for such fast MRI techniques in multicentre and multi-scanner studies.
  arXiv  Detail & Related papers  (2022-04-01T22:48:08Z)
• Towards performant and reliable undersampled MR reconstruction via diffusion model sampling [67.73698021297022]
  DiffuseRecon is a novel diffusion model-based MR reconstruction method. It guides the generation process based on the observed signals. It does not require additional training on specific acceleration factors.
  arXiv  Detail & Related papers  (2022-03-08T02:25:38Z)
• 20-fold Accelerated 7T fMRI Using Referenceless Self-Supervised Deep Learning Reconstruction [0.487576911714538]
  High-temporal resolution across the whole brain is essential to accurately resolve neural activities in fMRI. Deep learning (DL) reconstruction techniques have recently gained interest for improving highly-accelerated MRI imaging. In this study, we utilize a self-supervised physics-guided DL reconstruction on a 5-fold SMS and 4-fold inplane accelerated 7T fMRI data.
  arXiv  Detail & Related papers  (2021-05-12T17:39:16Z)
• Improved Simultaneous Multi-Slice Functional MRI Using Self-supervised Deep Learning [0.487576911714538]
  We extend self-supervised DL reconstruction to simultaneous multi-slice (SMS) imaging. Our results show that self-supervised DL reduces reconstruction noise and suppresses residual artifacts. Subsequent fMRI analysis remains unaltered by DL processing, while the improved temporal signal-to-noise ratio produces higher coherence estimates between task runs.
  arXiv  Detail & Related papers  (2021-05-10T17:36:27Z)
• Deep Representational Similarity Learning for analyzing neural signatures in task-based fMRI dataset [81.02949933048332]
  This paper develops Deep Representational Similarity Learning (DRSL), a deep extension of Representational Similarity Analysis (RSA) DRSL is appropriate for analyzing similarities between various cognitive tasks in fMRI datasets with a large number of subjects.
  arXiv  Detail & Related papers  (2020-09-28T18:30:14Z)
• Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks [55.0627904986664]
  Spiking Neural Networks (SNNs) usetemporal spike patterns to represent and transmit information, which is not only biologically realistic but also suitable for ultra-low-power event-driven neuromorphic implementation. This paper investigates the contribution of spike timing dynamics to information encoding, synaptic plasticity and decision making, providing a new perspective to design of future DeepSNNs and neuromorphic hardware systems.
  arXiv  Detail & Related papers  (2020-03-26T11:13:07Z)
• 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.