Learning Spatially-Continuous Fiber Orientation Functions

• URL: http://arxiv.org/abs/2312.05721v1
• Date: Sun, 10 Dec 2023 01:28:47 GMT
• Title: Learning Spatially-Continuous Fiber Orientation Functions
• Authors: Tyler Spears and P. Thomas Fletcher
• Abstract summary: We propose FENRI, a novel method that learns spatially-continuous fiber orientation density functions from low-resolution diffusion-weighted images. We demonstrate that FENRI accurately predicts high-resolution fiber orientations from realistic low-quality data.
• Score: 1.4504054468850665
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Our understanding of the human connectome is fundamentally limited by the resolution of diffusion MR images. Reconstructing a connectome's constituent neural pathways with tractography requires following a continuous field of fiber directions. Typically, this field is found with simple trilinear interpolation in low-resolution, noisy diffusion MRIs. However, trilinear interpolation struggles following fine-scale changes in low-quality data. Recent deep learning methods in super-resolving diffusion MRIs have focused on upsampling to a fixed spatial grid, but this does not satisfy tractography's need for a continuous field. In this work, we propose FENRI, a novel method that learns spatially-continuous fiber orientation density functions from low-resolution diffusion-weighted images. To quantify FENRI's capabilities in tractography, we also introduce an expanded simulated dataset built for evaluating deep-learning tractography models. We demonstrate that FENRI accurately predicts high-resolution fiber orientations from realistic low-quality data, and that FENRI-based tractography offers improved streamline reconstruction over the current use of trilinear interpolation.

Related papers

• ResFields: Residual Neural Fields for Spatiotemporal Signals [61.44420761752655]
  ResFields is a novel class of networks specifically designed to effectively represent complex temporal signals. We conduct comprehensive analysis of the properties of ResFields and propose a matrix factorization technique to reduce the number of trainable parameters. We demonstrate the practical utility of ResFields by showcasing its effectiveness in capturing dynamic 3D scenes from sparse RGBD cameras.
  arXiv  Detail & Related papers  (2023-09-06T16:59:36Z)
• Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems [64.29491112653905]
  We propose a novel and efficient diffusion sampling strategy that synergistically combines the diffusion sampling and Krylov subspace methods. Specifically, we prove that if tangent space at a denoised sample by Tweedie's formula forms a Krylov subspace, then the CG with the denoised data ensures the data consistency update to remain in the tangent space. Our proposed method achieves more than 80 times faster inference time than the previous state-of-the-art method.
  arXiv  Detail & Related papers  (2023-03-10T07:42:49Z)
• RecFNO: a resolution-invariant flow and heat field reconstruction method from sparse observations via Fourier neural operator [8.986743262828009]
  We propose an end-to-end physical field reconstruction method with both excellent performance and mesh transferability named RecFNO. The proposed method aims to learn the mapping from sparse observations to flow and heat field in infinite-dimensional space. The experiments conducted on fluid mechanics and thermology problems show that the proposed method outperforms existing POD-based and CNN-based methods in most cases.
  arXiv  Detail & Related papers  (2023-02-20T07:20:22Z)
• A Deep Learning Approach for SAR Tomographic Imaging of Forested Areas [10.477070348391079]
  We show that light-weight neural networks can be trained to perform the tomographic inversion with a single feed-forward pass. We train our encoder-decoder network using simulated data and validate our technique on real L-band and P-band data.
  arXiv  Detail & Related papers  (2023-01-20T14:34:03Z)
• Orthogonal Matrix Retrieval with Spatial Consensus for 3D Unknown-View Tomography [58.60249163402822]
  Unknown-view tomography (UVT) reconstructs a 3D density map from its 2D projections at unknown, random orientations. The proposed OMR is more robust and performs significantly better than the previous state-of-the-art OMR approach.
  arXiv  Detail & Related papers  (2022-07-06T21:40:59Z)
• PREF: Phasorial Embedding Fields for Compact Neural Representations [54.44527545923917]
  We present a phasorial embedding field emphPREF as a compact representation to facilitate neural signal modeling and reconstruction tasks. Our experiments show PREF-based neural signal processing technique is on par with the state-of-the-art in 2D image completion, 3D SDF surface regression, and 5D radiance field reconstruction.
  arXiv  Detail & Related papers  (2022-05-26T17:43:03Z)
• Zero-Shot Learning of Continuous 3D Refractive Index Maps from Discrete Intensity-Only Measurements [5.425568744312016]
  We present DeCAF as the first NF-based IDT method that can learn a high-quality continuous representation of a RI volume directly from its intensity-only and limited-angle measurements. We show on three different IDT modalities and multiple biological samples that DeCAF can generate high-contrast and artifact-free RI maps.
  arXiv  Detail & Related papers  (2021-11-27T06:05:47Z)
• Equivariant Spherical Deconvolution: Learning Sparse Orientation Distribution Functions from Spherical Data [0.0]
  We present a rotation-equivariant unsupervised learning framework for the sparse deconvolution of non-negative scalar fields defined on the unit sphere. We show improvements in terms of tractography and partial volume estimation on a multi-shell dataset of human subjects.
  arXiv  Detail & Related papers  (2021-02-17T16:04:35Z)
• Light Field Reconstruction via Deep Adaptive Fusion of Hybrid Lenses [67.01164492518481]
  This paper explores the problem of reconstructing high-resolution light field (LF) images from hybrid lenses. We propose a novel end-to-end learning-based approach, which can comprehensively utilize the specific characteristics of the input. Our framework could potentially decrease the cost of high-resolution LF data acquisition and benefit LF data storage and transmission.
  arXiv  Detail & Related papers  (2021-02-14T06:44:47Z)
• Non-line-of-Sight Imaging via Neural Transient Fields [52.91826472034646]
  We present a neural modeling framework for Non-Line-of-Sight (NLOS) imaging. Inspired by the recent Neural Radiance Field (NeRF) approach, we use a multi-layer perceptron (MLP) to represent the neural transient field or NeTF. We formulate a spherical volume NeTF reconstruction pipeline, applicable to both confocal and non-confocal setups.
  arXiv  Detail & Related papers  (2021-01-02T05:20:54Z)
• Large-scale kernelized GRANGER causality to infer topology of directed graphs with applications to brain networks [0.0]
  In large networks with short time-series, topology estimation becomes ill-posed. The present paper proposes a novel nonlinearity-preserving topology inference method for directed networks with co-evolving nodal processes. Tests on real datasets from a functional magnetic resonance imaging (fMRI) study demonstrate 96.3 percent accuracy in diagnosis tasks of schizophrenia patients.
  arXiv  Detail & Related papers  (2020-11-16T20:30:19Z)

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.