GDCNet: Calibrationless geometric distortion correction of echo planar imaging data using deep learning

• URL: http://arxiv.org/abs/2402.18777v1
• Date: Thu, 29 Feb 2024 00:42:33 GMT
• Title: GDCNet: Calibrationless geometric distortion correction of echo planar imaging data using deep learning
• Authors: Marina Manso Jimeno, Keren Bachi, George Gardner, Yasmin L. Hurd, John Thomas Vaughan Jr., Sairam Geethanath
• Abstract summary: This work implements a novel approach called GDCNet, which estimates a geometric distortion map by non-linear registration to T1-weighted anatomical images. GDCNet models achieved processing speeds 14 times faster than TOPUP in the prospective dataset.
• Score: 0.1398098625978622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Functional magnetic resonance imaging techniques benefit from echo-planar imaging's fast image acquisition but are susceptible to inhomogeneities in the main magnetic field, resulting in geometric distortion and signal loss artifacts in the images. Traditional methods leverage a field map or voxel displacement map for distortion correction. However, voxel displacement map estimation requires additional sequence acquisitions, and the accuracy of the estimation influences correction performance. This work implements a novel approach called GDCNet, which estimates a geometric distortion map by non-linear registration to T1-weighted anatomical images and applies it for distortion correction. GDCNet demonstrated fast distortion correction of functional images in retrospectively and prospectively acquired datasets. Among the compared models, the 2D self-supervised configuration resulted in a statistically significant improvement to normalized mutual information between distortion-corrected functional and T1-weighted images compared to the benchmark methods FUGUE and TOPUP. Furthermore, GDCNet models achieved processing speeds 14 times faster than TOPUP in the prospective dataset.

Related papers

• 3D Equivariant Pose Regression via Direct Wigner-D Harmonics Prediction [50.07071392673984]
  Existing methods learn 3D rotations parametrized in the spatial domain using angles or quaternions. We propose a frequency-domain approach that directly predicts Wigner-D coefficients for 3D rotation regression. Our method achieves state-of-the-art results on benchmarks such as ModelNet10-SO(3) and PASCAL3D+.
  arXiv  Detail & Related papers  (2024-11-01T12:50:38Z)
• Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detection [106.39544368711427]
  We study the problem of generalizable synthetic image detection, aiming to detect forgery images from diverse generative methods. We present a novel forgery-aware adaptive transformer approach, namely FatFormer. Our approach tuned on 4-class ProGAN data attains an average of 98% accuracy to unseen GANs, and surprisingly generalizes to unseen diffusion models with 95% accuracy.
  arXiv  Detail & Related papers  (2023-12-27T17:36:32Z)
• PtychoDV: Vision Transformer-Based Deep Unrolling Network for Ptychographic Image Reconstruction [12.780951605821238]
  PtychoDV is a novel deep model-based network designed for efficient, high-quality ptychographic image reconstruction. Results on simulated data demonstrate that PtychoDV is capable of outperforming existing deep learning methods for this problem.
  arXiv  Detail & Related papers  (2023-10-11T14:01:36Z)
• Geometric Constraints Enable Self-Supervised Sinogram Inpainting in Sparse-View Tomography [7.416898042520079]
  Sparse-angle tomographic scans reduce radiation and accelerate data acquisition, but suffer from image artifacts and noise. Existing image processing algorithms can restore CT reconstruction quality but often require large training data sets or can not be used for truncated objects. This work presents a self-supervised projection inpainting method that allows optimizing missing projective views via gradient-based optimization.
  arXiv  Detail & Related papers  (2023-02-13T15:15:18Z)
• Adapting the Hypersphere Loss Function from Anomaly Detection to Anomaly Segmentation [1.9458156037869137]
  We propose an incremental improvement to Fully Convolutional Data Description (FCDD) FCDD is an adaptation of the one-class classification approach from anomaly detection to image anomaly segmentation (a.k.a. anomaly localization) We analyze its original loss function and propose a substitute that better resembles its predecessor, the Hypersphere (HSC)
  arXiv  Detail & Related papers  (2023-01-23T18:06:35Z)
• Curvature regularization for Non-line-of-sight Imaging from Under-sampled Data [5.591221518341613]
  Non-line-of-sight (NLOS) imaging aims to reconstruct the three-dimensional hidden scenes from the data measured in the line-of-sight. We propose novel NLOS reconstruction models based on curvature regularization. We evaluate the proposed algorithms on both synthetic and real datasets.
  arXiv  Detail & Related papers  (2023-01-01T14:10:43Z)
• GradViT: Gradient Inversion of Vision Transformers [83.54779732309653]
  We demonstrate the vulnerability of vision transformers (ViTs) to gradient-based inversion attacks. We introduce a method, named GradViT, that optimize random noise into naturally looking images. We observe unprecedentedly high fidelity and closeness to the original (hidden) data.
  arXiv  Detail & Related papers  (2022-03-22T17:06:07Z)
• Wide-angle Image Rectification: A Survey [86.36118799330802]
  wide-angle images contain distortions that violate the assumptions underlying pinhole camera models. Image rectification, which aims to correct these distortions, can solve these problems. We present a detailed description and discussion of the camera models used in different approaches. Next, we review both traditional geometry-based image rectification methods and deep learning-based methods.
  arXiv  Detail & Related papers  (2020-10-30T17:28:40Z)
• Hyperspectral Anomaly Change Detection Based on Auto-encoder [40.32592332449066]
  Hyperspectral anomaly change detection (HACD) helps to find those small but important anomaly changes between hyperspectral images (HSI) In this paper, we propose an original HACD algorithm based on auto-encoder (ACDA) to give a nonlinear solution. The experiments results on public "Viareggio 2013" datasets demonstrate the efficiency and superiority over traditional methods.
  arXiv  Detail & Related papers  (2020-10-27T08:07:08Z)
• A Deep Ordinal Distortion Estimation Approach for Distortion Rectification [62.72089758481803]
  We propose a novel distortion rectification approach that can obtain more accurate parameters with higher efficiency. We design a local-global associated estimation network that learns the ordinal distortion to approximate the realistic distortion distribution. Considering the redundancy of distortion information, our approach only uses a part of distorted image for the ordinal distortion estimation.
  arXiv  Detail & Related papers  (2020-07-21T10:03:42Z)
• Data Consistent CT Reconstruction from Insufficient Data with Learned Prior Images [70.13735569016752]
  We investigate the robustness of deep learning in CT image reconstruction by showing false negative and false positive lesion cases. We propose a data consistent reconstruction (DCR) method to improve their image quality, which combines the advantages of compressed sensing and deep learning. The efficacy of the proposed method is demonstrated in cone-beam CT with truncated data, limited-angle data and sparse-view data, respectively.
  arXiv  Detail & Related papers  (2020-05-20T13:30:49Z)

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.