Improved Padding in CNNs for Quantitative Susceptibility Mapping

• URL: http://arxiv.org/abs/2106.15331v1
• Date: Mon, 21 Jun 2021 01:35:00 GMT
• Title: Improved Padding in CNNs for Quantitative Susceptibility Mapping
• Authors: Juan Liu
• Abstract summary: We propose an improved padding technique which utilizes the neighboring valid voxels to estimate the invalid voxels of feature maps at volume boundaries in the neural networks. Studies using simulated and in-vivo data show that the proposed padding greatly improves estimation accuracy and reduces artifacts in the results in the tasks of background field removal, field-to-source inversion, and single-step QSM reconstruction.
• Score: 5.421615560456378
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recently, deep learning methods have been proposed for quantitative susceptibility mapping (QSM) data processing: background field removal, field-to-source inversion, and single-step QSM reconstruction. However, the conventional padding mechanism used in convolutional neural networks (CNNs) can introduce spatial artifacts, especially in QSM background field removal and single-step QSM which requires inference from total fields with extreme large values at the edge boundaries of volume of interest. To address this issue, we propose an improved padding technique which utilizes the neighboring valid voxels to estimate the invalid voxels of feature maps at volume boundaries in the neural networks. Studies using simulated and in-vivo data show that the proposed padding greatly improves estimation accuracy and reduces artifacts in the results in the tasks of background field removal, field-to-source inversion, and single-step QSM reconstruction.

Related papers

• NeuroPMD: Neural Fields for Density Estimation on Product Manifolds [4.096453902709292]
  In our approach, the network directly parameterizes the unknown density function and is trained using a penalized maximum likelihood framework. The network architecture and estimation algorithm are carefully designed to handle the challenges of high-dimensional product manifold domains.
  arXiv  Detail & Related papers  (2025-01-06T13:13:13Z)
• Deep-Unrolling Multidimensional Harmonic Retrieval Algorithms on Neuromorphic Hardware [78.17783007774295]
  This paper explores the potential of conversion-based neuromorphic algorithms for highly accurate and energy-efficient single-snapshot multidimensional harmonic retrieval. A novel method for converting the complex-valued convolutional layers and activations into spiking neural networks (SNNs) is developed. The converted SNNs achieve almost five-fold power efficiency at moderate performance loss compared to the original CNNs.
  arXiv  Detail & Related papers  (2024-12-05T09:41:33Z)
• DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [55.48770333927732]
  We propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection. It consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion's denoising network, and a feature-space pre-trained feature extractor. Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-12-11T18:38:28Z)
• VoxNeRF: Bridging Voxel Representation and Neural Radiance Fields for Enhanced Indoor View Synthesis [73.50359502037232]
  VoxNeRF is a novel approach to enhance the quality and efficiency of neural indoor reconstruction and novel view synthesis. We propose an efficient voxel-guided sampling technique that allocates computational resources to selectively the most relevant segments of rays. Our approach is validated with extensive experiments on ScanNet and ScanNet++.
  arXiv  Detail & Related papers  (2023-11-09T11:32:49Z)
• 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)
• MA-RECON: Mask-aware deep-neural-network for robust fast MRI k-space interpolation [3.0821115746307672]
  High-quality reconstruction of MRI images from under-sampled kspace' data is crucial for shortening MRI acquisition times and ensuring superior temporal resolution. This paper introduces MA-RECON', an innovative mask-aware deep neural network (DNN) architecture and associated training method. It implements a tailored training approach that leverages data generated with a variety of under-sampling masks to stimulate the model's generalization of the under-sampled MRI reconstruction problem.
  arXiv  Detail & Related papers  (2022-08-31T15:57:38Z)
• Neural Posterior Estimation with Differentiable Simulators [58.720142291102135]
  We present a new method to perform Neural Posterior Estimation (NPE) with a differentiable simulator. We demonstrate how gradient information helps constrain the shape of the posterior and improves sample-efficiency.
  arXiv  Detail & Related papers  (2022-07-12T16:08:04Z)
• Robust lEarned Shrinkage-Thresholding (REST): Robust unrolling for sparse recover [87.28082715343896]
  We consider deep neural networks for solving inverse problems that are robust to forward model mis-specifications. We design a new robust deep neural network architecture by applying algorithm unfolding techniques to a robust version of the underlying recovery problem. The proposed REST network is shown to outperform state-of-the-art model-based and data-driven algorithms in both compressive sensing and radar imaging problems.
  arXiv  Detail & Related papers  (2021-10-20T06:15:45Z)
• NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo [97.07453889070574]
  We present a new multi-view depth estimation method that utilizes both conventional SfM reconstruction and learning-based priors. We show that our proposed framework significantly outperforms state-of-the-art methods on indoor scenes.
  arXiv  Detail & Related papers  (2021-09-02T17:54:31Z)
• Weakly-supervised Learning for Single-step Quantitative Susceptibility Mapping [5.590406494337628]
  We propose a weakly-supervised single-step QSM reconstruction method, denoted as wTFI, to directly reconstruct QSM from the total field without BFR. wTFI uses the BFR method RESHARP local fields as supervision to perform a multi-task learning of local tissue fields and QSM. We show that wTFI can generate high-quality local field and susceptibility maps in a variety of contexts.
  arXiv  Detail & Related papers  (2020-08-14T04:28:08Z)
• Learned Proximal Networks for Quantitative Susceptibility Mapping [9.061630971752464]
  We present a Learned Proximal Convolutional Neural Network (LP-CNN) for solving the ill-posed QSM dipole inversion problem. This framework is believed to be the first deep learning QSM approach that can naturally handle an arbitrary number of phase input measurements.
  arXiv  Detail & Related papers  (2020-08-11T22:35:24Z)

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.