Two-Stage Self-Supervised Cycle-Consistency Network for Reconstruction of Thin-Slice MR Images

• URL: http://arxiv.org/abs/2106.15395v1
• Date: Tue, 29 Jun 2021 13:29:18 GMT
• Title: Two-Stage Self-Supervised Cycle-Consistency Network for Reconstruction of Thin-Slice MR Images
• Authors: Zhiyang Lu, Zheng Li, Jun Wang, Jun shi, Dinggang Shen
• Abstract summary: The thick-slice magnetic resonance (MR) images are often structurally blurred in coronal and sagittal views. Deep learning has shown great potential to re-construct the high-resolution (HR) thin-slice MR images from those low-resolution (LR) cases. We propose a novel Two-stage Self-supervised Cycle-consistency Network (TSCNet) for MR slice reconstruction.
• Score: 62.4428833931443
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The thick-slice magnetic resonance (MR) images are often structurally blurred in coronal and sagittal views, which causes harm to diagnosis and image post-processing. Deep learning (DL) has shown great potential to re-construct the high-resolution (HR) thin-slice MR images from those low-resolution (LR) cases, which we refer to as the slice interpolation task in this work. However, since it is generally difficult to sample abundant paired LR-HR MR images, the classical fully supervised DL-based models cannot be effectively trained to get robust performance. To this end, we propose a novel Two-stage Self-supervised Cycle-consistency Network (TSCNet) for MR slice interpolation, in which a two-stage self-supervised learning (SSL) strategy is developed for unsupervised DL network training. The paired LR-HR images are synthesized along the sagittal and coronal directions of input LR images for network pretraining in the first-stage SSL, and then a cyclic in-terpolation procedure based on triplet axial slices is designed in the second-stage SSL for further refinement. More training samples with rich contexts along all directions are exploited as guidance to guarantee the improved in-terpolation performance. Moreover, a new cycle-consistency constraint is proposed to supervise this cyclic procedure, which encourages the network to reconstruct more realistic HR images. The experimental results on a real MRI dataset indicate that TSCNet achieves superior performance over the conventional and other SSL-based algorithms, and obtains competitive quali-tative and quantitative results compared with the fully supervised algorithm.

Related papers

• ICF-SRSR: Invertible scale-Conditional Function for Self-Supervised Real-world Single Image Super-Resolution [60.90817228730133]
  Single image super-resolution (SISR) is a challenging problem that aims to up-sample a given low-resolution (LR) image to a high-resolution (HR) counterpart. Recent approaches are trained on simulated LR images degraded by simplified down-sampling operators. We propose a novel Invertible scale-Conditional Function (ICF) which can scale an input image and then restore the original input with different scale conditions.
  arXiv  Detail & Related papers  (2023-07-24T12:42:45Z)
• Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction [68.80715727288514]
  We show how to unfold an iterative MGDUN algorithm into a novel model-guided deep unfolding network by taking the MRI observation matrix. In this paper, we propose a novel Model-Guided interpretable Deep Unfolding Network (MGDUN) for medical image SR reconstruction.
  arXiv  Detail & Related papers  (2022-09-15T03:58:30Z)
• Real-World Image Super-Resolution by Exclusionary Dual-Learning [98.36096041099906]
  Real-world image super-resolution is a practical image restoration problem that aims to obtain high-quality images from in-the-wild input. Deep learning-based methods have achieved promising restoration quality on real-world image super-resolution datasets. We propose Real-World image Super-Resolution by Exclusionary Dual-Learning (RWSR-EDL) to address the feature diversity in perceptual- and L1-based cooperative learning.
  arXiv  Detail & Related papers  (2022-06-06T13:28:15Z)
• Unsupervised Representation Learning for 3D MRI Super Resolution with Degradation Adaptation [28.296921790037725]
  High-resolution (HR) magnetic resonance imaging is critical in aiding doctors in their diagnoses and image-guided treatments. Deep learning-based super-resolution reconstruction (SRR) has emerged as a promising solution for generating super-resolution (SR) images from low-resolution (LR) images. Training such neural networks requires aligned authentic HR and LR image pairs, which are challenging to obtain due to patient movements during and between image acquisitions.
  arXiv  Detail & Related papers  (2022-05-13T21:07:26Z)
• Universal Generative Modeling for Calibration-free Parallel Mr Imaging [13.875986147033002]
  We present an unsupervised deep learning framework for calibration-free parallel MRI. We make use of the merits of both wavelet transform and the adaptive iteration strategy in a unified framework. We train a powerful noise conditional score network by forming wavelet tensor as the network input.
  arXiv  Detail & Related papers  (2022-01-25T10:05:39Z)
• Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling [139.25215100378284]
  We propose a hierarchical conditional flow (HCFlow) as a unified framework for image SR and image rescaling. HCFlow learns a mapping between HR and LR image pairs by modelling the distribution of the LR image and the rest high-frequency component simultaneously. To further enhance the performance, other losses such as perceptual loss and GAN loss are combined with the commonly used negative log-likelihood loss in training.
  arXiv  Detail & Related papers  (2021-08-11T16:11:01Z)
• Perception Consistency Ultrasound Image Super-resolution via Self-supervised CycleGAN [63.49373689654419]
  We propose a new perception consistency ultrasound image super-resolution (SR) method based on self-supervision and cycle generative adversarial network (CycleGAN) We first generate the HR fathers and the LR sons of the test ultrasound LR image through image enhancement. We then make full use of the cycle loss of LR-SR-LR and HR-LR-SR and the adversarial characteristics of the discriminator to promote the generator to produce better perceptually consistent SR results.
  arXiv  Detail & Related papers  (2020-12-28T08:24:04Z)
• Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-Scale Generative Adversarial Network [9.506036365253184]
  Arterial spin labeling (ASL) magnetic resonance imaging (MRI) is a powerful imaging technology that can measure cerebral blood flow (CBF) quantitatively. In this paper, we proposed a super-resolution method based on a multi-scale generative adversarial network (GAN) through unsupervised training.
  arXiv  Detail & Related papers  (2020-09-14T01:05:54Z)
• Two-Stage Deep Learning for Accelerated 3D Time-of-Flight MRA without Matched Training Data [33.549981359484406]
  We propose a novel two-stage unsupervised deep learning approach. The first network is trained in the square-root of sum of squares (SSoS) domain to achieve high quality parallel image reconstruction. The second refinement network is designed to efficiently learn the characteristics of highly-activated blood flow.
  arXiv  Detail & Related papers  (2020-08-04T06:36:38Z)

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.