Is Registering Raw Tagged-MR Enough for Strain Estimation in the Era of Deep Learning?

• URL: http://arxiv.org/abs/2401.17571v1
• Date: Wed, 31 Jan 2024 03:28:11 GMT
• Title: Is Registering Raw Tagged-MR Enough for Strain Estimation in the Era of Deep Learning?
• Authors: Zhangxing Bian, Ahmed Alshareef, Shuwen Wei, Junyu Chen, Yuli Wang, Jonghye Woo, Dzung L. Pham, Jiachen Zhuo, Aaron Carass, Jerry L. Prince
• Abstract summary: tMRI has long been utilized for quantifying tissue motion and strain during deformation. A phenomenon known as tag fading, a gradual decrease in tag visibility over time, often complicates post-processing.
• Score: 10.085929773807825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Magnetic Resonance Imaging with tagging (tMRI) has long been utilized for quantifying tissue motion and strain during deformation. However, a phenomenon known as tag fading, a gradual decrease in tag visibility over time, often complicates post-processing. The first contribution of this study is to model tag fading by considering the interplay between $T_1$ relaxation and the repeated application of radio frequency (RF) pulses during serial imaging sequences. This is a factor that has been overlooked in prior research on tMRI post-processing. Further, we have observed an emerging trend of utilizing raw tagged MRI within a deep learning-based (DL) registration framework for motion estimation. In this work, we evaluate and analyze the impact of commonly used image similarity objectives in training DL registrations on raw tMRI. This is then compared with the Harmonic Phase-based approach, a traditional approach which is claimed to be robust to tag fading. Our findings, derived from both simulated images and an actual phantom scan, reveal the limitations of various similarity losses in raw tMRI and emphasize caution in registration tasks where image intensity changes over time.

Related papers

• Autoregressive Image Diffusion: Generation of Image Sequence and Application in MRI [2.0318411357438086]
  Generative models learn image distributions and can be used to reconstruct high-quality images from undersampled k-space data. We present the autoregressive image diffusion (AID) model for image sequences and use it to sample the posterior for accelerated MRI reconstruction.
  arXiv  Detail & Related papers  (2024-05-23T08:57:10Z)
• Attention-aware non-rigid image registration for accelerated MR imaging [10.47044784972188]
  We introduce an attention-aware deep learning-based framework that can perform non-rigid pairwise registration for fully sampled and accelerated MRI. We extract local visual representations to build similarity maps between the registered image pairs at multiple resolution levels. We demonstrate that our model derives reliable and consistent motion fields across different sampling trajectories.
  arXiv  Detail & Related papers  (2024-04-26T14:25:07Z)
• Volumetric Reconstruction Resolves Off-Resonance Artifacts in Static and Dynamic PROPELLER MRI [76.60362295758596]
  Off-resonance artifacts in magnetic resonance imaging (MRI) are visual distortions that occur when the actual resonant frequencies of spins within the imaging volume differ from the expected frequencies used to encode spatial information. We propose to resolve these artifacts by lifting the 2D MRI reconstruction problem to 3D, introducing an additional "spectral" dimension to model this off-resonance.
  arXiv  Detail & Related papers  (2023-11-22T05:44:51Z)
• On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis [58.634791552376235]
  Deep Learning (DL) models have achieved state-of-the-art performance in diagnosing multiple diseases using reconstructed images as input. DL models are sensitive to varying artifacts as it leads to changes in the input data distribution between the training and testing phases. We propose to use other normalization techniques, such as Group Normalization and Layer Normalization, to inject robustness into model performance against varying image artifacts.
  arXiv  Detail & Related papers  (2023-06-23T03:09:03Z)
• A Long Short-term Memory Based Recurrent Neural Network for Interventional MRI Reconstruction [50.1787181309337]
  We propose a convolutional long short-term memory (Conv-LSTM) based recurrent neural network (RNN), or ConvLR, to reconstruct interventional images with golden-angle radial sampling. The proposed algorithm has the potential to achieve real-time i-MRI for DBS and can be used for general purpose MR-guided intervention.
  arXiv  Detail & Related papers  (2022-03-28T14:03:45Z)
• DeepTag: An Unsupervised Deep Learning Method for Motion Tracking on Cardiac Tagging Magnetic Resonance Images [10.434681088538866]
  We propose a novel deep learning-based fully unsupervised method for in vivo motion tracking on t-MRI images. Our method has been validated on a representative clinical t-MRI dataset.
  arXiv  Detail & Related papers  (2021-03-04T00:42:11Z)
• ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning [47.68307909984442]
  Single Image Super-Resolution (SISR) is a technique aimed to obtain high-resolution (HR) details from one single low-resolution input image. Deep learning extracts prior knowledge from big datasets and produces superior MRI images from the low-resolution counterparts.
  arXiv  Detail & Related papers  (2021-02-25T14:52:23Z)
• Dual-cycle Constrained Bijective VAE-GAN For Tagged-to-Cine Magnetic Resonance Image Synthesis [11.697141493937021]
  We propose a novel VAE-GAN approach to carry out tagged-to-cine MR image synthesis. Our framework has been trained, validated, and tested using 1,768, 416, and 1,560 subject-independent paired slices of tagged and cine MRI.
  arXiv  Detail & Related papers  (2021-01-14T03:27:16Z)
• A Novel Approach for Correcting Multiple Discrete Rigid In-Plane Motions Artefacts in MRI Scans [63.28835187934139]
  We propose a novel method for removing motion artefacts using a deep neural network with two input branches. The proposed method can be applied to artefacts generated by multiple movements of the patient.
  arXiv  Detail & Related papers  (2020-06-24T15:25:11Z)
• Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network [18.433956246011466]
  We propose a recurrent neural network to simultaneously extract both spatial and temporal features from motion-blurred cine cardiac images. The experimental results demonstrate substantially improved image quality on two clinical test datasets.
  arXiv  Detail & Related papers  (2020-06-23T01:55:57Z)
• 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.