Faster Diffusion Cardiac MRI with Deep Learning-based breath hold reduction

• URL: http://arxiv.org/abs/2206.10543v1
• Date: Tue, 21 Jun 2022 17:17:00 GMT
• Title: Faster Diffusion Cardiac MRI with Deep Learning-based breath hold reduction
• Authors: Michael Tanzer, Pedro Ferreira, Andrew Scott, Zohya Khalique, Maria Dwornik, Dudley Pennell, Guang Yang, Daniel Rueckert, Sonia Nielles-Vallespin
• Abstract summary: DT-CMR enables us to probe the microstructural arrangement of cardiomyocytes within the myocardium in vivo and non-invasively. DT-CMR is currently inefficient with over six minutes needed to acquire a single 2D static image. We propose to reduce the number of repetitions needed to produce DT-CMR datasets and subsequently de-noise them.
• Score: 7.559996316671546
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion Tensor Cardiac Magnetic Resonance (DT-CMR) enables us to probe the microstructural arrangement of cardiomyocytes within the myocardium in vivo and non-invasively, which no other imaging modality allows. This innovative technology could revolutionise the ability to perform cardiac clinical diagnosis, risk stratification, prognosis and therapy follow-up. However, DT-CMR is currently inefficient with over six minutes needed to acquire a single 2D static image. Therefore, DT-CMR is currently confined to research but not used clinically. We propose to reduce the number of repetitions needed to produce DT-CMR datasets and subsequently de-noise them, decreasing the acquisition time by a linear factor while maintaining acceptable image quality. Our proposed approach, based on Generative Adversarial Networks, Vision Transformers, and Ensemble Learning, performs significantly and considerably better than previous proposed approaches, bringing single breath-hold DT-CMR closer to reality.

Related papers

• Enhancing Angular Resolution via Directionality Encoding and Geometric Constraints in Brain Diffusion Tensor Imaging [70.66500060987312]
  Diffusion-weighted imaging (DWI) is a type of Magnetic Resonance Imaging (MRI) technique sensitised to the diffusivity of water molecules. This work proposes DirGeo-DTI, a deep learning-based method to estimate reliable DTI metrics even from a set of DWIs acquired with the minimum theoretical number (6) of gradient directions.
  arXiv  Detail & Related papers  (2024-09-11T11:12:26Z)
• Rotational Augmented Noise2Inverse for Low-dose Computed Tomography Reconstruction [83.73429628413773]
  Supervised deep learning methods have shown the ability to remove noise in images but require accurate ground truth. We propose a novel self-supervised framework for LDCT, in which ground truth is not required for training the convolutional neural network (CNN) Numerical and experimental results show that the reconstruction accuracy of N2I with sparse views is degrading while the proposed rotational augmented Noise2Inverse (RAN2I) method keeps better image quality over a different range of sampling angles.
  arXiv  Detail & Related papers  (2023-12-19T22:40:51Z)
• Cine cardiac MRI reconstruction using a convolutional recurrent network with refinement [9.173298795526152]
  We investigate the use of a convolutional recurrent neural network (CRNN) architecture to exploit temporal correlations in cardiac MRI reconstruction. This is combined with a single-image super-resolution refinement module to improve single coil reconstruction by 4.4% in structural similarity and 3.9% in normalised mean square error. The proposed model demonstrates considerable enhancements compared to the baseline case and holds promising potential for further improving cardiac MRI reconstruction.
  arXiv  Detail & Related papers  (2023-09-23T14:07:04Z)
• Unsupervised reconstruction of accelerated cardiac cine MRI using Neural Fields [3.684766600912547]
  We propose an unsupervised approach based on implicit neural field representations for cardiac cine MRI (so called NF-cMRI) The proposed method was evaluated in in-vivo undersampled golden-angle radial multi-coil acquisitions for undersampling factors of 26x and 52x.
  arXiv  Detail & Related papers  (2023-07-24T23:31:36Z)
• Using BOLD-fMRI to Compute the Respiration Volume per Time (RTV) and Respiration Variation (RV) with Convolutional Neural Networks (CNN) in the Human Connectome Development Cohort [55.41644538483948]
  This study proposes a one-dimensional CNN model for reconstruction of two respiratory measures, RV and RVT. Results show that a CNN can capture informative features from resting BOLD signals and reconstruct realistic RV and RVT timeseries.
  arXiv  Detail & Related papers  (2023-07-03T18:06:36Z)
• Generative AI for Rapid Diffusion MRI with Improved Image Quality, Reliability and Generalizability [3.6119644566822484]
  We employ a Swin UNEt Transformers model, trained on augmented Human Connectome Project data, to perform generalized denoising of dMRI. We demonstrate super-resolution with artificially downsampled HCP data in normal adult volunteers. We exceed current state-of-the-art denoising methods in accuracy and test-retest reliability of rapid diffusion tensor imaging requiring only 90 seconds of scan time.
  arXiv  Detail & Related papers  (2023-03-10T03:39:23Z)
• Physics-informed self-supervised deep learning reconstruction for accelerated first-pass perfusion cardiac MRI [2.023359976134555]
  We propose a physics-informed self-supervised deep learning FPP-CMR reconstruction approach for accelerating FPP-CMR scans. The proposed method provides high-quality FPP-CMR images from 10x undersampled data without using fully sampled reference data.
  arXiv  Detail & Related papers  (2023-01-05T12:11:17Z)
• 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)
• 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)
• Microvascular Dynamics from 4D Microscopy Using Temporal Segmentation [81.30750944868142]
  We are able to track changes in cerebral blood volume over time and identify spontaneous arterial dilations that propagate towards the pial surface. This new imaging capability is a promising step towards characterizing the hemodynamic response function upon which functional magnetic resonance imaging (fMRI) is based.
  arXiv  Detail & Related papers  (2020-01-14T22:55:03Z)

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.