Clinically Feasible Diffusion Reconstruction for Highly-Accelerated
Cardiac Cine MRI
- URL: http://arxiv.org/abs/2403.08749v1
- Date: Wed, 13 Mar 2024 17:51:01 GMT
- Title: Clinically Feasible Diffusion Reconstruction for Highly-Accelerated
Cardiac Cine MRI
- Authors: Shihan Qiu, Shaoyan Pan, Yikang Liu, Lin Zhao, Jian Xu, Qi Liu,
Terrence Chen, Eric Z. Chen, Xiao Chen, Shanhui Sun
- Abstract summary: We aim to develop a clinically feasible diffusion-model-based reconstruction pipeline to improve the image quality of cine MRI.
A multi-in multi-out diffusion enhancement model together with fast inference strategies were developed to be used in conjunction with a reconstruction model.
- Score: 20.86718191599198
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The currently limited quality of accelerated cardiac cine reconstruction may
potentially be improved by the emerging diffusion models, but the clinically
unacceptable long processing time poses a challenge. We aim to develop a
clinically feasible diffusion-model-based reconstruction pipeline to improve
the image quality of cine MRI. A multi-in multi-out diffusion enhancement model
together with fast inference strategies were developed to be used in
conjunction with a reconstruction model. The diffusion reconstruction reduced
spatial and temporal blurring in prospectively undersampled clinical data, as
validated by experts inspection. The 1.5s per video processing time enabled the
approach to be applied in clinical scenarios.
Related papers
- Improved Patch Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprinting [7.379135816468852]
Magnetic Resonance Fingerprinting (MRF) is a time-efficient approach to quantitative MRI.
achieving accurate reconstructions remains challenging, particularly in highly accelerated and undersampled acquisitions.
We propose for the first time a conditional diffusion probabilistic model for MRF image reconstruction.
arXiv Detail & Related papers (2024-10-29T21:38:54Z) - FLex: Joint Pose and Dynamic Radiance Fields Optimization for Stereo Endoscopic Videos [79.50191812646125]
Reconstruction of endoscopic scenes is an important asset for various medical applications, from post-surgery analysis to educational training.
We adress the challenging setup of a moving endoscope within a highly dynamic environment of deforming tissue.
We propose an implicit scene separation into multiple overlapping 4D neural radiance fields (NeRFs) and a progressive optimization scheme jointly optimizing for reconstruction and camera poses from scratch.
This improves the ease-of-use and allows to scale reconstruction capabilities in time to process surgical videos of 5,000 frames and more; an improvement of more than ten times compared to the state of the art while being agnostic to external tracking information
arXiv Detail & Related papers (2024-03-18T19:13:02Z) - Spatiotemporal Diffusion Model with Paired Sampling for Accelerated
Cardiac Cine MRI [20.86718191599198]
Current deep learning reconstruction for accelerated MRI suffers from spatial and temporal blurring.
A paired sampling strategy substantially reduced artificial noises in the generative results.
arXiv Detail & Related papers (2024-03-13T17:56:12Z) - K-space Cold Diffusion: Learning to Reconstruct Accelerated MRI without
Noise [2.982793366290863]
We propose a k-space cold diffusion model that performs image degradation and restoration in k-space without the need for Gaussian noise.
Our results show that this novel way of performing degradation can generate high-quality reconstruction images for accelerated MRI.
arXiv Detail & Related papers (2023-11-16T19:34:18Z) - Diffusion Modeling with Domain-conditioned Prior Guidance for
Accelerated MRI and qMRI Reconstruction [3.083408283778178]
This study introduces a novel approach for image reconstruction based on a diffusion model conditioned on the native data domain.
The proposed method demonstrates a significant promise, particularly for reconstructing images at high acceleration factors.
arXiv Detail & Related papers (2023-09-02T01:33:50Z) - Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion
Generative Models [75.52575380824051]
We present a learning method to optimize sub-sampling patterns for compressed sensing multi-coil MRI.
We use a single-step reconstruction based on the posterior mean estimate given by the diffusion model and the MRI measurement process.
Our method requires as few as five training images to learn effective sampling patterns.
arXiv Detail & Related papers (2023-06-05T22:09:06Z) - SNAF: Sparse-view CBCT Reconstruction with Neural Attenuation Fields [71.84366290195487]
We propose SNAF for sparse-view CBCT reconstruction by learning the neural attenuation fields.
Our approach achieves superior performance in terms of high reconstruction quality (30+ PSNR) with only 20 input views.
arXiv Detail & Related papers (2022-11-30T14:51:14Z) - Faster Diffusion Cardiac MRI with Deep Learning-based breath hold
reduction [7.559996316671546]
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.
arXiv Detail & Related papers (2022-06-21T17:17:00Z) - 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) - Towards performant and reliable undersampled MR reconstruction via
diffusion model sampling [67.73698021297022]
DiffuseRecon is a novel diffusion model-based MR reconstruction method.
It guides the generation process based on the observed signals.
It does not require additional training on specific acceleration factors.
arXiv Detail & Related papers (2022-03-08T02:25:38Z) - 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.