TC-DiffRecon: Texture coordination MRI reconstruction method based on
diffusion model and modified MF-UNet method
- URL: http://arxiv.org/abs/2402.11274v1
- Date: Sat, 17 Feb 2024 13:09:00 GMT
- Title: TC-DiffRecon: Texture coordination MRI reconstruction method based on
diffusion model and modified MF-UNet method
- Authors: Chenyan Zhang, Yifei Chen, Zhenxiong Fan, Yiyu Huang, Wenchao Weng,
Ruiquan Ge, Dong Zeng, Changmiao Wang
- Abstract summary: We propose a novel diffusion model-based MRI reconstruction method, named TC-DiffRecon, which does not rely on a specific acceleration factor for training.
We also suggest the incorporation of the MF-UNet module, designed to enhance the quality of MRI images generated by the model.
- Score: 2.626378252978696
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recently, diffusion models have gained significant attention as a novel set
of deep learning-based generative methods. These models attempt to sample data
from a Gaussian distribution that adheres to a target distribution, and have
been successfully adapted to the reconstruction of MRI data. However, as an
unconditional generative model, the diffusion model typically disrupts image
coordination because of the consistent projection of data introduced by
conditional bootstrap. This often results in image fragmentation and
incoherence. Furthermore, the inherent limitations of the diffusion model often
lead to excessive smoothing of the generated images. In the same vein, some
deep learning-based models often suffer from poor generalization performance,
meaning their effectiveness is greatly affected by different acceleration
factors. To address these challenges, we propose a novel diffusion model-based
MRI reconstruction method, named TC-DiffRecon, which does not rely on a
specific acceleration factor for training. We also suggest the incorporation of
the MF-UNet module, designed to enhance the quality of MRI images generated by
the model while mitigating the over-smoothing issue to a certain extent. During
the image generation sampling process, we employ a novel TCKG module and a
Coarse-to-Fine sampling scheme. These additions aim to harmonize image texture,
expedite the sampling process, while achieving data consistency. Our source
code is available at https://github.com/JustlfC03/TC-DiffRecon.
Related papers
- An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations [21.411327264448058]
We propose an expectation-maximization (EM) approach to train diffusion models from corrupted observations.
Our method alternates between reconstructing clean images from corrupted data using a known diffusion model (E-step) and refining diffusion model weights based on these reconstructions (M-step)
This iterative process leads the learned diffusion model to gradually converge to the true clean data distribution.
arXiv Detail & Related papers (2024-07-01T07:00:17Z) - Lossy Image Compression with Foundation Diffusion Models [10.407650300093923]
In this work we formulate the removal of quantization error as a denoising task, using diffusion to recover lost information in the transmitted image latent.
Our approach allows us to perform less than 10% of the full diffusion generative process and requires no architectural changes to the backbone.
arXiv Detail & Related papers (2024-04-12T16:23:42Z) - Ambient Diffusion Posterior Sampling: Solving Inverse Problems with
Diffusion Models trained on Corrupted Data [56.81246107125692]
Ambient Diffusion Posterior Sampling (A-DPS) is a generative model pre-trained on one type of corruption.
We show that A-DPS can sometimes outperform models trained on clean data for several image restoration tasks in both speed and performance.
We extend the Ambient Diffusion framework to train MRI models with access only to Fourier subsampled multi-coil MRI measurements.
arXiv Detail & Related papers (2024-03-13T17:28:20Z) - Denoising Diffusion Bridge Models [54.87947768074036]
Diffusion models are powerful generative models that map noise to data using processes.
For many applications such as image editing, the model input comes from a distribution that is not random noise.
In our work, we propose Denoising Diffusion Bridge Models (DDBMs)
arXiv Detail & Related papers (2023-09-29T03:24:24Z) - ExposureDiffusion: Learning to Expose for Low-light Image Enhancement [87.08496758469835]
This work addresses the issue by seamlessly integrating a diffusion model with a physics-based exposure model.
Our method obtains significantly improved performance and reduced inference time compared with vanilla diffusion models.
The proposed framework can work with both real-paired datasets, SOTA noise models, and different backbone networks.
arXiv Detail & Related papers (2023-07-15T04:48:35Z) - 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) - Hierarchical Integration Diffusion Model for Realistic Image Deblurring [71.76410266003917]
Diffusion models (DMs) have been introduced in image deblurring and exhibited promising performance.
We propose the Hierarchical Integration Diffusion Model (HI-Diff), for realistic image deblurring.
Experiments on synthetic and real-world blur datasets demonstrate that our HI-Diff outperforms state-of-the-art methods.
arXiv Detail & Related papers (2023-05-22T12:18:20Z) - Reflected Diffusion Models [93.26107023470979]
We present Reflected Diffusion Models, which reverse a reflected differential equation evolving on the support of the data.
Our approach learns the score function through a generalized score matching loss and extends key components of standard diffusion models.
arXiv Detail & Related papers (2023-04-10T17:54:38Z) - CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for
Low-Dose CT Denoising and Generalization [41.64072751889151]
Low-dose computed tomography (LDCT) images suffer from noise and artifacts due to photon starvation and electronic noise.
This paper presents a novel COntextual eRror-modulated gEneralized Diffusion model for low-dose CT (LDCT) denoising, termed CoreDiff.
arXiv Detail & Related papers (2023-04-04T14:13:13Z) - DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle
CT Reconstruction [42.028139152832466]
Limited-Angle Computed Tomography (LACT) is a non-destructive evaluation technique used in a variety of applications ranging from security to medicine.
We present DOLCE, a new deep model-based framework for LACT that uses a conditional diffusion model as an image prior.
arXiv Detail & Related papers (2022-11-22T15:30:38Z)
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.