DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion Models

• URL: http://arxiv.org/abs/2405.05763v1
• Date: Thu, 9 May 2024 13:37:18 GMT
• Title: DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion Models
• Authors: Mengxiao Geng, Jiahao Zhu, Xiaolin Zhu, Qiqing Liu, Dong Liang, Qiegen Liu,
• Abstract summary: We propose a comprehensive detail-preserving reconstruction method using multiple diffusion models. The framework effective-ly represents multi-scale sampled data, taking into ac-count the sparsity of the inverted pyramid architecture. The proposed method was evaluated by con-ducting experiments on clinical and public datasets.
• Score: 7.601874398726257
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detail features of magnetic resonance images play a cru-cial role in accurate medical diagnosis and treatment, as they capture subtle changes that pose challenges for doc-tors when performing precise judgments. However, the widely utilized naive diffusion model has limitations, as it fails to accurately capture more intricate details. To en-hance the quality of MRI reconstruction, we propose a comprehensive detail-preserving reconstruction method using multiple diffusion models to extract structure and detail features in k-space domain instead of image do-main. Moreover, virtual binary modal masks are utilized to refine the range of values in k-space data through highly adaptive center windows, which allows the model to focus its attention more efficiently. Last but not least, an inverted pyramid structure is employed, where the top-down image information gradually decreases, ena-bling a cascade representation. The framework effective-ly represents multi-scale sampled data, taking into ac-count the sparsity of the inverted pyramid architecture, and utilizes cascade training data distribution to repre-sent multi-scale data. Through a step-by-step refinement approach, the method refines the approximation of de-tails. Finally, the proposed method was evaluated by con-ducting experiments on clinical and public datasets. The results demonstrate that the proposed method outper-forms other methods.

Related papers

• ContextMRI: Enhancing Compressed Sensing MRI through Metadata Conditioning [51.26601171361753]
  We propose ContextMRI, a text-conditioned diffusion model for MRI that integrates granular metadata into the reconstruction process. We show that increasing the fidelity of metadata, ranging from slice location and contrast to patient age, sex, and pathology, systematically boosts reconstruction performance.
  arXiv  Detail & Related papers  (2025-01-08T05:15:43Z)
• Re-Visible Dual-Domain Self-Supervised Deep Unfolding Network for MRI Reconstruction [48.30341580103962]
  We propose a novel re-visible dual-domain self-supervised deep unfolding network to address these issues. We design a deep unfolding network based on Chambolle and Pock Proximal Point Algorithm (DUN-CP-PPA) to achieve end-to-end reconstruction. Experiments conducted on the fastMRI and IXI datasets demonstrate that our method significantly outperforms state-of-the-art approaches in terms of reconstruction performance.
  arXiv  Detail & Related papers  (2025-01-07T12:29:32Z)
• Partitioned Hankel-based Diffusion Models for Few-shot Low-dose CT Reconstruction [10.158713017984345]
  We propose a few-shot low-dose CT reconstruction method using Partitioned Hankel-based Diffusion (PHD) models. In the iterative reconstruction stage, an iterative differential equation solver is employed along with data consistency constraints to update the acquired projection data. The results approximate those of normaldose counterparts, validating PHD model as an effective and practical model for reducing artifacts and noise while preserving image quality.
  arXiv  Detail & Related papers  (2024-05-27T13:44:53Z)
• MSDiff: Multi-Scale Diffusion Model for Ultra-Sparse View CT Reconstruction [5.5805994093893885]
  We propose an ultra-sparse view CT reconstruction method utilizing multi-scale dif-fusion models (MSDiff) The proposed model ingeniously integrates information from both comprehensive sampling and selectively sparse sampling tech-niques. By leveraging the inherent correlations within the projec-tion data, we have designed an equidistant mask, enabling the model to focus its attention more effectively.
  arXiv  Detail & Related papers  (2024-05-09T14:52:32Z)
• DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [55.48770333927732]
  We propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection. It consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion's denoising network, and a feature-space pre-trained feature extractor. Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-12-11T18:38:28Z)
• Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs [35.46541584018842]
  Unsupervised Anomaly Detection (UAD) aims to identify any anomaly as an outlier from a healthy training distribution. generative models are used to learn the reconstruction of healthy brain anatomy for a given input image. We propose conditioning the denoising process of diffusion models with additional information derived from a latent representation of the input image.
  arXiv  Detail & Related papers  (2023-12-07T11:03:42Z)
• ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic Diffusion Models [69.9178140563928]
  Colonoscopy analysis is essential for assisting clinical diagnosis and treatment. The scarcity of annotated data limits the effectiveness and generalization of existing methods. We propose an Adaptive Refinement Semantic Diffusion Model (ArSDM) to generate colonoscopy images that benefit the downstream tasks.
  arXiv  Detail & Related papers  (2023-09-03T07:55:46Z)
• 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)
• Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentation [41.608617301275935]
  We propose a novel end-to-end framework, called Diff-UNet, for medical volumetric segmentation. Our approach integrates the diffusion model into a standard U-shaped architecture to extract semantic information from the input volume effectively. We evaluate our method on three datasets, including multimodal brain tumors in MRI, liver tumors, and multi-organ CT volumes.
  arXiv  Detail & Related papers  (2023-03-18T04:06:18Z)
• Multiscale Structure Guided Diffusion for Image Deblurring [24.09642909404091]
  Diffusion Probabilistic Models (DPMs) have been employed for image deblurring. We introduce a simple yet effective multiscale structure guidance as an implicit bias. We demonstrate more robust deblurring results with fewer artifacts on unseen data.
  arXiv  Detail & Related papers  (2022-12-04T10:40:35Z)
• Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning [62.17532253489087]
  Deep learning methods have been shown to produce superior performance on MR image reconstruction. These methods require large amounts of data which is difficult to collect and share due to the high cost of acquisition and medical data privacy regulations. We propose a federated learning (FL) based solution in which we take advantage of the MR data available at different institutions while preserving patients' privacy.
  arXiv  Detail & Related papers  (2021-03-03T03:04:40Z)

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.