A Diffusion-based Xray2MRI Model: Generating Pseudo-MRI Volumes From one Single X-ray

• URL: http://arxiv.org/abs/2410.06997v2
• Date: Thu, 17 Oct 2024 02:36:38 GMT
• Title: A Diffusion-based Xray2MRI Model: Generating Pseudo-MRI Volumes From one Single X-ray
• Authors: Zhe Wang, Rachid Jennane, Aladine Chetouani, Yung Hsin Chen, Fabian Bauer, Mohamed Jarraya,
• Abstract summary: We introduce a novel diffusion-based Xray2MRI model capable of generating pseudo-MRI volumes from a single X-ray image. Experimental results demonstrate that our proposed approach is capable of generating pseudo-MRI sequences that approximate real MRI scans.
• Score: 6.014316825270666
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knee osteoarthritis (KOA) is a prevalent musculoskeletal disorder, and X-rays are commonly used for its diagnosis due to their cost-effectiveness. Magnetic Resonance Imaging (MRI), on the other hand, offers detailed soft tissue visualization and has become a valuable supplementary diagnostic tool for KOA. Unfortunately, the high cost and limited accessibility of MRI hinders its widespread use, leaving many patients with KOA to rely solely on X-ray imaging. In this study, we introduce a novel diffusion-based Xray2MRI model capable of generating pseudo-MRI volumes from a single X-ray image. In addition to using X-rays as conditional input, our model integrates target depth, KOA probability distribution, and image intensity distribution modules to guide the synthesis process, ensuring that the generated corresponding slices accurately correspond to the anatomical structures. Experimental results demonstrate that by integrating information from X-rays with additional input data, our proposed approach is capable of generating pseudo-MRI sequences that approximate real MRI scans. In addition, by increasing the number of inference steps, the model achieves effective interpolation, which further improves the continuity and smoothness of the generated MRI sequences, representing a promising first attempt at cost-effective medical imaging solutions. This study is available on https://zwang78.github.io/.

Related papers

• Multimodal Deformable Image Registration for Long-COVID Analysis Based on Progressive Alignment and Multi-perspective Loss [0.0]
  Long COVID is characterized by persistent symptoms, particularly pulmonary impairment. Integrating functional data from XeMRI with structural data from CT is crucial for comprehensive analysis and effective treatment strategies. We propose an end-to-end multimodal deformable image registration method that achieves superior performance for aligning long-COVID lung CT and proton density MRI data.
  arXiv  Detail & Related papers  (2024-06-21T14:19:18Z)
• X-Diffusion: Generating Detailed 3D MRI Volumes From a Single Image Using Cross-Sectional Diffusion Models [6.046082223332061]
  X-Diffusion is a cross-sectional diffusion model tailored for Magnetic Resonance Imaging (MRI) data. X-Diffusion is able to generate detailed 3D MRI volume from a single full-body DXA. Remarkably, the resultant MRIs flawlessly retain essential features of the original MRI, including tumour profiles, spine curvature, brain volume, and beyond.
  arXiv  Detail & Related papers  (2024-04-30T14:53:07Z)
• NeuroPictor: Refining fMRI-to-Image Reconstruction via Multi-individual Pretraining and Multi-level Modulation [55.51412454263856]
  This paper proposes to directly modulate the generation process of diffusion models using fMRI signals. By training with about 67,000 fMRI-image pairs from various individuals, our model enjoys superior fMRI-to-image decoding capacity.
  arXiv  Detail & Related papers  (2024-03-27T02:42:52Z)
• 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)
• UMedNeRF: Uncertainty-aware Single View Volumetric Rendering for Medical Neural Radiance Fields [38.62191342903111]
  We propose an Uncertainty-aware MedNeRF (UMedNeRF) network based on generated radiation fields. We show the results of CT projection rendering with a single X-ray and compare our method with other methods based on generated radiation fields.
  arXiv  Detail & Related papers  (2023-11-10T02:47:15Z)
• Enhanced Synthetic MRI Generation from CT Scans Using CycleGAN with Feature Extraction [3.2088888904556123]
  We propose an approach for enhanced monomodal registration using synthetic MRI images from CT scans. Our methodology shows promising results, outperforming several state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-10-31T16:39:56Z)
• Style transfer between Microscopy and Magnetic Resonance Imaging via Generative Adversarial Network in small sample size settings [49.84018914962972]
  Cross-modal augmentation of Magnetic Resonance Imaging (MRI) and microscopic imaging based on the same tissue samples is promising. We tested a method for generating microscopic histological images from MRI scans of the corpus callosum using conditional generative adversarial network (cGAN) architecture.
  arXiv  Detail & Related papers  (2023-10-16T13:58:53Z)
• X-Ray2EM: Uncertainty-Aware Cross-Modality Image Reconstruction from X-Ray to Electron Microscopy in Connectomics [55.6985304397137]
  We propose an uncertainty-aware 3D reconstruction model that translates X-ray images to EM-like images with enhanced membrane segmentation quality. This shows its potential for developing simpler, faster, and more accurate X-ray based connectomics pipelines.
  arXiv  Detail & Related papers  (2023-03-02T00:52:41Z)
• Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction [68.80715727288514]
  We show how to unfold an iterative MGDUN algorithm into a novel model-guided deep unfolding network by taking the MRI observation matrix. In this paper, we propose a novel Model-Guided interpretable Deep Unfolding Network (MGDUN) for medical image SR reconstruction.
  arXiv  Detail & Related papers  (2022-09-15T03:58:30Z)
• BMD-GAN: Bone mineral density estimation using x-ray image decomposition into projections of bone-segmented quantitative computed tomography using hierarchical learning [1.8762753243053634]
  We propose an approach using the QCT for training a generative adversarial network (GAN) and decomposing an x-ray image into a projection of bone-segmented QCT. The evaluation of 200 patients with osteoarthritis using the proposed method demonstrated a Pearson correlation coefficient of 0.888 between the predicted and ground truth.
  arXiv  Detail & Related papers  (2022-07-07T10:33:12Z)
• 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)

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.