Novel-view X-ray Projection Synthesis through Geometry-Integrated Deep Learning

• URL: http://arxiv.org/abs/2504.11953v1
• Date: Wed, 16 Apr 2025 10:30:08 GMT
• Title: Novel-view X-ray Projection Synthesis through Geometry-Integrated Deep Learning
• Authors: Daiqi Liu, Fuxin Fan, Andreas Maier,
• Abstract summary: The DL-GIPS model synthesizes X-ray projections from new viewpoints by leveraging a single existing projection.<n>The model strategically manipulates geometry and texture features extracted from an initial projection to match new viewing angles.<n>It then synthesizes the final projection by merging these modified geometry features with consistent texture information through an advanced image generation process.
• Score: 3.4916237834391874
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: X-ray imaging plays a crucial role in the medical field, providing essential insights into the internal anatomy of patients for diagnostics, image-guided procedures, and clinical decision-making. Traditional techniques often require multiple X-ray projections from various angles to obtain a comprehensive view, leading to increased radiation exposure and more complex clinical processes. This paper explores an innovative approach using the DL-GIPS model, which synthesizes X-ray projections from new viewpoints by leveraging a single existing projection. The model strategically manipulates geometry and texture features extracted from an initial projection to match new viewing angles. It then synthesizes the final projection by merging these modified geometry features with consistent texture information through an advanced image generation process. We demonstrate the effectiveness and broad applicability of the DL-GIPS framework through lung imaging examples, highlighting its potential to revolutionize stereoscopic and volumetric imaging by minimizing the need for extensive data acquisition.

Related papers

• Interactive Tumor Progression Modeling via Sketch-Based Image Editing [54.47725383502915]
  We propose SkEditTumor, a sketch-based diffusion model for controllable tumor progression editing.<n>By leveraging sketches as structural priors, our method enables precise modifications of tumor regions while maintaining structural integrity and visual realism.<n>Our contributions include a novel integration of sketches with diffusion models for medical image editing, fine-grained control over tumor progression visualization, and extensive validation across multiple datasets, setting a new benchmark in the field.
  arXiv  Detail & Related papers  (2025-03-10T00:04:19Z)
• TomoGRAF: A Robust and Generalizable Reconstruction Network for Single-View Computed Tomography [3.1209855614927275]
  Traditional analytical/iterative CT reconstruction algorithms require hundreds of angular data samplings. We develop a novel TomoGRAF framework incorporating the unique X-ray transportation physics to reconstruct high-quality 3D volumes.
  arXiv  Detail & Related papers  (2024-11-12T20:07:59Z)
• XProspeCT: CT Volume Generation from Paired X-Rays [0.0]
  We build on previous research to convert X-ray images into simulated CT volumes. Model variations include UNet architectures, custom connections, activation functions, loss functions, and a novel back projection approach.
  arXiv  Detail & Related papers  (2024-02-11T21:57:49Z)
• GM-NeRF: Learning Generalizable Model-based Neural Radiance Fields from Multi-view Images [79.39247661907397]
  We introduce an effective framework Generalizable Model-based Neural Radiance Fields to synthesize free-viewpoint images. Specifically, we propose a geometry-guided attention mechanism to register the appearance code from multi-view 2D images to a geometry proxy.
  arXiv  Detail & Related papers  (2023-03-24T03:32:02Z)
• Oral-3Dv2: 3D Oral Reconstruction from Panoramic X-Ray Imaging with Implicit Neural Representation [3.8215162658168524]
  Oral-3Dv2 is a non-adversarial-learning-based model in 3D radiology reconstruction from a single panoramic X-ray image. Our model learns to represent the 3D oral structure in an implicit way by mapping 2D coordinates into density values of voxels in the 3D space. To the best of our knowledge, this is the first work of a non-adversarial-learning-based model in 3D radiology reconstruction from a single panoramic X-ray image.
  arXiv  Detail & Related papers  (2023-03-21T18:17:27Z)
• Vision-Language Generative Model for View-Specific Chest X-ray Generation [18.347723213970696]
  ViewXGen is designed to overcome the limitations of existing methods to generate frontal-view chest X-rays. Our approach takes into consideration the diverse view positions found in the dataset, enabling the generation of chest X-rays with specific views.
  arXiv  Detail & Related papers  (2023-02-23T17:13:25Z)
• Orientation-Shared Convolution Representation for CT Metal Artifact Learning [63.67718355820655]
  During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts. Existing deep-learning-based methods have gained promising reconstruction performance. We propose an orientation-shared convolution representation strategy to adapt the physical prior structures of artifacts.
  arXiv  Detail & Related papers  (2022-12-26T13:56:12Z)
• OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing [62.993663757843464]
  Optoacoustic (OA) imaging is based on excitation of biological tissues with nanosecond-duration laser pulses followed by detection of ultrasound waves generated via light-absorption-mediated thermoelastic expansion. OA imaging features a powerful combination between rich optical contrast and high resolution in deep tissues. No standardized datasets generated with different types of experimental set-up and associated processing methods are available to facilitate advances in broader applications of OA in clinical settings.
  arXiv  Detail & Related papers  (2022-06-17T08:11:26Z)
• MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray [14.10611608681131]
  Excessive ionising radiation can lead to deterministic and harmful effects on the body. This paper proposes a Deep Learning model that learns to reconstruct CT projections from a few or even a single-view X-ray.
  arXiv  Detail & Related papers  (2022-02-02T13:25:23Z)
• Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis [88.39466012709205]
  This paper builds a novel medical slice to increase the between-slice resolution. Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy. Our method outperforms state-of-the-art algorithms by clear margins.
  arXiv  Detail & Related papers  (2021-12-20T03:38:37Z)
• A Geometry-Informed Deep Learning Framework for Ultra-Sparse 3D Tomographic Image Reconstruction [13.44786774177579]
  We establish a geometry-informed deep learning framework for ultra-sparse 3D tomographic image reconstruction. We demonstrate that the seamless inclusion of known priors is essential to enhance the performance of 3D volumetric computed tomography imaging.
  arXiv  Detail & Related papers  (2021-05-25T06:20:03Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

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.