Zero-shot CT Field-of-view Completion with Unconditional Generative Diffusion Prior

• URL: http://arxiv.org/abs/2304.03760v1
• Date: Fri, 7 Apr 2023 17:54:40 GMT
• Title: Zero-shot CT Field-of-view Completion with Unconditional Generative Diffusion Prior
• Authors: Kaiwen Xu, Aravind R. Krishnan, Thomas Z. Li, Yuankai Huo, Kim L. Sandler, Fabien Maldonado, Bennett A. Landman
• Abstract summary: Anatomically consistent field-of-view (FOV) completion to recover truncated body sections has important applications in quantitative analyses of computed tomography (CT) with limited FOV. Existing solution based on conditional generative models relies on the fidelity of synthetic truncation patterns at training phase, which poses limitations for the generalizability of the method to potential unknown types of truncation. In this study, we evaluate a zero-shot method based on a pretrained unconditional generative diffusion prior, where truncation pattern with arbitrary forms can be specified at inference phase.
• Score: 4.084687005614829
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Anatomically consistent field-of-view (FOV) completion to recover truncated body sections has important applications in quantitative analyses of computed tomography (CT) with limited FOV. Existing solution based on conditional generative models relies on the fidelity of synthetic truncation patterns at training phase, which poses limitations for the generalizability of the method to potential unknown types of truncation. In this study, we evaluate a zero-shot method based on a pretrained unconditional generative diffusion prior, where truncation pattern with arbitrary forms can be specified at inference phase. In evaluation on simulated chest CT slices with synthetic FOV truncation, the method is capable of recovering anatomically consistent body sections and subcutaneous adipose tissue measurement error caused by FOV truncation. However, the correction accuracy is inferior to the conditionally trained counterpart.

Related papers

• CT-SDM: A Sampling Diffusion Model for Sparse-View CT Reconstruction across All Sampling Rates [16.985836345715963]
  Sparse views X-ray computed tomography has emerged as a contemporary technique to mitigate radiation dose degradation. Recent studies utilizing deep learning methods has made promising progress in removing artifacts for Sparse-View Computed Tomography (SVCT) Our study proposes a adaptive reconstruction method to achieve high-performance SVCT reconstruction at any sampling rate.
  arXiv  Detail & Related papers  (2024-09-03T03:06:15Z)
• Diffusion-based Generative Image Outpainting for Recovery of FOV-Truncated CT Images [10.350643783811174]
  Field-of-view (FOV) recovery of truncated chest CT scans is crucial for accurate body composition analysis. We present a method for recovering truncated CT slices using generative image outpainting. Our model reliably recovers the truncated anatomy and outperforms the previous state-of-the-art despite being trained on 87% less data.
  arXiv  Detail & Related papers  (2024-06-07T09:15:29Z)
• Diffeomorphic Template Registration for Atmospheric Turbulence Mitigation [50.16004183320537]
  We describe a method for recovering the irradiance underlying a collection of images corrupted by atmospheric turbulence. We select one of the images as a reference, and model the deformation in this image by the aggregation of the optical flow from it to other images. We achieve state-of-the-art performance despite its simplicity.
  arXiv  Detail & Related papers  (2024-05-06T17:39:53Z)
• CT-3DFlow : Leveraging 3D Normalizing Flows for Unsupervised Detection of Pathological Pulmonary CT scans [37.69303106863453]
  Normalizing Flows (NF) have the ability to directly learn the probability distribution of training examples through an invertible architecture. We leverage this property in a novel 3D NF-based model named CT-3DFlow, specifically tailored for patient-level pulmonary pathology detection in chest CT data. Our model is trained unsupervised on healthy 3D pulmonary CT patches, and detects deviations from its log-likelihood distribution as anomalies.
  arXiv  Detail & Related papers  (2024-03-27T12:44:57Z)
• ReshapeIT: Reliable Shape Interaction with Implicit Template for Anatomical Structure Reconstruction [59.971808117043366]
  ReShapeIT represents an anatomical structure with an implicit template field shared within the same category. It ensures the implicit template field generates valid templates by strengthening the constraint of the correspondence between the instance shape and the template shape. A template Interaction Module is introduced to reconstruct unseen shapes by interacting the valid template shapes with the instance-wise latent codes.
  arXiv  Detail & Related papers  (2023-12-11T07:09:32Z)
• CT Reconstruction using Diffusion Posterior Sampling conditioned on a Nonlinear Measurement Model [17.26789842018146]
  Diffusion posterior sampling has been used to produce high quality CT images given low-quality measurements. Current methods rely on a linear model of x-ray CT physics to reconstruct or restore images. We propose a new method that solves the inverse problem of nonlinear CT image reconstruction via diffusion posterior sampling.
  arXiv  Detail & Related papers  (2023-12-03T17:20:11Z)
• Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability [50.44439018155837]
  We propose to include a calibration term directly into the training objective of the neural model. By introducing a relaxation of the classical formulation of calibration error we enable end-to-end backpropagation. It is directly applicable to existing computational pipelines allowing reliable black-box posterior inference.
  arXiv  Detail & Related papers  (2023-10-20T10:20:45Z)
• Exploiting Diffusion Prior for Real-World Image Super-Resolution [75.5898357277047]
  We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution. By employing our time-aware encoder, we can achieve promising restoration results without altering the pre-trained synthesis model.
  arXiv  Detail & Related papers  (2023-05-11T17:55:25Z)
• Body Composition Assessment with Limited Field-of-view Computed Tomography: A Semantic Image Extension Perspective [5.373119949253442]
  Field-of-view (FOV) tissue truncation beyond the lungs is common in routine lung screening computed tomography (CT) In this work, we formulate the problem from the semantic image extension perspective which only requires image data as inputs. The proposed two-stage method identifies a new FOV border based on the estimated extent of the complete body and imputes missing tissues in the truncated region.
  arXiv  Detail & Related papers  (2022-07-13T23:19:22Z)
• A Self-Supervised Deep Framework for Reference Bony Shape Estimation in Orthognathic Surgical Planning [55.30223654196882]
  A virtual orthognathic surgical planning involves simulating surgical corrections of jaw deformities on 3D facial bony shape models. A reference facial bony shape model representing normal anatomies can provide an objective guidance to improve planning accuracy. We propose a self-supervised deep framework to automatically estimate reference facial bony shape models.
  arXiv  Detail & Related papers  (2021-09-11T05:24:40Z)
• Data-driven generation of plausible tissue geometries for realistic photoacoustic image synthesis [53.65837038435433]
  Photoacoustic tomography (PAT) has the potential to recover morphological and functional tissue properties. We propose a novel approach to PAT data simulation, which we refer to as "learning to simulate" We leverage the concept of Generative Adversarial Networks (GANs) trained on semantically annotated medical imaging data to generate plausible tissue geometries.
  arXiv  Detail & Related papers  (2021-03-29T11:30:18Z)

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.