Multi-Subject Image Synthesis as a Generative Prior for Single-Subject PET Image Reconstruction

• URL: http://arxiv.org/abs/2412.04324v1
• Date: Thu, 05 Dec 2024 16:40:33 GMT
• Title: Multi-Subject Image Synthesis as a Generative Prior for Single-Subject PET Image Reconstruction
• Authors: George Webber, Yuya Mizuno, Oliver D. Howes, Alexander Hammers, Andrew P. King, Andrew J. Reader,
• Abstract summary: We propose a novel method for synthesising diverse and realistic pseudo-PET images with improved signal-to-noise ratio. We show how our pseudo-PET images may be exploited as a generative prior for single-subject PET image reconstruction.
• Score: 40.34650079545031
• License:
• Abstract: Large high-quality medical image datasets are difficult to acquire but necessary for many deep learning applications. For positron emission tomography (PET), reconstructed image quality is limited by inherent Poisson noise. We propose a novel method for synthesising diverse and realistic pseudo-PET images with improved signal-to-noise ratio. We also show how our pseudo-PET images may be exploited as a generative prior for single-subject PET image reconstruction. Firstly, we perform deep-learned deformable registration of multi-subject magnetic resonance (MR) images paired to multi-subject PET images. We then use the anatomically-learned deformation fields to transform multiple PET images to the same reference space, before averaging random subsets of the transformed multi-subject data to form a large number of varying pseudo-PET images. We observe that using MR information for registration imbues the resulting pseudo-PET images with improved anatomical detail compared to the originals. We consider applications to PET image reconstruction, by generating pseudo-PET images in the same space as the intended single-subject reconstruction and using them as training data for a diffusion model-based reconstruction method. We show visual improvement and reduced background noise in our 2D reconstructions as compared to OSEM, MAP-EM and an existing state-of-the-art diffusion model-based approach. Our method shows the potential for utilising highly subject-specific prior information within a generative reconstruction framework. Future work may compare the benefits of our approach to explicitly MR-guided reconstruction methodologies.

Related papers

• Multibranch Generative Models for Multichannel Imaging with an Application to PET/CT Synergistic Reconstruction [42.95604565673447]
  This paper presents a novel approach for learned synergistic reconstruction of medical images using multibranch generative models. We demonstrate the efficacy of our approach on both Modified National Institute of Standards and Technology (MNIST) and positron emission tomography (PET)/ computed tomography (CT) datasets.
  arXiv  Detail & Related papers  (2024-04-12T18:21:08Z)
• Image2Points:A 3D Point-based Context Clusters GAN for High-Quality PET Image Reconstruction [47.398304117228584]
  We propose a 3D point-based context clusters GAN, namely PCC-GAN, to reconstruct high-quality SPET images from LPET. Experiments on both clinical and phantom datasets demonstrate that our PCC-GAN outperforms the state-of-the-art reconstruction methods.
  arXiv  Detail & Related papers  (2024-02-01T06:47:56Z)
• Score-Based Generative Models for PET Image Reconstruction [38.72868748574543]
  We propose several PET-specific adaptations of score-based generative models. The proposed framework is developed for both 2D and 3D PET. In addition, we provide an extension to guided reconstruction using magnetic resonance images.
  arXiv  Detail & Related papers  (2023-08-27T19:43:43Z)
• Contrastive Diffusion Model with Auxiliary Guidance for Coarse-to-Fine PET Reconstruction [62.29541106695824]
  This paper presents a coarse-to-fine PET reconstruction framework that consists of a coarse prediction module (CPM) and an iterative refinement module (IRM) By delegating most of the computational overhead to the CPM, the overall sampling speed of our method can be significantly improved. Two additional strategies, i.e., an auxiliary guidance strategy and a contrastive diffusion strategy, are proposed and integrated into the reconstruction process.
  arXiv  Detail & Related papers  (2023-08-20T04:10:36Z)
• TriDo-Former: A Triple-Domain Transformer for Direct PET Reconstruction from Low-Dose Sinograms [45.24575167909925]
  TriDoFormer is a transformer-based model that unites triple domains of sinogram, image, and frequency for direct reconstruction. It outperforms state-of-the-art methods qualitatively and quantitatively. GFP serves as a learnable frequency filter that adjusts the frequency components in the frequency domain, enforcing the network to restore high-frequency details.
  arXiv  Detail & Related papers  (2023-08-10T06:20:00Z)
• Fully 3D Implementation of the End-to-end Deep Image Prior-based PET Image Reconstruction Using Block Iterative Algorithm [0.0]
  Deep image prior (DIP) has attracted attention owing to its unsupervised positron emission tomography (PET) image reconstruction. We present the first attempt to implement an end-to-end DIP-based fully 3D PET image reconstruction method.
  arXiv  Detail & Related papers  (2022-12-22T16:25:58Z)
• List-Mode PET Image Reconstruction Using Deep Image Prior [3.6427817678422016]
  List-mode positron emission tomography (PET) image reconstruction is an important tool for PET scanners. Deep learning is one possible solution to enhance the quality of PET image reconstruction. In this study, we propose a novel list-mode PET image reconstruction method using an unsupervised CNN called deep image prior.
  arXiv  Detail & Related papers  (2022-04-28T10:44:33Z)
• Direct PET Image Reconstruction Incorporating Deep Image Prior and a Forward Projection Model [0.0]
  Convolutional neural networks (CNNs) have recently achieved remarkable performance in positron emission tomography (PET) image reconstruction. We propose an unsupervised direct PET image reconstruction method that incorporates a deep image prior framework. Our proposed method incorporates a forward projection model with a loss function to achieve unsupervised direct PET image reconstruction from sinograms.
  arXiv  Detail & Related papers  (2021-09-02T08:07:58Z)
• 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.