Two-Phase Multi-Dose-Level PET Image Reconstruction with Dose Level Awareness

• URL: http://arxiv.org/abs/2404.01563v2
• Date: Wed, 10 Apr 2024 13:02:59 GMT
• Title: Two-Phase Multi-Dose-Level PET Image Reconstruction with Dose Level Awareness
• Authors: Yuchen Fei, Yanmei Luo, Yan Wang, Jiaqi Cui, Yuanyuan Xu, Jiliu Zhou, Dinggang Shen,
• Abstract summary: We design a novel two-phase multi-dose-level PET reconstruction algorithm with dose level awareness. The pre-training phase is devised to explore both fine-grained discriminative features and effective semantic representation. The SPET prediction phase adopts a coarse prediction network utilizing pre-learned dose level prior to generate preliminary result.
• Score: 43.45142393436787
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: To obtain high-quality positron emission tomography (PET) while minimizing radiation exposure, a range of methods have been designed to reconstruct standard-dose PET (SPET) from corresponding low-dose PET (LPET) images. However, most current methods merely learn the mapping between single-dose-level LPET and SPET images, but omit the dose disparity of LPET images in clinical scenarios. In this paper, to reconstruct high-quality SPET images from multi-dose-level LPET images, we design a novel two-phase multi-dose-level PET reconstruction algorithm with dose level awareness, containing a pre-training phase and a SPET prediction phase. Specifically, the pre-training phase is devised to explore both fine-grained discriminative features and effective semantic representation. The SPET prediction phase adopts a coarse prediction network utilizing pre-learned dose level prior to generate preliminary result, and a refinement network to precisely preserve the details. Experiments on MICCAI 2022 Ultra-low Dose PET Imaging Challenge Dataset have demonstrated the superiority of our method.

Related papers

• S3PET: Semi-supervised Standard-dose PET Image Reconstruction via Dose-aware Token Swap [11.13611856305595]
  We propose a two-stage Semi-Supervised SPET reconstruction framework, namely S3PET, to accommodate the training of abundant unpaired and limited paired SPET and LPET images. Our S3PET involves an un-supervised pre-training stage (Stage I) to extract representations from unpaired images, and a supervised dose-aware reconstruction stage (Stage II) to achieve LPET-to-SPET reconstruction.
  arXiv  Detail & Related papers  (2024-07-30T14:56:06Z)
• HiDe-PET: Continual Learning via Hierarchical Decomposition of Parameter-Efficient Tuning [55.88910947643436]
  We propose a unified framework for continual learning (CL) with pre-trained models (PTMs) and parameter-efficient tuning (PET) We present Hierarchical Decomposition PET (HiDe-PET), an innovative approach that explicitly optimize the objective through incorporating task-specific and task-shared knowledge. Our approach demonstrates remarkably superior performance over a broad spectrum of recent strong baselines.
  arXiv  Detail & Related papers  (2024-07-07T01:50:25Z)
• 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)
• PET Synthesis via Self-supervised Adaptive Residual Estimation Generative Adversarial Network [14.381830012670969]
  Recent methods to generate high-quality PET images from low-dose counterparts have been reported to be state-of-the-art for low-to-high image recovery methods. To address these issues, we developed a self-supervised adaptive residual estimation generative adversarial network (SS-AEGAN) SS-AEGAN consistently outperformed the state-of-the-art synthesis methods with various dose reduction factors.
  arXiv  Detail & Related papers  (2023-10-24T06:43:56Z)
• 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)
• Self-Supervised Pre-Training for Deep Image Prior-Based Robust PET Image Denoising [0.5999777817331317]
  Deep image prior (DIP) has been successfully applied to positron emission tomography (PET) image restoration. We propose a self-supervised pre-training model to improve the DIP-based PET image denoising performance.
  arXiv  Detail & Related papers  (2023-02-27T06:55:00Z)
• 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)
• Does prior knowledge in the form of multiple low-dose PET images (at different dose levels) improve standard-dose PET prediction? [0.0]
  Deep learning methods have been introduced to predict standard PET images (S-PET) from the corresponding low-dose versions (L-PET) In this work, we proposed to exploit the prior knowledge in the form of multiple low-dose levels of PET images to estimate the S-PET images.
  arXiv  Detail & Related papers  (2022-02-22T15:58:32Z)

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.