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Updated: Feb 16, 2026

Imaging Intermediate Filaments and Microtubules with 2-dimensional Direct Stochastic Optical Reconstruction Microscopy
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List-mode TOF-PET 3D image reconstruction using stochastic primal-dual network.

Kun Tian1, Rui Hu1, Yiming Wan1

  • 1State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, People's Republic of China.

Medical Physics
|February 15, 2026
PubMed
Summary
This summary is machine-generated.

A new deep learning method, LM-SPD-Net, reconstructs positron emission tomography (PET) images directly from list-mode data. This approach enhances image quality and overcomes computational challenges in time-of-flight (TOF) PET reconstruction.

Keywords:
image reconstructionlist‐mode datapositron emission tomography (PET)

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Area of Science:

  • Nuclear Medicine Imaging
  • Medical Image Reconstruction
  • Deep Learning Applications

Background:

  • Positron Emission Tomography (PET) offers high sensitivity for visualizing biological activity.
  • Conventional PET imaging faces limitations in spatial resolution and signal-to-noise ratio (SNR).
  • Time-of-flight (TOF) data integration improves PET image quality but increases computational demands.

Purpose of the Study:

  • To introduce a novel deep learning framework for direct list-mode PET image reconstruction.
  • To address computational and memory challenges associated with TOF-PET data.
  • To enhance the quality of reconstructed PET images.

Main Methods:

  • LM-SPD-Net, a list-mode TOF-PET reconstruction framework utilizing a stochastic primal-dual network architecture.
  • A primal module (CNNs) for image domain processing and a dual module (FCNNs) for data domain features.
  • Integration of a physics-informed projection model and subset partitioning for efficient 3D reconstruction.

Main Results:

  • LM-SPD-Net demonstrated superior performance compared to LM-OSEM, LM-SPDHG, and Fast-PET.
  • Achieved 5%-20% improvements in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics.
  • Visibly enhanced image quality in both simulated and semi-real clinical data.

Conclusions:

  • The proposed LM-SPD-Net method effectively reconstructs overall subject structures with high fidelity.
  • Maintained excellent performance in clinically relevant regions like tumors and the thalamus.
  • Showcased robust performance, particularly under low-count conditions.