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<p>Reconstruction of dynamic single photon emission computed tomography (SPECT) images from a few angular projections is a challenging, ill-posed inverse problem. In addition, due to the high cost of image acquisition, previous works have mainly concentrated on restoring the dynamic images within a limited temporal sampling frequency, which raises the issue of low temporal resolution. In this paper, we propose a novel framework, Deep Spatial Prior with Continuous Temporal Representation (DSP-CTR), to reconstruct dynamic SPECT images with high resolution under scarce projection views and limited temporal sampling. Our method models SPECT image sequences by integrating a deep image prior for reconstructing the spatial structures and an implicit neural representation for learning time activity curves (TACs). Numerical experiments justify that the proposed method recovers high-quality image sequences from very few projection angles and time frames compared to the state-of-the-art methods.</p>

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Robust Dynamic SPECT Reconstruction with Scarce Angular and Limited Temporal Sampling

  • 期刊论文

  • 发表日期：

    2026年5月29日

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  • Yicheng Wu,
  • Roy Y. He,
  • Qiaoqiao Ding,
  • Xiaoqun Zhang,
  • Chao Wang

摘要

Reconstruction of dynamic single photon emission computed tomography (SPECT) images from a few angular projections is a challenging, ill-posed inverse problem. In addition, due to the high cost of image acquisition, previous works have mainly concentrated on restoring the dynamic images within a limited temporal sampling frequency, which raises the issue of low temporal resolution. In this paper, we propose a novel framework, Deep Spatial Prior with Continuous Temporal Representation (DSP-CTR), to reconstruct dynamic SPECT images with high resolution under scarce projection views and limited temporal sampling. Our method models SPECT image sequences by integrating a deep image prior for reconstructing the spatial structures and an implicit neural representation for learning time activity curves (TACs). Numerical experiments justify that the proposed method recovers high-quality image sequences from very few projection angles and time frames compared to the state-of-the-art methods.