ArticleAmerican journal of nuclear medicine and molecular imaging2025
Investigation of a deep learning-based reconstruction approach utilizing dual-view projection for myocardial perfusion SPECT imaging.
Article in American journal of nuclear medicine and molecular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Scatter correction for self-collimating SPECT using a 3D U-Net framework.American journal of nuclear medicine and molecular imaging · 2026Article
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7 authors.
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Abstract
Single-photon emission computed tomography (SPECT) is widely used in myocardial perfusion imaging (MPI) in clinic. However, conventional dual-head SPECT scanners require lengthy scanning times and gantry rotation, which limits the application of SPECT MPI. In this work, we proposed a deep learning-based approach to reconstruct dual-view projections, aiming to reduce acquisition time and enable non-rotational imaging for MPI based on conventional dual-head SPECT scanners. U-Net was adopted for the dual-view projection reconstruction. Initially, 2D U-Nets were used to evaluate various data organization schemes for dual-view projection as input, including paved projection, interleaved projection, and stacked projection, with and without an attenuation map. Subsequently, we developed 3D U-Nets using the optimal data organization scheme as input to further enhance reconstruction performance. The dataset consisted of a total of 116 SPECT/CT scans with
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