ArticleIEEE transactions on radiation and plasma medical sciences2025
Quantitative evaluation of spatially-variant deformations recovered by deep learning on clinical-like breast lesions.
Article in IEEE transactions on radiation and plasma medical sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The dedicated dual-panel breast PET scanner (B-PET) can potentially provide improved imaging of breast lesions with higher spatial resolution and sensitivity and lower scanner costs. On the other hand, the dual-panel systems suffer from spatially-variant deformations, due to the limited angular sampling and parallax errors, that need to be mitigated in order to improve visualization and quantification of the breast lesions. Our previous studies demonstrated that the deep-learning approach can significantly reduce spatially-variant deformations in the context of B-PET. However, these studies used only simplified geometric objects with a uniform background for training and testing, which does not fully represent the clinical complexity in breast PET imaging. In this work, we developed a methodology for generation of synthetic clinical-like breast images with complex lesion shapes, including spiculated lesions and following tracer-dependent activity characteristics for [
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