Improved image quality in CT pulmonary angiography using deep learning-based image reconstruction

We investigated the effect of deep learning-based image reconstruction (DLIR) compared to iterative reconstruction on image quality in CT pulmonary angiography (CTPA) for suspected pulmonary embolism (PE). For 220 patients with suspected PE, CTPA studies were reconstructed using filtered back projec...

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Bibliographic Details
Published in:Scientific reports 2024-01, Vol.14 (1), p.2494-11, Article 2494
Main Authors: Klemenz, Ann-Christin, Albrecht, Lasse, Manzke, Mathias, Dalmer, Antonia, Böttcher, Benjamin, Surov, Alexey, Weber, Marc-André, Meinel, Felix G.
Format: Article
Language:English
Subjects:
692/1807/1809
692/308
692/699/1785
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Angiography
Computed tomography
Deep learning
Embolism
Humanities and Social Sciences
Image processing
Medical imaging
multidisciplinary
Science
Science (multidisciplinary)
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Summary:We investigated the effect of deep learning-based image reconstruction (DLIR) compared to iterative reconstruction on image quality in CT pulmonary angiography (CTPA) for suspected pulmonary embolism (PE). For 220 patients with suspected PE, CTPA studies were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction (ASiR-V 30%, 60% and 90%) and DLIR (low, medium and high strength). Contrast-to-noise ratio (CNR) served as the primary parameter of objective image quality. Subgroup analyses were performed for normal weight, overweight and obese individuals. For patients with confirmed PE (n = 40), we further measured PE-specific CNR. Subjective image quality was assessed independently by two experienced radiologists. CNR was lowest for FBP and enhanced with increasing levels of ASiR-V and, even more with increasing strength of DLIR. High strength DLIR resulted in an additional improvement in CNR by 29–67% compared to ASiR-V 90% (p 
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-52517-2