AI Article Synopsis
- A study was conducted to assess the effectiveness of deep learning techniques in reconstructing virtual monoenergetic images (VMIs) from dual-energy CT scans of the liver, focusing on hypervascular lesions.
- The research included 109 patients and compared the image quality of VMIs reconstructed using both adaptive statistical iterative reconstruction (ASiR-V) and a deep learning method (ClariCT.AI) at different energy levels (40 keV and 70 keV).
- Results showed that the deep learning reconstructed images at 40 keV had significantly improved image quality, including lower noise, better sharpness, and higher diagnostic acceptability compared to traditional methods, making it more effective for evaluating liver lesions.
Article Abstract
Objective: To investigate clinical applicability of deep learning(DL)-based reconstruction of virtual monoenergetic images(VMIs) of arterial phase liver CT obtained by rapid kVp-switching dual-energy CT for evaluation of hypervascular liver lesions.
Materials And Methods: We retrospectively included 109 patients who had available late arterial phase liver CT images of the liver obtained with a rapid switching kVp DECT scanner for suspicious intra-abdominal malignancies. Two VMIs of 70 keV and 40 keV were reconstructed using adaptive statistical iterative reconstruction (ASiR-V) for arterial phase scans. VMIs at 40 keV were additionally reconstructed with a vendor-agnostic DL-based reconstruction technique (ClariCT.AI, ClariPi, DL 40 keV). Qualitative, quantitative image quality and subjective diagnostic acceptability were compared according to reconstruction techniques.
Results: In qualitative analysis, DL 40 keV images showed less image noise (4.55 vs 3.11 vs 3.95, p < 0.001), better image sharpness (4.75 vs 4.16 vs 4.3, p < 0.001), better image contrast (4.98 vs 4.72 vs 4.19, p < 0.017), better lesion conspicuity (4.61 vs 4.23 vs 3.4, p < 0.001) and diagnostic acceptability (4.59 vs 3.88 vs 4.09, p < 0.001) compared with ASiR-V 40 keV or 70 keV image sets. In quantitative analysis, DL 40 keV significantly reduced image noise relative to ASiR-V 40 keV images (49.9%, p < 0.001) and ASiR-V 70 keV images (85.2%, p = 0.012). DL 40 keV images showed significantly higher CNR and SNR than ASiR-V 40 keV image and 70 keV images (p < 0.001).
Conclusion: DL-based reconstruction of 40 keV images using vendor-agnostic software showed greater noise reduction, better lesion conspicuity, image contrast, image sharpness, and higher overall image diagnostic acceptability than ASiR for 40 keV or 70 keV images in patients with hypervascular liver lesions.
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| http://dx.doi.org/10.1016/j.ejrad.2022.110390 | DOI Listing |
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