深度学习重建DECT 55 keV成像用于胃肠道肿瘤

AIM: To qualitatively and quantitatively compare dual-energy computed tomography (DECT)-derived 55 keV virtual monochromatic images (VMIs) using deep learning image reconstruction (DLIR) with 70 keV VMIs using adaptive statistical iterative reconstruction (ASiR-V) in the Gastrointestinal (GI) system tumors and to investigate whether DLIR can influence the measured iodine parameters.

MATERIALS AND METHODS: This prospective, observational study included 47 patients with GI system tumors who underwent DECT. Qualitative and quantitative assessment was performed on 70 keV VMIs using ASiR-V40%, and 55 keV VMIs using DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H). Quantitative evaluation included calculation of the contrast-to-noise ratio (CNR) and the signal-to-noise ratio (SNR). Overall image quality, lesion conspicuity, and diagnostic confidence were evaluated qualitatively by 2 radiologists. Iodine parameters of tumor lesions were compared among four groups of algorithms.

RESULTS: There were no significant differences in the image noise between DLIR-M and ASiR-V40%, whereas image noise was significantly lower in DLIR-H than ASiR-V40% and DLIR-M (both phases, P < 0.001). DLIR-M and DLIR-H had significantly higher CNR and SNR compared with ASiR-V40% on both phases (all P ≤ 0.001). When compared to ASiR-V40%, DLIR-M and DLIR-H had similar or improved qualitative assessment metrics. DLIR-L yielded the highest image noise and the lowest overall image quality score among these algorithms. Lastly, the measured iodine parameters were equivalent among different algorithms (all P > 0.1).

CONCLUSION: Abdominal DECT at 55 keV with DLIR (-M and -H) provided qualitative and quantitative performance for GI tumor evaluation comparable to or exceeding that of ASiR-V 40% at 70 keV.