Low-contrast Dose Liver CT Using Lean Body Weight, Low Monoenergetic Images and Deep Learning-based Reconstruction for Hepatocellular Carcinoma
Low-contrast Dose Liver CT Using Lean Body Weight, Low Monoenergetic Images and Deep Learning-based Reconstruction for Hepatocellular Carcinoma
This study aims to assess whether the acceptable image quality is achievable using low monoenergetic imaging of dual-energy CT with deep learning-based denoising, and low contrast media dose calculated based on lean body weight for the detection of hepatocellular carcinoma.
The use of iodinated contrast media in CT is associated with an immediate hypersensitivity reaction in a dose-dependent manner. Therefore, it is important to reduce the contrast dose for CT exams in patients who are required repeated CT examinations, including patients with hepatocellular carcinoma (HCC). Low monoenergetic images of dual-energy CT can provide higher iodine contrast than conventional images, thus enabling reduction of contrast media. The high image noise in low monoenergetic images may be improved by using model-based IR techniques and deep learning-based denoising (DLD) algorithms. Besides, lean body weight (LBW)-based contrast dose determination can be another option to reduce contrast media dose compared with total body weight-based dose determination since the volumes of blood and liver are not strictly proportional to total body weight. Therefore, we surmised that 50 keV images reconstructed with DLD algorithms with reducing iodine load by 30% based on LBW could produce the comparable image quality and lesion conspicuity compared with standard iodine-dose 120kVp images.
In this single-center prospective, randomized clinical trial, we aimed to investigate the effectiveness of low-contrast dose CT using 50 keV and DLD technique compared with the standard contrast-dose protocol using model-based IR in patients at high risk of HCC.
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