Automatic scan range for dose-reduced multiphase CT imaging of the liver utilizing CNNs and Gaussian models

•We propose an automatic method based on liver detection and liver movement estimation for delineating scan range in multiphase CT imaging of liver cancer patients within a second with high accuracy compared to the best of state-of-the-art methods.•The study was carried out on 657 multiphase 3D CT images from several hospitals in various countries with two different liver cancer intervention applications.•The proposed method can significantly reduce the effect radiation dose inducting to the patients with a mount of 14.5% (2.56 mSv) on average.•Three radiologists from two hospitals assessed both the range-reduced CT images and the original images and independently concluded that no difference is found in their clinical decision-makings when using either of those images. [Display omitted] Multiphase CT scanning of the liver is performed for several clinical applications; however, radiation exposure from CT scanning poses a nontrivial cancer risk to the patients. The radiation dose may be reduced by determining the scan range of the subsequent scans by the location of the target of interest in the first scan phase. The purpose of this study is to present and assess an automatic method for determining the scan range for multiphase CT scans. Our strategy is to first apply a CNN-based method for detecting the liver in 2D slices, and to use a liver range search algorithm for detecting the liver range in the scout volume. The target liver scan range for subsequent scans can be obtained by adding safety margins achieved from Gaussian liver motion models to the scan range determined from the scout. Experiments were performed on 657 multiphase CT volumes obtained from multiple hospitals. The experiment shows that the proposed liver detection method can detect the liver in 223 out of a total of 224 3D volumes on average within one second, with mean intersection of union, wall distance and centroid distance of 85.5%, 5.7 mm and 9.7 mm, respectively. In addition, the performance of the proposed liver detection method is comparable to the best of the state-of-the-art 3D liver detectors in the liver detection accuracy while it requires less processing time. Furthermore, we apply the liver scan range generation method on the liver CT images acquired from radiofrequency ablation and Y-90 transarterial radioembolization (selective internal radiation therapy) interventions of 46 patients from two hospitals. The result shows that the automatic scan range generation can signif