Volumetric scout CT images reconstructed from conventional two-view radiograph localizers using deep learning (Conference Presentation)

Abstract

In this work, a deep neural network architecture was developed and trained to reconstruct volumetric CT images from two-view radiograph scout localizers. In clinical CT exams, each patient will receive a two-view scout scan to generate both lateral (LAT) and anterior-posterior (AP) radiographs to help CT technologist to prescribe scanning parameters. After that, patients go through CT scans to generate CT images for clinical diagnosis. Therefore, for each patient, we will have two-view radiographs as input data set and the corresponding CT images as output to form our training data set. In this work, more than 1.1 million diagnostic CT images and their corresponding projection data from 4214 clinically indicated CT studies were retrospectively collected. The dataset was used to train a deep neural network which inputs the AP and LAT projections and outputs a volumetric CT localizer. Once the model was trained, 3D localizers were reconstructed for a validation cohort and results were analyzed and compared with the standard MDCT images. In particular, we were interested in the use of 3D localizers for the purpose of optimizing tube current modulation schemes, therefore we compared water equivalent diameters (Dw), radiologic paths and radiation dose distributions. The quantitative evaluation yields the following results: The mean±SD percent difference in Dw was 0.6±4.7% in 3D localizers compared to the Dw measured from the conventional CT reconstructions. 3D localizers showed excellent agreement in radiologic path measurements. Gamma analysis of radiation dose distributions indicated a 97.3%, 97.3% and 98.2% of voxels with passing gamma index for anatomical regions in the chest, abdomen and pelvis respectively. These results demonstrate the great success of the developed deep learning reconstruction method to generate volumetric scout CT image volumes.