Abstract
Chest computed tomography (CT) is used to screen for lung cancer and evaluate pulmonary and extra-pulmonary abnormalities such as emphysema and coronary artery calcification, particularly in smokers. In real-world practice, lung abnormalities are visually assessed using high-contrast thin-slice images which are generated from raw scan data using sharp reconstruction kernels with the sacrifice of increased image noise. In contrast, accurate CT quantification requires low-contrast thin-slice images with low noise, which are generated using soft reconstruction kernels. However, only sharp-kernel thin-slice images are archived in many medical facilities due to limited data storage space. This study aimed to establish deep neural network (DNN) models to convert sharp-kernel images to soft-kernel-like images with a final goal to reuse historical chest CT images for robust quantitative measurements, particularly in completed previous longitudinal studies. By using pairs of sharp-kernel (input) and soft-kernel (ground-truth) images from 30 patients with chronic obstructive pulmonary disease (COPD), DNN models were trained. Then, the accuracy of kernel conversion based on the established DNN models was evaluated using CT from independent 30 smokers with and without COPD. Consequently, differences in CT values between new images converted from sharp-kernel images using the established DNN models and ground-truth soft-kernel images were comparable with the inter-scans variability derived from repeated phantom scans (6 times), showing that the conversion error was the same level as the measurement error of the CT device. Moreover, the Dice coefficients to quantify the similarity between low attenuation voxels on given images and the ground-truth soft-kernel images were significantly higher on the DNN-converted images than the Gaussian-filtered, median-filtered, and sharp-kernel images (p < 0.001). There were good agreements in quantitative measurements of emphysema, intramuscular adipose tissue, and coronary artery calcification between the converted and the ground-truth soft-kernel images. These findings demonstrate the validity of the new DNN model for kernel conversion and the clinical applicability of soft-kernel-like images converted from archived sharp-kernel images in previous clinical studies. The presented method to evaluate the validity of the established DNN model using repeated scans of phantom could be applied to various deep learning-based image conversions for robust quantitative evaluation.
Highlights
Long-term exposure to cigarette smoke causes damage to the parenchyma and airways in the lungs and lung cancer
The full-deep convolutional neural network (DNN) model was trained using paired sharpkernel images and soft-kernel images to convert sharp-kernel images to new images whose values were as close to the ground-truth soft-kernel images as possible for the whole range of computed tomography (CT) values
The clinical validity of the kernel conversion was confirmed by showing that quantitative measurements of the well-established CT indices, LAV%, intramuscular adipose tissue (IMAT)%, and CAC volume on the converted and ground-truth images were matched well
Summary
Long-term exposure to cigarette smoke causes damage to the parenchyma and airways in the lungs and lung cancer. Chest computed tomography (CT) is widely used to screen for lung cancer and simultaneously provides information of emphysema, airway diseases, and even extra-pulmonary abnormalities regarding coronary artery disease, muscle wasting, and bone mineral density loss (Ohara et al, 2008; Mcdonald et al, 2014; Labaki et al, 2017). In real-world clinical practice, lung abnormalities are visually assessed by experts on high-contrast thin-slice images that are generated from raw scan data using sharp reconstruction kernels with the sacrifice of increased image noise. Lowcontrast thin-slice images with low noise, which are generated using soft reconstruction kernels, are more appropriate for quantitative measurements. Since the acquisition of CT data imposes radiation exposure and prospective collection of longitudinal CT data requires a long time and cost, computational methods to convert sharp-kernel high-contrast images to soft-kernel-like low-contrast images should be established to reuse archived sharp-kernel CT data and to perform robust quantitative measurements in completed previous studies
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