For computed tomography (CT) imaging, it is important that the imaging protocols be optimized so that the scan is performed at the lowest dose that yields diagnostic images in order to minimize patients’ exposure to ionizing radiation. To accomplish this, it is important to verify that image quality of the acquired scan is sufficient for the diagnostic task at hand. Since the image quality strongly depends on both the characteristics of the patient as well as the imager, both of which are highly variable, using simplistic parameters like noise to determine the quality threshold is challenging. In this work, we apply deep learning using convolutional neural network (CNN) to predict whether CT scans meet the minimal image quality threshold for diagnosis. The dataset consists of 74 cases of high resolution axial CT scans acquired for the diagnosis of interstitial lung disease. The quality of the images is rated by a radiologist. While the number of cases is relatively small for deep learning tasks, each case consists of more than 200 slices, comprising a total of 21,257 images. The deep learning involves fine-tuning of a pre-trained VGG19 network, which results in an accuracy of 0.76 (95% CI: 0.748 – 0.773) and an AUC of 0.78 (SE: 0.01). While the number of total images is relatively large, the result is still significantly limited by the small number of cases. Despite the limitation, this work demonstrates the potential for using deep learning to characterize the diagnostic quality of CT scans.