Development and performance evaluation of a deep learning lung nodule detection system.

BACKGROUND: Lung cancer is the leading cause of cancer-related deaths throughout the world. Chest computed tomography (CT) is now widely used in the screening and diagnosis of lung cancer due to its effectiveness. Radiologists must identify each small nodule shadow from 3D volume images, which is very burdensome and often results in missed nodules. To address these challenges, we developed a computer-aided detection (CAD) system that automatically detects lung nodules in CT images. METHODS: A total of 1997 chest CT scans were collected for algorithm development. The algorithm was designed using deep learning technology. In addition to evaluating detection performance on various public datasets, its robustness to changes in radiation dose was assessed by a phantom study. To investigate the clinical usefulness of the CAD system, a reader study was conducted with 10 doctors, including inexperienced and expert readers. This study investigated whether the use of the CAD as a second reader could prevent nodular lesions in lungs that require follow-up examinations from being overlooked. Analysis was performed using the Jackknife Free-Response Receiver-Operating Characteristic (JAFROC). RESULTS: The CAD system achieved sensitivity of 0.98/0.96 at 3.1/7.25 false positives per case on two public datasets. Sensitivity did not change within the range of practical doses for a study using a phantom. A second reader study showed that the use of this system significantly improved the detection ability of nodules that could be picked up clinically (p = 0.026). CONCLUSIONS: We developed a deep learning-based CAD system that is robust to imaging conditions. Using this system as a second reader increased detection performance.

Improved image quality of temporal bone CT with an ultrahigh-resolution CT scanner: clinical pilot studies.

PURPOSE: Ultrahigh-resolution CT (UHRCT) with slice collimation of 0.25 mm × 160 and matrix size of 1024 × 1024 has become clinically available. We compared the image quality of temporal bone CT (TBCT) between UHRCT and conventional multidetector CT (MDCT). MATERIALS AND METHODS: We retrospectively enrolled 20 patients who underwent TBCT by MDCT (matrix size, 512 × 512) and subsequently by UHRCT (matrix size, 1024 × 1024). Two independent reviewers subjectively graded delineation of normal stapes, oval window, facial nerve canal, incudostapedial joint, and tympanic tegmen. We also quantified image noise in the cerebellar hemisphere. Between MDCT and UHRCT, we compared mean subjective grades using the Wilcoxon signed-rank test and the image noise using paired t test. RESULTS: Grades were significantly higher with UHRCT than with MDCT for all the anatomies (P < 0.001), whereas noise was significantly higher with UHRCT than with MDCT (P = 0.002). CONCLUSION: For TBCT, UHRCT shows better delineation of the fine anatomical structures compared with MDCT.

Clinical applications of digital angiography with the harmonization function in body interventional radiology.

Digital subtraction angiography (DSA) is frequently applied in interventional radiology (IR). When DSA is not useful due to misregistration, digital angiography (DA) as an alternative option is used. In DA, the harmonization function (HF) works in real time by harmonizing the distribution of gray steps or reducing the dynamic range; thus, it can compress image gradations, decrease image contrast, and suppress halation artifacts. DA with HF as a good alternative to DSA is clinically advantageous in body IR for generating DSA-like images and simultaneously reducing various motion artifacts and misregistrations caused by patient body motion, poor breath-holding, bowel and ureter peristalsis, and cardiac pulsation as well as halation artifacts often stemming from the lung field. Free-breath DA with HF can improve body IR workflow and decrease the procedure time by reducing the risk of catheter dislocation and using background structures as anatomical landmarks, demonstrating reduced radiation exposure relative to DSA. Thus, HF should be more widely and effectively utilized for appropriate purposes in body IR. This article illustrates the basic facts and principles of HF in DA, and demonstrates clinical advantages and limitations of this function in body IR.