Pneumonia Detection in Chest X-Ray Dose-Equivalent CT: Impact of Dose Reduction on Detectability by Artificial Intelligence.

RATIONALE AND OBJECTIVES: There has been a significant increase of immunocompromised patients in recent years due to new treatment modalities for previously fatal diseases. This comes at the cost of an elevated risk for infectious diseases, most notably pathogens affecting the respiratory tract. Because early diagnosis and treatment of pneumonia can help reducing morbidity and mortality, we assessed the performance of a deep neural network in the detection of pulmonary infection in chest X-ray dose-equivalent computed tomography (CT). MATERIALS AND METHODS: The 100 patients included in this retrospective study were referred to our department for suspicion of pulmonary infection and/or follow-up of known pulmonary nodules. Every patient was scanned with a standard dose (1.43 ± 0.54 mSv) and a 20 times dose-reduced (0.07 ± 0.03 mSv) CT protocol. We trained a deep neural network to perform binary classification (pulmonary consolidation present or not) and assessed diagnostic performance on both standard dose and reduced dose CT images. RESULTS: The areas under the curve of the deep learning algorithm for the standard dose CT was 0.923 (confidence interval [CI] 95%: 0.905-0.941) and significantly higher than the areas under the curve (0.881, CI 95%: 0.859-0.903) of the reduced dose CT (p = 0.001). Sensitivity and specificity of the standard dose CT was 82.9% and 93.8%, and of the reduced dose CT 71.0% and 93.3%. CONCLUSION: Pneumonia detection with X-ray dose-equivalent CT using artificial intelligence is feasible and may contribute to a more robust and reproducible diagnostic performance. Dose reduction lowered the performance of the deep neural network, which calls for optimization and adaption of CT protocols when using AI algorithms at reduced doses.

Flexible needle and patient tracking using fractional scanning in interventional CT procedures.

PURPOSE: We present a new method for flexible needle and patient localization in interventional CT procedures based on fractional CT scanning. Our method accurately localizes the trajectory of a flexible needle to which a spherical marker is attached at a known distance from the tip with respect to a baseline scan of patient in the CT scanner coordinate frame. METHODS: The localization is achieved with a significantly lower dose compared to a full scan using sparse view angle sampling and without reconstructing the CT image of the repeat scan. Our method starts by performing rigid registration between the patient and the baseline scan in 3D Radon space computed from the sparse projection data. It then computes 2D projection difference images in which the flexible needle and the spherical marker appear as prominent features. Their 3D spatial locations are then automatically extracted from the projection images to accurately trace the flexible needle trajectory. To validate our method, we conducted registration and needle trajectory localization experiments in seven abdomen phantom scans using a short and a long flexible needle. RESULTS: Our experimental results yield a mean needle trajectory localization error of 0.7 ± 0.2 mm and a mean tip localization error of 2.4 ± 0.9 mm with a [Formula: see text]7.5 radiation dose reduction with respect to a full CT scan. CONCLUSIONS: The significant radiation dose reduction enables more frequent needle trajectory localization during the needle insertion for a similar total dose, or a reduced total dose for the same localization frequency.