Phantom evaluation of feasibility and applicability of artificial intelligence based pulmonary nodule detection in chest radiographs.

The aim of our study was to evaluate the specific performance of an artificial intelligence (AI) algorithm for lung nodule detection in chest radiography for a larger number of nodules of different sizes and densities using a standardized phantom approach. A total of 450 nodules with varying density (d1 to d3) and size (3, 5, 8, 10 and 12 mm) were inserted in a Lungman phantom at various locations. Radiographic images with varying projections were acquired and processed using the AI algorithm for nodule detection.

Assessment of Image Quality in Chest CT With Precision Matrix and Increased Framing Rate Using Single Source CT: A Phantom Study.

Background/aim: The aim was to evaluate the effect of a combined precision matrix and high sampling rate on the delineation of anatomical structures and objective image quality in single source CT in a qualitative approach.

Materials And Methods: An anthropomorphic thoracic phantom was used to evaluate the objective image quality parameters, including image noise, noise power spectrum, image stepness and Q for different CT scanners including high/standard matrix and framing frequency setups. Scan parameters were standardized over all scanners.