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Lung cancer is one of the leading cause of cancer death worldwide. Computed Tomography (CT) is the best imaging modality for the detection of small pulmonary nodules and for this reason its employment as a screening tool has been widely studied. However, radiation dose delivered in a chest CT examination must be considered, especially when potentially healthy people are examined in screening programs. In this context, iterative reconstruction (IR) algorithms have shown the potential to reduce image noise and radiation dose and computer aided detection (CAD) systems can be employed for supporting radiologists. Thus, the combined use of IR algorithms and CAD systems can be of practical interest. In this preliminary work we studied the potential improvements in the quality of phantom and clinical chest images reconstructed trough the Adaptive Statistical Iterative Reconstruction (ASIR, GE Healthcare, Waukesha, WI, USA) algorithm, in order to evaluate a possible employment of this algorithm in low dose chest CT imaging with CAD analysis. We analysed both clinical and phantom CT images. Noise, noise power spectrum (NPS) and modulation transfer function (MTF) were estimated for different inserts in the phantom images. Image contrast and contrast-to-noise ratio (CNR) of different nodules contained in clinical chest images were evaluated. Noise decreases non-linearly when increasing the ASIR blending level of reconstruction. ASIR modified the NPS. The MTF for ASIR-reconstructed images depended on tube load, contrast and blending level. Both image contrast and CNR increased with the ASIR blending level.

Evaluation of the Adaptive Statistical Iterative Reconstruction Algorithm in Chest CT (Computed Tomography)