Sparse-sampling computed tomography for pulmonary imaging

Abstract

Computed tomography (CT) is a valuable imaging modality for pulmonary imaging. Fast acquisition times and sharp cross-sectional images guarantee high diagnostic confidence. With the introduction of low-dose CT, it has been established as standard for lung screening of heavy smokers in many countries around the world. However, at some point the limits for dose reduction with conventional CT are reached and further reduction would suffer from poor image quality. Sparsesampling CT is one technology that would allow a further radiation dose reduction by reducing the number of acquired projection images. Recently, the feasibility of a fast pulsing X-ray tube for CT has been demonstrated, indicating that sparse sampling could become available in future generations of CT scanners. Therefore, we investigated the effect of sparse sampling by a stepwise reduction of the projection images. A lung phantom with synthetic pulmonary nodules was scanned with a clinical CT system. Sparse sampling was simulated by removing projection images prior to reconstruction. The phantom was scanned at the iso-center and at the highest possible table position (off-center). The modulation transfer function (MTF) was determined for different degrees of sparse sampling. Image quality was evaluated by comparing the reduced dose simulations against the full dose image using the structural similarity index (SSIM). MTF was stable down to using 1/4th of the projection images (4-times sparse sampling, SpS-4) with high degradation at the off-center position (full sampling (FS) 10% MTF, iso-center: 0.64; off-center: 0.47). SSIM indicates a small image quality degradation of FS images compared to sparse-sampling images at low radiation doses at the iso-center (35 mAs; FS: 0.91; SpS-4: 0.93) and stronger degradations at the off-center position (35 mAs; FS: 0.65; SpS-4: 0.84). In conclusion, sparse sampling provides stable MTF results down to 1/4th of the projection images. At low dose levels (iso-center: ≤43 mAs; off-center: ≤86 mAs), sparse sampling performs better in terms of SSIM compared to FS.