High resolution propagation-based lung imaging at clinically relevant X-ray dose levels

Absorption-based clinical computed tomography (CT) is the current imaging method of choice in the diagnosis of lung diseases. Many pulmonary diseases are affecting microscopic structures of the lung, such as terminal bronchi, alveolar spaces, sublobular blood vessels or the pulmonary interstitial tissue. As spatial resolution in CT is limited by the clinically acceptable applied X-ray dose, a comprehensive diagnosis of conditions such as interstitial lung disease, idiopathic pulmonary fibrosis or the characterization of small pulmonary nodules is limited and may require additional validation by invasive lung biopsies. Propagation-based imaging (PBI) is a phase sensitive X-ray imaging technique capable of reaching high spatial resolutions at relatively low applied radiation dose levels. In this publication, we present technical refinements of PBI for the characterization of different artificial lung pathologies, mimicking clinically relevant patterns in ventilated fresh porcine lungs in a human-scale chest phantom. The combination of a very large propagation distance of 10.7 m and a photon counting detector with [Formula: see text] pixel size enabled high resolution PBI CT with significantly improved dose efficiency, measured by thermoluminescence detectors. Image quality was directly compared with state-of-the-art clinical CT. PBI with increased propagation distance was found to provide improved image quality at the same or even lower X-ray dose levels than clinical CT. By combining PBI with iodine k-edge subtraction imaging we further demonstrate that, the high quality of the calculated iodine concentration maps might be a potential tool for the analysis of lung perfusion in great detail. Our results indicate PBI to be of great value for accurate diagnosis of lung disease in patients as it allows to depict pathological lesions non-invasively at high resolution in 3D. This will especially benefit patients at high risk of complications from invasive lung biopsies such as in the setting of suspected idiopathic pulmonary fibrosis (IPF)

Phase-preserving approach in denoising computed tomography medical images

The denoising procedure attenuates the image noise while preserving its edges and fine details. In computed tomography (CT), images are degraded by additive white Gaussian noise because of different acquisition and system errors. Due to noise existence, specialists may encounter certain difficulties to analyse or extract the useful information from noisy images. This article presents a novel implementation of the phase-preserving algorithm to denoise CT images. The phase preserving is a powerful noise reduction algorithm, but it tends to remove specific details from the processed images supposing them as noise. Therefore, a Wiener filter that uses 2D Gaussian point spread function is used along with a modified version of the latter algorithm to reduce the noise and conserve the minor medical details. The performance of the proposed approach is assessed on naturally and synthetically degraded CT images using the universal image quality indexand peak signal-to-noise ratio accuracy metrics. Results show major improvement not only in noise attenuation but also in preserving the small details