By integrating three absorption gratings with a medical X-ray tube and a medical flat panel detector, a preclinical X-ray multi-contrast lung imaging prototype with a large field-of-view up to 40 × 50 cm2 was developed in this work. The prototype system provides phase-contrast and dark-field images simultaneously in addition to the conventional X-ray absorption imaging. Unlike the traditional grating-based phase-contrast imaging (GPCI) system, the scanning object is placed behind the pattern-generating grating G1, reducing the radiation dose to probably half of the original system design. For a fast scan process, three absorption gratings inducing a large field-of-view were utilized. Besides, the X-ray tube, the three gratings and the detector are all fixed on the same mechanical arm, scanning the object along the vertical direction. A numerical simulation platform was developed to help optimize the imaging parameters. Finally, the preliminary experimental results of a full-body enthanized rabbit demonstrate the capability of the multi-contrast prototype system, and its dark-field images are capable of providing additional diagnostic value for the diagnosis of lung diseases. Direct evaluation of the potential clinical unity of the multi-contrast lung imaging can be further performed on this prototype system.
X-ray imaging with grating interferometry (GI) can obtain additional phase and dark-field contrasts simultaneously with the traditional absorption contrast. Due to higher sensitivity of phase contrast and subpixel spatial resolution of dark-field contrast, this technique has been established as a promising technique for low-density materials imaging. The information retrieval algorithm of three contrasts plays the key role in applications of the technique. The existing algorithms can be divided into two major types, the cosine-model analysis (CMA) method and the small angle x-ray scattering (SAXS) method. However, CMA method is established on the approximate cosine-model assumption and SAXS method requires relatively complicated and time-consuming iteration process of deconvolution. To overcome the aforementioned limitations, we introduce the convolution neural network (CNN) technique for the first time. With collected detector data as the input and retrieved information via SAXS method as the label, we design two CNN architectures. We train every network with 2160 exposure images of 6 breast specimen and test on another 720 images of 2 breast specimen. With structural similarity (SSIM) index as the quantitative standard, the results indicate retrieved images via the much faster CNN algorithms are consistent with SAXS method (best SSIM values are 0.9852, 0.9760 and 0.9006 respectively for absorption, phase and dark-field contrasts).