Wave optics simulation of grating-based X-ray phase-contrast imaging using 4D Mouse Whole Body (MOBY) phantom

Deep-learning-based denoising of X-ray differential phase and dark-field images

PURPOSE

Statistical photon noise has always been a common problem in X-ray multi-contrast imaging and significantly influenced the quality of retrieved differential phase and dark-field images. We intend to develop a deep learning-based denoising algorithm to reduce the noise of retrieved X-ray differential phase and dark-field images.

METHODS

A novel deep learning based image noise suppression algorithm (named DnCNN-P) is presented. We proposed two different denoising modes: Retrieval-Denoising mode (R-D mode) and Denoising-Retrieval mode (D-R mode). While the R-D mode denoises the retrieved images, the D-R mode denoises the raw phase stepping data. The two denoising modes are evaluated under different photon counts and visibilities.

RESULTS

Experimental results show that with the algorithm DnCNN-P used, the D-R mode always exhibits a better noise reduction under diverse experimental conditions, even in the case of a low photon count and/or a low visibility. With a detected photon count of 1800 and a visibility of 0.3, compared to the differential phase images without denoising, the standard deviation is reduced by 89.1% and 16.4% in the D-R and R-D modes. Compared to the dark-field images without denoising, the standard deviation is reduced by 83.7% and 12.6% in the D-R and R-D modes, respectively.

CONCLUSIONS

The novel supervised DnCNN-P algorithm can significantly reduce the noise in retrieved X-ray differential phase and dark-field images. We believe this novel algorithm can be a promising approach to improve the quality of X-ray differential phase and dark-field images, and therefore dose efficiency in future biomedical applications.

Tomography of dark-field scatter including single-exposure Moiré fringe analysis with X-ray biprism interferometry-A simulation study

PURPOSE

In this work, we present tomographic simulations of a new hardware concept for X-ray phase-contrast interferometry wherein the phase gratings are replaced with an array of Fresnel biprisms, and Moiré fringe analysis is used instead of "phase stepping" popular with grating-based setups.

METHODS

Projections of a phantom consisting of four layers of parallel carbon microfibers is simulated using wave optics representation of X-ray electromagnetic waves. Simulated projections of a phantom with preferential scatter perpendicular to the direction of the fibers are performed to analyze the extraction of small-angle scatter from dark-field projections for the following: (1) biprism interferometry using Moiré fringe analysis; (2) grating interferometry using phase stepping with eight grating steps; and (3) grating interferometry using Moiré fringe analysis. Dark-field projections are modeled as projections of voxel intensities represented by a fixed finite vector basis set of scattering directions. An iterative MLEM algorithm is used to reconstruct, from simulated projection data, the coefficients of a fixed set of seven basis vectors at each voxel representing the small-angle scatter distribution.

RESULTS

Results of reconstructed vector coefficients are shown comparing the three simulated imaging configurations. The single-exposure Moiré fringe analysis shows not only an increase in noise because of one-eighth the number of projection samples but also is obtained with less dose and faster acquisition times. Furthermore, replacing grating interferometry with biprism interferometry provides better contrast-to-noise.

CONCLUSION

The simulations demonstrate the feasibility of the reconstruction of dark-field data acquired with a biprism interferometry system. With the potential of higher fringe visibility, biprism interferometry with Moiré fringe analysis might provide equal or better image quality to that of phase stepping methods with less imaging time and lower dose.