This paper presents analytical approach to modeling of a full planar and volumetric acquisition system with image reconstructions originated from partial illumination x-ray phase-contrast imaging at a human scale using graphics processor units. The model is based on x-ray tracing and wave optics methods to develop a numerical framework for predicting the performance of a preclinical phase-contrast imaging system of a human-scaled phantom. In this study, experimental images of simple numerical phantoms and high resolution anthropomorphic phantoms of head and thorax based on non-uniform rational b-spline shapes (NURBS) prove the correctness of the model. Presented results can be used to simulate the performance of partial illumination x-ray phase-contrast imaging system on various preclinical applications. (Wiley, New Jersey 2003). 2. T. Weitkamp, C. David, O. Bunk, J. Bruder, P. Cloetens, and F. Pfeiffer, "X-ray phase radiography and tomography of soft tissue using grating interferometry," Eur. J. Radiol. 68(3 Suppl), S13-S17 (2008). 3. S. A. Zhou and A. Brahme, "Development of phase-contrast x-ray imaging techniques and potential medical applications," Phys. Med. 24(3), 129-148 (2008). 4. C. Raven, A. Snigirev, I. Snigireva, P. Spanne, A. Souvorov, and V. Kohn, "Phase-contrast microtomography with coherent high-energy synchrotron X-rays," Appl. Phys. Lett. 69(13), 1826-1828 (1996). 5. F. Arfelli, M. Assante, V. Bonvicini, A. Bravin, G. Cantatore, E. Castelli, L. Dalla Palma, M. Di Michiel, R. ©2015 Optical Society of America References and links A. R. Webb, Introduction to Biomedical ImagingLongo, A. Olivo, S. Pani, D. Pontoni, P. Poropat, M. Prest, A. Rashevsky, G. Tromba, A. Vacchi, E. Vallazza, and F. Zanconati, "Low-dose phase contrast X-ray medical imaging," Phys. Med. Biol. 43(10), 2845-2852 (1998). 6. S. W. Wilkins, T. E. Gureyev, D. Gao, A. Pogany, and A. W. Stevenson, "Phase-contrast imaging using polychromatic hard X-rays," Nature 384(6607), 335-338 (1996). 7. D. Chapman, W. Thomlinson, R. E. Johnston, D. Washburn, E. Pisano, N. Gmür, Z. Zhong, R. Menk, F. Arfelli, and D. Sayers, "Diffraction enhanced x-ray imaging," Phys. Med. Biol. 42(11), 2015-2025 (1997). 8. A. Snigirev, I. Snigireva, V. Kohn, S. Kuznetsov, and I. Schelokov, "On the possibilities of x-ray phase contrast microimaging by coherent high-energy synchrotron radiation," Rev. Sci. Instrum. 66(12), 5486 (1995). 9. X. Wu and H. Liu, "Clarification of aspects in in-line phase-sensitive x-ray imaging," Med. Phys. 34(2), 737-743 (2007). 10. A. Momose, "Demonstration of phase-contrast X-ray computed tomography using an X-ray interferometer," Nucl. Instrum. Methods Phys.

The most accurate technique to model the X- and gamma radiation path through a numerically defined object is the Monte Carlo simulation which follows single photons according to their interaction probabilities. A simplified and much faster approach, which just integrates total interaction probabilities along selected paths, is known as ray tracing. Both techniques are used in medical imaging for simulating real imaging systems and as projectors required in iterative tomographic reconstruction algorithms. These approaches are ready for massive parallel implementation e.g. on Graphics Processing Units (GPU), which can greatly accelerate the computation time at a relatively low cost. In this paper we describe the application of the NVIDIA OptiX ray-tracing engine, popular in professional graphics and rendering applications, as a new powerful tool for X- and gamma ray-tracing in medical imaging. It allows the implementation of a variety of physical interactions of rays with pixel-, mesh- or nurbs-based objects, and recording any required quantities, like path integrals, interaction sites, deposited energies, and others. Using the OptiX engine we have implemented a code for rapid Monte Carlo simulations of Single Photon Emission Computed Tomography (SPECT) imaging, as well as the ray-tracing projector, which can be used in reconstruction algorithms. The engine generates efficient, scalable and optimized GPU code, ready to run on multi GPU heterogeneous systems. We have compared the results our simulations with the GATE package. With the OptiX engine the computation time of a Monte Carlo simulation can be reduced from days to minutes.

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We present an image rotation algorithm in which the values of pixels of the rotated image are computed as weighted averages of the original grid pixels, with the weights proportional to the overlapping areas of each corresponding pixel pair. The method preserves virtually perfectly the image uniformity and single pixel pulse value in the rotated image. These properties are particularly important to accurately model projections in iterative reconstruction algorithms. The matrix of pixel redistribution coefficients can be precomputed analytically and the algorithm is much faster that e.g. the widely used Gaussian rotator.