Information-based analysis of X-ray in-line phase tomography with application to the detection of iron oxide nanoparticles in the brain

The study analyzes noise in X-ray in-line phase tomography in a biomedical context. The impact of noise on detection of iron oxide nanoparticles in mouse brain is assessed. The part of the noise due to the imaging system and the part due to biology are quantitatively expressed in a Neyman Pearson detection strategy with two models of noise. This represents a practical extension of previous work on noise in phase-contrast X-ray imaging which focused on the theoretical expression of the signal-tonoise ratio in mono-dimensional phantoms, taking account of the statistical noise of the imaging system only. We also report the impact of the phase retrieval step on detection performance. Taken together, this constitutes a general methodology of practical interest for quantitative extraction of information from X-ray in-line phase tomography, and is also relevant to assessment of contrast agents with a blob-like signature in high resolution imaging.