From Event: SPIE Optical Engineering + Applications, 2017
Bone properties at all length scales have a major impact on the fracture risk in disease such as osteoporosis. However, quantitative 3D data on bone tissue at the cellular scale are still rare. Here we propose to use magnified X-ray phase nano-CT to quantify bone ultra-structure in human bone, on the new setup developed on the beamline ID16A at the ESRF, Grenoble. Obtaining 3D images requires the application of phase retrieval prior to tomographic reconstruction. Phase retrieval is an ill-posed problem for which various approaches have been developed. Since image quality has a strong impact on the further quantification of bone tissue, our aim here is to evaluate different phase retrieval methods for imaging bone samples at the cellular scale. Samples from femurs of female donors were scanned using magnified phase nano-CT at voxel sizes of 120 and 30 nm with an energy of 33 keV. Four CT scans at varying sample-to-detector distances were acquired for each sample. We evaluated three phase retrieval methods adapted to these conditions: Paganin’s method at single distance, Paganin’s method extended to multiple distances, and the contrast transfer function (CTF) approach for pure phase objects. These methods were used as initialization to an iterative refinement step. Our results based on visual and quantitative assessment show that the use of several distances (as opposed to single one) clearly improves image quality and the two multi-distance phase retrieval methods give similar results. First results on the segmentation of osteocyte lacunae and canaliculi from such images are presented.
Boliang Yu, Max Langer, Alexandra Pacureanu, Remy Gauthier, Helene Follet, David Mitton, Cecile Olivier, Peter Cloetens, and Francoise Peyrin, "Assessment of imaging quality in magnified phase CT of human bone tissue at the nanoscale," Proc. SPIE 10391, Developments in X-Ray Tomography XI, 103910L (Presented at SPIE Optical Engineering + Applications: August 09, 2017; Published: 3 October 2017); https://doi.org/10.1117/12.2272561.
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