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Background: Data-driven development of medical biomarkers of bone requires a large amount of image data but physical measurements are generally too restricted in size and quality to perform a robust training., Purpose: This study aims to provide a reliable in silico method for the generation of realistic bone microstructure with defined microarchitectural properties. Synthetic bone samples may improve training of neural networks and serve for the development of new diagnostic parameters of bone architecture and mineralization., Methods: One hundred-fifty cadaveric lumbar vertebrae from 48 different male human spines were scanned with a high resolution peripheral quantitative CT. After prepocessing the scans, we extracted 10,795 purely spongeous bone patches, each with a side length of 32 voxels (5 mm) and isotropic voxel size of 164 μm. We trained a volumetric generative adversarial network (GAN) in a progressive manner to create synthetic microstructural bone samples. We then added a style transfer technique to allow the generation of synthetic samples with defined microstructure and gestalt by simultaneously optimizing two entangled loss functions. Reliability testing was performed by comparing real and synthetic bone samples on 10 well-understood microstructural parameters., Results: The method was able to create synthetic bone samples with visual and quantitative properties that effectively matched with the real samples. The GAN contained a well-formed latent space allowing to smoothly morph bone samples by their microstructural parameters, visual appearance or both. Optimum performance has been obtained for bone samples with voxel size 32 × 32 × 32, but also samples of size 64 × 64 × 64 could be synthesized., Conclusions: Our two-step-approach combines a parameter-agnostic GAN with a parameter-specific style transfer technique. It allows to generate an unlimited anonymous database of microstructural bone samples with sufficient realism to be used for the development of new data-driven methods of bone-biomarkers. Particularly, the style transfer technique can generate datasets of bone samples with specific conditions to simulate certain bone pathologies., (© 2023 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Objectives: Photon counting computed tomography (PCCT) might offer an effective spatial resolution that is significantly improved compared with conventional state-of-the-art computed tomography (CT) and even provide a microstructural level of detail similar to high-resolution peripheral CT (HR-pQCT). The aim of this study was to evaluate the volumetric effective spatial resolution of clinically approved PCCT as an alternative to HR-pQCT for ex vivo or preclinical high-resolution imaging of bone microstructure., Materials and Methods: The experiment contained 5 human vertebrae embedded in epoxy resin, which were scanned 3 times each, and on 3 different clinical CT scanners: a PCCT (Naeotom Alpha), a dual-energy CT (Somatom Force [SF]), and a single-energy CT (Somatom Sensation 40 [S40]), all manufactured by Siemens Healthineers (Erlangen, Germany). Scans were performed with a tube voltage of 120 kVp and, to provide maximum scan performance and minimum noise deterioration, with exposures of 1500 mAs (SF), 2400 mAs (S40), and 4500 mAs (PCCT) and low slice increments of 0.1 (PCCT) and 0.3 mm (SF, S40). Images were reconstructed with sharp and very sharp bone kernels, Br68 and Br76 (PCCT), Br64 (SF), and B65s and B75h (S40). Ground truth information was obtained from an XtremeCT scanner (Scanco, Brüttisellen, Switzerland). Voxel-wise comparison was performed after registration, calibration, and resampling of the volumes to isotropic voxel size of 0.164 mm. Three-dimensional point spread- and modulation-transfer functions were calculated with Wiener's deconvolution in the anatomical trabecular structure, allowing optimum estimation of device- and kernel-specific smoothing properties as well as specimen-related diffraction effects on the measurement., Results: At high contrast (modulation transfer function [MTF] of 10%), radial effective resolutions of PCCT were 10.5 lp/cm (minimum resolvable object size 476 μm) for kernel Br68 and 16.9 lp/cm (295 μm) for kernel Br76. At low contrast (MTF 5%), radial effective spatial resolutions were 10.8 lp/cm (464 μm) for kernel Br68 and 30.5 lp/cm (164 μm) for kernel Br76. Axial effective resolutions of PCCT for both kernels were between 27.0 (185 μm) and 29.9 lp/cm (167 μm). Spatial resolutions with kernel Br76 might possibly be still higher but were technically limited by the isotropic voxel size of 164 μm. The effective volumetric resolutions of PCCT with kernel Br76 ranged between 61.9 (MTF 10%) and 222.4 (MTF 5%) elements per cubic mm. Photon counting CT improved the effective volumetric resolution by factor 5.5 (MTF 10%) and 18 (MTF 5%) compared with SF and by a factor of 8.7 (MTF 10%) and 20 (MTF 5%) compared with S40. Photon counting CT allowed obtaining similar structural information as HR-pQCT., Conclusions: The effective spatial resolution of PCCT in trabecular bone imaging was comparable with that of HR-pQCT and more than 5 times higher compared with conventional CT. For ex vivo samples and when patient radiation dose can be neglected, PCCT allows imaging bone microstructure at a preclinical level of detail., Competing Interests: Conflicts of interest and sources of funding: This study was supported by a grant from the German Academic Exchange Service (DAAD). F.S.L.T. is currently receiving grants from Salesforce AI, USA, from the Agencia Nacional de Promoción Científica y Tecnológica, Argentina (PICT 2017-1731), and from Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina (PIP 2021-2023 GI). For the remaining authors, none were declared., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Micro-structural parameters of the thoracic or lumbar spine generally carry insufficient accuracy and precision for clinical in vivo studies when assessed on quantitative computed tomography (QCT). We propose a 3D convolutional neural network with specific loss functions for QCT noise reduction to compute micro-structural parameters such as tissue mineral density (TMD) and bone volume ratio (BV/TV) with significantly higher accuracy than using no or standard noise reduction filters. The vertebra-phantom study contained high resolution peripheral and clinical CT scans with simulated in vivo CT noise and nine repetitions of three different tube currents (100, 250 and 360 mAs). Five-fold cross validation was performed on 20466 purely spongy pairs of noisy and ground-truth patches. Comparison of training and test errors revealed high robustness against over-fitting. While not showing effects for the assessment of BMD and voxel-wise densities, the filter improved thoroughly the computation of TMD and BV/TV with respect to the unfiltered data. Root-mean-square and accuracy errors of low resolution TMD and BV/TV decreased to less than 17% of the initial values. Furthermore filtered low resolution scans revealed still more TMD- and BV/TV-relevant information than high resolution CT scans, either unfiltered or filtered with two state-of-the-art standard denoising methods. The proposed architecture is threshold and rotational invariant, applicable on a wide range of image resolutions at once, and likely serves for an accurate computation of further micro-structural parameters. Furthermore, it is less prone for over-fitting than neural networks that compute structural parameters directly. In conclusion, the method is potentially important for the diagnosis of osteoporosis and other bone diseases since it allows to assess relevant 3D micro-structural information from standard low exposure CT protocols such as 100 mAs and 120 kVp., (Copyright © 2020 Elsevier Ltd. All rights reserved.)