19In silico trials of treatments in a virtual physiological human (VPH) would revolutionize research in 20 the biomedical field. Hallmarks of bone disease and treatments can already be simulated in pre-21 clinical models and in ex vivo data of humans using microstructural bone adaptation simulations. The 22 an input regularisation approach to reduce initialisation shocks observed in microstructural bone 31 adaptation simulations and evaluated supersampling as a way to improve the accuracy of model 32 inputs. Finally, we compared our ex vivo results to simulations run on in vivo images to investigate 33 whether in vivo image artefacts further affect simulation outcomes. 34 35Keywords 36 Supersampling, HR-pQCT, Simulation, Mechanoregulation, Microstructure, Bone Adaptation 37With the introduction and increased use of HR-pQCT, large amounts of clinically relevant data have 50 been gathered which provide the basis to validate and parameterise in silico models of bone [8][9][10][11][12][13][14][15][16][17]. 51However, combining current microstructural bone adaptation simulations with HR-pQCT is non-52 trivial. Existing simulations either utilize synthetic images [4] or high-resolution micro-CT images 53 which cannot be obtained clinically [3,[5][6][7]. Furthermore, HR-pQCT images tend to have more noise 54[18] and other potential imaging artefacts, such as those due to movement [19]. 55The reduction in resolution is a known obstacles for the translation of computational techniques 56 from the lab into the clinical setting [20][21][22]. Thus, the use of clinical images in microstructural bone 57 adaptation simulations requires us to first understand the convergence of existing algorithms with 58 respect to image resolution [22,23] and second, evaluate whether supersampling of HR-pQCT data to 59 the resolution of desktop micro-CT images on which the algorithms have been validated produces 60 accurate results [18,24]. Supersampling of magnetic resonance imaging (MRI) data has been shown 61 to produce micro-FE results in good agreement with those from micro-CT images of a higher 62 4 resolution [18]. While it is clear that supersampling does not yield the same effects as scanning at a 63 higher resolution [25], as supersampling cannot compensate for information missing in the image, 64 techniques, like mesh refinement, are widely used in numerical applications to improve simulation 65 accuracy by providing a better digital representation of the information contained in the images. 66While several models exist [4,5,[26][27][28], we chose the advection based remodelling simulation of 67 Adachi [28] to test the appropriateness of HR-pQCT data input as it has been used previously on pre-68 clinical data [3,6] and ex-vivo large data [7]. In comparison to Ruimerman et al. [4], the model 69 simulation is also deterministic which simplifies the comparison of results. 70In previous studies using the algorithm by Adachi et al. [28], initial iterations, which showed aberrant 71 results, were regarded as part of the model initializ...
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