Quantification of bone tissue growth and adaptation in longitudinal murine studies

Lead Research Organisation: University of Sheffield
Department Name: Oncology and Metabolism


The goal of the study is to develop a novel elastic registration algorithm is developed, based on the Sheffield Registration Toolkit version 2 (ShIRTv2), for the investigation of the bone growth in mice models. The algorithm will provide growth displacement map and will improve current measurements of bone remodeling which ignore growth.

Background: Brief summary to allow assignment to appropriate internal reviewers in field.

Traditionally the effect of bone enhancing drugs or other interventions are investigated in murine models by sacrificing a number of animals at each time point, and then analysing bone 3D histomorphometry with micro-Computed Tomography (microCT) on dissected bones. Within an NC3R project, led by Prof Bellantuono Prof Viceconti and Dr Dall'Ara, we recently introduced new methodology whereby in vivo microCT is used to follow the evolution of bone tissue in the same mouse over time. Current methods make the assumption that except for small local changes that are the adaptation to be measured, the bone remains the same at two distinct time points. Thus, rigid image registration is used to align multiple 3D images of the same animal at different time points, and then Boolean operators are used to compute the tissue adaptation [Lambers, 2011; Lu, 2015]. Unfortunately, most studies use 10-15 week old mice but this is an age where the long bones are still growing. Thus, the tissue adaptation produced by the intervention, is superimposed on the normal growth of the bone.

Research Plan: Including detail of experiments to be undertaken in the first year and a list of expected deliverables at 6 and 12 months plus a general outline of expected direction of project in years 2 and 3 if a PhD is anticipated.

To address this problem we need to develop an elastic registration algorithm that essentially simultaneously computes an affine scaling that can describe the growth component, and a local displacement scaling that quantifies the actual tissue adaptation. The problem is further complicated by the fact that these 3D images are acquired at a high resolution (8 billion voxels), which makes the elastic registration and image interpolation computationally intensive. In this project we aim to optimise the standard ShIRT elastic registration algorithm to a) register such large datasets efficiently, and b) accurately separate growth from adaptation. We will explore the use of both multi-resolution techniques and global-local methods that are able to tackle both problems at the same time. The developed methods will be tested for accuracy on a series of digital phantoms, and on a collection of repeated scans performed on the same mouse at the same time (zero-growth accuracy, on at least 5 mice). They will then be applied to the entire cohort of experimental results collected during the NC3R project (different groups of mice wild type, ovariectomized and treated with PTH), and from any follow-up projects (MULTISIM project, longitudinal data are currently being collected on another 20 mice). The resulting registration algorithm will be packaged as a high-throughput big data analytics pipeline to be used by Insigneo biological researchers to analyse their bone research results.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/N509735/1 01/10/2016 30/09/2021
1785630 Studentship EP/N509735/1 03/10/2016 03/10/2019 Samuel Lapworth