Austria'
author:
- Zhipeng Ding
- Greg Fleishman
- Xiao Yang
- Paul Thompson
- Roland Kwitt
- Marc Niethammer
- 'The Alzheimer’s Disease Neuroimaging Initiative'
bibliography:
- 'allBibShort.bib'
title: Fast Predictive Simple Geodesic Regression
---

Fast prediction, image regression, ADNI dataset, longitudinal data

Introduction
============

Longitudinal image data provides us with a wealth of information to study aging processes, brain development and disease progression. Such studies, for example ADNI [@jack2015magnetic] and the Rotterdam study [@ikram2015rotterdam], involve analyzing thousands of images. In fact, even larger studies will be available in the near future. For example, the UK Biobank [@biobankWebsite] targets on the order of 100,000 images once completed. With the number of images increasing, large-scale image analysis typically resorts to using compute clusters for parallel processing. While this is, in principle, a viable solution, increasingly larger compute clusters will become necessary for such studies. Alternatively, more efficient algorithms can reduce computational requirements, which then facilitates computations on individual computers or much smaller compute clusters, interactive (e.g., clinical) applications, efficient algorithm development, and use of these efficient algorithms as components in more sophisticated analysis approaches (which may use them as part of iterative processes).

1ex Image registration is a key task 