Contributions of Imprecision in PET-MRI Rigid Registration to Imprecision in Amyloid PET SUVR Measurements Christopher G. Schwarz , 1 * David T. Jones, 2 Jeffrey L. Gunter, 1,3 Val J. Lowe, 1 Prashanthi Vemuri, 1 Matthew L. Senjem, 1,3 Ronald C. Petersen, 2 David S. Knopman, 2 and Clifford R. Jack Jr. 1 The Alzheimer’s Disease Neuroimaging Initiative 1 Department of Radiology, Mayo Clinic and Foundation, Rochester, Minnesota 2 Department of Neurology, Mayo Clinic and Foundation, Rochester, Minnesota 3 Department of Information Technology, Mayo Clinic and Foundation, Rochester, Minnesota r r Abstract: Quantitative measurement of b-amyloid from amyloid PET scans typically relies on localiz- ing target and reference regions by image registration to MRI. In this work, we present a series of simulations where 50 small random perturbations of starting location and orientation were applied to each subject’s PET scan, and rigid registration using spm_coreg was performed between each perturbed PET scan and its corresponding MRI. We then measured variation in the output PET-MRI registrations and how this variation affected the resulting SUVR measurements. We performed these experiments using scans of 1196 participants, half using 18F florbetapir and half using 11C PiB. From these experi- ments, we measured the magnitude of the imprecision in the rigid registration steps used to localize measurement regions, and how this contributes to the overall imprecision in SUVR measurements. Unexpectedly, we found for both tracers that the imprecision in these measurements depends on the degree of amyloid tracer uptake, and thus also indirectly on Alzheimer’s disease clinical status. We then examined common choices of reference regions, and we show that SUVR measurements using supratentorial white matter references are relatively resistant to this source of error. We also show that the use of partial volume correction further magnifies the effects of registration imprecision on SUVR Additional Supporting Information may be found in the online version of this article. The Alzheimer’s Disease Neuroimaging Initiative: A portion of data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_ apply/ADNI_Acknowledgement_List.pdf. Contract grant sponsor: NIH grants; Contract grant number: R01 AG011378, R00 AG37573, R01 AG041851, U01 AG24904, U01 AG06786, P50 AG16574, R01 AG034676; Contract grant sponsor: The Alexander Family Professorship of Alzheimer’s Disease Research, Mayo Clinic; the GHR Foundation; Contract grant spon- sor: Elsie and Marvin Dekelboum Family Foundation *Correspondence to: Christopher G. Schwarz, Ph.D., Mayo Clinic, Diagnostic Radiology, 200 First Street SW, Rochester, MN, 55905. E-mail: schwarz.christopher@mayo.edu Received for publication 7 December 2016; Revised 6 April 2017; Accepted 9 April 2017. DOI: 10.1002/hbm.23622 Published online 22 April 2017 in Wiley Online Library (wileyon- linelibrary.com). r Human Brain Mapping 38:3323–3336 (2017) r V C 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.