RATIONALE AND OBJECTIVES: Significant effort has been spent during the past decades to develop innovative image-processing algorithms and improve existing methods in terms of precision, reproducibility, and computational efficiency, but relatively little research was undertaken to find out the extent to which the validity of results obtained with these methods is limited by inherent imperfections of the input images. This observation is especially true for magnetic resonance imaging (MRI)-based morphometry, which aims at precise and highly reproducible determination of geometric properties of anatomic structures, although MRI images are geometrically distorted. MATERIALS AND METHODS: A method for characterization of site-specific geometric distortions and results of a long-term study designed to find the extent to which imperfections in the data-acquisition process limit the reliable detection of subtle morphological changes in MRI data acquired with state-of-the-art scanners are presented. Because of the long-term character of the study, results include effects resulting from limited hardware stability, as well as from imperfections in patient repositioning. RESULTS: Maximal relative morphological changes detected in our phantom data series were 1.0 mm positional and 2.0% volumetric difference (relative to a 7600-mm3 cuboid) in a subvolume relevant for whole-brain morphometry. Morphological variability was even greater for human volunteer data (up to 5% in local gray matter volume) because of movements during scan, natural morphological variability, and a presumably less precise segmentation procedure. CONCLUSION: Imperfections in the MRI data-acquisition process in combination with practical limitations in patient repositioning can substantially confound studies of subtle morphological changes.
RATIONALE AND OBJECTIVES: Significant effort has been spent during the past decades to develop innovative image-processing algorithms and improve existing methods in terms of precision, reproducibility, and computational efficiency, but relatively little research was undertaken to find out the extent to which the validity of results obtained with these methods is limited by inherent imperfections of the input images. This observation is especially true for magnetic resonance imaging (MRI)-based morphometry, which aims at precise and highly reproducible determination of geometric properties of anatomic structures, although MRI images are geometrically distorted. MATERIALS AND METHODS: A method for characterization of site-specific geometric distortions and results of a long-term study designed to find the extent to which imperfections in the data-acquisition process limit the reliable detection of subtle morphological changes in MRI data acquired with state-of-the-art scanners are presented. Because of the long-term character of the study, results include effects resulting from limited hardware stability, as well as from imperfections in patient repositioning. RESULTS: Maximal relative morphological changes detected in our phantom data series were 1.0 mm positional and 2.0% volumetric difference (relative to a 7600-mm3 cuboid) in a subvolume relevant for whole-brain morphometry. Morphological variability was even greater for human volunteer data (up to 5% in local gray matter volume) because of movements during scan, natural morphological variability, and a presumably less precise segmentation procedure. CONCLUSION: Imperfections in the MRI data-acquisition process in combination with practical limitations in patient repositioning can substantially confound studies of subtle morphological changes.
Authors: Ashley L Ware; Naomi J Goodrich-Hunsaker; Catherine Lebel; Ayushi Shukla; Elisabeth A Wilde; Tracy J Abildskov; Erin D Bigler; Daniel M Cohen; Leslie K Mihalov; Ann Bacevice; Barbara A Bangert; H Gerry Taylor; Keith Owen Yeates Journal: J Neurotrauma Date: 2020-05-11 Impact factor: 5.269
Authors: Gunnar Krueger; Cristina Granziera; Clifford R Jack; Jeffrey L Gunter; Arne Littmann; Bénédicte Mortamet; Stephan Kannengiesser; Alma Gregory Sorensen; Chadwick P Ward; Denise A Reyes; Paula J Britson; Hubertus Fischer; Matt A Bernstein Journal: J Magn Reson Imaging Date: 2012-05-08 Impact factor: 4.813
Authors: Cynthia M Stonnington; Geoffrey Tan; Stefan Klöppel; Carlton Chu; Bogdan Draganski; Clifford R Jack; Kewei Chen; John Ashburner; Richard S J Frackowiak Journal: Neuroimage Date: 2007-10-13 Impact factor: 6.556