The Impact of the Siemens Trio to Prisma Upgrade and Volumetric Navigators on MRI Indices: A Reliability Study with Implications for Longitudinal Study Designs

Many magnetic resonance imaging (MRI) indices are being studied longitudinally to explore topics such as biomarker detection and clinical staging. A pertinent concern to longitudinal work is MRI scanner upgrades. When upgrades occur during the course of a longitudinal MRI neuroimaging investigation, there may be an impact on the compatibility of pre- and post-upgrade measures. Similarly, subject motion is another issue that may be detrimental to longitudinal MRI work; however, embedding volumetric navigators (vNavs) within acquisition sequences has emerged as a technique that allows for prospective motion correction. Our research group recently implemented an upgrade from a Siemens MAGNETOM 3T Trio system to a Siemens MAGNETOM 3T Prisma Fit system. The goals of the current work were to: 1) investigate the impact of this upgrade on commonly used structural imaging measures and proton magnetic resonance spectroscopy indices (“Prisma Upgrade protocol”) and 2) examine structural imaging measures in a sequence with vNavs alongside a standard acquisition sequence (“vNav protocol”). In both protocols, while high reliability was observed for most of the investigated MRI outputs, suboptimal reliability was observed for certain indices. Across the scanner upgrade, increases in frontal, temporal, and cingulate cortical thickness (CT) and thalamus volume, along with decreases in parietal CT, amygdala, globus pallidus, hippocampus, and striatum volumes were observed across the Prisma upgrade, and were linked to increases in signal-to-noise ratios. No significant impact of the upgrade was found in 1H-MRS analyses. Further, CT estimates were found to be larger in MPRAGE acquisitions compared to vNav-MPRAGE acquisitions mainly within temporal areas, while the opposite was found mostly in parietal brain regions. The results from this work should be considered in longitudinal study designs and comparable prospective motion correction investigations are warranted in cases of marked head movement.

[1]  S. Provencher Automatic quantitation of localized in vivo 1H spectra with LCModel , 2001, NMR in biomedicine.

[2]  M. Mallar Chakravarty,et al.  Manual segmentation of the fornix, fimbria, and alveus on high-resolution 3T MRI: Application via fully-automated mapping of the human memory circuit white and grey matter in healthy and pathological aging , 2016, NeuroImage.

[3]  R. Gruetter,et al.  In vivo 1H NMR spectroscopy of rat brain at 1 ms echo time , 1999, Magnetic resonance in medicine.

[4]  M. Mallar Chakravarty,et al.  Evaluating accuracy of striatal, pallidal, and thalamic segmentation methods: Comparing automated approaches to manual delineation , 2017, NeuroImage.

[5]  Rolf Gruetter,et al.  MR spectroscopy of the human brain with enhanced signal intensity at ultrashort echo times on a clinical platform at 3T and 7T , 2009, Magnetic resonance in medicine.

[6]  M. Mallar Chakravarty,et al.  Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates , 2014, NeuroImage.

[7]  C. Jack,et al.  Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers , 2013, The Lancet Neurology.

[8]  Brian R. Tietz,et al.  Deciding Which Way to Go: How Do Insects Alter Movements to Negotiate Barriers? , 2012, Front. Neurosci..

[9]  D Louis Collins,et al.  Warping an atlas derived from serial histology to 5 high-resolution MRIs , 2018, Scientific Data.

[10]  Tyrone D. Cannon,et al.  Progressive Reduction in Cortical Thickness as Psychosis Develops: A Multisite Longitudinal Neuroimaging Study of Youth at Elevated Clinical Risk , 2015, Biological Psychiatry.

[11]  D. Louis Collins,et al.  An Object-Based Method for Rician Noise Estimation in MR Images , 2009, MICCAI.

[12]  Alan C. Evans,et al.  Cortical thickness analysis examined through power analysis and a population simulation , 2005, NeuroImage.

[13]  M. Mallar Chakravarty,et al.  Normative brain size variation and brain shape diversity in humans , 2018, Science.

[14]  Anders M. Dale,et al.  The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites , 2018, Developmental Cognitive Neuroscience.

[15]  Brian B. Avants,et al.  N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.

[16]  Jon Pipitone,et al.  Hippocampal (subfield) volume and shape in relation to cognitive performance across the adult lifespan , 2015, Human brain mapping.

[17]  J. Giedd,et al.  Subtle in‐scanner motion biases automated measurement of brain anatomy from in vivo MRI , 2016, Human brain mapping.

[18]  D. Selkoe Alzheimer's disease. , 2011, Cold Spring Harbor perspectives in biology.

[19]  D. Collins,et al.  Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space , 1994, Journal of computer assisted tomography.

[20]  Robin A. de Graaf,et al.  In Vivo NMR Spectroscopy , 2019 .

[21]  Esben Thade Petersen,et al.  Improvement in diagnostic quality of structural and angiographic MRI of the brain using motion correction with interleaved, volumetric navigators , 2019, PloS one.

[22]  M. Mallar Chakravarty,et al.  Illness Progression, Recent Stress, and Morphometry of Hippocampal Subfields and Medial Prefrontal Cortex in Major Depression , 2014, Biological Psychiatry.

[23]  S. A. Wijtenburg,et al.  In vivo assessment of neurotransmitters and modulators with magnetic resonance spectroscopy: Application to schizophrenia , 2015, Neuroscience & Biobehavioral Reviews.

[24]  Arno Klein,et al.  101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol , 2012, Front. Neurosci..

[25]  Hyunwoo Lee,et al.  Estimating and accounting for the effect of MRI scanner changes on longitudinal whole-brain volume change measurements , 2019, NeuroImage.

[26]  Heath R. Pardoe,et al.  Motion and morphometry in clinical and nonclinical populations , 2016, NeuroImage.

[27]  D. Collins,et al.  The creation of a brain atlas for image guided neurosurgery using serial histological data , 2003, NeuroImage.

[28]  Margot J. Taylor,et al.  Large-scale analyses of the relationship between sex, age and intelligence quotient heterogeneity and cortical morphometry in autism spectrum disorder , 2019, Molecular Psychiatry.

[29]  R. Hoge,et al.  Measurement Variability Following MRI System Upgrade , 2019, Front. Neurol..

[30]  A. Dale,et al.  Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System , 1999, NeuroImage.

[31]  Alan C. Evans,et al.  Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification , 2005, NeuroImage.

[32]  Alan C. Evans,et al.  Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis , 2002, IEEE Transactions on Medical Imaging.

[33]  D. Collins,et al.  Performing label‐fusion‐based segmentation using multiple automatically generated templates , 2013, Human brain mapping.

[34]  R Gruetter,et al.  Field mapping without reference scan using asymmetric echo‐planar techniques , 2000, Magnetic resonance in medicine.

[35]  Naoto Hayashi,et al.  Effects of the use of multiple scanners and of scanner upgrade in longitudinal voxel‐based morphometry studies , 2013, Journal of magnetic resonance imaging : JMRI.

[36]  Brian B. Avants,et al.  Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain , 2008, Medical Image Anal..

[37]  Lei Ai,et al.  Is it time to switch your T1W sequence? Assessing the impact of prospective motion correction on the reliability and quality of structural imaging , 2019, NeuroImage.

[38]  D. Cicchetti Guidelines, Criteria, and Rules of Thumb for Evaluating Normed and Standardized Assessment Instruments in Psychology. , 1994 .

[39]  Stine K. Krogsrud,et al.  Development and aging of cortical thickness correspond to genetic organization patterns , 2015, Proceedings of the National Academy of Sciences.

[40]  R. Woods,et al.  Sex differences in cortical thickness mapped in 176 healthy individuals between 7 and 87 years of age. , 2007, Cerebral cortex.

[41]  R. Berman,et al.  Longitudinal four-dimensional mapping of subcortical anatomy in human development , 2014, Proceedings of the National Academy of Sciences.

[42]  Sabina Sonia Tangaro,et al.  Integrating longitudinal information in hippocampal volume measurements for the early detection of Alzheimer's disease , 2016, NeuroImage.

[43]  Borys Shuter,et al.  Reproducibility of brain tissue volumes in longitudinal studies: Effects of changes in signal-to-noise ratio and scanner software , 2008, NeuroImage.

[44]  M. Dylan Tisdall,et al.  Head motion during MRI acquisition reduces gray matter volume and thickness estimates , 2015, NeuroImage.

[45]  M. Dylan Tisdall,et al.  Prospective motion correction with volumetric navigators (vNavs) reduces the bias and variance in brain morphometry induced by subject motion , 2016, NeuroImage.

[46]  Jennifer Fedor,et al.  Cortical and subcortical brain morphometry differences between patients with autism spectrum disorders (ASD) and healthy individuals across the lifespan: results from the ENIGMA-ASD working group , 2017 .

[47]  Peter Jezzard,et al.  Advanced processing and simulation of MRS data using the FID appliance (FID‐A)—An open source, MATLAB‐based toolkit , 2017, Magnetic resonance in medicine.

[48]  Bruce Fischl,et al.  Volumetric navigators for prospective motion correction and selective reacquisition in neuroanatomical MRI , 2012, Magnetic resonance in medicine.

[49]  Karolinska Schizophrenia Cortical brain abnormalities in 4474 individuals with schizophrenia and 5098 control subjects via the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) Consortium , 2018 .

[50]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[51]  A. Dale,et al.  Whole Brain Segmentation Automated Labeling of Neuroanatomical Structures in the Human Brain , 2002, Neuron.

[52]  Adrian Preda,et al.  Cortical Brain Abnormalities in 4474 Individuals With Schizophrenia and 5098 Control Subjects via the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) Consortium , 2018, Biological Psychiatry.

[53]  Lachlan T. Strike,et al.  Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group , 2015, Molecular Psychiatry.

[54]  M. Mallar Chakravarty,et al.  A novel in vivo atlas of human hippocampal subfields using high-resolution 3T magnetic resonance imaging , 2013, NeuroImage.

[55]  Christine L. Tardif,et al.  MR‐based age‐related effects on the striatum, globus pallidus, and thalamus in healthy individuals across the adult lifespan , 2019, Human brain mapping.

[56]  I. Melle,et al.  Erratum: Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium (Molecular Psychiatry (2015) DOI:10.1038/mp.2015.63) , 2016 .

[57]  Alan C. Evans,et al.  Changes in cortical thickness during the course of illness in schizophrenia. , 2011, Archives of general psychiatry.