Cortical Flattening Applied to High-Resolution 18F-FDG PET

Group studies using PET and other types of neuroimaging require some means to achieve congruence of brain structures across subjects, such that scans from individuals varying in brain shape and gyral anatomy can be analyzed together. Volume registration methods are the most widely used approach to achieve this congruence. They are fast and typically require little manual interaction, but, unfortunately, it is difficult to achieve a good match between cortical areas in volume space, especially where folding patterns vary across subjects. Cortical flattening is a recent, alternative strategy: Its key features are explicit definition of cortex, such that white matter or cerebrospinal fluid compartments are largely excluded from the analysis volume, and subsequent registration of the cortical sheet in its natural, 2-dimensional topology. This type of registration has been demonstrated to provide better matching of congruent cortical structures than volume methods and, thus, offers a potentially more robust way of analyzing PET data. Methods: Here, we explore the applicability of cortical flattening of coregistered MRI to 18F-FDG PET on the HRRT system (high-resolution research tomograph), the highest-resolution whole-head scanner available to date. Results: We report average values and SD of cortical metabolism in a pilot study of the dominant hemisphere in 9 control subjects and provide estimates of group sizes necessary for studies using this technique. Conclusion: We conclude that cortical flattening with subsequent surface registration is a feasible and promising strategy for group studies on the HRRT, providing the highest fidelity maps of human cortical glucose consumption to date.

[1]  E. Hoffman,et al.  TOMOGRAPHIC MEASUREMENT OF LOCAL CEREBRAL GLUCOSE METABOLIC RATE IN HUMANS WITH (F‐18)2‐FLUORO-2‐DEOXY-D‐GLUCOSE: VALIDATION OF METHOD , 1980, Annals of neurology.

[2]  A. Rahmim,et al.  The second generation HRRT - a multi-centre scanner performance investigation , 2005, IEEE Nuclear Science Symposium Conference Record, 2005.

[3]  D. V. van Essen,et al.  Computerized Mappings of the Cerebral Cortex: A Multiresolution Flattening Method and a Surface-Based Coordinate System , 1996, Journal of Cognitive Neuroscience.

[4]  A. Alavi,et al.  Measurement of local cerebral glucose metabolism in man with 18F-2-fluoro-2-deoxy-d-glucose. , 1977, Acta neurologica Scandinavica. Supplementum.

[5]  山浦 晶 Atlas of the Cerebral Sulci, Michio Ono, Stefan Kubik and Chad D. Abernathey著, Georg Thieme Verlag, Stuttgart, New York 1990(らいぶらりい) , 1992 .

[6]  G. Smith,et al.  Die Cytoarchitektonik der Hirnrinde des erwachsenen Menschen. , 1927 .

[7]  David C. Van Essen,et al.  Windows on the brain: the emerging role of atlases and databases in neuroscience , 2002, Current Opinion in Neurobiology.

[8]  et al.,et al.  Discrimination between Alzheimer Dementia and Controls by Automated Analysis of Multicenter FDG PET , 2002, NeuroImage.

[9]  A. Schleicher,et al.  Broca's region revisited: Cytoarchitecture and intersubject variability , 1999, The Journal of comparative neurology.

[10]  Rainer Schrader,et al.  Fast and robust registration of PET and MR images of human brain , 2004, NeuroImage.

[11]  J S McGlone,et al.  Three-dimensional representation and analysis of brain energy metabolism. , 1987, Science.

[12]  K. Herholz,et al.  Regional Kinetic Constants and Cerebral Metabolic Rate for Glucose in Normal Human Volunteers Determined by Dynamic Positron Emission Tomography of [18F]-2-Fluoro-2-Deoxy-D-Glucose , 1984, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[13]  Michael E. Casey,et al.  HeinzelCluster: accelerated reconstruction for FORE and OSEM3D. , 2002 .

[14]  D. Lindsley,et al.  The human brain in figures and tables : a quantitative handbook , 1968 .

[15]  David C. Van Essen,et al.  Application of Information Technology: An Integrated Software Suite for Surface-based Analyses of Cerebral Cortex , 2001, J. Am. Medical Informatics Assoc..

[16]  M. Reivich,et al.  Labeled 2-deoxy-D-glucose analogs. 18F-labeled 2-deoxy-2-fluoro-D-glucose, 2-deoxy-2-fluoro-D-mannose and 14C-2-deoxy-2-fluoro-D-glucose , 1978 .

[17]  K Wienhard,et al.  The ECAT EXACT HR: Performance of a New High Resolution Positron Scanner , 1994, Journal of computer assisted tomography.

[18]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[19]  Karl Herholz,et al.  Metabolic rates in small brain nuclei determined by high-resolution PET. , 2004, Journal of Nuclear Medicine.

[20]  K Amunts,et al.  Quantitative analysis of sulci in the human cerebral cortex: Development, regional heterogeneity, gender difference, asymmetry, intersubject variability and cortical architecture , 1997, Human brain mapping.

[21]  小野 道夫,et al.  Atlas of the Cerebral Sulci , 1990 .

[22]  A. Dale,et al.  High‐resolution intersubject averaging and a coordinate system for the cortical surface , 1999, Human brain mapping.

[23]  A. Schleicher,et al.  Architectonics of the human cerebral cortex and transmitter receptor fingerprints: reconciling functional neuroanatomy and neurochemistry , 2002, European Neuropsychopharmacology.

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

[25]  A. Schleicher,et al.  The human pattern of gyrification in the cerebral cortex , 2004, Anatomy and Embryology.

[26]  H. Malcolm Hudson,et al.  Accelerated image reconstruction using ordered subsets of projection data , 1994, IEEE Trans. Medical Imaging.

[27]  K. Herholz,et al.  NeuroPET: Positron Emission Tomography in Neuroscience and Clinical Neurology , 2004 .

[28]  P. Grangeat,et al.  Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine , 1996, Computational Imaging and Vision.

[29]  H. Alkadhi,et al.  Localization of the motor hand area to a knob on the precentral gyrus. A new landmark. , 1997, Brain : a journal of neurology.

[30]  K Wienhard,et al.  Estimation of Local Cerebral Glucose Utilization by Positron Emission Tomography of [18F]2-Fluoro-2-Deoxy-D-Glucose: A Critical Appraisal of Optimization Procedures , 1985, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[31]  D. V. van Essen,et al.  Windows on the brain: the emerging role of atlases and databases in neuroscience , 2002, Current Opinion in Neurobiology.

[32]  Tom R. Miller,et al.  PET: Molecular Imaging and Its Biological Applications. , 2005 .

[33]  A. Schleicher,et al.  Areas 3a, 3b, and 1 of Human Primary Somatosensory Cortex 1. Microstructural Organization and Interindividual Variability , 1999, NeuroImage.

[34]  E. Hoffman,et al.  Tomographic measurement of local cerebral glucose metabolic rate in humans with (F‐18)2‐fluoro‐2‐deoxy‐D‐glucose: Validation of method , 1979, Annals of neurology.

[35]  J. Mugler,et al.  Three‐dimensional magnetization‐prepared rapid gradient‐echo imaging (3D MP RAGE) , 1990, Magnetic resonance in medicine.

[36]  R. Cabeza,et al.  Imaging Cognition II: An Empirical Review of 275 PET and fMRI Studies , 2000, Journal of Cognitive Neuroscience.

[37]  G. Bruyn Atlas of the Cerebral Sulci, M. Ono, S. Kubik, Chad D. Abernathey (Eds.). Georg Thieme Verlag, Stuttgart, New York (1990), 232, DM 298 , 1990 .

[38]  Alberto Pupi,et al.  Visual rating of medial temporal lobe metabolism in mild cognitive impairment and Alzheimer’s disease using FDG-PET , 2006, European Journal of Nuclear Medicine and Molecular Imaging.

[39]  M. Defrise,et al.  HeinzelCluster: accelerated reconstruction for FORE and OSEM3D , 2001, 2001 IEEE Nuclear Science Symposium Conference Record (Cat. No.01CH37310).

[40]  D. Newport,et al.  A Single Scatter Simulation Technique for Scatter Correction in 3D PET , 1996 .