Michael Rullmann1, Juergen Dukart2, Karl-Titus Hoffmann3, Julia Luthardt4, Solveig Tiepolt4, Marianne Patt4, Hermann-Josef Gertz5, Matthias L Schroeter6, John Seibyl7, Walter J Schulz-Schaeffer8, Osama Sabri4, Henryk Barthel4. 1. Department of Nuclear Medicine, University of Leipzig, Leipzig, Germany rullmann@medizin.uni-leipzig.de. 2. LREN, Département des Neurosciences Cliniques, CHUV, Université de Lausanne, Lausanne, Switzerland Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. 3. Department of Neuroradiology, University of Leipzig, Leipzig, Germany. 4. Department of Nuclear Medicine, University of Leipzig, Leipzig, Germany. 5. Department of Psychiatry, University of Leipzig, Leipzig, Germany. 6. Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany Clinic for Cognitive Neurology, University Hospital Leipzig, Leipzig, Germany. 7. Molecular NeuroImaging, L.L.C., New Haven, Connecticut; and. 8. Department of Neuropathology, University Medical Center, Goettingen, Germany.
Abstract
UNLABELLED: Neocortical atrophy reduces PET signal intensity, potentially affecting the diagnostic efficacy of β-amyloid (Aβ) brain PET imaging. This study investigated whether partial-volume effect correction (PVEC), adjusting for this atrophy bias, improves the accuracy of (18)F-florbetaben Aβ PET. METHODS: We analyzed (18)F-florbetaben PET and MRI data obtained from 3 cohorts. The first was 10 patients with probable Alzheimer disease (AD) and 10 age-matched healthy controls (HCs), the second was 31 subjects who underwent in vivo imaging and postmortem histopathology for Aβ plaques, and the third was 5 subjects who underwent PET and MRI at baseline and 1 y later. The imaging data were coregistered and segmented. PVEC was performed using the voxel-based modified Müller-Gärtner method (PVELab, SPM8). From the PET data, regional and composite SUV ratios (SUVRs) with and without PVEC were obtained. In the MRI data, mesial temporal lobe atrophy was determined by the Scheltens mesial temporal atrophy scale and gray matter volumes by voxel-based morphometry. RESULTS: In cohort 1, PVEC increased the effect on AD-versus-HC discrimination from a Cohen d value of 1.68 to 2.0 for composite SUVRs and from 0.04 to 1.04 for mesial temporal cortex SUVRs. The PVEC-related increase in mesial temporal cortex SUVR correlated with the Scheltens score (r = 0.84, P < 0.001), and that of composite SUVR correlated with the composite gray matter volume (r = -0.75, P < 0.001). In cohort 2, PVEC increased the correlation coefficient between mesial temporal cortex SUVR and histopathology score for Aβ plaque load from 0.28 (P = 0.09) to 0.37 (P = 0.03). In cohort 3, PVEC did not affect the composite SUVR dynamics over time for the Aβ-negative subject. This finding was in contrast to the 4 Aβ-positive subjects, in 2 of whom PVEC changed the composite SUVR dynamics. CONCLUSION: The influence of PVEC on (18)F-florbetaben PET data is associated with the degree of brain atrophy. Thus, PVEC increases the ability of (18)F-florbetaben PET to discriminate between AD patients and HCs, to detect Aβ plaques in the atrophic mesial temporal cortex, and potentially to evaluate changes in brain Aβ load over time. As such, the use of PVEC should be considered for quantitative (18)F-florbetaben PET scans, especially in assessing patients with brain atrophy.
UNLABELLED: Neocortical atrophy reduces PET signal intensity, potentially affecting the diagnostic efficacy of β-amyloid (Aβ) brain PET imaging. This study investigated whether partial-volume effect correction (PVEC), adjusting for this atrophy bias, improves the accuracy of (18)F-florbetaben Aβ PET. METHODS: We analyzed (18)F-florbetaben PET and MRI data obtained from 3 cohorts. The first was 10 patients with probable Alzheimer disease (AD) and 10 age-matched healthy controls (HCs), the second was 31 subjects who underwent in vivo imaging and postmortem histopathology for Aβ plaques, and the third was 5 subjects who underwent PET and MRI at baseline and 1 y later. The imaging data were coregistered and segmented. PVEC was performed using the voxel-based modified Müller-Gärtner method (PVELab, SPM8). From the PET data, regional and composite SUV ratios (SUVRs) with and without PVEC were obtained. In the MRI data, mesial temporal lobe atrophy was determined by the Scheltens mesial temporal atrophy scale and gray matter volumes by voxel-based morphometry. RESULTS: In cohort 1, PVEC increased the effect on AD-versus-HC discrimination from a Cohen d value of 1.68 to 2.0 for composite SUVRs and from 0.04 to 1.04 for mesial temporal cortex SUVRs. The PVEC-related increase in mesial temporal cortex SUVR correlated with the Scheltens score (r = 0.84, P < 0.001), and that of composite SUVR correlated with the composite gray matter volume (r = -0.75, P < 0.001). In cohort 2, PVEC increased the correlation coefficient between mesial temporal cortex SUVR and histopathology score for Aβ plaque load from 0.28 (P = 0.09) to 0.37 (P = 0.03). In cohort 3, PVEC did not affect the composite SUVR dynamics over time for the Aβ-negative subject. This finding was in contrast to the 4 Aβ-positive subjects, in 2 of whom PVEC changed the composite SUVR dynamics. CONCLUSION: The influence of PVEC on (18)F-florbetaben PET data is associated with the degree of brain atrophy. Thus, PVEC increases the ability of (18)F-florbetaben PET to discriminate between ADpatients and HCs, to detect Aβ plaques in the atrophic mesial temporal cortex, and potentially to evaluate changes in brain Aβ load over time. As such, the use of PVEC should be considered for quantitative (18)F-florbetaben PET scans, especially in assessing patients with brain atrophy.
Authors: Janusch Blautzik; Matthias Brendel; Julia Sauerbeck; Sebastian Kotz; Franziska Scheiwein; Peter Bartenstein; John Seibyl; Axel Rominger Journal: Eur J Nucl Med Mol Imaging Date: 2017-03-22 Impact factor: 9.236
Authors: Pawel J Markiewicz; Julian C Matthews; John Ashburner; David M Cash; David L Thomas; Enrico De Vita; Anna Barnes; M Jorge Cardoso; Marc Modat; Richard Brown; Kris Thielemans; Casper da Costa-Luis; Isadora Lopes Alves; Juan Domingo Gispert; Mark E Schmidt; Paul Marsden; Alexander Hammers; Sebastien Ourselin; Frederik Barkhof Journal: Neuroimage Date: 2021-02-12 Impact factor: 6.556
Authors: Charles M Laymon; Davneet S Minhas; Sarah K Royse; Howard J Aizenstein; Ann D Cohen; Dana L Tudorascu; William E Klunk Journal: EJNMMI Phys Date: 2021-07-20
Authors: Christopher G Schwarz; David T Jones; Jeffrey L Gunter; Val J Lowe; Prashanthi Vemuri; Matthew L Senjem; Ronald C Petersen; David S Knopman; Clifford R Jack Journal: Hum Brain Mapp Date: 2017-04-22 Impact factor: 5.038