Subject-specific models for the analysis of pathological FDG PET data

TY - CHAP

T1 - Subject-specific models for the analysis of pathological FDG PET data

AU - Burgos, Ninon

AU - Cardoso, M. Jorge

AU - Mendelson, Alex F.

AU - Schott, Jonathan M.

AU - Atkinson, David

AU - Arridge, Simon R.

AU - Hutton, Brian F.

AU - Ourselin, Sébastien

PY - 2015/1/1

Y1 - 2015/1/1

N2 - Abnormalities in cerebral glucose metabolism detectable on fluorodeoxyglucose positron emission tomography (FDG PET) can be assessed on a regional or voxel-wise basis. In regional analysis, the average relative uptake over a region of interest is compared with the average relative uptake obtained for normal controls. Prior knowledge is required to determine the regions where abnormal uptake is expected, which can limit its usability. On the other hand, voxel-wise analysis consists of comparing the metabolic activity of the patient to the normal controls voxel-by-voxel, usually in a groupwise space. Voxel-based techniques are limited by the inter-subject morphological and metabolic variability in the normal population, which can limit their sensitivity. In this paper, we combine the advantages of both regional and voxelwise approaches through the use of subject-specific PET models for glucose metabolism. By accounting for inter-subject morphological differences, the proposed method aims to remove confounding variation and increase the sensitivity of group-wise approaches. The method was applied to a dataset of 22 individuals: 17 presenting four distinct neurodegenerative syndromes, and 5 controls. The proposed method more accurately distinguishes subgroups in this set, and improves the delineation of disease-specific metabolic patterns.

AB - Abnormalities in cerebral glucose metabolism detectable on fluorodeoxyglucose positron emission tomography (FDG PET) can be assessed on a regional or voxel-wise basis. In regional analysis, the average relative uptake over a region of interest is compared with the average relative uptake obtained for normal controls. Prior knowledge is required to determine the regions where abnormal uptake is expected, which can limit its usability. On the other hand, voxel-wise analysis consists of comparing the metabolic activity of the patient to the normal controls voxel-by-voxel, usually in a groupwise space. Voxel-based techniques are limited by the inter-subject morphological and metabolic variability in the normal population, which can limit their sensitivity. In this paper, we combine the advantages of both regional and voxelwise approaches through the use of subject-specific PET models for glucose metabolism. By accounting for inter-subject morphological differences, the proposed method aims to remove confounding variation and increase the sensitivity of group-wise approaches. The method was applied to a dataset of 22 individuals: 17 presenting four distinct neurodegenerative syndromes, and 5 controls. The proposed method more accurately distinguishes subgroups in this set, and improves the delineation of disease-specific metabolic patterns.

UR - http://www.scopus.com/inward/record.url?scp=84951071143&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-24571-3_78

DO - 10.1007/978-3-319-24571-3_78

M3 - Conference paper

AN - SCOPUS:84951071143

SN - 9783319245706

SN - 9783319245706

SN - 9783319245706

VL - 9350

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 651

EP - 658

BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference, Proceedings

PB - Springer Verlag

T2 - 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015

Y2 - 5 October 2015 through 9 October 2015

ER -