Deformation-based atrophy computation by surface propagation and its application to Alzheimer’s disease

TY - JOUR

T1 - Deformation-based atrophy computation by surface propagation and its application to Alzheimer’s disease

AU - Pai, Akshay Sadananda Uppinakudru

AU - Sporring, Jon

AU - Darkner, Sune

AU - Dam, Erik Bjørnager

AU - Lillholm, Martin

AU - Jørgensen, Dan

AU - Oh, Joonmi

AU - Chen, Gennan

AU - Suhy, Joyce

AU - Sørensen, Lauge

AU - Nielsen, Mads

PY - 2016/1/1

Y1 - 2016/1/1

N2 - Obtaining regional volume changes from a deformation field is more precise when using simplex counting (SC) compared with Jacobian integration (JI) due to the numerics involved in the latter. Although SC has been proposed before, numerical properties underpinning the method and a thorough evaluation of the method against JI is missing in the literature. The contributions of this paper are: (a) we propose surface propagation (SP) - a simplification to SC that significantly reduces its computational complexity; (b) we will derive the orders of approximation of SP which can also be extended to SC. In the experiments, we will begin by empirically showing that SP is indeed nearly identical to SC, and that both methods are more stable than JI in presence of moderate to large deformation noise. Since SC and SP are identical, we consider SP as a representative of both the methods for a practical evaluation against JI. In a real application on Alzheimer's disease neuroimaging initiative data, we show the following: (a) SP produces whole brain and medial temporal lobe atrophy numbers that are significantly better than JI at separating between normal controls and Alzheimer's disease patients; (b) SP produces disease group atrophy differences comparable to or better than those obtained using FreeSurfer, demonstrating the validity of the obtained clinical results. Finally, in a reproducibility study, we show that the voxel-wise application of SP yields significantly lower variance when compared to JI.

AB - Obtaining regional volume changes from a deformation field is more precise when using simplex counting (SC) compared with Jacobian integration (JI) due to the numerics involved in the latter. Although SC has been proposed before, numerical properties underpinning the method and a thorough evaluation of the method against JI is missing in the literature. The contributions of this paper are: (a) we propose surface propagation (SP) - a simplification to SC that significantly reduces its computational complexity; (b) we will derive the orders of approximation of SP which can also be extended to SC. In the experiments, we will begin by empirically showing that SP is indeed nearly identical to SC, and that both methods are more stable than JI in presence of moderate to large deformation noise. Since SC and SP are identical, we consider SP as a representative of both the methods for a practical evaluation against JI. In a real application on Alzheimer's disease neuroimaging initiative data, we show the following: (a) SP produces whole brain and medial temporal lobe atrophy numbers that are significantly better than JI at separating between normal controls and Alzheimer's disease patients; (b) SP produces disease group atrophy differences comparable to or better than those obtained using FreeSurfer, demonstrating the validity of the obtained clinical results. Finally, in a reproducibility study, we show that the voxel-wise application of SP yields significantly lower variance when compared to JI.

U2 - 10.1117/1.JMI.3.1.014005

DO - 10.1117/1.JMI.3.1.014005

M3 - Journal article

C2 - 27014717

SN - 2329-4302

VL - 3

JO - Journal of Medical Imaging

JF - Journal of Medical Imaging

IS - 1

M1 - 014005

ER -