Deformable group-wise registration using a physiological model: application to DIffusion-Weighted MRI

Evgenios N. Kornaropoulos (1, 2) , Evangelia I. Zacharaki (1, 2) , Pierre Zerbib (3) , Chieh Lin (4) , Alain Rahmouni (3) , Nikos Paragios (1, 2)

1 CVN - Centre de vision numérique
2 GALEN - Organ Modeling through Extraction, Representation and Understanding of Medical Image Content
3 CHU Henri Mondor [Créteil]
4 CGMH - Chang Gung Memorial Hospital [Taipei]

Intensity variations can often be described by a physiological or temporal model applied on a voxel-wise basis across a group of images. However the voxel correspondence might be unknown, imposing the need for a group-wise deformable registration coupled with the computation of the model parameters. In this paper we propose a group-wise registration method of medical images that incorporates the temporal dimension (reflecting the change of signal amplitude) of the acquisition process. Consistency on the spatiotemporal physiological model, as well as deformation smoothness, is imposed in order to produce anatomically meaningful representations of the 3D images. The performance of the proposed method is compared to two different group-wise registration approaches; one that penalizes the absolute differences in the intensities and one that penalizes the intensity range among the images on corresponding regions. We chose as an application paradigm the registration of diffusion-weighted magnetic resonance (DW-MR) images for the evaluation of patients with lymphomas. A dataset consisting of 25 patients, each scanned with 3 " b values " , was used to evaluate the method's accuracy. The proposed registration method outperfomed the other two registration approaches, making it a very promising method for highlighting the importance of DWI as an imaging biomarker.

Keywords: ADC; DW-MRI; Group-wise registration

Domains: Bioinformatics [q-bio.QM]

Evgenios N. Kornaropoulos, Evangelia I. Zacharaki, Pierre Zerbib, Chieh Lin, Alain Rahmouni, et al.. Deformable group-wise registration using a physiological model: application to DIffusion-Weighted MRI. ICIP 2016 - IEEE International Conference on Image Processing, Sep 2016, Phoenix, Arizona, United States. pp.2345-2349, ⟨10.1109/icip.2016.7532778⟩. ⟨hal-01324238⟩