Collaborative patch-based super-resolution for diffusion-weighted images.

Pierrick Coupé (1) , José V Manjón (2) , Maxime Chamberland (3) , Maxime Descoteaux (3) , Bassem Hiba (4)

1 LaBRI - Laboratoire Bordelais de Recherche en Informatique
2 ITACA
3 SCIL - Sherbrooke Connectivity Imaging Lab [Sherbrooke]
4 CRMSB - Centre de résonance magnétique des systèmes biologiques

In this paper, a new single image acquisition super-resolution method is proposed to increase image resolution of diffusion weighted (DW) images. Based on a nonlocal patch-based strategy, the proposed method uses a non-diffusion image (b0) to constrain the reconstruction of DW images. An extensive validation is presented with a gold standard built on averaging 10 high-resolution DW acquisitions. A comparison with classical interpolation methods such as trilinear and B-spline demonstrates the competitive results of our proposed approach in terms of improvements on image reconstruction, fractional anisotropy (FA) estimation, generalized FA and angular reconstruction for tensor and high angular resolution diffusion imaging (HARDI) models. Besides, first results of reconstructed ultra high resolution DW images are presented at 0.6×0.6×0.6mm(3) and 0.4×0.4×0.4mm(3) using our gold standard based on the average of 10 acquisitions, and on a single acquisition. Finally, fiber tracking results show the potential of the proposed super-resolution approach to accurately analyze white matter brain architecture.

Domaines: Imagerie médicale; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]; Ingénierie biomédicale; Applications [stat.AP]

Pierrick Coupé, José V Manjón, Maxime Chamberland, Maxime Descoteaux, Bassem Hiba. Collaborative patch-based super-resolution for diffusion-weighted images.. NeuroImage, 2013, epub ahead of print. ⟨10.1016/j.neuroimage.2013.06.030⟩. ⟨hal-00842213⟩