Interactive local super-resolution reconstruction of whole-body MRI mouse data: A pilot study with applications to bone and kidney metastases

Interactive local super-resolution reconstruction of whole-body MRI mouse data: A pilot study with applications to bone and kidney metastases

Dzyubachyk, O.
Khmelinskii, A.
Plenge, E.
Kok, P.
Snoeks, T.J.
Poot, D.H.
Löwik, C.W.
Botha, C.P.
Niessen, W.J.
Van der Weerd, L.
Meijering, E.
Lelieveldt, B.P.F.

Applied Sciences

ImPhys/Imaging Physics

2014-09-29

In small animal imaging studies, when the locations of the micro-structures of interest are unknown a priori, there is a simultaneous need for full-body coverage and high resolution. In MRI, additional requirements to image contrast and acquisition time will often make it impossible to acquire such images directly. Recently, a resolution enhancing post-processing technique called super-resolution reconstruction (SRR) has been demonstrated to improve visualization and localization of micro-structures in small animal MRI by combining multiple low-resolution acquisitions. However, when the field-of-view is large relative to the desired voxel size, solving the SRR problem becomes very expensive, in terms of both memory requirements and computation time. In this paper we introduce a novel local approach to SRR that aims to overcome the computational problems and allow researchers to efficiently explore both global and local characteristics in whole-body small animal MRI. The method integrates state-of-the-art image processing techniques from the areas of articulated atlas-based segmentation, planar reformation, and SRR. A proof-of-concept is provided with two case studies involving CT, BLI, and MRI data of bone and kidney tumors in a mouse model. We show that local SRR-MRI is a computationally efficient complementary imaging modality for the precise characterization of tumor metastases, and that the method provides a feasible high-resolution alternative to conventional MRI.

http://resolver.tudelft.nl/uuid:88c264c7-8027-47d0-906c-55fb94ae9041

https://doi.org/10.1371/journal.pone.0108730

Public Library of Science

1932-6203

PLoS ONE, 9 (9), 2014

Institutional Repository

journal article

© 2014 The Author(s)
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