24 February 2017 Whole-body diffusion-weighted MR image stitching and alignment to anatomical MRI
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Abstract
Whole-body diffusion-weighted (WB-DW) MRI in combination with anatomical MRI has shown a great poten- tial in bone and soft tissue tumour detection, evaluation of lymph nodes and treatment response assessment. Because of the vast body coverage, whole-body MRI is acquired in separate stations, which are subsequently combined into a whole-body image. However, inter-station and inter-modality image misalignments can occur due to image distortions and patient motion during acquisition, which may lead to inaccurate representations of patient anatomy and hinder visual assessment. Automated and accurate whole-body image formation and alignment of the multi-modal MRI images is therefore crucial. We investigated several registration approaches for the formation or stitching of the whole-body image stations, followed by a deformable alignment of the multi- modal whole-body images. We compared a pairwise approach, where diffusion-weighted (DW) image stations were sequentially aligned to a reference station (pelvis), to a groupwise approach, where all stations were simultaneously mapped to a common reference space while minimizing the overall transformation. For each, a choice of input images and corresponding metrics was investigated. Performance was evaluated by assessing the quality of the obtained whole-body images, and by verifying the accuracy of the alignment with whole-body anatomical sequences. The groupwise registration approach provided the best compromise between the formation of WB- DW images and multi-modal alignment. The fully automated method was found to be robust, making its use in the clinic feasible.
Conference Presentation
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Jakub Ceranka, Jakub Ceranka, Mathias Polfliet, Mathias Polfliet, Frederic Lecouvet, Frederic Lecouvet, Nicolas Michoux, Nicolas Michoux, Jef Vandemeulebroucke, Jef Vandemeulebroucke, } "Whole-body diffusion-weighted MR image stitching and alignment to anatomical MRI", Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 1013311 (24 February 2017); doi: 10.1117/12.2253838; https://doi.org/10.1117/12.2253838
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