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An Evaluation of the Benefits of Simultaneous Acquisition on PET/MR Coregistration in Head/Neck Imaging

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Coregistration of multimodal diagnostic images is crucial for qualitative and quantitative multiparametric analysis. While retrospective coregistration is computationally intense and could be inaccurate, hybrid PET/MR scanners allow acquiring implicitly coregistered… Click to show full abstract

Coregistration of multimodal diagnostic images is crucial for qualitative and quantitative multiparametric analysis. While retrospective coregistration is computationally intense and could be inaccurate, hybrid PET/MR scanners allow acquiring implicitly coregistered images. Aim of this study is to assess the performance of state-of-the-art coregistration methods applied to PET and MR acquired as single modalities, comparing the results with the implicitly coregistration of a hybrid PET/MR, in complex anatomical regions such as head/neck (HN). A dataset consisting of PET/CT and PET/MR subsequently acquired in twenty-three patients was considered: performance of rigid (RR) and deformable (DR) registration obtained by a commercial software and an open-source registration package was evaluated. Registration accuracy was qualitatively assessed in terms of visual alignment of anatomical structures and qualitatively measured by the Dice scores computed on segmented tumors in PET and MRI. The resulting scores highlighted that hybrid PET/MR showed higher registration accuracy than retrospectively coregistered images, because of an overall misalignment after RR, unrealistic deformations and volume variations after DR. DR revealed superior performance compared to RR due to complex nonrigid movements of HN district. Moreover, simultaneous PET/MR offers unique datasets serving as ground truth for the improvement and validation of coregistration algorithms, if acquired with PET/CT.

Keywords: pet; coregistration; registration; hybrid pet; evaluation benefits; head neck

Journal Title: Journal of Healthcare Engineering
Year Published: 2017

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