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Whole-body diffusion-weighted MR image stitching and alignment to anatomical MRI

机译:全身扩散加权MR图像拼接和解剖MRI对准

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Whole-body diffusion-weighted (WB-DW) MRI in combination with anatomical MRI has shown a great potential 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 multimodal 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.
机译:全身扩散加权(WB-DW)MRI与解剖MRI结合在骨骼和软组织肿瘤检测,淋巴结评估和治疗反应评估中显示出巨大潜力。由于全身覆盖范围广,因此需要在单独的工作站中获取全身MRI,然后将其合并为全身图像。但是,由于采集期间的图像变形和患者运动,可能会发生站间和模态间图像未对准,这可能会导致患者解剖结构的表示不正确,并妨碍视觉评估。因此,自动化和准确的全身图像形成以及多模式MRI图像的对齐至关重要。我们研究了几种配准方法,用于形成或缝合全身图像站,然后对多模式全身图像进行可变形对齐。我们将成对方法与成组方法进行了比较,成对方法是将扩散加权(DW)图像站顺序对齐到参考站(骨盆),而成组方法是将所有站点同时映射到公共参考空间,同时将总体转换减到最少。对于每种图像,研究了输入图像和相应指标的选择。通过评估获得的全身图像的质量并验证与全身解剖序列对齐的准确性来评估性能。逐组配准方法在WB-DW图像的形成与多模式对齐之间提供了最佳折中方案。发现全自动方法是鲁棒的,使其在临床上可行。

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