脑MRI非刚性配准:更快更便宜

Yixun Liu, Andrey Fedorov, R. Kikinis, N. Chrisochoides
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引用次数: 5

摘要

我们研究了非刚性配准(NRR)在图像引导神经外科术中脑移位恢复中的应用问题。肿瘤切除过程的时间关键性质是常规临床使用许多可用的NRR方法的主要障碍。在本文中,我们利用具有先进显卡的单个多核工作站的资源来并行化和评估临床验证的NRR方法的端到端实现。临床脑MRI数据表明,并行NRR可以达到临床实时性要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Non-Rigid Registration for brain MRI: faster and cheaper
We study the problem of Non-Rigid Registration (NRR) for intra-operative recovery of brain shift during image-guided neurosurgery. Time-critical nature of the tumour resection procedure presents a major obstacle to the routine clinical use of many available NRR approaches. In this paper, we utilise the resources of a single multicore workstation with an advanced graphics card to parallelise and evaluate an end-to-end implementation of a clinically validated NRR method. The results on clinical brain MRI data show the parallel NRR can reach real-time clinical requirement.
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