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Image registration of ex-vivo MRI to sparsely sectioned histology of hippocampal and neocortical temporal lobe specimens.

机译:MRI图像配准到海马和新皮质颞叶标本的稀疏切片组织学。

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摘要

Intractable or drug-resistant epilepsy occurs in up to 30% of epilepsy patients, with many of these patients undergoing surgical excision of the affected brain region to achieve seizure control. Recent magnetic resonance imaging (MRI) sequences and analysis techniques have the potential to detect abnormalities not identified with diagnostic MRI protocols. Prospective studies involving pre-operative imaging and collection of surgically-resected tissue provide a unique opportunity for verification and tuning of these image analysis techniques, since direct comparison can be made against histopathology, and can lead to better prediction of surgical outcomes and potentially less invasive procedures. To carry out MRI and histology comparison, spatial correspondence between the MR images and the histology images must be found. Towards this goal, a novel pipeline is presented here for bringing ex-vivo MRI of surgically-resected temporal lobe specimens and digital histology into spatial correspondence. The sparsely-sectioned histology images represent a challenge for 3D reconstruction which we address with a combined 3D and 2D registration algorithm that alternates between slice-based and volume-based registration with the ex-vivo MRI. We evaluated our registration method on specimens resected from patients undergoing anterior temporal lobectomy (N=7) and found our method to have a mean target registration error of 0.76±0.66 and 0.98±0.60 mm for hippocampal and neocortical specimens respectively. This work allows for the spatially-local comparison of histology with post-operative MRI and paves the way for eventual correlation with pre-operative MRI image analysis techniques.
机译:高达30%的癫痫患者发生顽固性或耐药性癫痫,其中许多患者接受手术切除受影响的大脑区域以控制癫痫发作。最近的磁共振成像(MRI)序列和分析技术具有检测未通过诊断MRI协议识别的异常的潜力。涉及术前成像和手术切除组织收集的前瞻性研究为验证和调整这些图像分析技术提供了独特的机会,因为可以与组织病理学进行直接比较,并且可以更好地预测手术结果并减少侵入性程序。为了进行MRI和组织学比较,必须找到MR图像和组织学图像之间的空间对应性。为了实现这一目标,这里提出了一条新颖的管道,用于将手术切除的颞叶标本的离体MRI和数字组织学进行空间对应。稀疏的组织学图像代表了3D重建的挑战,我们将通过结合3D和2D配准算法来解决这个问题,该算法在离体MRI与基于切片的配准和基于体积的配准之间交替。我们评估了从前颞叶切除术(N = 7)患者切除的标本上的配准方法,发现我们的方法对海马和新皮层标本的平均目标配准误差分别为0.76±0.66和0.98±0.60 mm。这项工作允许组织学与术后MRI的空间局部比较,并为与术前MRI图像分析技术的最终相关性铺平道路。

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