Medical image processing

医学图像处理
  • 文章类型: Journal Article
    泰尔防腐,然后冷冻在所需的位置,并获得CT+MRI扫描,预计是理想的方法,以获得准确的,增强的CT数据用于勾画指南的开发。Thiel防腐和冷冻对MRI图像质量的影响尚不清楚。这项研究评估了上述过程,以获得增强的CT数据集,专注于从冷冻获得的MRI数据的整合,泰尔防腐的标本。
    将三个Thiel防腐的标本冷冻在俯卧爬行位置,并根据对比细节和相应结构的3D渲染之间的结构一致性评估MRI扫描方案。在相应的MRI和CT扫描上进行分段。还评估了数据集配准程序的测量误差。
    扫描协议T1VIBEFS能够根据对比细节快速区分软组织,甚至允许臂丛神经的完全详细的分割。在CT和MRI上重建的结构之间的结构一致性良好,神经和血管输入到CT扫描中从不与骨骼相交。图像配准程序的平均测量误差始终在亚毫米范围内(范围0.77-0.94mm)。
    基于出色的MRI图像质量和亚毫米误差余量,建议在治疗位置扫描冷冻的Thiel防腐标本以获得增强CT扫描。该程序可用于支持划界指南的假设,或者训练深度学习算法,考虑自动分割。
    Thiel embalming followed by freezing in the desired position and acquiring CT + MRI scans is expected to be the ideal approach to obtain accurate, enhanced CT data for delineation guideline development. The effect of Thiel embalming and freezing on MRI image quality is not known. This study evaluates the above-described process to obtain enhanced CT datasets, focusing on the integration of MRI data obtained from frozen, Thiel-embalmed specimens.
    Three Thiel-embalmed specimens were frozen in prone crawl position and MRI scanning protocols were evaluated based on contrast detail and structural conformity between 3D renderings from corresponding structures, segmented on corresponding MRI and CT scans. The measurement error of the dataset registration procedure was also assessed.
    Scanning protocol T1 VIBE FS enabled swift differentiation of soft tissues based on contrast detail, even allowing a fully detailed segmentation of the brachial plexus. Structural conformity between the reconstructed structures on CT and MRI was excellent, with nerves and blood vessels imported into the CT scan never intersecting with the bones. The mean measurement error for the image registration procedure was consistently in the submillimeter range (range 0.77-0.94 mm).
    Based on the excellent MRI image quality and the submillimeter error margin, the procedure of scanning frozen Thiel-embalmed specimens in the treatment position to obtain enhanced CT scans is recommended. The procedure can be used to support the postulation of delineation guidelines, or for training deep learning algorithms, considering automated segmentations.
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