Biomedical Imaging Group Rotterdam, Department of Radiology and Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands.
PLoS One. 2011;6(8):e22835. doi: 10.1371/journal.pone.0022835. Epub 2011 Aug 29.
Magnetic resonance imaging (MRI), together with histology, is widely used to diagnose and to monitor treatment in oncology. Spatial correspondence between these modalities provides information about the ability of MRI to characterize cancerous tissue. However, registration is complicated by deformations during pathological processing, and differences in scale and information content.
METHODOLOGY/PRINCIPAL FINDINGS: This study proposes a methodology for establishing an accurate 3D relation between histological sections and high resolution in vivo MRI tumor data. The key features of the methodology are: 1) standardized acquisition and processing, 2) use of an intermediate ex vivo MRI, 3) use of a reference cutting plane, 4) dense histological sampling, 5) elastic registration, and 6) use of complete 3D data sets. Five rat pancreatic tumors imaged by T2*-w MRI were used to evaluate the proposed methodology. The registration accuracy was assessed by root mean squared (RMS) distances between manually annotated landmark points in both modalities. After elastic registration the average RMS distance decreased from 1.4 to 0.7 mm. The intermediate ex vivo MRI and the reference cutting plane shared by all three 3D images (in vivo MRI, ex vivo MRI, and 3D histology data) were found to be crucial for the accurate co-registration between the 3D histological data set and in vivo MRI. The MR intensity in necrotic regions, as manually annotated in 3D histology, was significantly different from other histologically confirmed regions (i.e., viable and hemorrhagic). However, the viable and the hemorrhagic regions showed a large overlap in T2(*)-w MRI signal intensity.
The established 3D correspondence between tumor histology and in vivo MRI enables extraction of MRI characteristics for histologically confirmed regions. The proposed methodology allows the creation of a tumor database of spatially registered multi-spectral MR images and multi-stained 3D histology.
磁共振成像（MRI）与组织学一起广泛用于肿瘤的诊断和治疗监测。这些模态之间的空间对应关系提供了关于 MRI 对癌组织进行特征描述的能力的信息。然而，注册过程因病理处理过程中的变形以及尺度和信息量的差异而变得复杂。
方法/主要发现：本研究提出了一种在组织学切片和高分辨率体内 MRI 肿瘤数据之间建立准确 3D 关系的方法。该方法的关键特征是：1）标准化采集和处理，2）使用中间离体 MRI，3）使用参考切割平面，4）密集的组织学采样，5）弹性配准，以及 6）使用完整的 3D 数据集。使用 T2*-w MRI 成像的 5 只大鼠胰腺肿瘤来评估所提出的方法。通过在两种模态中手动注释标志点之间的均方根（RMS）距离来评估配准精度。经过弹性配准后，平均 RMS 距离从 1.4 毫米降低到 0.7 毫米。发现中间离体 MRI 和所有三个 3D 图像（体内 MRI、离体 MRI 和 3D 组织学数据）共有的参考切割平面对于准确地将 3D 组织学数据集与体内 MRI 配准至关重要。在 3D 组织学中手动注释的坏死区域的 MR 强度与其他经组织学确认的区域（即存活和出血）显著不同。然而，存活和出血区域在 T2(*)-w MRI 信号强度上表现出很大的重叠。
在肿瘤组织学和体内 MRI 之间建立的 3D 对应关系使得可以提取经过组织学确认的区域的 MRI 特征。所提出的方法允许创建具有空间配准的多光谱 MRI 图像和多染色 3D 组织学的肿瘤数据库。
