Axente Marian, He Jun, Bass Christopher P, Sundaresan Gobalakrishnan, Zweit Jamal, Williamson Jeffrey F, Pugachev Andrei
Department of Radiation Oncology, Virginia Commonwealth University Medical Center, United States; Department of Radiation Oncology, Stanford University School of Medicine, United States.
Department of Radiation Oncology, Virginia Commonwealth University Medical Center, United States.
Radiother Oncol. 2014 Feb;110(2):309-16. doi: 10.1016/j.radonc.2013.12.017. Epub 2014 Jan 30.
In radiotherapy, PET images can be used to guide the delivery of selectively escalated doses to biologically relevant tumour subvolumes. Validation of PET for such applications requires demonstration of spatial coincidence between PET tracer uptake pattern and the histopathologically confirmed target. This study introduces a novel approach to histopathological validation of PET image segmentation for radiotherapy guidance.
Sequential tissue sections from surgically excised whole-tumour specimens were used to acquire full 3D-sets of both histopathological images (microscopy) and PET tracer distribution images (autoradiography). After these datasets were accurately registered, a full 3D autoradiographic distribution of PET tracer was reconstructed and used to obtain synthetic PET images (sPET) by simulating the image deterioration induced by processes involved in PET image formation. To illustrate the method, sPET images were used in this study to investigate spatial coincidence between high FDG uptake areas and the distribution of viable tissue in two small animal tumour models.
The reconstructed 3D autoradiographic distribution of the PET tracer was spatially coherent, as indicated by the high average value of the normalised pixel-by-pixel correlation of intensities between successive slices (0.84 ± 0.05 and 0.94 ± 0.02). The loss of detail in the sPET images versus the 3D autoradiography was significant as indicated by Dice coefficient values corresponding to the two tumours (0 and 0.1 at 70% threshold). The maximum overlap between the FDG segmented volumes and the extent of the viable tissue as indicated by Dice coefficient values, was 0.8 for one tumour (for the image thresholded at 22% of max intensity) and 0.88 for the other (threshold of 14% of max intensity).
It was demonstrated that the use of synthetic PET images for histopathological validation allows for bypassing a technically challenging and error-prone step of registering non-invasive PET images with histopathology.
在放射治疗中，正电子发射断层扫描（PET）图像可用于指导向生物学相关的肿瘤亚体积选择性递增剂量的输送。对于此类应用，PET的验证需要证明PET示踪剂摄取模式与组织病理学证实的靶标之间的空间一致性。本研究引入了一种新方法，用于对放射治疗指导的PET图像分割进行组织病理学验证。
使用手术切除的全肿瘤标本的连续组织切片来获取组织病理学图像（显微镜检查）和PET示踪剂分布图像（放射自显影）的完整三维数据集。在这些数据集精确配准后，重建PET示踪剂的完整三维放射自显影分布，并通过模拟PET图像形成过程中引起的图像退化来获得合成PET图像（sPET）。为了说明该方法，本研究使用sPET图像来研究两个小动物肿瘤模型中高氟代脱氧葡萄糖（FDG）摄取区域与存活组织分布之间的空间一致性。
PET示踪剂的重建三维放射自显影分布在空间上是连贯的，连续切片之间强度的逐像素归一化相关性的高平均值表明了这一点（0.84±0.05和0.94±0.02）。如对应于两个肿瘤的Dice系数值所示，sPET图像与三维放射自显影相比细节的丢失是显著的（在70%阈值下为0和0.1）。如Dice系数值所示，FDG分割体积与存活组织范围之间的最大重叠，对于一个肿瘤为0.8（图像阈值为最大强度的22%），对于另一个肿瘤为0.88（阈值为最大强度的14%）。
结果表明，使用合成PET图像进行组织病理学验证可以绕过将非侵入性PET图像与组织病理学配准这一技术上具有挑战性且容易出错的步骤。
