Department of Hematology, Oncology and Radiation Physics, Skåne University Hospital, Lund, 221 85, Sweden.
Department of Medical Radiation Physics, Lund University, Malmö, 205 02, Sweden.
Med Phys. 2017 Nov;44(11):5563-5574. doi: 10.1002/mp.12516. Epub 2017 Sep 11.
The superior soft tissue contrast of magnetic resonance imaging (MRI) compared to computed tomography (CT) has urged the integration of MRI and elimination of CT in radiotherapy treatment (RT) for prostate. An intraprostatic gold fiducial marker (GFM) appears hyperintense on CT. On T2-weighted (T2w) MRI target delineation images, the GFM appear as a small signal void similar to calcifications and post biopsy fibrosis. It can therefore be difficult to identify the markers without CT. Detectability of GFMs can be improved using additional MR images, which are manually registered to target delineation images. This task requires manual labor, and is associated with interoperator differences and image registration errors. The aim of this work was to develop and evaluate an automatic method for identification of GFMs directly in the target delineation images without the need for image registration.
T2w images, intended for target delineation, and multiecho gradient echo (MEGRE) images intended for GFM identification, were acquired for prostate cancer patients. Signal voids in the target delineation images were identified as GFM candidates. The GFM appeared as round, symmetric, signal void with increasing area for increasing echo time in the MEGRE images. These image features were exploited for automatic identification of GFMs in a MATLAB model using a patient training dataset (n = 20). The model was validated on an independent patient dataset (n = 40). The distances between the identified GFM in the target delineation images and the GFM in CT images were measured. A human observatory study was conducted to validate the use of MEGRE images.
The sensitivity, specificity, and accuracy of the automatic method and the observatory study was 84%, 74%, 81% and 98%, 94%, 97%, respectively. The mean absolute difference in the GFM distances for the automatic method and observatory study was 1.28 ± 1.25 mm and 1.14 ± 1.06 mm, respectively.
Multiecho gradient echo images were shown to be a feasible and reliable way to perform GFM identification. For clinical practice, visual inspection of the results from the automatic method is needed at the current stage.
磁共振成像（MRI）的软组织对比度优于计算机断层扫描（CT），这促使将 MRI 与 CT 整合并消除 CT，以用于前列腺放射治疗（RT）。前列腺内的金标（GFM）在 CT 上呈高信号。在 T2 加权（T2w）MRI 靶区勾画图像上，GFM 表现为类似于钙化和活检后纤维化的小信号缺失。因此，如果没有 CT，很难识别标记物。通过使用手动配准到靶区勾画图像的额外 MR 图像，可以提高 GFM 的可检测性。此任务需要人工劳动，并且与操作员之间的差异和图像配准错误相关。本研究旨在开发并评估一种无需图像配准即可直接在靶区勾画图像中识别 GFM 的自动方法。
为前列腺癌患者采集用于靶区勾画的 T2w 图像和用于 GFM 识别的多回波梯度回波（MEGRE）图像。在靶区勾画图像中识别信号缺失作为 GFM 候选者。在 MEGRE 图像中，GFM 表现为圆形、对称、信号缺失，随着回波时间的增加，面积逐渐增大。利用这些图像特征，在使用患者训练数据集（n=20）的 MATLAB 模型中，自动识别 GFM。该模型在独立的患者数据集（n=40）上进行验证。测量靶区勾画图像中识别的 GFM 与 CT 图像中 GFM 之间的距离。进行了一项人类观察研究，以验证 MEGRE 图像的使用。
自动方法和观察研究的灵敏度、特异性和准确性分别为 84%、74%和 81%，98%、94%和 97%。自动方法和观察研究中 GFM 距离的平均绝对差值分别为 1.28±1.25mm 和 1.14±1.06mm。
多回波梯度回波图像被证明是一种可行且可靠的方法来进行 GFM 识别。在当前阶段，对于临床实践，需要对自动方法的结果进行视觉检查。
