Istituto di Bioimmagini e Fisiologia Molecolare - Consiglio Nazionale delle Ricerche (IBFM-CNR), Cefalù (PA), Italy; Dipartimento di Informatica, Sistemistica e Comunicazione (DISCo), Università degli Studi di Milano-Bicocca, Milano, Italy.
Istituto di Bioimmagini e Fisiologia Molecolare - Consiglio Nazionale delle Ricerche (IBFM-CNR), Cefalù (PA), Italy; Dipartimento di Ingegneria Chimica, Gestionale, Informatica, Meccanica (DICGIM), Università degli Studi di Palermo, Palermo, Italy.
Comput Methods Programs Biomed. 2017 Jun;144:77-96. doi: 10.1016/j.cmpb.2017.03.011. Epub 2017 Mar 19.
Nowadays, clinical practice in Gamma Knife treatments is generally based on MRI anatomical information alone. However, the joint use of MRI and PET images can be useful for considering both anatomical and metabolic information about the lesion to be treated. In this paper we present a co-segmentation method to integrate the segmented Biological Target Volume (BTV), using [C]-Methionine-PET (MET-PET) images, and the segmented Gross Target Volume (GTV), on the respective co-registered MR images. The resulting volume gives enhanced brain tumor information to be used in stereotactic neuro-radiosurgery treatment planning. GTV often does not match entirely with BTV, which provides metabolic information about brain lesions. For this reason, PET imaging is valuable and it could be used to provide complementary information useful for treatment planning. In this way, BTV can be used to modify GTV, enhancing Clinical Target Volume (CTV) delineation.
A novel fully automatic multimodal PET/MRI segmentation method for Leksell Gamma Knife treatments is proposed. This approach improves and combines two computer-assisted and operator-independent single modality methods, previously developed and validated, to segment BTV and GTV from PET and MR images, respectively. In addition, the GTV is utilized to combine the superior contrast of PET images with the higher spatial resolution of MRI, obtaining a new BTV, called BTV. A total of 19 brain metastatic tumors, undergone stereotactic neuro-radiosurgery, were retrospectively analyzed. A framework for the evaluation of multimodal PET/MRI segmentation is also presented. Overlap-based and spatial distance-based metrics were considered to quantify similarity concerning PET and MRI segmentation approaches. Statistics was also included to measure correlation among the different segmentation processes. Since it is not possible to define a gold-standard CTV according to both MRI and PET images without treatment response assessment, the feasibility and the clinical value of BTV integration in Gamma Knife treatment planning were considered. Therefore, a qualitative evaluation was carried out by three experienced clinicians.
The achieved experimental results showed that GTV and BTV segmentations are statistically correlated (Spearman's rank correlation coefficient: 0.898) but they have low similarity degree (average Dice Similarity Coefficient: 61.87 ± 14.64). Therefore, volume measurements as well as evaluation metrics values demonstrated that MRI and PET convey different but complementary imaging information. GTV and BTV could be combined to enhance treatment planning. In more than 50% of cases the CTV was strongly or moderately conditioned by metabolic imaging. Especially, BTV enhanced the CTV more accurately than BTV in 25% of cases.
The proposed fully automatic multimodal PET/MRI segmentation method is a valid operator-independent methodology helping the clinicians to define a CTV that includes both metabolic and morphologic information. BTV and GTV should be considered for a comprehensive treatment planning.
目前，伽玛刀治疗的临床实践通常仅基于 MRI 解剖学信息。然而，联合使用 MRI 和 PET 图像对于考虑待治疗病变的解剖学和代谢信息可能是有用的。本文提出了一种基于 [C]-蛋氨酸-PET（MET-PET）图像的生物靶区（BTV）分割的共分割方法，并将其与各自的配准 MR 图像上的大体靶区（GTV）分割相结合。生成的体积可提供增强的脑肿瘤信息，用于立体定向神经放射外科治疗计划。GTV 通常与提供脑病变代谢信息的 BTV 不完全匹配。因此，PET 成像具有价值，可提供对治疗计划有用的补充信息。通过这种方式，可以使用 BTV 修改 GTV，增强临床靶区（CTV）的勾画。
提出了一种用于 Leksell 伽玛刀治疗的新型全自动多模态 PET/MRI 分割方法。该方法改进并结合了两种计算机辅助和操作员独立的单模态方法，这些方法先前已经得到开发和验证，用于分别从 PET 和 MRI 图像中分割 BTV 和 GTV。此外，GTV 用于结合 PET 图像的高对比度和 MRI 的高空间分辨率，获得一个新的 BTV，称为 BTV。共回顾性分析了 19 例接受立体定向神经放射外科治疗的脑转移瘤患者。还提出了一种多模态 PET/MRI 分割评估框架。考虑了基于重叠和基于空间距离的度量来量化 PET 和 MRI 分割方法的相似性。还包括统计信息来衡量不同分割过程之间的相关性。由于在没有治疗反应评估的情况下，不可能根据 MRI 和 PET 图像定义一个金标准 CTV，因此考虑了在伽玛刀治疗计划中整合 BTV 的可行性和临床价值。因此，由三位经验丰富的临床医生进行了定性评估。
实验结果表明，GTV 和 BTV 分割具有统计学相关性（Spearman 等级相关系数：0.898），但相似程度较低（平均 Dice 相似系数：61.87±14.64）。因此，体积测量值和评估指标值表明，MRI 和 PET 传递不同但互补的成像信息。可以结合 GTV 和 BTV 来增强治疗计划。在超过 50%的情况下，代谢成像强烈或适度地影响 CTV。特别是，BTV 在 25%的情况下比 BTV 更准确地增强了 CTV。
所提出的全自动多模态 PET/MRI 分割方法是一种有效的独立于操作人员的方法，可帮助临床医生定义包含代谢和形态信息的 CTV。BTV 和 GTV 应考虑用于全面的治疗计划。
