PET-GUIDED TUMOR VOLUME DELINEATION FOR RADIATION THERAPY

描述（由申请人提供）：用于放射治疗的 PET 引导肿瘤体积描绘 正电子发射断层扫描 (PET)，特别是 18F-FDG，越来越多地用于放射治疗，以协助放射肿瘤学家描绘三维肿瘤形状以制定治疗计划。虽然 PET 成像可以获取非常灵敏的分子探针，但 PET 成像过程的空间分辨率相对较差。这与 PET 图像采集和重建过程一起，导致表观 PET 放射性同位素摄取分布远远超出物理放射性同位素摄取分布。现有的使用 PET 图像分割肿瘤边界的方法主要集中在基于强度的阈值处理，这受到阈值选择模糊性的限制。基于强度的阈值方法对 PET 图像效果不佳的原因是强度直方图不是双峰形状，因此没有可以自动客观检测的稳定阈值。我们提出了一种新颖的两阶段图像分析过程，克服了现有分割方法的困难。第一阶段是自适应区域生长 (ARG)，分析 PET 图像，从而生成可靠且可重复的标志，定义“初步”肿瘤体积，该体积大于实际肿瘤体积。第二阶段采用双前沿活动轮廓 (DFAC) 模型，使初步肿瘤表面符合 PET 放射性同位素定义的肿瘤体积。我们将在体模实验、蒙特卡罗模拟数据以及最后的头颈癌患者研究上开发和优化这个两阶段模型，最终通过空间联合配准 PET/CT 图像和基于病理学的肿瘤体积相关性进行前瞻性试点研究。头颈癌是开发和验证分割过程的理想选择，因为它在解剖学上复杂、可治愈，可以将解剖和代谢成像结果与病理学相关联，肿瘤容易代谢 18F-FDG，并且由于周围有众多关键结构，因此非常需要精确的肿瘤分割。该提案响应题为“体内癌症成像探索/开发资助（R21）”的计划公告 PA-06-371，特别是“体内癌症图像显示和分析”和“体内图像引导癌症干预”两个主题。我们将利用R21机制来开发这项有前景的技术，并为后续R01应用提供初步数据。如果这个过程有效，它将首次为放射肿瘤科医生提供客观、可靠和准确的 PET 分割工具，帮助他们描绘肿瘤，提高放射治疗计划的准确性和一致性，并最终提高放射治疗护理的质量。公共健康相关性：我们建议开发并验证可靠的方法，用于在头颈癌患者的正电子发射断层扫描 (PET) 中描绘肿瘤。改善肿瘤轮廓可能会提高放射剂量输送的质量和准确性。这可能会改善局部控制，并最终提高这些癌症患者的生存率。

DESCRIPTION (provided by applicant): PET-guided tumor volume delineation for radiation therapy Positron-emission tomography (PET), especially 18F-FDG, is increasingly being used in radiation therapy to assist the radiation oncologist in delineating the three-dimensional tumor shape for treatment planning. While PET imaging provides access to very sensitive molecular probes, the PET imaging process suffers from relatively poor spatial resolution. This, along with the PET image acquisition and reconstruction process, causes the apparent PET radioisotope uptake distribution to spread well beyond the physical radioisotope uptake distribution. Existing methods to segment the tumor boundary using PET images have focused on intensity-based thresholding, which is limited by ambiguity with respect to the threshold selection. The reason that intensity-based thresholding methods do not work well with PET images is because the intensity histograms are not bimodal in shape, so there is no stable threshold that can be automatically and objectively detected. We propose a novel two-stage image analysis process that overcomes the difficulties with the existing segmentation methods. The first stage, adaptive region growing (ARG), analyzes the PET image such that a reliable and reproducible landmark is produced that defines a "preliminary" tumor volume, which is larger than the actual tumor volume. The second stage employs a dual-front active contour (DFAC) model to conform the preliminary tumor surface to the PET radioisotope defined tumor volume. We will develop and optimize this two-stage model on phantom experiments, Monte Carlo simulated data, and finally on head-and-neck cancer patient studies, culminating in a prospective pilot study with spatially co-registered PET/CT images and pathology-based tumor volume correlation. Head- and neck cancer is ideal for developing and validating the segmentation process because it is anatomically complex, curable, it is possible to correlate anatomic and metabolic imaging findings with pathology, the tumors readily metabolize 18F-FDG, and accurate tumor segmentation is highly desirable due to the numerous surrounding critical structures. This proposal responds to the Program Announcement PA-06-371 titled "In vivo Cancer Imaging Exploratory/Developmental Grants (R21)", particularly, the two topics "in vivo cancer image displays and analyses" and "in vivo image-guided cancer interventions". We will use the R21 mechanism to develop this promising technique and provide preliminary data for a subsequent R01 application. If this process works, it will provide to the radiation oncologist, for the first time, an objective, reliable, and accurate PET segmentation tool to aid them in delineating the tumor, improving the accuracy and consistency of radiation therapy treatment plans and ultimately improving the quality of radiation therapy care. PUBLIC HEALTH RELEVANCE: We propose to develop and validate reliable methods for delineating tumors in Positron Emission Tomography (PET) for patients with head and neck cancer. Improved tumor delineation may lead to improved quality and accuracy of radiation dose delivery. This may improve the local control, and eventually the survival of these cancer patients.