Organ-aware CBCT enhancement via dual path learning for prostate cancer treatment.
Organ-aware CBCT enhancement via dual path learning for prostate cancer treatment.
复制标题
DOI：
10.1002/mp.16752
复制
发表时间：
2023-09
期刊：
影响因子：
3.8
通讯作者：
Xu Chen;Yunkui Pang;Sahar Ahmad;Trevor J Royce;Andrew Wang;Jun Lian;P. Yap
中科院分区：
文献类型：
--
作者：
Xu Chen;Yunkui Pang;Sahar Ahmad;Trevor J Royce;Andrew Wang;Jun Lian;P. Yap
背景锥束计算机断层扫描（CBCT）在前列腺癌的调强放射治疗（IMRT）中起着至关重要的作用。然而，较差的图像对比度和模糊的器官边界给剂量输送的精确定位和适应性治疗的计划重新优化带来了挑战。目的在这项工作中，我们的目标是通过将骨盆 CBCT 图像转换为高质量 CT 图像来增强盆腔 CBCT 图像，特别关注对放射治疗重要的解剖结构。方法我们开发了一种新颖的双路径学习框架，涵盖全局和局部信息，用于前列腺、膀胱和直肠的器官感知增强。全局路径在图像级别学习粗略的模态间转换。本地路径在区域级别学习器官感知翻译。这种双路径学习架构可以作为即插即用模块，适用于其他医学图像到图像转换框架。结果我们定量和定性地评估了所提出方法的性能。训练数据集由未配对的 40 个 CBCT 和 40 个 CT 扫描组成，验证数据集由 5 个配对的 CBCT-CT 扫描组成，测试数据集由 10 个配对的 CBCT-CT 扫描组成。增强CBCT与参考CT图像之间的峰值信噪比（PSNR）为27.22±1.79，增强CBCT与参考CT图像之间的结构相似性（SSIM）为0.71±0.03。我们还将我们的方法与最先进的图像到图像翻译方法进行了比较，我们的方法实现了最佳性能。此外，统计分析证实我们的方法所取得的改进具有统计显着性。结论 与相关方法相比，所提出的方法证明了其在增强盆腔 CBCT 图像方面的优越性，特别是在器官水平上。
BACKGROUND Cone-beam computed tomography (CBCT) plays a crucial role in the intensity modulated radiotherapy (IMRT) of prostate cancer. However, poor image contrast and fuzzy organ boundaries pose challenges to precise targeting for dose delivery and plan reoptimization for adaptive therapy. PURPOSE In this work, we aim to enhance pelvic CBCT images by translating them to high-quality CT images with a particular focus on the anatomical structures important for radiotherapy. METHODS We develop a novel dual-path learning framework, covering both global and local information, for organ-aware enhancement of the prostate, bladder and rectum. The global path learns coarse inter-modality translation at the image level. The local path learns organ-aware translation at the regional level. This dual-path learning architecture can serve as a plug-and-play module adaptable to other medical image-to-image translation frameworks. RESULTS We evaluated the performance of the proposed method both quantitatively and qualitatively. The training dataset consists of unpaired 40 CBCT and 40 CT scans, the validation dataset consists of 5 paired CBCT-CT scans, and the testing dataset consists of 10 paired CBCT-CT scans. The peak signal-to-noise ratio (PSNR) between enhanced CBCT and reference CT images is 27.22 ± 1.79, and the structural similarity (SSIM) between enhanced CBCT and the reference CT images is 0.71 ± 0.03. We also compared our method with state-of-the-art image-to-image translation methods, where our method achieves the best performance. Moreover, the statistical analysis confirms that the improvements achieved by our method are statistically significant. CONCLUSIONS The proposed method demonstrates its superiority in enhancing pelvic CBCT images, especially at the organ level, compared to relevant methods.