SBIR Phase I: Single-shot X-ray Phase-contrast Imaging Using Deep Learning Approaches

SBIR 第一阶段：使用深度学习方法的单次 X 射线相衬成像

• 批准号: 2321552
• 负责人: Yao-Te Cheng
• 金额: $ 27.5万
• 依托单位: 3FATES-XRAY, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2321552&HistoricalAwards=false
• 关键词: SBIR; Phase; Single; shot; ray

The broader impact of this Small Business Innovation Research (SBIR) Phase I project relates to the benefits of next-generation X-ray imaging systems. The proposed single-shot platform overcomes the current barriers to widespread commercialization of Differential Phase Contrast (DPC) X-ray imaging. If successful, the single-shot technology will enable the development and application of next generation X-ray imagers for detecting liquids, small explosives, and other security threats for aviation and business applications. Reducing false alarm rates at airports will increase customer satisfaction, improve security, and reduce cost. DPC imaging could also substantially increase the detection of food pests, thereby reducing food waste and saving billions of dollars. In another market, non-destructive testing could significantly improve the inspection of additive manufacturing products, reducing manufacturing time through fewer iterations and creating high-quality products. Medical DPC imagers would provide MRI (Magnetic Resonance Imaging)-like resolution and diagnostics at an order of magnitude lower cost than current MRI.This Small Business Innovation Research Phase I project aims to develop a deep-learning approach to realize “single-shot” X-ray phase-contrast imaging. To commercialize the technology, the deep-learning algorithm needs to identify more complicated real-world objects effectively and accurately. Deep-learning methods require thousands to millions of training samples to make a reliable model. However, no imaging library for this unique technology currently exists. The research and development plan initially incorporates the standard slow scanning method of X-ray phase-contrast imaging to obtain DPC tri-signature computed tomography (CT) images. The tri-signature CT images provide the basis for precise material characterization (e.g., absorption coefficients, indices of refraction, and scatter characteristics). Once the materials have been characterized, they form the basis for creating millions of numerical representations of real-world objects. These objects subsequently form the core for effectively and efficiently training deep-learning models without further experimentation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项小型企业创新研究（SBIR）I阶段项目的更广泛影响与下一代X射线成像系统的好处有关。提出的单发平台克服了当前的差异差异相比（DPC）X射线成像的宽度商业化的障碍。如果成功的话，单发技术将使下一代X射线图像的开发和应用用于检测液体，小型爆炸物以及航空和业务应用的其他安全威胁。降低机场的误报率将提高客户满意度，提高安全性并降低成本。 DPC成像还可以大大增加对食物害虫的检测，从而减少食物浪费并节省数十亿美元。在另一个市场中，非破坏性测试可以显着改善对添加剂制造产品的检查，从而减少迭代次数并创建高质量产品的制造时间。医疗DPC Imagesrs将提供MRI（磁共振成像）类似于分辨率和诊断，其成本低于当前MRI。此小型企业创新研究阶段I项目旨在开发一种深度学习方法，以实现“单杆” X射线相对比成像。为了使技术商业化，深度学习算法需要有效，准确地识别更复杂的现实对象。深度学习方法需要数千到数百万培训样本才能制作出可靠的模型。但是，目前没有针对这种独特技术的成像库。研究与开发计划最初结合了X射线相位对比成像的标准慢扫描方法，以获得DPC三符号计算机断层扫描（CT）图像。三符号CT图像为精确材料表征提供了基础（例如，吸收系数，折射率和散射字符）。一旦材料进行了表征，它们就构成了创建数百万个现实世界对象的数值表示的基础。这些对象随后构成了无需进一步实验的有效和有效训练深度学习模型的核心。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响来审查标准，认为通过评估被认为是宝贵的支持。