Unsupervised Deep Photon-Counting Computed Tomography Reconstruction for Human Extremity Imaging

用于人体肢体成像的无监督深度光子计数计算机断层扫描重建

• 批准号: 10718303
• 负责人: Hengyong Yu
• 金额: $ 60.83万
• 依托单位: UNIVERSITY OF MASSACHUSETTS LOWELL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10718303
• 关键词: Acceleration; Algorithms; Architecture; Awareness; Back; Big Data; Bismuth; Clinical; Clinical Research; Clinical Trials; Computer Systems; Computer software; Contrast Media; Data; Data Compression; Data Set; Dictionary; Dose; Evaluation; FDA approved; Goals; High Performance Computing; Human; Image; Image Enhancement; Knowledge; Learning; Limb structure; Low Dose Radiation; Maps; Mathematics; Methods; Modeling; Molecular; Nature; New Zealand; Performance; Photons; Planet Mars; Procedures; Protocols documentation; Publishing; Radiation Dose Unit; Reader; Reporting; Resolution; Roentgen Rays; Scanning; Software Engineering; Source; Speed; System; Techniques; Testing; Time; Tissues; Training; Validation; X-Ray Computed Tomography; X-Ray Medical Imaging; clinical application; clinical imaging; cluster computing; computational platform; data acquisition; data reduction; deep learning; design; detector; empowerment; experimental study; frontier; image reconstruction; imaging modality; improved; nanoGold; nanoparticle; novel; open source; photon-counting detector; prototype; reconstruction; simulation; spectral energy; stability testing; temporal measurement; theories; tomography

Abstract The state-of-the-art x-ray photon-counting CT (PCCT) generates images in multi-energy bins simultaneously with high spatial resolution and low radiation dose for tissue characterization and material decomposition. FDA has approved the techniques in 2021. Both clinical PCCT and micro-PCCT scanners are now commercially available. This opens a new door to opportunities for functional, cellular, and molecular x-ray imaging with novel contrast agents such as bismuth and gold nanoparticles. However, x-ray photon-counting detectors are not perfect, and it remains challenging to reconstruct high-quality PCCT images for various clinical applications. Over the past several years, deep learning-based tomographic imaging has become a new frontier of image reconstruction. Different from compressive sensing (CS) methods, which totally rely on the prior information in terms of an accurate mathematical constraint, the emerging deep learning-based approach is empowered by big data with which a deep network can be trained for superior tomographic reconstruction. However, a recent study published in PNAS revealed three types of instabilities of deep tomographic reconstruction networks, which are believed to be fundamental due to lack of kernel awareness and “nontrivial to overcome”, but CS-based reconstruction was reported in that study to be stable because of its kernel awareness. Meanwhile, it is hard to collect large amounts of data with ground-truths for supervised network training up to the clinical image quality. To overcome the aforementioned challenges in the context of a clinical trial with PCCT using Medipix detectors, our overall goal is to develop an Unsupervised Deep Learning Approach (UDLA) for few-view and low-dose image reconstruction based on our Analytic Compressive Iterative Deep (ACID) architecture but specific to PCCT data, with much higher spatial resolution and computational efficiency, and without the requirement of ground- truth for training. ACID combines the data-driven power of deep learning, the kernel-awareness of CS, and iterative refinement to deliver image reconstruction results accurately and stably. To achieve our goal, three specific aims are defined as follows. Aim 1: UDLA will be designed, developed, optimized, and integrated into an open-source platform, including a deep end-to-end reconstruction network and an advanced CS module with a multi-constraint model; Aim 2: UDLA will be tested for stability and generalizability, and accelerated via software optimization on a high-performance computing platform; and Aim 3: UDLA will be evaluated and validated in simulation, experiments, and retrospective use of clinical extremity imaging PCCT data. Upon the completion of this project, the UDLA software should have been characterized for clinical extremity imaging using Medpix-based PCCT to outperform contemporary iterative algorithms, without the vulnerabilities of existing deep reconstruction networks and the requirements of ground-truth for network training. In a broader perspective, our approach represents a paradigm shift towards the integration of model-based and data-driven reconstruction methods, and may have a lasting impact on PCCT and other tomographic imaging modalities.

抽象的 最先进的 X 射线光子计数 CT (PCCT) 可同时在多能量箱中生成图像 具有高空间分辨率和低辐射剂量，用于组织表征和材料分解。FDA 已于 2021 年批准了该技术。临床 PCCT 和微型 PCCT 扫描仪现已商业化 这为功能性、细胞和分子 X 射线成像提供了新的机会。 然而，X 射线光子计数探测器则不然。 完美，但为各种临床应用重建高质量的 PCCT 图像仍然具有挑战性。 过去几年，基于深度学习的断层成像已成为图像新领域 与压缩感知（CS）方法不同，压缩感知方法完全依赖于先验信息。 根据精确的数学约束，新兴的基于深度学习的方法得到了大数据的支持 然而，最近的一项研究表明，可以使用这些数据来训练深度网络以进行高级断层扫描重建。 发表在 PNAS 上的文章揭示了深度断层重建网络的三种不稳定性，它们是 由于缺乏内核意识和“克服困难”，被认为是根本性的，但基于 CS 该研究报告称重建是稳定的，因为其内核意识，但很难做到。 收集大量具有基本事实的数据，以进行监督网络训练，直至达到临床图像质量。 为了克服使用 Medipix 探测器进行 PCCT 临床试验的挑战， 我们的总体目标是开发一种用于少视图和低剂量的无监督深度学习方法（UDLA） 基于我们的深度分析压缩迭代 (ACID) 架构但特定于 PCCT 的图像重建 数据，具有更高的空间分辨率和计算效率，并且不需要地面 ACID 结合了深度学习的数据驱动能力、CS 的内核意识和 迭代细化以准确稳定地提供图像重建结果 为了实现我们的目标，三个。 具体目标定义如下： 目标 1：UDLA 将被设计、开发、优化并集成到其中。 一个开源平台，包括深度端到端重建网络和先进的 CS 模块 目标 2：UDLA 将进行稳定性和通用性测试，并通过以下方式加速： 高性能计算平台上的软件优化；以及目标 3：将评估并确定 UDLA 通过模拟、实验和临床肢体成像 PCCT 数据的回顾性使用进行了验证。 该项目完成后，UDLA 软件应已针对临床肢体进行了表征 使用基于 Medpix 的 PCCT 进行成像，其性能优于当代迭代算法，且没有漏洞 现有的深度重建网络以及网络训练的地面实况要求。 从角度来看，我们的方法代表了向基于模型和数据驱动的集成的范式转变 重建方法，并可能对 PCCT 和其他断层扫描成像方式产生持久影响。