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）简单地生成多能箱中的图像 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 已批准了2021年的技术。 可用的。这为功能，细胞和分子X射线成像的机会打开了新的大门 对比剂，例如苯丁香和金纳米颗粒。但是，X射线光子计数检测器不是 完美，重建用于各种临床应用的高质量PCCT图像仍然是挑战。 在过去的几年中，深度学习的层析成像成像已成为图像的新领域 重建。与压缩感应（CS）方法不同，该方法完全依赖于先前的信息 准确的数学约束条款，新兴的深度学习方法受到了大型授权 可以使用深层网络的数据进行培训以进行出色的层析成像重建。但是，最近的研究 在PNAS中发表了三种类型的深层断层重建网络的不稳定性，它们是 由于缺乏内核意识和“克服不平凡”，因此被认为是基本的，但是 在该研究中，由于其内核意识，重建是稳定的。刻那一刻，很难 收集大量的数据，该数据具有地面真相，以进行监督的网络培训，直至临床图像质量。 为了克服使用Medipix探测器的PCCT临床试验中的优先挑战， 我们的总体目标是开发一种无监督的深度学习方法（UDLA），以进行几种观看和低剂量 基于我们的分析压缩迭代深（酸）结构的图像重建，但特定于PCCT 数据，具有更高的空间分辨率和计算效率，而无需地面 培训的真相。酸结合了深度学习的数据驱动力，CS的内核意识和 迭代精致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，优化和集成到 一个开源平台，包括一个深端到端重建网络和带有高级CS模块 多构造模型； AIM 2：UDLA将进行稳定性和概括性测试，并通过 高性能计算平台上的软件优化；目标3：将评估Udla，并 在模拟，实验和回顾性使用PCCT数据中验证。 该项目完成后，UDLA软件应以临床末端为特征 使用基于MEDPIX的PCCT进行成像以优于当代迭代算法，而没有漏洞 现有的深层重建网络以及网络培训的基础真相的要求。更广泛 视角，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，可能会对PCCT和其他断层造影成像方式产生持久影响。