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

摘要 最先进的X射线光子计数CT(PCCT)同时在多个能源箱中产生图像 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。林业局 已于2021年批准了这项技术。临床PCCT和微型PCCT扫描仪现在都已商业化 可用。这为功能、细胞和分子x射线成像打开了一扇新的大门， 对比剂，如铋和金纳米颗粒。然而，x射线光子计数探测器不是 完美，但重建各种临床应用的高质量PCCT图像仍然具有挑战性。 在过去的几年里，基于深度学习的层析成像已经成为图像领域的一个新的前沿 重建。不同于压缩感知(CS)方法，压缩感知方法完全依赖先验信息。 就精确的数学约束而言，新兴的基于深度学习的方法是由BIG 可以用来训练深层网络以进行更好的层析重建的数据。然而，最近的一项研究 发表在PNAS上的文章揭示了深层层析重建网络的三种类型的不稳定性，这三种类型是 被认为是根本性的，因为缺乏内核意识和“不容易克服的”，但基于CS 据报道，该研究的重建是稳定的，因为它的核心意识。与此同时，很难做到 收集大量具有地面事实的数据，用于监督网络训练，直至达到临床图像质量。 为了在使用Medipix检测器的PCCT临床试验的背景下克服上述挑战， 我们的总体目标是开发一种用于少视角和低剂量的无监督深度学习方法(UDLA 基于我们的解析压缩迭代深部(ACID)结构但特定于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- 实事求是的训练。ACID结合了深度学习的数据驱动能力、CS的核心感知能力，以及 迭代细化，以准确和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：将UDLA设计、开发、优化并整合到 开源平台，包括深度端到端重建网络和高级CS模块 多约束模型；目标2：将测试UDLA的稳定性和泛化能力，并通过 高性能计算平台上的软件优化；目标3：将对UDLA进行评估和 在模拟、实验和临床肢体成像PCCT数据的回顾使用中得到了验证。 在这个项目完成后，UDLA软件应该已经针对临床肢体进行了表征 使用基于Medpix的PCCT进行成像，在没有漏洞的情况下超越当代迭代算法 现有深度重建网络的现状和地面实况对网络训练的要求。在更广泛的范围内 从角度来看，我们的方法代表着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和其他断层成像方式产生持久影响。

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.