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年批准该技术。临床PCCT和微型PCCT扫描仪现已商业化。 available.这为功能性、细胞和分子X射线成像打开了一扇新的大门， 造影剂如铋和金纳米颗粒。然而，X射线光子计数探测器不是 完美的，它仍然具有挑战性，重建高质量的PCCT图像的各种临床应用。 在过去的几年里，基于深度学习的断层成像已经成为图像的新前沿 重建与完全依赖先验信息的压缩感知（CS）方法不同， 就精确的数学约束而言，新兴的基于深度学习的方法是由大 可以用来训练深度网络以进行上级层析重建的数据。然而，最近的一项研究 发表在PNAS上的论文揭示了深层层析重建网络的三种不稳定性， 由于缺乏内核意识和“非平凡的克服”， 在该研究中，重建被报告为稳定的，因为其内核感知。与此同时， 收集大量的数据与地面实况监督网络训练高达临床图像质量。 为了克服在使用Medipix探测器的PCCT临床试验的背景下的上述挑战， 我们的总体目标是开发一种无监督的深度学习方法（UDLA）， 图像重建基于我们的分析压缩迭代深度（ACID）架构，但特定于PCCT 数据，具有更高的空间分辨率和计算效率，并且不需要地面， 真理训练ACID结合了深度学习的数据驱动能力、CS的内核感知能力， 迭代细化以准确且稳定地提供图像重建结果。为了实现我们的目标，三 具体目标界定如下。目标1：UDLA将被设计、开发、优化和集成到 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的稳定性和可推广性，并通过 在高性能计算平台上进行软件优化;目标3：将对UDLA进行评估， 在模拟、实验和临床肢体成像PCCT数据的回顾性使用中得到验证。 本项目完成后，UDLA软件应针对临床肢体进行表征 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 现有的深度重建网络和地面实况对网络训练的要求。从更广泛的 从这个角度来看，我们的方法代表了一种范式的转变，即基于模型和数据驱动的整合 重建方法，并可能对PCCT和其他断层成像方式产生持久的影响。

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.