Liang Kaichao, Zhang Li, Xing Yuxiang
Department of Engineering Physics, Tsinghua University, Beijing, 100084, People's Republic of China.
Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, People's Republic of China.
Phys Med Biol. 2023 Mar 15;68(6). doi: 10.1088/1361-6560/acc001.
X-ray diffraction (XRD) has been considered as a valuable diagnostic technology providing material specific 'finger-print' information i.e. XRD pattern to distinguish different biological tissues. XRD tomography (XRDT) further obtains spatial-resolved XRD pattern distribution, which has become a frontier biological sample inspection method. Currently, XRD computed tomography (XRD-CT) featured by the conventional CT scan mode with rotation has the best spatial resolution among various XRDT methods, but its scan process takes hours. Meanwhile, snapshot XRDT methods such as coded-aperture XRDT (CA-XRDT) aim at direct imaging without scan movements. With compressed-sensing acquisition applied, CA-XRDT significantly shortens data acquisition time. However, the snapshot acquisition results in a significant drop in spatial resolution. Hence, we need an advanced XRDT method that significantly accelerates XRD-CT acquisition and still maintains an acceptable imaging accuracy for biological sample inspection.Inspired by the high spatial resolution of XRD-CT from rotational scan and the fast compressed-sensing acquisition in snapshot CA-XRDT (SnapshotCA-XRDT), we proposed a new XRDT imaging method: sparse-view rotational CA-XRDT (RotationCA-XRDT). It takes SnapshotCA-XRDT as a preliminary depth-resolved XRDT method, and combines rotational scan to significantly improve the spatial resolution. A model-based iterative reconstruction (MBIR) method is adopted for RotationCA-XRDT. Moreover, we suggest a refined system model calculation for the RotationCA-XRDT MBIR which is a key factor to improve reconstruction image quality.We conducted our experimental validation based on Monte-Carlo simulation for a breast sample. The results show that the proposed RotationCA-XRDT method succeeded in producing good images for detecting 2 mm square carcinoma with a 15-view scan. The spatial resolution is significantly improved from current SnapshotCA-XRDT methods. With our refined system model, MBIR can obtain high quality images with little artifacts.In this work, we proposed a new high spatial resolution XRDT method combining coded-aperture compressed-sensing acquisition and sparse-view scan. The proposed RotationCA-XRDT method obtained significantly better image resolution than current SnapshotCA-XRDT methods in the field. It is of great potential for biological sample XRDT inspection. The proposed RotationCA-XRDT is the fastest millimetre-resolution XRDT method in the field which reduces the scan time from hours to minutes.
X射线衍射（XRD）被视为一种有价值的诊断技术，可提供特定材料的“指纹”信息，即XRD图谱，以区分不同的生物组织。XRD断层扫描（XRDT）进一步获取空间分辨的XRD图谱分布，已成为前沿的生物样品检测方法。目前，以传统旋转CT扫描模式为特征的XRD计算机断层扫描（XRD-CT）在各种XRDT方法中具有最佳的空间分辨率，但其扫描过程需要数小时。同时，诸如编码孔径XRDT（CA-XRDT）之类的快照XRDT方法旨在无需扫描移动即可直接成像。通过应用压缩感知采集，CA-XRDT显著缩短了数据采集时间。然而，快照采集导致空间分辨率显著下降。因此，我们需要一种先进的XRDT方法，该方法能显著加速XRD-CT采集，同时仍能保持可接受的生物样品检测成像精度。受旋转扫描的XRD-CT的高空间分辨率以及快照CA-XRDT（SnapshotCA-XRDT）中快速压缩感知采集的启发，我们提出了一种新的XRDT成像方法：稀疏视图旋转CA-XRDT（RotationCA-XRDT）。它将SnapshotCA-XRDT作为初步的深度分辨XRDT方法，并结合旋转扫描以显著提高空间分辨率。RotationCA-XRDT采用基于模型的迭代重建（MBIR）方法。此外，我们为RotationCA-XRDT MBIR建议了一种改进的系统模型计算，这是提高重建图像质量的关键因素。我们基于蒙特卡罗模拟对乳腺样本进行了实验验证。结果表明，所提出的RotationCA-XRDT方法通过15视图扫描成功生成了用于检测2平方毫米癌灶的良好图像。空间分辨率比当前的SnapshotCA-XRDT方法有显著提高。通过我们改进的系统模型，MBIR可以获得几乎没有伪影的高质量图像。在这项工作中，我们提出了一种结合编码孔径压缩感知采集和稀疏视图扫描的新的高空间分辨率XRDT方法。所提出的RotationCA-XRDT方法在该领域获得了比当前SnapshotCA-XRDT方法显著更好的图像分辨率。它在生物样品XRDT检测方面具有巨大潜力。所提出的RotationCA-XRDT是该领域最快的毫米级分辨率XRDT方法，将扫描时间从数小时缩短至数分钟。
