FAIR-CT: a practical approach to enable ultra-low dose CT for longitudinal disease and treatment monitoring

项目摘要/摘要 超低剂量CT，定义为整个胸部、腹部或骨盆的亚毫西弗（sub-mSv）成像， 慢性病和癌症患者的医疗保健急需。不幸的是，光子饥饿 和电子噪声使得在这样的剂量水平下的成像具有挑战性。光子饥饿指的是 传输的光子当没有光子传输时，测量基本上是无用的。如果很少的光子 测量携带信息，但其解释和价值受到以下因素的干扰： 电子噪音已经为亚mSv胸部成像提供了具有令人鼓舞的结果的解决方案，但是这些 并不广泛使用，也不容易在解剖部位、供应商和扫描仪型号中推广。 我们提出了一种新的，强大的解决方案，超低剂量CT，将克服这些问题。我们参考我们的 解决方案为FAIR-CT，其代表直角积分射线CT。FAIR-CT的工作原理是 光子饥饿和电子噪声的混杂效应最好通过避免它们来处理， 通过增加源-探测器旋转期间的数据积分时间，FAIR-CT数据 严重偏离经典CT数据模型，并且共享稀疏视图的条纹伪影问题 取样. FAIR-CT数据采集也影响方位分辨率。我们预计，这些问题可以 使用先进的图像重建技术适当地处理。一旦可用，FAIR-CT将允许 改善慢性疾病患者的纵向监测，如COPD、尿石症和 糖尿病，从而降低死亡率和并发症。FAIR-CT还将促进癌症治疗 通过使得能够在剂量分次之间调整放射治疗计划， 增加CT辐射暴露，并通过促进早期发现炎症， 治疗为了使FAIR-CT取得成果，我们将致力于两个具体目标：（1）创建一个 全面收集FAIR-CT数据集，从而能够严格开发、验证和评估 图像重建算法;（2）高级图像重建的开发、验证和评估 算法FAIR-CT数据集将使用最先进的扫描仪，并包括真实的 从标准护理采集的高剂量扫描合成的患者数据。两互补图像 将研究重建方法。即，基于模型的迭代重建具有非线性 前向模型和专用压缩感知正则化;以及基于深度学习的FBP细化 使用具有任务适应图像质量的目标图像进行重建。图像质量评价将考虑 关键的生物变量，并涉及客观指标，如结构相似性和对比度噪声比 用于临床证实的病变，以及涉及人类读者的基于任务的性能指标。

Project Abstract/Summary Ultra-low dose CT, defined as sub-millisievert (sub-mSv) imaging of the entire chest, abdomen or pelvis, is critically needed for healthcare of patients with chronic diseases and cancer. Unfortunately, photon starvation and electronic noise make imaging at such dose levels challenging. Photon starvation refers to the number of transmitted photons. When no photons are transmitted, the measurement is essentially useless. If few photons are transmitted, the measurement carries information, but its interpretation and value are confounded by electronic noise. Solutions with encouraging results have been offered for sub-mSv chest imaging, but these are not widely available and not easily generalizable across anatomical sites, vendors and scanner models. We propose a novel, robust solution for ultra-low dose CT that will overcome these issues. We refer to our solution as FAIR-CT, which stands for Finite-Angle Integrated-Ray CT. FAIR-CT operates under the principle that photon starvation and the confounding effect of electronic noise are best handled by avoiding them, which is made possible by increasing the data integration time during the source-detector rotation. FAIR-CT data strongly deviate from the classical CT data model and share the streak artifact problem of sparse view sampling. FAIR-CT data acquisition also affects azimuthal resolution. We anticipate that these issues can be suitably handled using advanced image reconstruction techniques. Once available, FAIR-CT will allow improvements in longitudinal monitoring of patients with chronic diseases such as COPD, urolithiasis and diabetes, thereby reducing mortality and co-morbidities. FAIR-CT will also allow advancing cancer therapy treatments by enabling adjustments in radiation therapy plans between dose fractions without increasing CT radiation exposure, and by facilitating early detection of inflammations in drug-based therapies. To bring FAIR-CT towards fruition, we will work on two specific aims: (1) Creation of a comprehensive collection of FAIR-CT data sets enabling rigorous development, validation and evaluation of image reconstruction algorithms; (2) Development, validation and evaluation of advanced image reconstruction algorithms. The FAIR-CT data sets will involve the utilization of state-of-the-art scanners and include real patient data synthesized from high dose scans acquired for standard of care. Two complementary image reconstruction approaches will be investigated. Namely, model-based iterative reconstruction with non-linear forward model and dedicated compressed sensing regularization; and deep learning-based refinement of FBP reconstructions using target images with task-adapted image quality. Image quality evaluation will account for critical biological variables and involve objective metrics such as structure similarity and contrast-to-noise ratio for clinically-proven lesions, as well as task-based performance metrics involving human readers.