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

FAIR-CT：一种利用超低剂量 CT 进行纵向疾病和治疗监测的实用方法

基本信息

批准号：
10158473
负责人：
Frederic Noo
金额：
$ 22.73万
依托单位：
UNIVERSITY OF UTAH
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-15 至 2023-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10158473
关键词：
Abdomen Academia Advanced Malignant Neoplasm Affect Algorithms Anatomy Biological Body mass index Chest Chronic Disease Chronic Obstructive Airway Disease Clinical Collection Computers Cystic Fibrosis Data Data Set Development Diabetes Mellitus Diagnostic Disease Dose Early Diagnosis Epidemic Evaluation Fruit Goals Healthcare Human Image Image Analysis Industry Inflammation Inflammatory Bowel Diseases Lesion Malignant Neoplasms Measurement Metabolic Diseases Modeling Modernization Monitor Morphologic artifacts Noise Obesity Patient Monitoring Patients Pelvis Performance Pharmaceutical Preparations Photons Physics Polycystic Kidney Diseases Process Pulmonary Inflammation Radiation exposure Radiation therapy Radiology Specialty Reader Research Resolution Rotation Sampling Scanning Source Starvation Structure Techniques Technology Time Validation Vendor Work X-Ray Computed Tomography base cancer risk cancer therapy clinical practice clinical translation clinically translatable comorbidity data acquisition data integration data modeling data sharing deep learning detector expectation image reconstruction improved low dose computed tomography mortality novel reconstruction sex side effect standard of care targeted imaging urolithiasis

项目摘要

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.

项目摘要/摘要超低剂量CT，定义为整个胸部，腹部或骨盆的子米利列赛（sub-msv）成像慢性疾病和癌症患者的医疗保健至关重要。不幸的是，光子饥饿电子噪声使这种剂量水平的成像具有挑战性。光子饥饿是指传输光子。当没有传输光子时，测量本质上是没有用的。如果很少的光子被传输，测量带有信息，但其解释和价值与电子噪声。为子MSV胸部成像提供了令人鼓舞的结果的解决方案，但是这些在解剖站点，供应商和扫描仪模型中不可广泛使用，也不容易概括。我们为超低剂量CT提出了一种新颖的，可靠的解决方案，该解决方案将克服这些问题。我们指的是我们的解决方案作为Fair-CT，代表有限角度的集成射线CT。 Fair-CT根据原则运行最好通过避免它们来处理光子饥饿和电子噪声的混杂效应，通过增加源检测器旋转期间的数据积分时间来使其成为可能。 Fair-CT数据强烈偏离经典CT数据模型，并共享稀疏视图的条纹伪像问题采样。 Fair-CT数据采集还会影响方位角分辨率。我们预计这些问题可能是使用高级图像重建技术适当处理。一旦可用，公平将允许改善了慢性疾病患者（例如COPD，尿道病和尿道病）的纵向监测糖尿病，从而降低死亡率和合并症。 Fair-CT还将允许前进的癌症治疗通过对剂量分数之间的放射治疗计划进行调整，可以进行治疗增加CT辐射暴露，并通过促进基于药物的炎症的早期检测疗法。为了实现公平委员会，我们将以两个具体的目的致力：（1）创建全面收集公平CT数据集，可实现严格的开发，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数据集将涉及最先进的扫描仪的利用，并包括真实的扫描仪从高剂量扫描中合成的患者数据是为了获得标准的护理。两个互补图像将研究重建方法。也就是说，基于模型的迭代重建与非线性重建正向模型和专用压缩感应正则化；和FBP的深度基于学习的改进使用具有任务适应图像质量的目标图像的重建。图像质量评估将考虑关键的生物学变量，涉及客观指标，例如结构相似性和对比度比率用于临床验证的病变以及涉及人类读者的基于任务的绩效指标。