Data Consistency Based Motion Artifact Reduction for Head CT

基于数据一致性的头部 CT 运动伪影减少

• 批准号: 7491540
• 负责人: Hengyong Yu
• 金额: $ 7.77万
• 依托单位: VIRGINIA POLYTECHNIC INST AND ST UNIV
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7491540
• 关键词: Algorithms; Allergic Reaction; Automobile Driving; Biomedical Research; Blur; Child; Childhood; Clinical; Collaborations; Compatible; Condition; Data; Detection; Diagnosis; Diagnostic; Future; Goals; Head; Healthcare; Image; Intravenous; Lead; Medical; Methods; Modeling; Monitor; Morphologic artifacts; Motion; Noise; Numbers; Patients; Pattern; Performance; Physiological; Process; Radiation; Radiation Dosage; Resolution; Retinal Cone; Roentgen Rays; Rotation; Scanning; Sedation procedure; Slice; Solid; Solutions; Source; Specific qualifier value; System; Techniques; Testing; Time; Translating; Translations; United States National Institutes of Health; Validation; Virginia; Work; base; clinical application; cost; design; feeding; follow-up; image reconstruction; improved; indexing; instrument; interest; novel; prevent; reconstruction; research study; simulation; Algorithms; Allergic Reaction; Automobile Driving; Biomedical Research; Blur; Child; Childhood; Clinical; Collaborations; Compatible; Condition; Data; Detection; Diagnosis; Diagnostic; Future; Goals; Head; Healthcare; Image; Intravenous; Lead; Medical; Methods; Modeling; Monitor; Morphologic artifacts; Motion; Noise; Numbers; Patients; Pattern; Performance; Physiological; Process; Radiation; Radiation Dosage; Resolution; Retinal Cone; Roentgen Rays; Rotation; Scanning; Sedation procedure; Slice; Solid; Solutions; Source; Specific qualifier value; System; Techniques; Testing; Time; Translating; Translations; United States National Institutes of Health; Validation; Virginia; Work; base; clinical application; cost; design; feeding; follow-up; image reconstruction; improved; indexing; instrument; interest; novel; prevent; reconstruction; research study; simulation

DESCRIPTION (provided by applicant): Most of routine CT methods assume that a subject being imaged is stationary during the scan. In important clinical applications and biomedical researches, such as pediatric head CT, the patient-motion is often unavoidable, leading to significant motion artifacts. The long-term goal of this project is to develop effective CT methods for motion estimation based image reconstruction, improve the diagnostic performance, and enable new clinical applications. Because motion artifact reduction has been a major problem in CT, our proposed work may have a significant impact on the healthcare by producing better image quality and extracting physiological and pathological features more accurately. In this R03 project, we will focus on the circular scanning trajectory and in-plane head motion, which is not only a satisfactory model in the cases we are interested but also a solid basis for our future study on more complicated motion patterns and scan modes. The specific aims are to (1) develop motion models and estimate the corresponding parameters from fan-beam/multi-slice data collected along a circular locus to describe the in-plane motion precisely; (2) adapt generalized cone-beam reconstruction methods to reconstruct a moving object in the fan-beam/multi-slice geometry; and (3) evaluate the proposed methods in numerical simulations, phantom experiments and retrospective patient studies. On completion, the proposed techniques will have been optimized, and their superior performance demonstrated in numerical simulation, phantom experiments and clinical applications. Using our proposed methods, the motion-induced blurring will have been reduced down to at least 30% of that associated with the current commercial methods of choice in the representative cases. This project will generate pilot data for a follow-up NIH R01 proposal targeting dynamic CT with noncircular scanning and general subject motion.Project Narrative: Motion artifact reduction has been a major problem in CT. Our project will develop a novel and effective solution to this long-standing problem, and may produce significantly better image quality to extract physiological and pathological features more accurately and more robustly. Hence, the proposed techniques promise to be translated for clinical use and should generate great healthcare benefits, especially for children.

描述（由申请人提供）：大多数常规CT方法都假定在扫描过程中所成像的受试者是固定的。在重要的临床应用和生物医学研究（例如小儿头部CT）中，患者运动通常是不可避免的，导致了大量的运动伪像。该项目的长期目标是开发有效的CT方法，用于基于运动估计的图像重建，改善诊断性能并实现新的临床应用。由于减少运动伪像是CT的主要问题，因此我们提出的工作可能通过产生更好的图像质量并更准确地提取生理和病理特征对医疗保健产生重大影响。 在这个R03项目中，我们将重点关注循环扫描轨迹和面内头运动，这不仅是我们感兴趣的情况下令人满意的模型，而且是我们对更复杂运动模式和扫描模式的未来研究的扎实基础。具体目的是（1）开发运动模型并估算沿圆形基因座收集的风扇束/多片数据的相应参数，以精确地描述平面运动； （2）适应广义锥形束重建方法，以重建风扇束/多片几何形状中的移动对象； （3）评估数值模拟，幻影实验和回顾性患者研究中所提出的方法。 完成后，提出的技术将得到优化，并且在数值模拟，幻影实验和临床应用中证明了它们的出色性能。使用我们提出的方法，在代表性情况下，运动引起的模糊将减少至至少30％与当前的商业选择方法相关的30％。该项目将生成针对随访的NIH R01提案的试点数据，该建议针对动态CT进行非圆形扫描和一般主题运动。项目叙述： 减少运动伪影一直是CT的主要问题。我们的项目将针对这个长期存在的问题开发一种新颖而有效的解决方案，并可能产生更高的图像质量，以更准确，更健壮地提取生理和病理特征。因此，拟议的技术有望被翻译以供临床使用，并应带来巨大的医疗保健益处，尤其是对于儿童。