Optimization of diagnostic accuracy, radiation dose, and patient throughput for cardiac SPECT via advanced and clinically practical cardiac-respiratory motion correction and deep learning

通过先进且临床实用的心肺运动校正和深度学习，优化心脏 SPECT 的诊断准确性、辐射剂量和患者吞吐量

• 批准号: 10685488
• 负责人: Michael A King
• 金额: $ 77.33万
• 依托单位: ILLINOIS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10685488
• 关键词: 3-Dimensional; 4D Imaging; Accounting; Address; Advocate; Algorithms; American; Cardiac; Clinic; Clinical; Compensation; Computer software; Coronary Arteriosclerosis; Data; Defect; Development; Diagnosis; Diagnostic; Disease; Dose; Effectiveness; Elderly; Electromagnetic Energy; Evaluation; Exposure to; Functional Imaging; Goals; Health Care Costs; Image; Imaging Techniques; Institution; Left Ventricular Function; Life Expectancy; Measures; Mechanics; Medical Imaging; Methods; Modality; Morphologic artifacts; Motion; Myocardial perfusion; Noise; Nuclear; Obesity; Patients; Performance; Perfusion; Photons; Play; Population; Prevalence; Protocols documentation; Radiation Dose Unit; Radiation exposure; Reader; Recommendation; Resolution; Risk; Role; Scanning; Signal Transduction; Societies; System; Task Performances; Techniques; Technology; Time; Translating; Validation; Visualization; Work; X-Ray Computed Tomography; attenuation; base; cardiac single photon emission computed tomography; clinical care; cost; deep learning; denoising; denoising deep learning; diagnostic accuracy; heart motion; hemodynamics; image processing; image reconstruction; imaging modality; imaging system; improved; innovation; obese patients; perfusion imaging; preservation; prognostic; quantum; radiation risk; radiologist; reconstruction; respiratory; single photon emission computed tomography; tool; visual tracking

Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is widely used to detect and evaluate coronary artery disease. The goal of this project is to reduce the radiation dose and/or scan time of SPECT MPI by a combined factor of 16x, while maintaining or increasing diagnostic accuracy. This would enable SPECT MPI to be performed, e.g., with 4x reduced radiation dose and 4x shorter scan time (~2.5 minutes) than typical protocols. Radiation dose in SPECT MPI has been recognized as an important issue, accounting for ~25% of all radiation exposure to patients in medical imaging. Dose reduction particularly addresses the increased prevalence of obese patients (who receive higher dose) and younger cardiac patients (whose radiation risk is higher due to longer life expectancy). Reduction in scan time would improve comfort for elderly and infirm cardiac patients, while mitigating body-motion image artifacts and reducing healthcare costs by increasing clinical throughput. We will reduce dose and scan time through innovative image reconstruction methods that involve little or no cost and require no additional patient setup steps. We will employ new respiratory and cardiac motion compensation to reduce image artifacts, as well as new deep learning techniques, which will be used for both respiratory-signal estimation and high-performance denoising. We will methodically optimize these techniques and then validate our algorithms in multicenter clinical reader studies. SA1: Develop clinically practical respiratory motion surrogates for low-count studies. T1: Perfect data- driven respiratory surrogate estimation; T2: Optimize data-driven surrogate estimation at reduced counts; T3: Develop and clinically validate depth-sensing cameras for respiratory and body-motion surrogate estimation; T4: Generalization of data-driven surrogate estimation to SPECT systems not having a CT. SA2: Develop deep-learning reconstruction methods and optimize for diagnostic accuracy and dose/scan time. T1: Post-reconstruction DL denoising algorithms for 3D perfusion images for reduced-count and standard- count studies; T2: DL denoising algorithms for 4D cardiac-gated studies; T3: 4D reconstruction with embedded DL denoising, cardiac motion estimation and correction; and T4: DL reconstruction methods with both RMC and CMC, with projection data binned using respiratory surrogate signals derived in SA1. SA3: Perform multicenter clinical reader studies (6 clinicians, 3 institutions) to validate the new algorithms and compare to current clinically-available methods based on diagnostic performance and repeatability in assessing both perfusion and wall motion defects. T1: In comparison to baseline clinical reconstruction, evaluate added benefit of: a) including attenuation and scatter correction, and b) additionally including RMC; T2: Validate DL for improvement of perfusion and function (wall motion) task performance at full-count levels; and T3: Validate DL for improvement of task performance at reduced counts.

单光子发射计算机断层扫描（SPECT）心肌灌注成像（MPI）被广泛使用 检测和评估冠状动脉疾病。该项目的目的是减少辐射剂量和/或 在保持或提高诊断准确性的同时，扫描SPECT MPI的扫描时间为16倍。这 可以执行SPECT MPI，例如，降低了4倍的辐射剂量和短4倍的扫描时间（〜2.5 分钟）比典型协议。 SPECT MPI中的辐射剂量已被认为是重要的 问题，占医疗成像中患者所有放射线的约25％。减少剂量 特别解决了肥胖患者（接受较高剂量）和年轻患者的患病率的增加 心脏患者（由于预期寿命较长，其辐射风险更高）。减少扫描时间 在减轻身体运动图像的同时，会改善老年人和体弱的心脏病患者的舒适感 通过增加临床吞吐量来减少伪像和降低医疗保健成本。我们将减少剂量和扫描时间 通过创新的图像重建方法几乎没有成本，不需要额外 患者设置步骤。我们将采用新的呼吸和心脏运动补偿来减少图像 工件以及新的深度学习技术，将用于两种呼吸估计 和高性能的降级。我们将有条不紊地优化这些技术，然后验证我们的 多中心临床读取器研究中的算法。 SA1：开发临床上实用的呼吸运动代理，以进行低计数研究。 T1：完美数据 - 驱动的呼吸替代估计； T2：在减少计数时优化数据驱动的替代估计； T3： 开发和临床验证深度感应摄像头，以进行呼吸和运动替代估计； T4：数据驱动的替代估计对没有CT的SPECT系统的概括。 SA2：开发深度学习的重建方法并优化以诊断准确性和剂量/扫描 时间。 T1：用于3D灌注图像的重建后DL DENO算法，用于降低计数和标准 - 计数研究； T2：DL DeNo算法进行4D心脏门控研究； T3：4D重建与嵌入式的重建 DL降解，心运动估计和校正；和T4：两种RMC的DL重建方法 和CMC，具有使用SA1中呼吸道替代信号进行投影数据。 SA3：执行多中心临床读者研究（6位临床医生，3个机构）来验证新的 算法和基于诊断性能和 评估灌注和壁运动缺陷时的可重复性。 T1：与基线临床相比 重建，评估附加的好处：a）包括衰减和分散校正，b）另外 包括RMC； T2：验证DL以改善灌注和功能（壁运动）任务性能 全计数；和T3：验证DL以改善降低计数的任务绩效。

项目成果

A cadaveric breast cancer tissue phantom for phase-contrast X-ray imaging applications.

• DOI: 10.1002/ame2.12340
• 发表时间: 2023-10
• 期刊: ANIMAL MODELS AND EXPERIMENTAL MEDICINE
• 影响因子: 3.7
• 作者: Rounds, Cody C.;Li, Chengyue;Zhou, Wei;Tichauer, Kenneth M.;Brankov, Jovan G.
• 通讯作者: Brankov, Jovan G.

Improving detection accuracy of perfusion defect in standard dose SPECT-myocardial perfusion imaging by deep-learning denoising.

• DOI: 10.1007/s12350-021-02676-w
• 发表时间: 2022-10
• 期刊: Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology

Data-driven respiratory signal estimation from temporally finely sampled projection data in conventional cardiac perfusion SPECT imaging.

• DOI: 10.1002/mp.15391
• 发表时间: 2022-01
• 期刊: Medical physics
• 影响因子: 3.8

Deep learning with noise-to-noise training for denoising in SPECT myocardial perfusion imaging.

• DOI: 10.1002/mp.14577
• 发表时间: 2021-01
• 期刊: Medical physics
• 影响因子: 3.8
• 作者: Liu J;Yang Y;Wernick MN;Pretorius PH;King MA
• 通讯作者: King MA