Functional Magnetic Resonance Imaging and Deep Learning to Improve Deep Brain Stimulation Therapy

功能磁共振成像和深度学习改善脑深部刺激疗法

• 批准号: 10717563
• 负责人: Afis Ajala
• 金额: --
• 依托单位: GENERAL ELECTRIC GLOBAL RESEARCH CTR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-19 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10717563
• 关键词: Adoption; Adverse effects; Alzheimer' s Disease; Artificial Intelligence; Bilateral; Brain; Classification; Clinical; Complication; Consumption; Data; Data Collection; Deep Brain Stimulation; Dystonia; Electrodes; Engineering; Ensure; Essential Tremor; Financial Hardship; Frequencies; Functional Magnetic Resonance Imaging; Goals; Health; Hour; Implant; Implanted Electrodes; Improve Access; Location; Magnetic Resonance Imaging; Major Depressive Disorder; Manuals; Maps; Methods; Modeling; Neurologist; Neurosurgeon; Operative Surgical Procedures; Outcome; Parkinson Disease; Pathologic; Patient-Focused Outcomes; Patients; Physicians; Physiologic pulse; Pilot Projects; Protocols documentation; Publishing; Qualifying; Quality of life; Reporting; Research; STN stimulation; Sampling; Signal Transduction; System; Testing; Therapeutic; Time; Tissues; Training; Universities; Visit; Width; brain volume; clinical care; comparative efficacy; data acquisition; deep learning; deep learning model; energy efficiency; feature extraction; improved; interest; mild cognitive impairment; multidisciplinary; novel; predictive modeling; programs; radiologist; rapid technique; response; side effect; standard of care; success; treatment center; validation studies; voltage; Adoption; Adverse effects; Alzheimer' s Disease; Artificial Intelligence; Bilateral; Brain; Classification; Clinical; Complication; Consumption; Data; Data Collection; Deep Brain Stimulation; Dystonia; Electrodes; Engineering; Ensure; Essential Tremor; Financial Hardship; Frequencies; Functional Magnetic Resonance Imaging; Goals; Health; Hour; Implant; Implanted Electrodes; Improve Access; Location; Magnetic Resonance Imaging; Major Depressive Disorder; Manuals; Maps; Methods; Modeling; Neurologist; Neurosurgeon; Operative Surgical Procedures; Outcome; Parkinson Disease; Pathologic; Patient-Focused Outcomes; Patients; Physicians; Physiologic pulse; Pilot Projects; Protocols documentation; Publishing; Qualifying; Quality of life; Reporting; Research; STN stimulation; Sampling; Signal Transduction; System; Testing; Therapeutic; Time; Tissues; Training; Universities; Visit; Width; brain volume; clinical care; comparative efficacy; data acquisition; deep learning; deep learning model; energy efficiency; feature extraction; improved; interest; mild cognitive impairment; multidisciplinary; novel; predictive modeling; programs; radiologist; rapid technique; response; side effect; standard of care; success; treatment center; validation studies; voltage

PROJECT SUMMARY/ABSTRACT Successful treatment of Parkinson's disease (PD) using deep brain stimulation (DBS) therapy requires an optimal setting of stimulation parameters to correct brain function anomalies. The commonly employed DBS 1.0 electrodes have only four contact locations (with no stimulation directionality) that are used to electric pulses to a target volume of the brain. DBS 1.0 electrodes require the optimization of four stimulation parameters: signal frequency, voltage, pulse width, and contact location. In current standard-of-care optimization protocol, the DBS parameters are adjusted (via trial and error) until the physician determines an optimal set of parameters. This empirical optimization protocol requires numerous clinical visits (~6 weeks interval) that substantially increases the time to optimization (TTO) per patient (~1 year), financial burden, and ultimately limits the number of patients that can have access to DBS therapy. Even though there are more effective electrodes, DBS 1.0 electrodes are mostly used by clinicians because their smaller parameter space pose less difficulty during manual clinical optimization. However, DBS 1.0 electrodes cannot be directed to stimulate a smaller volume of tissue, which can lead to extraneous stimulations that can reduce patient clinical benefits and increase side effects. By contrast, the newer DBS electrodes (dubbed DBS 2.0) have a greater number of contact locations and can be programmed to stimulate a smaller volume of tissue at multiple levels and directions. Several published reports have shown that DBS 2.0 electrodes (compared to DBS 1.0) are more energy-efficient and improve patient outcomes with lesser side-effects and a wider therapeutic window. However, the expanded DBS 2.0 parameter space has made empirical programming of the electrodes difficult as the TTO per patient is beyond acceptable clinical timeframes. This increased difficulty has hindered adoption of DBS 2.0 electrodes by clinicians. To significantly shorten and simplify DBS 2.0 parameter optimization—thus enabling its wider adoption for more precise therapy—a uniquely qualified multi- disciplinary team of magnetic resonance imaging (MRI) physicists, artificial intelligence (AI) engineers, and clinicians from GE Research and the University Health Network propose to: 1) develop a semi-automated functional MRI (fMRI) and deep learning (DL)-based system for rapid optimization of DBS 2.0 parameters; 2) demonstrate its clinical benefit in the treatment of PD patients using bilateral stimulation of the sub-thalamic nucleus with DBS 2.0 electrodes in a pilot study. Success of this program will decrease the TTO per patient for PD patients with DBS 2.0 implants to ~1 hour, and will improve patient throughput and outcomes in the treatment of PD. The proposed fMRI-DL-based optimization method may also improve access by making it possible for non-expert centers (without highly specialized clinicians) to carry out stimulation parameters optimization in patients after the electrode insertion surgery have been completed in expert centers.

项目摘要/摘要 使用深脑刺激（DBS）治疗成功治疗帕金森氏病（PD）需要 模拟参数的最佳设置以纠正大脑功能异常。常用的DBS 1.0电子只有四个接触位置（没有刺激方向性） 脉冲到大脑的目标体积。 DBS 1.0电子需要优化四个刺激 参数：信号频率，电压，脉冲宽度和接触位置。在当前的护理标准中 优化协议，调整了DBS参数（通过反复试验），直到物理确定 最佳参数集。这种经验优化方案需要大量的临床访问（约6周） 间隔）大大增加了每名患者（〜1年）的优化时间（TTO），金融伯恩， 并最终限制了可以使用DBS治疗的患者数量。即使还有更多 有效的电子，DBS 1.0电子主要由临床医生使用，因为其较小的参数空间 在手动临床优化期间，构成不太困难。但是，DBS 1.0电极不能直接到 刺激少量的组织，这可能导致可减少患者临床的外部模拟 益处并增加副作用。相比之下，较新的DBS电极（称为DBS 2.0）具有更大的 接触位置的数量，可以编程以刺激多个级别的较小组织 和指示。几份已发表的报告表明，DBS 2.0电极（与DBS 1.0相比）更多 节能和改善副作用较小和更广泛的治疗窗口的患者预后。 但是，扩展的DBS 2.0参数空间使电子的经验编程变得困难 因为每位患者的TTO超出了可接受的临床时间表。这增加了困难受到阻碍 临床医生采用DBS 2.0电极。显着缩短并简化DBS 2.0参数 优化 - 使其更广泛地采用更精确的疗法 - 一种独特的合格多种 磁共振成像（MRI）物理学家，人工智能（AI）工程师和 GE研究和大学健康网络建议的临床医生：1）开发一个半自动 功能性MRI（fMRI）和深度学习（DL）的系统，用于快速优化DBS 2.0参数； 2） 使用双侧刺激对PD患者进行临床益处，以对PD患者进行双侧刺激 在一项试点研究中，带有DBS 2.0电子的核。该程序的成功将减少每位患者的TTO DB 2.0的PD患者实施到约1小时，并将改善患者的吞吐量和结果 Pd的处理。提出的基于fMRI-DL的优化方法也可能通过使其改善访问 非专家中心（没有高度专业的临床医生）进行刺激参数可能可能 电极插入手术后患者的优化已在专业中心完成。