Noninvasive measurement of oxygenation using quantitative susceptibility mapping (supplement)

项目摘要/摘要 本研究的目的是开发和验证心脏定量易感性地形图(QSM) 评估二尖瓣环钙化的严重程度，以期改善二尖瓣环钙化的预测 二尖瓣脱垂患者的治疗反应、治疗决策和临床结果 (MVP)。美国有700多万人患有MVP，全球有超过1.7亿人患有MVP。这是一个 退行性二尖瓣返流(DMR)是二尖瓣最常见的类型 需要手术的反流(MR)。经皮(二尖瓣夹置术)或外科(二尖瓣修补术) 治疗可以减少MR，从而避免有害的未经治疗的反流或二尖瓣置换术(MVR)。 然而，10%-30%的患者MR复发，这影响了预后并增加了充血性心脏的风险 失败和死亡。因此，拥有治疗成功的早期预测指标对于优化治疗至关重要。 并改善MVP患者的预后。二尖瓣环状钙(MAC)是这种反应的关键预测指标之一 对于MVP患者的手术和经皮修补。众所周知，Mac会降低手术治疗的成功率 并增加发病率和死亡率。目前使用超声(ECHO)诊断Mac，缺乏 定量，或CT，它使患者暴露在电离辐射中，无法测量其他 预测指标，如纤维化或直接评估MR本身。心脏磁共振成像(CMRI)可以测量这些预测因素 但目前无法量化MAC--从而限制了这一强大的评估方式的实用性 MR的生理决定因素及其对治疗的反应。尽管如此，钙化对 由于其强烈的抗磁敏感性，MR信号显著地改变了在和周围的磁场 苹果公司。虽然使用产生的低幅度信号可以定性地检测到严重的MAC，但它更少 对轻度或中度MAC水平敏感，且不是定量的。QSM--我们首创的磁共振成像技术 组--能够直接测量磁化率，从而测量钙含量。我们获得了鼓舞人心的消息 初步数据显示心脏QSM检测MAC的可行性，并已初步显示 对照心脏CT参考值对该方法进行了验证。在这项提案中，我们建议开发心脏QSM 获取和处理方法并在一组MVP患者中通过 遵循特定的目标。(1)我们将比较传统的和加速的QSM对MAC的量化 接受经皮或手术修复的MVP患者。(2)我们将开发和验证一个全自动的 机器学习算法对MAC进行量化。(3)我们将测试QSM是否独立预测治疗 二尖瓣修补术的反应。这项研究的预期结果是一种基于非侵入性MRI的方法 测量二尖瓣环钙化程度，这是经皮或外科手术成功的关键预测因素 二尖瓣脱垂的治疗。心脏QSM对治疗的目标具有广泛的意义 针对瓣膜疾病的优化。

PROJECT SUMMARY/ABSTRACT The goal of this research is to develop and validate cardiac quantitative susceptibility mapping (QSM) for the assessment of mitral annular calcification severity, towards the long-term objective of improving prediction of therapeutic response, therapeutic decision-making and clinical outcomes for patients with mitral valve prolapse (MVP). MVP occurs in over 7 million individuals in the United States and over 170 million worldwide. It is a leading cause of degenerative mitral regurgitation (DMR) which represents the most frequent form of mitral regurgitation (MR) requiring surgery. Percutaneous (mitral valve clip placement) or surgical (mitral valve repair) treatment can reduce MR, thus avoiding harmful untreated regurgitation or mitral valve replacement (MVR). However, MR recurs in 10-30% of patients, which impacts prognosis and increases risk for congestive heart failure and death. It is therefore critical to have early predictors of therapeutic success to optimize treatments and improve outcomes for patients with MVP. Mitral annular calcium (MAC) is one such key predictor of response to surgical and percutaneous repair in patients with MVP. MAC is known to decrease surgical treatment success and to increase morbidity and mortality. MAC is currently diagnosed using ultrasound (echo), which lacks quantitation, or CT, which exposes the patient to ionizing radiation and is not capable of measuring other predictors such as fibrosis or directly assessing MR itself. Cardiac MRI (CMRI) can measures these predictors but is currently incapable of quantifying MAC – thus limiting the utility of this powerful modality for assessment of physiologic determinants of MR and its response to therapy. Nevertheless, calcification has a strong effect on the MR signal due to its strong diamagnetic susceptibility, which significantly modifies the magnetic at and round the MAC. While severe MAC can be qualitatively detected using the resulting low magnitude signal, it is less sensitive at mild or moderate MAC levels and is not quantitative. QSM – an MRI technique pioneered by our group – is able to directly measure susceptibility and thus calcium content. We have obtained encouraging preliminary data showing the feasibility of using cardiac QSM to detect MAC and have shown preliminary validation of this method against cardiac CT reference. In this proposal, we propose to develop cardiac QSM acquisition and processing methods and perform its validation among a cohort of MVP patients through the following Specific Aims. (1) We will compare conventional to accelerated QSM for quantification of MAC among patients with MVP undergoing percutaneous or surgical repair. (2) We will develop and validate a fully automated machine learning algorithm to quantify MAC. (3) We will test whether QSM independently predicts therapeutic response to mitral valve repair. The expected outcome of this research is a non-invasive MRI based method to measure mitral annulus calcification severity, which is a key predictor of success of percutaneous or surgical treatment for mitral valve prolapse. Cardiac QSM holds broad significance towards the goal of therapeutic optimization for valvular disease.