A New Multimodal Perfusion Imaging Method Using Concurrent NIRS and fMRI

描述(申请人提供)：该R21项目将开发和验证一种新的血流动力学参数(CBV和CBF)成像方法，该方法基于对同时记录的fMRI和NIRS数据的交叉模式处理，使用累进时间延迟(RIPTID)的Regressor插值法。这一新的非侵入性成像技术允许常规生成具有粗体成像数据的定量CBF和CBV图像，并避免了现有方法(DSC MRI、ASL和VASO)的许多缺陷。背景：激流成像利用了NIRS和fMRI都测量血氧和浓度波动的事实，但没有仪器噪声机制。因此，NIRS和BOLD数据的时间相关性代表了内源性血氧和血容量波动通过血管系统传播的强度和时间。我们可以在高信噪比(1)的短扫描中量化这些信号在单个受试者中的幅度和到达时间(1)，并使用这项技术从BOLD数据中过滤生理性噪声(2)，并测量脑血管对屏气挑战的反应性(3)。利用现有的BOLD效应生物物理模型，我们建议使用这些数据在高空间分辨率下定量估计脑血流量和体积。测量可以与传统的fMRI采集同时进行，并且不需要特殊的fMRI采集序列或参数。此外，这种类型的测量所需的近红外采集硬件原则上可以相当便宜，这使得将其添加到现有的磁共振扫描仪中是可行的。我们将把这种测量简化到实践中，并将其结果和数据质量与ASL和VASO进行比较。意义：同时采集的fMRI/NIRS数据使用Riptie进行处理，使我们能够分离出血流动力学波动对每个体素BOLD信号的贡献。这可以显著减少BOLD数据中的生理噪声，同时产生每个位置的血流量和体积的估计。这允许真正同时获取高质量的BOLD、CBV和CBF信息。具体目标：1)比较使用近红外光谱从四个不同记录位置获得的数据质量。NIRS记录的位置影响到测量的血流动力学信号的纯度。激流波图像将使用来自四个探头位置的近红外数据进行计算，并进行比较，以选择标准记录位置；2)评估激流波数据作为气球模型输入的使用，以生成血流量和血流量的定量估计。我们将使用激流数据(最佳延迟NIRS[HBR]和[THB]，以及BOLD)作为气球模型的输入，计算CBV和CBF，并将结果和SNR/单位时间与ASL和VASO进行比较；3)实现激流处理程序包。这一目标将开发一种简化的数据记录和分析套件，以简化激流数据的使用，并允许其他研究人员使用这种方法。

DESCRIPTION (provided by applicant): This R21 project will develop and validate a novel method of imaging hemodynamic parameters (CBV and CBF) in vivo, based on crossmodal processing of concurrently recorded fMRI and NIRS data using Regressor Interpolation at Progressive Time Delays (RIPTiDe). This new, non-invasive imaging technique allows the routine generation of quantitative CBF and CBV images simultaneously with BOLD imaging data, and avoids many pitfalls of existing methods (DSC MRI, ASL and VASO). Background: RIPTiDe imaging exploits the fact the NIRS and fMRI both measure blood oxygenation and concentration fluctuations, but share no instrumental noise mechanisms. Therefore the temporal crosscorrelation of the NIRS and BOLD data represents the strength and timing of the propagation of endogenous fluctuations in blood oxygenation and volume through the vasculature. We can quantify the amplitude and arrival time of these signals in single subjects during brief scans at high signal to noise(1), and have used this technique for filtering physiological noise from BOLD data (2), and measuring cerebrovascular reactivity to a breathhold challenge (3). Using existing biophysical models of the BOLD effect, we propose using this data to quantitatively estimate cerebral blood flow and volume at high spatial resolution. Measurements can be made concurrently with conventional fMRI acquisitions, and require no special fMRI acquisition sequences or parameters. Moreover, the near infrared acquisition hardware required for this type of measurement can in principal be quite inexpensive, making it practical to add it to existing MR scanners. We will reduce this measurement to practice, and compare its results and data quality to ASL and VASO. Significance: Simultaneously acquired fMRI/NIRS data processed using RIPTiDe allows us to isolate the contribution of hemodynamic fluctuations to the BOLD signal in every voxel. This permits significant reduction in the physiological noise in the BOLD data, and simultaneously yields an estimate of blood flow and volume at every location. This allows truly concurrent acquisition of high quality BOLD, CBV, and CBF information. Specific Aims: 1) Compare data quality obtained using NIRS from four different recording locations. The location of NIRS recording affects the purity of the measured hemodynamic signal. RIPTiDe images will be calculated using NIRS data from four probe locations, and compared to choose a standard recording location; 2) Evaluate the use of RIPTiDe data as input to the Balloon Model to generate quantitative estimates of blood flow and volume. We will use the RIPTiDe data (optimally delayed NIRS [HbR] and [tHb], and BOLD) as inputs to the balloon model, calculate CBV and CBF, and compare results and SNR/unit time with ASL and VASO; 3) Implement a RIPTiDe processing package. This aim will develop a streamlined data recording and analysis suite to simplify the use of RIPTiDe data, and allow other researchers to use this method.