Institute of Biomedical Engineering, University of Oxford, United Kingdom.
Neuroimage. 2013 Jan 1;64:560-70. doi: 10.1016/j.neuroimage.2012.08.083. Epub 2012 Sep 5.
DSC-MRI analysis is based on tracer kinetic theory and typically involves the deconvolution of the MRI signal in tissue with an arterial input function (AIF), which is an ill-posed inverse problem. The current standard singular value decomposition (SVD) method typically underestimates perfusion and introduces non-physiological oscillations in the resulting residue function. An alternative vascular model (VM) based approach permits only a restricted family of shapes for the residue function, which might not be appropriate in pathologies like stroke. In this work a novel deconvolution algorithm is presented that can estimate both perfusion and residue function shape accurately without requiring the latter to belong to a specific class of functional shapes. A control point interpolation (CPI) method is proposed that represents the residue function by a number of control points (CPs), each having two degrees of freedom (in amplitude and time). A complete residue function shape is then generated from the CPs using a cubic spline interpolation. The CPI method is shown in simulation to be able to estimate cerebral blood flow (CBF) with greater accuracy giving a regression coefficient between true and estimated CBF of 0.96 compared to 0.83 for VM and 0.71 for the circular SVD (oSVD) method. The CPI method was able to accurately estimate the residue function over a wide range of simulated conditions. The CPI method has also been demonstrated on clinical data where a marked difference was observed between the residue function of normally appearing brain parenchyma and infarcted tissue. The CPI method could serve as a viable means to examine the residue function shape under pathological variations.
DSC-MRI 分析基于示踪动力学理论，通常涉及使用动脉输入函数（AIF）对组织中的 MRI 信号进行反卷积，这是一个不适定的反问题。目前的标准奇异值分解（SVD）方法通常会低估灌注，并在得到的残差函数中引入非生理的振荡。基于血管模型（VM）的替代方法仅允许残差函数具有受限的形状家族，在像中风这样的病理情况下可能不合适。在这项工作中，提出了一种新的反卷积算法，它可以在不要求残差函数属于特定的函数形状类的情况下，准确地估计灌注和残差函数的形状。提出了一种控制点插值（CPI）方法，通过多个控制点（CPs）来表示残差函数，每个 CP 具有两个自由度（幅度和时间）。然后使用三次样条插值从 CP 生成完整的残差函数形状。CPI 方法在模拟中被证明能够更准确地估计脑血流量（CBF），真实和估计的 CBF 之间的回归系数为 0.96，而 VM 为 0.83，圆形 SVD（oSVD）方法为 0.71。CPI 方法能够在广泛的模拟条件下准确地估计残差函数。CPI 方法也在临床数据上得到了验证，在正常出现的脑实质和梗死组织的残差函数之间观察到明显的差异。CPI 方法可以作为一种可行的方法来检查病理变化下的残差函数形状。
