White matter fiber tractography: why we need to move beyond DTI Clinical article
White matter fiber tractography: why we need to move beyond DTI Clinical article
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DOI：
10.3171/2013.2.jns121294
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发表时间：
2013-06-01
影响因子：
4.1
通讯作者：
Connelly, Alan
中科院分区：
文献类型：
--
作者：
Farquharson, Shawna;Tournier, J. Donald;Connelly, Alan
物体。基于扩散的MRI纤维束成像是一种成像工具，越来越多地应用于神经外科手术中，以生成白质路径的3D地图，以帮助确定安全的切除边缘。目前临床医生可用的大多数白质纤维束成像软件包依赖于一个有根本缺陷的框架来根据扩散加权数据生成纤维方向，即扩散张量成像(DTI)。这项工作首次广泛和系统地探索了基于DTI的纤维束成像的实际局限性，并调查了约束球面反卷积的高阶纤维束成像模型是否在临床可行的时间框架内提供了合理的解决方案。使用45名健康对照组和10名接受术前影像评估的患者的弥散加权数据集，比较了不同的纤维束成像方法在显示皮质脊髓束中的作用。患者和对照组均采用了基于张量和基于约束球面去卷积的纤维束成像方法。基于扩散张量成像的纤维束成像方法(使用确定性和概率性的纤维束成像算法)大大低估了对照组中所有参与者连接到感觉运动皮质的轨迹的范围。相比之下，受约束的球面反卷积光路造影术方法一致地产生了生物学上预期的扇形轨迹。在临床病例中，在神经外科手术后伴有神经功能缺陷风险的患者进行脑束成像以显示皮质脊髓通路，基于约束球面去卷积的方法和基于张量的方法显示出明显不同的切除安全边缘；基于受限球面去卷积的方法发现皮质脊髓束延伸至整个感觉运动皮质，而基于张量的方法仅发现向内侧延伸到顶点的一小部分束束。这项全面的研究表明，最广泛使用的临床纤维束成像方法(基于扩散张量成像的纤维束成像)会导致系统性的不可靠和临床误导信息。高阶纤维束成像模型，使用相同的扩散加权数据，更准确地显示纤维束，提供更好的安全裕度估计，这可能在神经外科手术中有用。因此，如果我们要开始为神经外科医生提供生物学上可靠的纤维束成像信息，我们需要超越扩散张量框架。
Object. Diffusion-based MRI tractography is an imaging tool increasingly used in neurosurgical procedures to generate 3D maps of white matter pathways as an aid to identifying safe margins of resection. The majority of white matter fiber tractography software packages currently available to clinicians rely on a fundamentally flawed framework to generate fiber orientations from diffusion-weighted data, namely diffusion tensor imaging (DTI). This work provides the first extensive and systematic exploration of the practical limitations of DTI-based tractography and investigates whether the higher-order tractography model constrained spherical deconvolution provides a reasonable solution to these problems within a clinically feasible timeframe.Methods. Comparison of tractography methodologies in visualizing the corticospinal tracts was made using the diffusion-weighted data sets from 45 healthy controls and 10 patients undergoing presurgical imaging assessment. Tensor-based and constrained spherical deconvolution based tractography methodologies were applied to both patients and controls.Results. Diffusion tensor imaging based tractography methods (using both deterministic and probabilistic tractography algorithms) substantially underestimated the extent of tracks connecting to the sensorimotor cortex in all participants in the control group. In contrast, the constrained spherical deconvolution tractography method consistently produced the biologically expected fan-shaped configuration of tracks. In the clinical cases, in which tractography was performed to visualize the corticospinal pathways in patients with concomitant risk of neurological deficit following neurosurgical resection, the constrained spherical deconvolution based and tensor-based tractography methodologies indicated very different apparent safe margins of resection; the constrained spherical deconvolution based method identified corticospinal tracts extending to the entire sensorimotor cortex, while the tensor-based method only identified a narrow subset of tracts extending medially to the vertex.Conclusions. This comprehensive study shows that the most widely used clinical tractography method (diffusion tensor imaging based tractography) results in systematically unreliable and clinically misleading information. The higher-order tractography model, using the same diffusion-weighted data, clearly demonstrates fiber tracts more accurately, providing improved estimates of safety margins that may be useful in neurosurgical procedures. We therefore need to move beyond the diffusion tensor framework if we are to begin to provide neurosurgeons with biologically reliable tractography information.