应用于心脏扩散张量磁共振成像的线性不变张量插值
Linear invariant tensor interpolation applied to cardiac diffusion tensor MRI.
作者信息
Gahm Jin Kyu, Wisniewski Nicholas, Kindlmann Gordon, Kung Geoffrey L, Klug William S, Garfinkel Alan, Ennis Daniel B
机构信息
Department of Radiological Sciences, UCLA, CA 90095, USA.
出版信息
Med Image Comput Comput Assist Interv. 2012;15(Pt 2):494-501. doi: 10.1007/978-3-642-33418-4_61.
PURPOSE
Various methods exist for interpolating diffusion tensor fields, but none of them linearly interpolate tensor shape attributes. Linear interpolation is expected not to introduce spurious changes in tensor shape.
METHODS
Herein we define a new linear invariant (LI) tensor interpolation method that linearly interpolates components of tensor shape (tensor invariants) and recapitulates the interpolated tensor from the linearly interpolated tensor invariants and the eigenvectors of a linearly interpolated tensor. The LI tensor interpolation method is compared to the Euclidean (EU), affine-invariant Riemannian (AI), log-Euclidean (LE) and geodesic-loxodrome (GL) interpolation methods using both a synthetic tensor field and three experimentally measured cardiac DT-MRI datasets.
RESULTS
EU, AI, and LE introduce significant microstructural bias, which can be avoided through the use of GL or LI.
CONCLUSION
GL introduces the least microstructural bias, but LI tensor interpolation performs very similarly and at substantially reduced computational cost.
目的
存在多种用于插值扩散张量场的方法，但它们均未对张量形状属性进行线性插值。线性插值预计不会在张量形状中引入虚假变化。
方法
在此我们定义一种新的线性不变（LI）张量插值方法，该方法对张量形状的分量（张量不变量）进行线性插值，并从线性插值的张量不变量和线性插值张量的特征向量中重构插值张量。使用合成张量场和三个实验测量的心脏DT - MRI数据集，将LI张量插值方法与欧几里得（EU）、仿射不变黎曼（AI）、对数欧几里得（LE）和测地线斜航线（GL）插值方法进行比较。
结果
EU、AI和LE会引入显著的微观结构偏差，通过使用GL或LI可避免这种偏差。
结论
GL引入的微观结构偏差最小，但LI张量插值的表现非常相似，且计算成本大幅降低。
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