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Combining the boundary shift integral and tensor-based morphometry for brain atrophy estimation

机译:结合边界移位积分和基于张量的形态学估计脑萎缩

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Brain atrophy from structural magnetic resonance images (MRIs) is widely used as an imaging surrogate marker for Alzheimers disease. Their utility has been limited due to the large degree of variance and subsequently high sample size estimates. The only consistent and reasonably powerful atrophy estimation methods has been the boundary shift integral (BSI). In this paper, we first propose a tensor-based morphometry (TBM) method to measure voxel-wise atrophy that we combine with BSI. The combined model decreases the sample size estimates significantly when compared to BSI and TBM alone.
机译:结构性磁共振图像(MRI)引起的脑萎缩被广泛用作阿尔茨海默氏病的影像学替代指标。由于较大的方差和随后的高样本量估计,其效用受到了限制。唯一一致且合理有效的萎缩估计方法是边界偏移积分(BSI)。在本文中,我们首先提出了一种基于张量的形态计量学(TBM)方法来测量与BSI结合的体素性萎缩。与单独的BSI和TBM相比,组合模型显着减少了样本量估计。

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