RubiX: combining spatial resolutions for Bayesian inference of crossing fibers in diffusion MRI.
RubiX: combining spatial resolutions for Bayesian inference of crossing fibers in diffusion MRI.
复制标题
DOI：
10.1109/tmi.2012.2231873
复制
发表时间：
2013-06
影响因子：
10.6
通讯作者：
Behrens TE
中科院分区：
文献类型：
--
作者：
Sotiropoulos SN;Jbabdi S;Andersson JL;Woolrich MW;Ugurbil K;Behrens TE
信噪比（SNR）和空间特异性之间的权衡决定了磁共振成像（MRI）中空间分辨率的选择;弥散加权（DW）MRI也不例外。较低分辨率的图像具有较高的信噪比，但也有更多的部分体积伪影。我们提出了一种数据融合的方法来解决这个权衡结合DW MRI数据采集在高和低的空间分辨率。我们将所有数据联合收割机组合成一个单一的贝叶斯模型，以估计潜在的纤维模式和扩散参数。因此，拟议的模式结合了每次收购的好处。我们表明，在最高的空间分辨率的光纤交叉可以推断出更强大和更准确地使用这样的模型相比，一个简单的模型，只对高分辨率的数据，当这两种方法是匹配的采集时间。
The trade-off between signal-to-noise ratio (SNR) and spatial specificity governs the choice of spatial resolution in magnetic resonance imaging (MRI); diffusion-weighted (DW) MRI is no exception. Images of lower resolution have higher signal to noise ratio, but also more partial volume artifacts. We present a data-fusion approach for tackling this trade-off by combining DW MRI data acquired both at high and low spatial resolution. We combine all data into a single Bayesian model to estimate the underlying fiber patterns and diffusion parameters. The proposed model, therefore, combines the benefits of each acquisition. We show that fiber crossings at the highest spatial resolution can be inferred more robustly and accurately using such a model compared to a simpler model that operates only on high-resolution data, when both approaches are matched for acquisition time.