基于模型的多参数映射
Model-based multi-parameter mapping
摘要：
定量磁共振成像因其信息内容丰富、测量标准化而越来越受到青睐。然而，计算定量参数图，例如编码纵向弛豫速率（R1）、表观横向弛豫速率（R2*）或磁化转移饱和度（MTsat）的那些，涉及反转高度非线性函数。许多用于导出参数图的方法假设完美的测量，并且不考虑噪声如何通过估计过程传播，从而导致不必要的噪声图。相反，我们提出了整个数据集的概率生成（前向）模型，该模型被公式化和反转以联合恢复（log）具有明确定义的概率解释的参数图（例如，最大似然或最大后验）。二阶优化，我们提出的模型拟合实现快速和稳定的收敛，由于一个新的近似海森。我们证明了我们的灵活的框架的实用程序的背景下，从使用流行的多参数映射协议获得的数据恢复更准确的地图。我们还展示了如何将联合总变差之前，进一步降低噪声的地图，注意到概率公式允许恢复参数地图的不确定性估计。我们的实现使用PyTorch后端并受益于GPU加速。可在https://github.com/balbasty/nitorch上查阅。
英文摘要：
Quantitative MR imaging is increasingly favoured for its richer information content and standardised measures. However, computing quantitative parameter maps, such as those encoding longitudinal relaxation rate (R1), apparent transverse relaxation rate (R2*) or magnetisation-transfer saturation (MTsat), involves inverting a highly non-linear function. Many methods for deriving parameter maps assume perfect measurements and do not consider how noise is propagated through the estimation procedure, resulting in needlessly noisy maps. Instead, we propose a probabilistic generative (forward) model of the entire dataset, which is formulated and inverted to jointly recover (log) parameter maps with a well-defined probabilistic interpretation (e.g., maximum likelihood or maximum a posteriori). The second order optimisation we propose for model fitting achieves rapid and stable convergence thanks to a novel approximate Hessian. We demonstrate the utility of our flexible framework in the context of recovering more accurate maps from data acquired using the popular multi-parameter mapping protocol. We also show how to incorporate a joint total variation prior to further decrease the noise in the maps, noting that the probabilistic formulation allows the uncertainty on the recovered parameter maps to be estimated. Our implementation uses a PyTorch backend and benefits from GPU acceleration. It is available at https://github.com/balbasty/nitorch.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
