Multi-model sequential analysis of MRI data for microstructure prediction in heterogeneous tissue.
Multi-model sequential analysis of MRI data for microstructure prediction in heterogeneous tissue.
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DOI：
10.1038/s41598-023-43329-x
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发表时间：
2023-10-01
影响因子：
4.6
通讯作者：
Bourne, Roger
中科院分区：
文献类型：
--
作者：
Enriquez-Mier-y-Teran, Francisco E.;Chatterjee, Aritrick;Antic, Tatjana;Oto, Aytekin;Karczmar, Gregory;Bourne, Roger
我们提出了一种结合多种模型来预测组织微观结构的通用方法，并以体内扩散-弛豫MRI数据为例。提出的方法避免了在异质组织中选择单一的“最佳”结构模型进行数据分析的需要，其中最佳模型根据局部环境而变化。我们将信号解释分为三个阶段：(1)应用多个半现象学模型来预测组织水池对观测信号的物理性质；(2)从每个阶段1半现象学模型出发，应用组织微观结构模型预测构成每个水池的组织结构组分的相对体积；(3)对组织结构的预测进行汇总，权重基于模型似然和第1阶段水池的分数体积。多模型方法有望减少复杂模型过度参数化的组织区域的预测方差，以及模型参数化不足的偏差。信号表征（阶段1）与生物赋值（阶段2）的分离，通过应用不同的组织结构模型，可以对观察到的系统物理特性进行不同的生物学解释。所提出的方法以人类前列腺弥散-弛豫MRI数据为例，但具有潜在的应用于广泛的分析，其中单个模型在整个采样域可能不是最佳的。
We propose a general method for combining multiple models to predict tissue microstructure, with an exemplar using in vivo diffusion-relaxation MRI data. The proposed method obviates the need to select a single ’optimum’ structure model for data analysis in heterogeneous tissues where the best model varies according to local environment. We break signal interpretation into a three-stage sequence: (1) application of multiple semi-phenomenological models to predict the physical properties of tissue water pools contributing to the observed signal; (2) from each Stage-1 semi-phenomenological model, application of a tissue microstructure model to predict the relative volumes of tissue structure components that make up each water pool; and (3) aggregation of the predictions of tissue structure, with weightings based on model likelihood and fractional volumes of the water pools from Stage-1. The multiple model approach is expected to reduce prediction variance in tissue regions where a complex model is overparameterised, and bias where a model is underparameterised. The separation of signal characterisation (Stage-1) from biological assignment (Stage-2) enables alternative biological interpretations of the observed physical properties of the system, by application of different tissue structure models. The proposed method is exemplified with human prostate diffusion-relaxation MRI data, but has potential application to a wide range of analyses where a single model may not be optimal throughout the sampled domain.
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影响因子：
2.6
作者：
Gordetsky J;Epstein J
通讯作者：
Epstein J
乳腺癌和周围基质中扩散MRI的微观结构模型：一项离体研究。
影响因子：
2.9
作者：
Bailey C;Siow B;Panagiotaki E;Hipwell JH;Mertzanidou T;Owen J;Gazinska P;Pinder SE;Alexander DC;Hawkes DJ
通讯作者：
Hawkes DJ
DOI：
10.1007/s00261-021-03371-7
发表时间：
2022-03
期刊：
Abdominal radiology (New York)
影响因子：
--
作者：
Chatterjee A;Antic T;Gallan AJ;Paner GP;Lin LI;Karczmar GS;Oto A
通讯作者：
Oto A
影响因子：
3.3
作者：
Grant, SC;Buckley, DL;Blackband, SJ
通讯作者：
Blackband, SJ
影响因子：
3.7
作者：
Banks, H. T.;Joyner, Michele L.
通讯作者：
Joyner, Michele L.