Information Processing in Medical Imaging - 28th International Conference, IPMI 2023, San Carlos de Bariloche, Argentina, June 18-23, 2023, Proceedings
Information Processing in Medical Imaging - 28th International Conference, IPMI 2023, San Carlos de Bariloche, Argentina, June 18-23, 2023, Proceedings
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医学影像信息处理 - 第 28 届国际会议，IPMI 2023，阿根廷圣卡洛斯-德巴里洛切，2023 年 6 月 18-23 日，会议记录
DOI：
10.1007/978-3-031-34048-2_1
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Guerreri M
中科院分区：
文献类型：
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作者：
Guerreri M
定量MRI （qMRI）旨在通过将这些未知量与测量的MRI信号相关联的模型，非侵入性地绘制组织特性。估计这些未知数，传统上需要模型拟合，这通常是一个迭代的过程，现在可以用一次性机器学习（ML）方法来完成。这种参数估计可能会因qMRI信号模型的内在退化而变得复杂：组织特性的不同组合产生相同的信号。尽管ML方法有很多优点，但它是否能解决这个问题还不清楚。越来越多的经验证据似乎表明ML方法仍然容易受到模型退化的影响。这里我们演示了在适当的情况下ML可以解决这个问题。受最近研究训练数据分布对基于ml的参数估计影响的工作的启发，我们提出通过设计训练数据分布来解决模型退化问题。我们提出了一种模型退化的分类方法，并确定了一种适合这种攻击的模型退化。以标准多壳扩散MRI数据为例，对修正的NODDI模型进行了验证。我们的结果说明了训练集设计的重要性，它有可能允许用ML准确估计组织特性。
Quantitative MRI (qMRI) aims to map tissue properties non-invasively via models that relate these unknown quantities to measured MRI signals. Estimating these unknowns, which has traditionally required model fitting - an often iterative procedure, can now be done with one-shot machine learning (ML) approaches. Such parameter estimation may be complicated by intrinsic qMRI signal model degeneracy: different combinations of tissue properties produce the same signal. Despite their many advantages, it remains unclear whether ML approaches can resolve this issue. Growing empirical evidence appears to suggest ML approaches remain susceptible to model degeneracy. Here we demonstrate under the right circumstances ML can address this issue. Inspired by recent works on the impact of training data distributions on ML-based parameter estimation, we propose to resolve model degeneracy by designing training data distributions. We put forward a classification of model degeneracies and identify one particular kind of degeneracies amenable to the proposed attack. The strategy is demonstrated successfully using the Revised NODDI model with standard multi-shell diffusion MRI data as an exemplar. Our results illustrate the importance of training set design which has the potential to allow accurate estimation of tissue properties with ML.