Introduction

During the last decade, a multitude of novel quantitative and semiquantitative MRI techniques have provided new information about the pathophysiology of neurological diseases. Yet, selection of the most relevant contrasts for a given pathology remains challenging. In this work, we developed and validated a method, Gated-Attention MEchanism Ranking of multi-contrast MRI in brain pathology (GAMER MRI), to rank the relative importance of MR measures in the classification of well understood ischemic stroke lesions. Subsequently, we applied this method to the classification of multiple sclerosis (MS) lesions, where the relative importance of MR measures is less understood.

Methods

GAMER MRI was developed based on the gated attention mechanism, which computes attention weights (AWs) as proxies of importance of hidden features in the classification. In the first two experiments, we used Trace-weighted (Trace), apparent diffusion coefficient (ADC), Fluid-Attenuated Inversion Recovery (FLAIR), and T1-weighted (T1w) images acquired in 904 acute/subacute ischemic stroke patients and in 6,230 healthy controls and patients with other brain pathologies to assess if GAMER MRI could produce clinically meaningful importance orders in two different classification scenarios. In the first experiment, GAMER MRI with a pretrained convolutional neural network (CNN) was used in conjunction with Trace, ADC, and FLAIR to distinguish patients with ischemic stroke from those with other pathologies and healthy controls. In the second experiment, GAMER MRI with a patch-based CNN used Trace, ADC and T1w to differentiate acute ischemic stroke lesions from healthy tissue. The last experiment explored the performance of patch-based CNN with GAMER MRI in ranking the importance of quantitative MRI measures to distinguish two groups of lesions with different pathological characteristics and unknown quantitative MR features. Specifically, GAMER MRI was applied to assess the relative importance of the myelin water fraction (MWF), quantitative susceptibility mapping (QSM), T1 relaxometry map (qT1), and neurite density index (NDI) in distinguishing 750 juxtacortical lesions from 242 periventricular lesions in 47 MS patients. Pair-wise permutation t-tests were used to evaluate the differences between the AWs obtained for each quantitative measure.

Results

In the first experiment, we achieved a mean test AUC of 0.881 and the obtained AWs of FLAIR and the sum of AWs of Trace and ADC were 0.11 and 0.89, respectively, as expected based on previous knowledge. In the second experiment, we achieved a mean test F1 score of 0.895 and a mean AW of Trace = 0.49, of ADC = 0.28, and of T1w = 0.23, thereby confirming the findings of the first experiment. In the third experiment, MS lesion classification achieved test balanced accuracy = 0.777, sensitivity = 0.739, and specificity = 0.814. The mean AWs of T1map, MWF, NDI, and QSM were 0.29, 0.26, 0.24, and 0.22 (p < 0.001), respectively.

Conclusions

This work demonstrates that the proposed GAMER MRI might be a useful method to assess the relative importance of MRI measures in neurological diseases with focal pathology. Moreover, the obtained AWs may in fact help to choose the best combination of MR contrasts for a specific classification problem.

中文翻译：

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GAMER MRI：脑病理学中多对比度MRI的门控注意机制排名

介绍

在过去的十年中，大量新颖的定量和半定量MRI技术为神经疾病的病理生理学提供了新的信息。然而，针对给定病理的最相关对比的选择仍然具有挑战性。在这项工作中，我们开发并验证了一种方法，即在脑病理学中的多对比度MRI的门控注意机制分级（GAMER MRI），以在公认的缺血性中风病变分类中对MR措施的相对重要性进行排名。随后，我们将这种方法应用于多发性硬化症（MS）病变的分类，其中对MR措施的相对重要性了解得很少。

方法

GAMER MRI是基于门控注意力机制开发的，该机制计算注意力权重（AW）作为分类中隐藏特征重要性的代理。在前两个实验中，我们使用了904例急性/亚急性缺血性脑卒中患者和患者中获得的痕量加权（Trace），表观扩散系数（ADC），体液倒置恢复（FLAIR）和T1加权（T1w）图像。 6,230名健康对照组和其他脑部疾病患者评估了GAMER MRI是否可以在两种不同的分类方案中产生具有临床意义的重要性顺序。在第一个实验中，将带有预训练卷积神经网络（CNN）的GAMER MRI与Trace，ADC和FLAIR结合使用，以将缺血性卒中患者与其他病理和健康对照患者区分开。在第二个实验中 GAMER MRI带有基于补丁的CNN，使用Trace，ADC和T1w区分健康组织的急性缺血性中风病灶。上一个实验探索了GAMER MRI的基于补丁的CNN在确定定量MRI测量对区分具有不同病理特征和未知定量MR特征的两组病变的重要性方面的重要性。具体而言，GAMER MRI用于评估髓鞘水分数（MWF），定量磁化图（QSM），T1弛豫法图（qT1）和神经突密度指数（NDI）的相对重要性，以区分750例皮层皮损和242个脑室周围皮损在47名MS患者中。使用成对排列t检验来评估每种定量度量获得的AW之间的差异。ADC和T1w可以区分健康组织的急性缺血性中风病变。上一个实验探索了GAMER MRI的基于补丁的CNN在确定定量MRI测量对区分具有不同病理特征和未知定量MR特征的两组病变的重要性方面的重要性。具体而言，GAMER MRI用于评估髓鞘水分数（MWF），定量磁化图（QSM），T1弛豫法图（qT1）和神经突密度指数（NDI）的相对重要性，以区分750例皮层皮损和242个脑室周围皮损在47名MS患者中。使用成对排列t检验来评估每种定量度量获得的AW之间的差异。ADC和T1w可以区分健康组织的急性缺血性中风病变。最后一个实验探索了GAMER MRI的基于补丁的CNN在确定定量MRI措施区分两组具有不同病理特征和未知定量MR特征的病变的重要性方面的性能。具体而言，GAMER MRI用于评估髓鞘水分数（MWF），定量磁化图（QSM），T1弛豫法图（qT1）和神经突密度指数（NDI）的相对重要性，以区分750例皮层皮损和242个脑室周围皮损在47名MS患者中。使用成对排列t检验来评估每种定量度量获得的AW之间的差异。上一个实验探索了GAMER MRI的基于补丁的CNN在量化定量MRI措施以区分两组具有不同病理特征和未知定量MR特征的病变的重要性方面的性能。具体而言，GAMER MRI用于评估髓鞘水分数（MWF），定量磁化图（QSM），T1弛豫法图（qT1）和神经突密度指数（NDI）的相对重要性，以区分750例皮层皮损和242个脑室周围皮损在47名MS患者中。使用成对排列t检验来评估每种定量度量获得的AW之间的差异。上一个实验探索了GAMER MRI的基于补丁的CNN在确定定量MRI测量对区分具有不同病理特征和未知定量MR特征的两组病变的重要性方面的重要性。具体而言，GAMER MRI用于评估髓鞘水分数（MWF），定量磁化图（QSM），T1弛豫法图（qT1）和神经突密度指数（NDI）的相对重要性，以区分750例皮层皮损和242个脑室周围皮损在47名MS患者中。使用成对排列t检验来评估每种定量度量获得的AW之间的差异。

结果

在第一个实验中，我们获得了平均测试AUC为0.881，并且获得的FLAIR的AW和Trace和ADC的AW之和分别为0.11和0.89，这是基于先前的知识所期望的。在第二个实验中，我们获得的平均测试F1分数为0.895，平均AW为迹线= 0.49，ADC = 0.28和T1w = 0.23，从而证实了第一项实验的发现。在第三个实验中，MS病变分类达到了测试平衡准确度= 0.777，灵敏度= 0.739，特异性= 0.814。T1map，MWF，NDI和QSM的平均AW分别为0.29、0.26、0.24和0.22（p <0.001）。

结论

这项工作表明，提出的GAMER MRI可能是评估MRI措施在局灶性神经疾病中相对重要性的有用方法。此外，对于特定的分类问题，获得的AW实际上可以帮助选择MR对比的最佳组合。