Robust thalamic nuclei segmentation from T1-weighted MRI using polynomial intensity transformation

Brain Structure and Function Pub Date : 2024-03-28 DOI:10.1007/s00429-024-02777-5

Julie P. Vidal, Lola Danet, Patrice Péran, Jérémie Pariente, Meritxell Bach Cuadra, Natalie M. Zahr, Emmanuel J. Barbeau, Manojkumar Saranathan

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Abstract

Accurate segmentation of thalamic nuclei, crucial for understanding their role in healthy cognition and in pathologies, is challenging to achieve on standard T1-weighted (T1w) magnetic resonance imaging (MRI) due to poor image contrast. White-matter-nulled (WMn) MRI sequences improve intrathalamic contrast but are not part of clinical protocols or extant databases. In this study, we introduce histogram-based polynomial synthesis (HIPS), a fast preprocessing transform step that synthesizes WMn-like image contrast from standard T1w MRI using a polynomial approximation for intensity transformation. HIPS was incorporated into THalamus Optimized Multi-Atlas Segmentation (THOMAS) pipeline, a method developed and optimized for WMn MRI. HIPS-THOMAS was compared to a convolutional neural network (CNN)-based segmentation method and THOMAS modified for the use of T1w images (T1w-THOMAS). The robustness and accuracy of the three methods were tested across different image contrasts (MPRAGE, SPGR, and MP2RAGE), scanner manufacturers (PHILIPS, GE, and Siemens), and field strengths (3 T and 7 T). HIPS-transformed images improved intra-thalamic contrast and thalamic boundaries, and HIPS-THOMAS yielded significantly higher mean Dice coefficients and reduced volume errors compared to both the CNN method and T1w-THOMAS. Finally, all three methods were compared using the frequently travelling human phantom MRI dataset for inter- and intra-scanner variability, with HIPS displaying the least inter-scanner variability and performing comparably with T1w-THOMAS for intra-scanner variability. In conclusion, our findings highlight the efficacy and robustness of HIPS in enhancing thalamic nuclei segmentation from standard T1w MRI.

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利用多项式强度变换从 T1 加权磁共振成像中稳健地分割丘脑核团

摘要丘脑核的精确分割对于了解其在健康认知和病理中的作用至关重要，但由于图像对比度差，在标准 T1 加权（T1w）磁共振成像（MRI）上实现丘脑核的精确分割具有挑战性。白质剔除（WMn）磁共振成像序列可改善鞘内对比度，但并不属于临床方案或现有数据库的一部分。在这项研究中，我们引入了基于直方图的多项式合成（HIPS），这是一个快速预处理转换步骤，它利用强度转换的多项式近似值从标准 T1w MRI 合成类似 WMn 的图像对比度。HIPS 被纳入 "丘脑优化多图集分割"（THOMAS）管道，这是一种针对 WMn MRI 开发和优化的方法。HIPS-THOMAS与基于卷积神经网络（CNN）的分割方法以及为使用T1w图像而修改的THOMAS（T1w-THOMAS）进行了比较。在不同的图像对比度（MPRAGE、SPGR 和 MP2RAGE）、扫描仪制造商（PHILIPS、GE 和 Siemens）和场强（3 T 和 7 T）下测试了三种方法的稳健性和准确性。与 CNN 方法和 T1w-THOMAS 相比，HIPS 转换图像改善了丘脑内对比度和丘脑边界，HIPS-THOMAS 得到的平均 Dice 系数明显更高，体积误差也更小。最后，我们使用频繁移动的人体模型 MRI 数据集对所有三种方法的扫描仪间和扫描仪内变异性进行了比较，HIPS 的扫描仪间变异性最小，扫描仪内变异性与 T1w-THOMAS 不相上下。总之，我们的研究结果凸显了 HIPS 在增强标准 T1w MRI 丘脑核分割方面的有效性和稳健性。

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