T1图比其他MRI影像更适合作为丘脑核团分割的输入
Segmenting Thalamic Nuclei: T1 Maps Provide a Reliable and Efficient Solution
- 用梯度显著性分析与蒙特卡洛丢弃法筛选最佳多TI图像
- 仅用T1图即达优秀分割效果，定量指标领先
- 对临床和研究有指导意义，适合关注丘脑结构者
准确分割丘脑核团对理解神经疾病、脑功能及指导临床干预至关重要，但最优影像输入尚不明确。本研究系统评估了MPRAGE、FGATIR、定量质子密度（PD）图、T1图以及不同反转时间的多T1加权图像（multi-TI）等多种MRI对比度。针对multi-TI图像，采用基于梯度的显著性分析结合蒙特卡洛丢弃法，提出总体重要性评分以筛选对分割贡献最大的图像。使用3D U-Net在每种配置上训练模型。结果表明，仅使用T1图即可实现优异的定量性能和更优的定性分割效果，而PD图未带来额外增益。这些发现强调了T1图作为可靠且高效的输入价值，为丘脑结构相关临床与研究中的成像协议优化提供了重要依据。
原文摘要 · Abstract (English)
Accurate thalamic nuclei segmentation is crucial for understanding neurological diseases, brain functions, and guiding clinical interventions. However, the optimal inputs for segmentation remain unclear. This study systematically evaluates multiple MRI contrasts, including MPRAGE and FGATIR sequences, quantitative PD and T1 maps, and multiple T1-weighted images at different inversion times (multi-TI), to determine the most effective inputs. For multi-TI images, we employ a gradient-based saliency analysis with Monte Carlo dropout and propose an Overall Importance Score to select the images contributing most to segmentation. A 3D U-Net is trained on each of these configurations. Results show that T1 maps alone achieve strong quantitative performance and superior qualitative outcomes, while PD maps offer no added value. These findings underscore the value of T1 maps as a reliable and efficient input among the evaluated options, providing valuable guidance for optimizing imaging protocols when thalamic structures are of clinical or research interest.
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