ICHOR：一种用于ASL CBF图的鲁棒表示学习方法，基于自监督掩码自编码器
ICHOR: A Robust Representation Learning Approach for ASL CBF Maps with Self-Supervised Masked Autoencoders
AI总结：
ICHOR通过自监督预训练方法，利用3D掩码自编码器提升ASL CBF图的表示学习，实现跨研究和协议的鲁棒性与泛化能力。
摘要：
动脉自旋标记（ASL）灌注MRI允许直接量化区域脑血流量（CBF），而无需外源性对比剂，从而实现无创测量，且不受对比剂注射限制。ASL在研究和临床MRI协议中日益增多。在结构成像成功的基础上，最近的研究尝试通过深度学习方法提高图像质量、实现自动化质量控制，并从ASL衍生的CBF中获得稳健的定量和预测生物标志物。然而，进展受到变量图像质量、显著的跨站点、厂商和协议差异以及有限的标注数据集的限制，这些数据集需要训练能够跨人群泛化的模型。为了解决这些挑战，我们引入了ICHOR，一种用于ASL CBF图的自监督预训练方法，利用3D掩码自编码器学习可转移的表示。ICHOR通过使用视觉Transformer主干进行掩码图像建模进行预训练，并可作为通用编码器用于下游ASL任务。在预训练过程中，我们精心挑选了迄今为止最大的ASL数据集之一，包含11,405个ASL CBF扫描，来自14项研究，涵盖多个站点和采集协议。我们评估了预训练的ICHOR编码器在三个下游诊断分类任务和一个ASL CBF图质量预测回归任务上的表现。在所有评估中，ICHOR均优于现有神经影像自监督预训练方法适应于ASL的情况。预训练权重和代码将向公众开放。
英文摘要：
Arterial spin labeling (ASL) perfusion MRI allows direct quantification of regional cerebral blood flow (CBF) without exogenous contrast, enabling noninvasive measurements that can be repeated without constraints imposed by contrast injection. ASL is increasingly acquired in research studies and clinical MRI protocols. Building on successes in structural imaging, recent efforts have implemented deep learning based methods to improve image quality, enable automated quality control, and derive robust quantitative and predictive biomarkers with ASL derived CBF. However, progress has been limited by variable image quality, substantial inter-site, vendor and protocol differences, and limited availability of labeled datasets needed to train models that generalize across cohorts. To address these challenges, we introduce ICHOR, a self supervised pre-training approach for ASL CBF maps that learns transferable representations using 3D masked autoencoders. ICHOR is pretrained via masked image modeling using a Vision Transformer backbone and can be used as a general-purpose encoder for downstream ASL tasks. For pre-training, we curated one of the largest ASL datasets to date, comprising 11,405 ASL CBF scans from 14 studies spanning multiple sites and acquisition protocols. We evaluated the pre-trained ICHOR encoder on three downstream diagnostic classification tasks and one ASL CBF map quality prediction regression task. Across all evaluations, ICHOR outperformed existing neuroimaging self-supervised pre-training methods adapted to ASL. Pre-trained weights and code will be made publicly available.
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