SSL-QALAS：利用自监督学习通过3D-QALAS快速估计定量MRI中的多参数
SSL-QALAS: Self-Supervised Learning for Rapid Multiparameter Estimation in Quantitative MRI Using 3D-QALAS
AI总结：
提出SSL-QALAS自监督方法，无需外部字典或标注真值，即可从3D-QALAS测量中快速准确重建T1、T2、PD和IE多参数图。
摘要：
目的：开发并评估一种方法，利用自监督学习（SSL）从带T2准备脉冲的三维交错Look-Locker采集序列（3D-QALAS）测量中快速估计多参数T1、T2、质子密度（PD）和反转效率（IE）图，且无需外部字典。方法：开发了一种基于SSL的QALAS映射方法（SSL-QALAS），用于从3D-QALAS测量中快速、无字典地估计多参数图。在ISMRM/NIST体模上，通过将估计的T1和T2值与参考方法所得值进行比较，评估了字典匹配和SSL-QALAS重建定量图的准确性。还在体内比较了SSL-QALAS与字典匹配方法，并通过比较扫描特定模型、预训练模型和迁移学习模型评估了泛化能力。结果：体模实验表明，字典匹配和SSL-QALAS方法产生的T1和T2估计值均与ISMRM/NIST体模中的参考值具有很强的线性一致性。此外，在体内数据上重建T1、T2、PD和IE图时，SSL-QALAS表现出与字典匹配相似的性能。通过使用预训练的SSL-QALAS模型在10秒内推断数据，实现了多参数图的快速重建。通过在15分钟内利用目标受试者数据对预训练模型进行微调，还展示了快速的扫描特定调优。结论：所提出的SSL-QALAS方法无需外部字典或带标签的真值训练数据，即可从3D-QALAS测量中快速重建多参数图。
英文摘要：
Purpose: To develop and evaluate a method for rapid estimation of multiparametric T1, T2, proton density (PD), and inversion efficiency (IE) maps from 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) measurements using self-supervised learning (SSL) without the need for an external dictionary. Methods: A SSL-based QALAS mapping method (SSL-QALAS) was developed for rapid and dictionary-free estimation of multiparametric maps from 3D-QALAS measurements. The accuracy of the reconstructed quantitative maps using dictionary matching and SSL-QALAS was evaluated by comparing the estimated T1 and T2 values with those obtained from the reference methods on an ISMRM/NIST phantom. The SSL-QALAS and the dictionary matching methods were also compared in vivo, and generalizability was evaluated by comparing the scan-specific, pre-trained, and transfer learning models. Results: Phantom experiments showed that both the dictionary matching and SSL-QALAS methods produced T1 and T2 estimates that had a strong linear agreement with the reference values in the ISMRM/NIST phantom. Further, SSL-QALAS showed similar performance with dictionary matching in reconstructing the T1, T2, PD, and IE maps on in vivo data. Rapid reconstruction of multiparametric maps was enabled by inferring the data using a pre-trained SSL-QALAS model within 10 s. Fast scan-specific tuning was also demonstrated by fine-tuning the pre-trained model with the target subject's data within 15 min. Conclusion: The proposed SSL-QALAS method enabled rapid reconstruction of multiparametric maps from 3D-QALAS measurements without an external dictionary or labeled ground-truth training data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
