磁共振影像（magnetic resonance imaging, MRI）具有無輻射線與非侵入性等優勢。近年來磁共振造影的快速發展，modified look-locker inversion recovery (MOLLI)目前被公認為最可靠且重現性極佳的T1定量影像技術。但MOLLI技術要求患者在掃描期間必須全程閉氣，對許多患有疾病或是肺容量限制的患者來說是極為困難的，因此先前研究提出了自由呼吸下的MOLLI，但自由呼吸衍生了像是心臟移動等問題，造成T1量測結果的誤差。本研究藉由自動化圈選切割影像、機器學習、合成影像、影像對位、雲端計算等方法改善自由呼吸下MOLLI的T1量測結果，利用美國心臟協會的心肌標準化分區做分析，並和先前的研究比較T1量測的精確度有顯著的提升。最後將全自動化的處理流程透過雲端計算服務平台簡化臨床T1圖譜的處理流程，使改善後的自由呼吸 MOLLI成為臨床上量測T1圖譜的一個可靠工具。

Amount the myocardial T1 mapping techniques, the modified look-locker inversion recovery (MOLLI) sequence is accurate and reproducible. The free-breathing MOLLI (FB-MOLLI) presented in our previous study allowed T1 mapping in vivo without breath-hold. In this study, we attempted to implement unsupervised reconstruction for FB-MOLLI data sets and used a deformable method for image registration to improve the reliability of free-breathing T1 mapping. The results supported that the method improved the image alignments of the FB-MOLLI data sets and thus increased the quality of the T1 map. The variations of the repeated T1 measurements were significantly reduced in the anterolateral of the LV walls. In summary, this study presents a functional and robust cloud computing service for application of automatic image processing for FB-MOLLI data sets.

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