使用深度学习和集成 UTE/multi-echo Dixon 序列进行衰减校正：在淀粉样蛋白和 tau PET 成像中的评估。
Attenuation correction using deep Learning and integrated UTE/multi-echo Dixon sequence: evaluation in amyloid and tau PET imaging.
机构信息
Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.
Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.
出版信息
Eur J Nucl Med Mol Imaging. 2021 May;48(5):1351-1361. doi: 10.1007/s00259-020-05061-w. Epub 2020 Oct 27.
PURPOSE
PET measures of amyloid and tau pathologies are powerful biomarkers for the diagnosis and monitoring of Alzheimer's disease (AD). Because cortical regions are close to bone, quantitation accuracy of amyloid and tau PET imaging can be significantly influenced by errors of attenuation correction (AC). This work presents an MR-based AC method that combines deep learning with a novel ultrashort time-to-echo (UTE)/multi-echo Dixon (mUTE) sequence for amyloid and tau imaging.
METHODS
Thirty-five subjects that underwent both 11C-PiB and 18F-MK6240 scans were included in this study. The proposed method was compared with Dixon-based atlas method as well as magnetization-prepared rapid acquisition with gradient echo (MPRAGE)- or Dixon-based deep learning methods. The Dice coefficient and validation loss of the generated pseudo-CT images were used for comparison. PET error images regarding standardized uptake value ratio (SUVR) were quantified through regional and surface analysis to evaluate the final AC accuracy.
RESULTS
The Dice coefficients of the deep learning methods based on MPRAGE, Dixon, and mUTE images were 0.84 (0.91), 0.84 (0.92), and 0.87 (0.94) for the whole-brain (above-eye) bone regions, respectively, higher than the atlas method of 0.52 (0.64). The regional SUVR error for the atlas method was around 6%, higher than the regional SUV error. The regional SUV and SUVR errors for all deep learning methods were below 2%, with mUTE-based deep learning method performing the best. As for the surface analysis, the atlas method showed the largest error (> 10%) near vertices inside superior frontal, lateral occipital, superior parietal, and inferior temporal cortices. The mUTE-based deep learning method resulted in the least number of regions with error higher than 1%, with the largest error (> 5%) showing up near the inferior temporal and medial orbitofrontal cortices.
CONCLUSION
Deep learning with mUTE can generate accurate AC for amyloid and tau imaging in PET/MR.
目的
正电子发射断层扫描（PET）测量淀粉样蛋白和tau 病理学是诊断和监测阿尔茨海默病（AD）的有力生物标志物。由于皮质区域靠近骨骼，因此淀粉样蛋白和 tau PET 成像的定量准确性会受到衰减校正（AC）误差的显著影响。本研究提出了一种基于磁共振的 AC 方法，该方法将深度学习与新型超短回波时间（UTE）/多回波 Dixon（mUTE）序列相结合，用于淀粉样蛋白和 tau 成像。
方法
本研究纳入了 35 名同时接受 11C-PiB 和 18F-MK6240 扫描的受试者。该方法与 Dixon 图谱法以及基于磁化准备快速采集梯度回波（MPRAGE）或 Dixon 图谱的深度学习方法进行了比较。生成的虚拟 CT 图像的 Dice 系数和验证损失用于比较。通过区域和表面分析来量化 PET 标准化摄取值比（SUVR）误差图像，以评估最终的 AC 准确性。
结果
基于 MPRAGE、Dixon 和 mUTE 图像的深度学习方法的 Dice 系数分别为 0.84（0.91）、0.84（0.92）和 0.87（0.94），适用于全脑（眼上）骨区域，高于图谱法的 0.52（0.64）。图谱法的区域 SUVR 误差约为 6%，高于区域 SUV 误差。所有深度学习方法的区域 SUV 和 SUVR 误差均低于 2%，其中 mUTE 图谱法表现最佳。对于表面分析，图谱法在额上、外侧枕、顶和颞下回等皮质区域的顶点附近显示出最大误差（>10%）。mUTE 图谱法导致具有大于 1%误差的区域数量最少，最大误差（>5%）出现在颞下回和内侧眶额皮质附近。
结论
基于 mUTE 的深度学习可实现 PET/MR 中淀粉样蛋白和 tau 成像的精确 AC。
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