近年來有許多研究都指出，在健康人的黑質部位可以觀察到燕尾特徵，而在帕金森氏症的患者中則會發現燕尾特徵單邊或雙邊消失的狀況。目前尚沒有研究針對如何清楚觀察到燕尾特徵作詳細的討論，因此在這篇論文中我們利用3T MRI觀察健康黑質的燕尾特徵，並比較在不同醫學影像以及不同年齡族群中燕尾特徵的表現。
論文中利用3D ME-GRE收取實驗資料，並且在實驗中為了減少掃描時間，我們減少腦部掃描的範圍，後續進行分析的影像包含T2*-weighted image、T2* mapping、SWI、QSM、FLAIR，接著我們利用分群方法區分黑質區域及對燕尾特徵的清晰度進行評分。在比較不同醫學影像的結果中，使用T2* mapping觀察燕尾特徵的表現明顯比T2*-weighted image 更好，而當年齡大於40歲的族群中，SWI的表現也明顯比T2*-weighted image好，在此篇論文中，我們針對健康人黑質部位的燕尾特徵進行詳細的比較，希望可以做為往後利用燕尾特徵作為帕金森氏症之生物標記物相關研究的基準。

Recently, many MRI studies have reported that the swallowtail sign at substantia nigra can be visualized in healthy substantia nigra and which is unilaterally or bilaterally absent in PD patient. But there is no study focuses on the clarity of swallowtail sign. Our study aims to investigate swallowtail sign in health subject and compare quality of different MRI imaging methods and different age groups at 3T.
The MRI data were acquired by 3D uni-polar multi-echo gradient echo sequence. In order to reduce scan time, limited volume coverage of brain data was acquired in experiments. The MRI imaging method included T2*-weighted image, T2* mapping, susceptibility weighted image, quantitative susceptibility mapping and FLAIR are then post-processed. In addition, we use clustering method to decompose substantia nigra in order to clearly distinguish nigrosome-1 and utilize rating to quantify the quality of swallowtail sign. In the comparison between different MRI imaging method, T2* mapping is significantly better than magnitude image in visualization of swallowtail sign and SWI is significantly better than magnitude image in elder group. In our study, we investigate swallowtail sign in health subject at 3T in detail. This study will serve as a baseline for future studies using the swallowtail sign as a biomarker for PD.

1. Wang, J., et al., Biomarkers of Parkinson's disease: current status and future perspectives. Drug Discov Today, 2013. 18(3-4): p. 155-62.
2. Blazejewska, A.I., et al., Visualization of nigrosome 1 and its loss in PD: pathoanatomical correlation and in vivo 7 T MRI. Neurology, 2013. 81(6): p. 534-40.
3. Kim, J.M., et al., Loss of substantia nigra hyperintensity on 7 Tesla MRI of Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy. Parkinsonism Relat Disord, 2016. 26: p. 47-54.
4. Lehericy, S., et al., 7 Tesla magnetic resonance imaging: a closer look at substantia nigra anatomy in Parkinson's disease. Mov Disord, 2014. 29(13): p. 1574-81.
5. Noh, Y., et al., Nigrosome 1 Detection at 3T MRI for the Diagnosis of Early-Stage Idiopathic Parkinson Disease: Assessment of Diagnostic Accuracy and Agreement on Imaging Asymmetry and Clinical Laterality. AJNR Am J Neuroradiol, 2015. 36(11): p. 2010-6.
6. Schwarz, S.T., et al., The 'swallow tail' appearance of the healthy nigrosome - a new accurate test of Parkinson's disease: a case-control and retrospective cross-sectional MRI study at 3T. PLoS One, 2014. 9(4): p. e93814.
7. Azuma, M., et al., Lateral Asymmetry and Spatial Difference of Iron Deposition in the Substantia Nigra of Patients with Parkinson Disease Measured with Quantitative Susceptibility Mapping. AJNR Am J Neuroradiol, 2016. 37(5): p. 782-8.
8. PhD, J.N., The Human Brain: in Photographs and Diagrams. 2000.
9. Ben-Shachar, D. and M.B. Youdim, Iron, melanin and dopamine interaction: relevance to Parkinson's disease. Prog Neuropsychopharmacol Biol Psychiatry, 1993. 17(1): p. 139-50.
10. Quinn, N., P. Critchley, and C.D. Marsden, Young onset Parkinson's disease. Movement Disorders, 1987. 2(2): p. 73-91.
11. Hammond, K.E., et al., Development of a robust method for generating 7.0 T multichannel phase images of the brain with application to normal volunteers and patients with neurological diseases. Neuroimage, 2008. 39(4): p. 1682-92.
12. Cusack, R. and N. Papadakis, New robust 3-D phase unwrapping algorithms: application to magnetic field mapping and undistorting echoplanar images. Neuroimage, 2002. 16(3 Pt 1): p. 754-64.
13. Gilbert, G., et al., Quantitative comparison between a multiecho sequence and a single-echo sequence for susceptibility-weighted phase imaging. Magn Reson Imaging, 2012. 30(5): p. 722-30.
14. Liu, T., et al., Calculation of susceptibility through multiple orientation sampling (COSMOS): a method for conditioning the inverse problem from measured magnetic field map to susceptibility source image in MRI. Magn Reson Med, 2009. 61(1): p. 196-204.
15. de Rochefort, L., et al., Quantitative susceptibility map reconstruction from MR phase data using bayesian regularization: validation and application to brain imaging. Magn Reson Med, 2010. 63(1): p. 194-206.
16. Liu, T., et al., Morphology enabled dipole inversion (MEDI) from a single-angle acquisition: comparison with COSMOS in human brain imaging. Magn Reson Med, 2011. 66(3): p. 777-83.
17. Schweser, F., et al., Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: an approach to in vivo brain iron metabolism? Neuroimage, 2011. 54(4): p. 2789-807.
18. Liu, T., et al., Accuracy of the morphology enabled dipole inversion (MEDI) algorithm for quantitative susceptibility mapping in MRI. IEEE Trans Med Imaging, 2012. 31(3): p. 816-24.
19. Bilgic, B., et al., Fast image reconstruction with L2-regularization. J Magn Reson Imaging, 2014. 40(1): p. 181-91.
20. Liu, T., et al., A novel background field removal method for MRI using projection onto dipole fields (PDF). NMR Biomed, 2011. 24(9): p. 1129-36.
21. Haacke, E.M., et al., Susceptibility weighted imaging (SWI). Magn Reson Med, 2004. 52(3): p. 612-8.
22. Lin, P.Y., T.C. Chao, and M.L. Wu, Quantitative susceptibility mapping of human brain at 3T: a multisite reproducibility study. AJNR Am J Neuroradiol, 2015. 36(3): p. 467-74.
23. Kressler, B., et al., Nonlinear regularization for per voxel estimation of magnetic susceptibility distributions from MRI field maps. IEEE Trans Med Imaging, 2010. 29(2): p. 273-81.
24. Rokach, L. and O. Maimon, Clustering Methods, in Data Mining and Knowledge Discovery Handbook, O. Maimon and L. Rokach, Editors. 2005, Springer US: Boston, MA. p. 321-352.
25. Ghasemi, A. and S. Zahediasl, Normality Tests for Statistical Analysis: A Guide for Non-Statisticians. International Journal of Endocrinology and Metabolism, 2012. 10(2): p. 486-489.
26. Gibbons, J.D. and S. Chakraborti, Nonparametric statistical inference. 2011: Springer.