目的：加速後的擴散影像配合高角解析度，可處理面迴訊造影造成的影像變形與未加速的較長掃描時間。然而，重建回去的資料能否保留腦白質纖維的資訊尚有待商榷。我們的目標是透過量化擴散的特性去比較未加速跟加速資料的相似度。想法是比較未加速跟加速資料間量化後的擴散特性相似度。
方法：在此研究中，使用單次與多次激發式面迴訊造影收資料，以及快速擴散影像配合高角解析度與敏感度編碼技術，重建出單次激發式面迴訊造影加速之後沒有混疊假影的影像。K空間中有兩倍與四倍加速，而後者在D空間中的加速保留了二分之一、三分之一及四分之一的梯度方向。為了評估加速的保真度，將不同加速倍率下的資料與128個梯度方向的未加速資料作比較。在此方法下，兩種比較方法分別是以纖維束通過的立體像素、纖維束成像為基底的比較。在纖維束經過的立體像素裡，是比較重疊比率以及擴散非等向性指標。擴散非等向性指標包含擴散不等向性、平均擴散度、廣泛擴散不等向性。不同於上述，基於纖維束成像是直接將每根纖維配對好，並計算出纖維之間的型態距離差。所測試的纖維束包含：胼胝體、額枕下束與鉤束。箱型圖讓不同受試者、加速倍率及纖維束的比較更為一目了然。
結果：纖維束經過的立體像素的重疊比較中，真陽性的比率在胼胝體、額枕下束和鉤束分別大概落在80至90%、40至80%與50至80%。雖然在額枕下束和鉤束的比率較低，但真陽性的區域出現在纖維束的主幹部分。擴散非等向性指標的相關係數在四倍加速下至少高於0.7，但是兩倍加速因為變形程度不同，使得相關係數低至0.4。在纖維線的中央，每點間的型態距離只有1至2立體像素，不同加速倍率及不同受試者下的差異也很小。
結論：在影像與纖維束成像的品質都可以兼顧的情形下，最多可以將K空間條數減少至四分之一及擴散方向減少至三分之一。在臨床上，跟常用的兩倍加速相比，可以把掃描時間降到三分之一。

Purpose: Accelerated high angular resolution diffusion imaging (HARDI) deals with distortion problem from echo planar imaging (EPI) and longer scan time from unaccelerated HARDI. However, whether the reconstructed data can keep the information of white matter tract is an open question. This study is to compare the similarity of the quantified tract features between the fully sampled and accelerated datasets.
Method: In this article, single-shot and multi-shot EPI are used for data acquisition. In k-space acceleration, the two-fold and four-fold datasets underwent the SENSE and fast HARDI reconstruction respectively. The acceleration in d-space contains half, a third and a quarter of gradient directions within the four-fold acceleration. To assess the fidelity of the accelerated data, it will be compared with the fully sampled dataset including 128 gradient directions. In this way, two methods were prepared for comparison: track-volume based and tractographic based comparison. Track-volume coverage and diffusion indices involve in track-based comparison. The diffusion indices contain the fractional anisotropy (FA), the mean diffusivity (MD) and the generalized fractional anisotropy (GFA). Tractography based comparison, different from above, measures the morphology distance between streamlines directly after paired matching. The white matter tracts for measurement are the corpus callosum (CC), the inferior fronto-occipital fasciculus (IFO) and uncinate fasciculus (UNC). The box-plot makes the results present at a glance for comparison of different subjects, accelerations and fibers.
Result: In track-volume coverage, the percentage of true positive was about 80~90%, 40~80% and 50~80% in the CC, IFO and UNC respectively. Although the percentage was lower in the IFO and UNC, the region of true positive occurred at backbone of the tractography. The correlation coefficient value of diffusion indices was at least up to 0.7 in the four-fold acceleration, but was down to 0.4 because of different distortion level in the two-fold acceleration. The morphology distance was only 1 to 2 voxels per point in the center of a streamline approximately, with little difference in inter-subject and intra-subject.
Conclusion: On the balance of image and tractography quality, reserving a quarter of ky-line and a third of diffusion direction is acceptable at most in this reconstruction. In clinical, the scan time can be reduced to one-third compared with commonly used the two-fold SENSE.

1. Tuch, D.S., Q-ball imaging. Magn Reson Med, 2004. 52(6): p. 1358-72.
2. Horsfield, M.A. and D.K. Jones, Applications of diffusion-weighted and diffusion tensor MRI to white matter diseases - a review. NMR Biomed, 2002. 15(7-8): p. 570-7.
3. Goodlett, C.B., et al., Group analysis of DTI fiber tract statistics with application to neurodevelopment. Neuroimage, 2009. 45(1 Suppl): p. S133-42.
4. Mueller, H.P., et al., Diffusion tensor imaging and tractwise fractional anisotropy statistics: quantitative analysis in white matter pathology. Biomed Eng Online, 2007. 6: p. 42.
5. Jaermann, T., et al., SENSE-DTI at 3 T. Magn Reson Med, 2004. 51(2): p. 230-6.
6. Madore, B., et al., Accelerated multi-shot diffusion imaging. Magn Reson Med, 2014. 72(2): p. 324-36.
7. Chao, T.C., et al., Fast diffusion imaging with high angular resolution. Magn Reson Med, 2016.
8. Smith, S.M., et al., Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage, 2006. 31(4): p. 1487-505.
9. Zimmerman-Moreno, G., et al., Whole brain fiber-based comparison (FBC)-A tool for diffusion tensor imaging-based cohort studies. Hum Brain Mapp, 2016. 37(2): p. 477-90.
10. Tsao, J., P. Boesiger, and K.P. Pruessmann, k-t BLAST and k-t SENSE: dynamic MRI with high frame rate exploiting spatiotemporal correlations. Magn Reson Med, 2003. 50(5): p. 1031-42.
11. Atkinson, D., et al., Nonlinear phase correction of navigated multi-coil diffusion images. Magn Reson Med, 2006. 56(5): p. 1135-9.
12. Rosenfeld, D., New approach to gridding using regularization and estimation theory. Magn Reson Med, 2002. 48(1): p. 193-202.
13. Tournier, J.D., et al., Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. Neuroimage, 2004. 23(3): p. 1176-85.
14. Tournier, J.D., F. Calamante, and A. Connelly, Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. Neuroimage, 2007. 35(4): p. 1459-72.
15. Landman, B.A., et al., Resolution of Crossing Fibers with Constrained Compressed Sensing using Traditional Diffusion Tensor MRI. Proc SPIE Int Soc Opt Eng, 2010. 7623: p. 76231H.
16. Garyfallidis, E., et al., Dipy, a library for the analysis of diffusion MRI data. Front Neuroinform, 2014. 8: p. 8.
17. Garyfallidis, E., Towards an accurate brain tractography. 2012, University of Cambridge. p. 181.
18. Panigrahy, A., M. Borzage, and S. Bluml, Basic principles and concepts underlying recent advances in magnetic resonance imaging of the developing brain. Semin Perinatol, 2010. 34(1): p. 3-19.
19. Wahl, M., et al., Variability of homotopic and heterotopic callosal connectivity in partial agenesis of the corpus callosum: a 3T diffusion tensor imaging and Q-ball tractography study. AJNR Am J Neuroradiol, 2009. 30(2): p. 282-9.
20. Kaur, S., et al., Reliability and repeatability of quantitative tractography methods for mapping structural white matter connectivity in preterm and term infants at term-equivalent age. PLoS One, 2014. 9(1): p. e85807.
21. Zhang, A., et al., Quantitative tract-specific measures of uncinate and cingulum in major depression using diffusion tensor imaging. Neuropsychopharmacology, 2012. 37(4): p. 959-67.
22. Wakana, S., et al., Fiber tract-based atlas of human white matter anatomy. Radiology, 2004. 230(1): p. 77-87.
23. Ashburner, J., et al., SPM8 Manual. 2013.
24. Pajevic, S. and C. Pierpaoli, Color schemes to represent the orientation of anisotropic tissues from diffusion tensor data: application to white matter fiber tract mapping in the human brain. Magn Reson Med, 1999. 42(3): p. 526-40.
25. Mukherjee, P., et al., Diffusion tensor MR imaging and fiber tractography: technical considerations. AJNR Am J Neuroradiol, 2008. 29(5): p. 843-52.