最近許多研究認為，利用磁振造影達成的大腦組織磁化率定量技術Quantitative Susceptibility Mapping (QSM)對神經疾病的研究及診斷具有極大的潛力。研究團隊提出不同的演算法想減少在計算QSM時，在磁化率影像上由於dipole model deconvolution所產生的線型雜訊(streaking artifact)的影響。這些演算法其中一個主要的目標就是，減少線型雜訊的影響，並且盡可能地保留原始組織結構資訊。
另外一方面，為了使QSM能夠更為廣泛的被應用到疾病研究上，我們想利用Generalized Autocalibraing Partially Parallel Acquisition (GRAPPA)的技術來簡短掃描時間。但是在QSM的過程中加入GRAPPA是否會造成磁化率定量上的誤差是未曾在文獻中被探討的。
本篇研究提出一個自動線型假影偵測的方法來決定最佳的QSM重建結果。並且探討利用GRAPPA來加速掃描是否會對QSM的結果產生影響。實驗結果顯示本篇提出的線型結構偵測方法(streaking pattern detection)除了能在不同的重建結果中決定最佳的磁化率影像，且也能作為不同QSM演算法對於線型雜訊抑制效果評估的一個依據。另外，在QSM流程加入GRAPPA的研究中，在實際加速倍率R=3或更高時，QSM影像上已有可見的疊影(aliasing artifact)產生，儘管若以感興趣區域(region of interest)分析方法計算，各個區域的磁化率定量結果差異並不大。因此，若以臨床診斷使用來說，將GRAPPA設定為R=2的加速方式，將得到既快速又較為可靠的磁振影像，以作為QSM重建使用。

Recent studies have shown that Quantitative Susceptibility Mapping (QSM) of brain tissues has great potential for neuronal diseases. Researchers have proposed various algorithms to reduce the influence of the streaking artifact, which is generated during dipole model deconvolution calculation. One of the main purposes of these algorithms is to reduce the impact of the streaking artifact while simultaneously keeping anatomical structures on susceptibility maps as intact as possible.
On the other hand, in order to make QSM widely applicable for clinical diagnosis and research, we use Generalized Autocalibraing Partially Parallel Acquisition (GRAPPA) to accelerate the image acquisition. However, whether acceleration using GRAPPA will have any influence on QSM remains to be studied.
This study proposed automatic streaking artifact detection to achieve optimal QSM reconstruction results. We also study the influence of GRAPPA acceleration on QSM. Results show that streaking pattern detection not only can be used for parameter optimization in QSM algorithms, but also provide an objective method for comparing residual streaking artifacts in different QSM algorithms. In addition, results from QSM accelerated with GRAPPA show that there are visually perceptible aliasing artifacts on susceptibility maps with R=3 or higher even though, in ROI analysis, the measured susceptibility values seem to be less affected. Therefore, it is concluded that GRAPPA with R=2 results in accelerated image acquisition for reliable QSM reconstruction for clinical applications.

[1] J. M. Shulman, P. L. De Jager, and M. B. Feany, “Parkinson’s disease: genetics and pathogenesis.,” Annual review of pathology, vol. 6, pp. 193—222, Jan. 2011.

[2] M. Alegre and M. Valencia, “Oscillatory activity in the human basal ganglia: more than just beta, more than just Parkinson’s disease.,” Experimental neurology, vol. 248, pp. 183—6, Oct. 2013.

[3] N. Pyatigorskaya, C. Gallea, D. Garcia-Lorenzo, M. Vidailhet, and S. Lehericy, “A review of the use of magnetic resonance imaging in Parkinson’s disease.,” Therapeutic advances in neurological disorders, vol. 7, pp. 206—20, July 2014.

[4] F. J. A. Meijer and B. Goraj, “Brain MRT in Parkinson’s disease.,” Frontiers in bio science (Elite edition), vol. 6, pp. 360—9, Jan. 2014.

[5] T. Liu, K. Surapaneni, M. Lou, L. Cheng, P. Spincemaille, and Y. Wang, “Cerebral microbleeds: burden assessment by using quantitative susceptibility mapping.,” Radi ology, vol. 262, pp. 269—78, Jan. 2012.

[6] C. Langkammer, T. Liu, M. Khalil, C. Enzinger, M. Jehna, S. Fuchs, F. Fazekas,

Y. Wang, and S. Ropele, “Quantitative susceptibility mapping in multiple sclerosis.,”

Radiology, vol. 267, pp. 551—9, May 2013.

[7] D. Haddar, E. Haacke, V. Sehgal, Z. Delproposto, G. Salamon, O. Seror, and N. Sellier, “[Susceptibility weighted imaging. Theory and applications].,” Journal de radiologie, vol. 85, pp. 1901—8, Nov. 2004.

[8] E. M. Haacke, Y. Xu, Y.-C. N. Cheng, and J. R. Reichenbach, “Susceptibility weighted imaging (SWT).,” Magnetic resonance in medicine • official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine, vol. 52, pp. 612—8, Sept. 2004.

[9] E. M. Haacke, S. Mittal, Z. Wu, J. Neelavalli, and Y.-C. N. Cheng, “Susceptibility- weighted imaging: technical aspects and clinical applications, part 1.,” AJNR. American journal of neuroradiology, vol. 30, pp. 19—30, Jan. 2009.

[10] S. Mittal, Z. Wu, J. Neelavalli, and E. M. Haacke, “Susceptibility-weighted imaging: technical aspects and clinical applications, part 2.,” AJNR. American journal of neuro radiology, vol. 30, pp. 232—52, Feb. 2009.

[11] A. J. Walsh and A. H. Wilman, “Susceptibility phase imaging with comparison to R2 mapping of iron-rich deep grey matter.,” Neurolmage, vol. 57, pp. 452—61, July 2011.

[12] S. Tde, S. Kakeda, T. Ueda, K. Watanabe, Y. Murakami, J. Moriya, A. Ogasawara,

K. Futatsuya, T. Sato, N. Ohnari, K. Okada, A. Matsuyama, H. Fujiwara, M. Hisaoka,

S. Tsuji, T. Liu, Y. Wang, and Y. Korogi, “Tnternal structures of the globus pallidus in patients with Parkinson’s disease: evaluation with quantitative susceptibility mapping (QSM),” Eur Radiol, Nov 2014.

[13] J. Li, S. Chang, T. Liu, Q. Wang, D. Cui, X. Chen, M. Jin, B. Wang, M. Pei, C. Wisnieff,

P. Spincemaille, M. Zhang, and Y. Wang, “Reducing the object orientation dependence of susceptibility effects in gradient echo MRT through quantitative susceptibility map- ping,” Magn Reson Med, vol. 68, pp. 1563—1569, Nov 2012.

[14] T. Liu, P. Spincemaille, L. de Rochefort, B. Kressler, and Y. Wang, “Calculation of susceptibility through multiple orientation sampling (COSMOS): a method for condi- tioning the inverse problem from measured magnetic field map to susceptibility source image in MRT,” Magn Reson Med, vol. 61, pp. 196—204, Jan 2009.

[15] T. Liu, J. Liu, L. de Rochefort, P. Spincemaille, I. Khalidov, J. R. Ledoux, and Y. Wang, “Morphology enabled dipole inversion (MEDI) from a single-angle acquisition: com- parison with COSMOS in human brain imaging,” Magn Reson Med, vol. 66, pp. 777— 783, Sep 2011.

[16] L. de Rochefort, T. Liu, B. Kressler, J. Liu, P. Spincemaille, V. Lebon, J. Wu, and

Y. Wang, “Quantitative susceptibility map reconstruction from MR phase data using bayesian regularization: validation and application to brain imaging,” Magn Reson Med, vol. 63, pp. 194—206, Jan 2010.

[17] C. Poynton, M. Jenkinson, E. Adalsteinsson, E. Sullivan, A. Pfefferbaum, and W. Wells, “Quantitative Susceptibility Mapping by Inversion of a Perturbation Field Model: Correlation with Brain Iron in Normal Aging,” IEEE Trans Med Imaging, Sep 2014.

[18] M. A. Griswold, P. M. Jakob, R. M. Heidemann, M. Nittka, V. Jellus, J. Wang,

B. Kiefer, and A. Haase, “Generalized autocalibrating partially parallel acquisitions (GRAPPA).,” Magnetic resonance in medicine • official journal of the Society of Mag netic Resonance in Medicine / Society of Magnetic Resonance in Medicine, vol. 47, pp. 1202—10, June 2002.

[19] K. P. Pruessmann, M. Weiger, M. B. Scheidegger, and P. Boesiger, “SENSE: sensitivity encoding for fast MRI,” Magn Reson Med, vol. 42, pp. 952—962, Nov 1999.

[20] K. E. Hammond, J. M. Lupo, D. Xu, M. Metcalf, D. A. Kelley, D. Pelletier, S. M. Chang, P. Mukherjee, D. B. Vigneron, and S. J. Nelson, “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, vol. 39, pp. 1682—1692, Feb 2008.

[21] R. Cusack and N. Papadakis, “New robust 3-D phase unwrapping algorithms: appli- cation to magnetic field mapping and undistorting echoplanar images,” Neuroimage, vol. 16, pp. 754—764, Jul 2002.

[22] S. Wharton, A. Schafer, and R. Bowtell, “Susceptibility mapping in the human brain using threshold-based k-space division,” Magn Reson Med, vol. 63, pp. 1292—1304, May 2010.

[23] B. Bilgic, I. Chatnuntawech, A. P. Fan, K. Setsompop, S. F. Cauley, L. L. Wald, and

E. Adalsteinsson, “Fast image reconstruction with L2-regularization,” J Magn Reson Imaging, vol. 40, pp. 181—191, Jul 2014.

[24] T. Liu, I. Khalidov, L. de Rochefort, P. Spincemaille, J. Liu, A. J. Tsiouris, and

Y. Wang, “A novel background field removal method for MRI using projection onto dipole fields (PDF),” NMR Biomed, vol. 24, pp. 1129—1136, Nov 2011.

[25] P. Y. Lin, T. C. Chao, and M. L. Wu, “Quantitative Susceptibility Mapping of Human Brain at 3T: A Multisite Reproducibility Study,” AJNR Am J Neuroradiol, Oct 2014.

[26] B. Kressler, L. de Rochefort, T. Liu, P. Spincemaille, Q. Jiang, and Y. Wang, “Nonlinear regularization for per voxel estimation of magnetic susceptibility distributions from MRI field maps,” IEEE Trans Med Imaging, vol. 29, pp. 273—281, Feb 2010.

[27] H. C, “Method and means for recognizing complex patterns,” Dec. 18 1962. US Patent 3,069,654.

[28] W. S. Hoge and D. H. Brooks, “Using GRAPPA to improve autocalibrated coil sensi- tivity estimation for the SENSE family of parallel imaging reconstruction algorithms,” Magn Reson Med, vol. 60, pp. 462—467, Aug 2008.