Comparison of linear combination modeling strategies for edited magnetic resonance spectroscopy at 3 T.
Comparison of linear combination modeling strategies for edited magnetic resonance spectroscopy at 3 T.
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DOI：
10.1002/nbm.4618
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发表时间：
2022-01
影响因子：
2.9
通讯作者：
Oeltzschner G
中科院分区：
文献类型：
--
作者：
Zöllner HJ;Tapper S;Hui SCN;Barker PB;Edden RAE;Oeltzschner G
J-差分编辑光谱法是一种用于γ-氨基丁酸（GABA）磁共振波谱（MRS）活体检测的有效方法。最近的一篇专家共识文章建议对编辑后的MRS进行线性组合建模（LCM），但没有给出有关实施的具体细节。这项研究探讨了不同的建模策略，以适应LCM GABA编辑的MRS。六十一个内侧顶叶GABA编辑的MEGA-PRESS光谱从最近的3-T多位点研究建模使用102种不同的策略，结合六种不同的方法，以解释共同编辑的大分子（COMEX），三个建模范围，三个基线结间距，并使用基组有或没有高肌肽。使用所得GABA和GABA+估计值（相对于总肌酸定量）、不同范围的残差、标准差和变异系数（CV）以及赤池信息标准来评估模型的性能。显着不同的GABA+和GABA的估计，发现一个参数化的MM 3co基函数时，包括在模型中。当建模MM 3co时，平均GABA估计值显著较低，而CV相似。稀疏样条节点间距导致GABA和GABA+估计值的变化较低，并且较窄的建模范围（仅包括感兴趣的信号）并未显著改善或降低建模性能。此外，结果表明LCM可以分离GABA和潜在的共同编辑的MM 3co。将高肌肽加入模型中并没有显著改善GABA+估计值的方差。总之，GABA编辑的MRS最适合通过LCM进行量化，LCM具有良好参数化的共同编辑的MM 3co基函数，对非重叠MM 0.93进行约束，结合稀疏样条节点间距（0.55 ppm）和0.5-4 ppm的建模范围。
J-difference-edited spectroscopy is a valuable approach for the in vivo detection of γ-aminobutyric-acid (GABA) with magnetic resonance spectroscopy (MRS). A recent expert consensus article recommends linear combination modeling (LCM) of edited MRS but does not give specific details regarding implementation. This study explores different modeling strategies to adapt LCM for GABA-edited MRS. Sixty-one medial parietal lobe GABA-edited MEGA-PRESS spectra from a recent 3-T multisite study were modeled using 102 different strategies combining six different approaches to account for co-edited macromolecules (MMs), three modeling ranges, three baseline knot spacings, and the use of basis sets with or without homocarnosine. The resulting GABA and GABA+ estimates (quantified relative to total creatine), the residuals at different ranges, standard deviations and coefficients of variation (CVs), and Akaike information criteria, were used to evaluate the models’ performance. Significantly different GABA+ and GABA estimates were found when a well-parameterized MM3co basis function was included in the model. The mean GABA estimates were significantly lower when modeling MM3co, while the CVs were similar. A sparser spline knot spacing led to lower variation in the GABA and GABA+ estimates, and a narrower modeling range—only including the signals of interest—did not substantially improve or degrade modeling performance. Additionally, the results suggest that LCM can separate GABA and the underlying co-edited MM3co. Incorporating homocarnosine into the modeling did not significantly improve variance in GABA+ estimates. In conclusion, GABA-edited MRS is most appropriately quantified by LCM with a well-parameterized co-edited MM3co basis function with a constraint to the non-overlapped MM0.93, in combination with a sparse spline knot spacing (0.55 ppm) and a modeling range of 0.5–4 ppm.
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影响因子：
5.7
作者：
Mikkelsen M;Barker PB;Bhattacharyya PK;Brix MK;Buur PF;Cecil KM;Chan KL;Chen DY;Craven AR;Cuypers K;Dacko M;Duncan NW;Dydak U;Edmondson DA;Ende G;Ersland L;Gao F;Greenhouse I;Harris AD;He N;Heba S;Hoggard N;Hsu TW;Jansen JFA;Kangarlu A;Lange T;Lebel RM;Li Y;Lin CE;Liou JK;Lirng JF;Liu F;Ma R;Maes C;Moreno-Ortega M;Murray SO;Noah S;Noeske R;Noseworthy MD;Oeltzschner G;Prisciandaro JJ;Puts NAJ;Roberts TPL;Sack M;Sailasuta N;Saleh MG;Schallmo MP;Simard N;Swinnen SP;Tegenthoff M;Truong P;Wang G;Wilkinson ID;Wittsack HJ;Xu H;Yan F;Zhang C;Zipunnikov V;Zöllner HJ;Edden RAE
通讯作者：
Edden RAE
影响因子：
2.9
作者：
通讯作者：
--
影响因子：
3.2
作者：
Endres DM;Chiovetto E;Giese MA
通讯作者：
Giese MA
影响因子：
3.3
作者：
KLOSE, U
通讯作者：
KLOSE, U
3T 下大分子抑制 GABA 编辑的前瞻性频率校正。
影响因子：
4.4
作者：
Edden, Richard A. E.;Oeltzschner, Georg;Harris, Ashley D.;Puts, Nicolaas A. J.;Chan, Kimberly L.;Boer, Vincent O.;Schar, Michael;Barker, Peter B.
通讯作者：
Barker, Peter B.