T2 mapping is a parametric imaging approach that provides quantification of T2-weighted images for a more accurate diagnosis of pathology. 2D multi-slice T2 estimation techniques cannot be used for thin slice isotropic imaging. To overcome this limitation, we present a T2 mapping technique using a 3D radial FSE pulse sequence with a variable flip angle scheme for optimal T2-weighting and T2 mapping within SAR constraints. Data is acquired in a stack-of-stars radial trajectory and T2 maps are reconstructed using model based iterative algorithms. The method is demonstrated in phantoms and in vivo brain and musculoskeletal imaging.
A 3D Cartesian FSE pulse sequence was modified to support a stack-of-stars trajectory where each kz plane contains radial views corresponding to multiple TE points (Fig.1). A varFA design based on a 3 flip angle parameter approach4 was implemented. The refocusing flip angles were optimized to maximize the area under the T2 decay curve (for improved overall SNR) and minimize SAR. Figure 2 shows (a) the flip angle scheme and (b) effect of the flip angle modulation on the signal intensity compared to a constant flip angle scheme.
To achieve fast imaging, data for each TE point is highly under-sampled (3 views/TE per kz plane). Thus, 3D TE images are reconstructed using sparsity-based iterative reconstruction algorithms. Previously our group had proposed1 a principal component (PC) based T2 mapping technique with indirect echo compensation for 2D radial FSE data based on the slice-resolved extended phase graph model. For 3D T2 mapping, the reconstruction problem is written as:
$$argmin_{I_0,T2,T1,B1} \sum_{j=1}^{ETL}||FT_j\{C_j(I_0,T2,T1,B1,TR,\alpha_0,\ldots,\alpha_j)\} - K_j||_2^2$$
where $$$FT_j$$$ is the Fourier operator, $$$K_j$$$ is the undersampled k-space data at the jth TE, $$$C_j$$$ is the EPG signal model as a function of I0, T2, T1, B1, TR and the flip angles of the excitation and refocusing RF pulses. The signal model is linearized using a PC basis generated from a set of training curves. The linearized problem is expressed as:
$$argmin_{M}\sum_{l=1}^{coils}\sum_{j=1}^{L}||FT_j\{S_lMP_j^T\} - K_{l,j}||_2^2 + \lambda ||\psi M||_1$$
where $$$P$$$ is a matrix of the first principal components, $$$M$$$ is a matrix corresponding to the PC coefficients, $$$S_l$$$ are the complex coil sensitivities. The penalty term exploits the spatial compressibility of the PC coefficient maps using a sparsity transform $$$\psi$$$. The images at $$$TE_j$$$ are obtained using the $$$L$$$ PC coefficients as $$$MP_j^T$$$ , where $$$P_j$$$ is the jth row of the matrix $$$P$$$. T2 maps are estimated by using a pattern recognition technique3 using pre-computed dictionaries based on the EPG model.
1. Huang C., Bilgin A., Barr T., Altbach M, T2 relaxometry with indirect echo compensation from highly undersampled data, Magnetic Resonance in Medicine, 70(4) 2013
2. Altbach M., Bilgin A., Li Z., Clarkson E., Trouard T., Gmitro A., Processing of radial fast spin-echo data for obtaining T2 estimates from a single k-space data set, Magnetic Resonance in Medicine, 54 2005
3. Keerthivasan MB, Bilgin A, Martin DR, Altbach MI. Isotropic T2 mapping using a 3D radial FSE (or TSE) pulse sequence. In Proceedings of ISMRM, Vol. 23, Toronto, Ontario, Canada, 2015. p. 323
4. Busse RF, Brau ACS, Vu A, Michelich CR, Bayram E, Kijowski R, Reeder SB, Rowley HA. Effects of refocusing flip angle modulation and view ordering in 3D fast spin echo. Magn Reson Med 2008;60: 640–649
5. Huang C., Altbach M, Fakhri G., Pattern recognition for rapid T2 mapping with stimulated echo compensation, Magnetic Resonance Imaging, 32, 2014