A few MWT experimental prototypes have been developed in the last twenty years [17; 18; 19; 20; 21; 22], and various methods have been applied to the resulting EM inverse scattering problem [23]. Most of the work, however, has focused on two-dimensional (2-D) algorithms to avoid the added complexity of three-dimensional (3-D) implementations. MWT 3-D implementations for medical imaging have been proposed in the literature for a few years now, but to the best of the authors' knowledge, they have been tested primarily with simulation data [24; 25; 26; 27; 28]. A notable exception is the 3-D MWT algorithm in [2], which produced clinical 3-D microwave tomographic images of the breast by combining the discrete dipole approximation (DDA) as the forward solver with the Gauss-Newton (GN) algorithm. MWT 2-D problems are easier to model and solve numerically, and they can often produce reconstructions of sufficient quality. In contrast, 3-D algorithms can be computationally expensive and require solving a more severely ill-posed inverse problem due to the much higher ratio of unknowns relative to data points. In iterative GN implementations such as the distorted Born iterative method (DBIM), a 3-D forward scattering problem must be solved at each iteration. This can be computationally prohibitive without the use of semi-analytical approaches [2] or numerical methods implemented in high performance hardware such as graphic processing units (GPUs) [25; 29]. CPU parallelization can also model 3-D MWI forward solvers efficiently with CG type inverse solvers as in [30], where the finite element method (FEM) was combined with the contrast source inversion (CSI) method to image an experimental breast phantom, and in [31], which uses FDTD as a forward solver for reconstructing a weak scatterer. The latter is implemented on a parallel Linux cluster with a 20-core CPU and a running time of 1 h for each iteration is required. For microwave head imaging, studies have analysed scattering from 3-D head models using FDTD [32] or FEM [33], and the FFT-based volume integral equation (VIE) method [29]. Most imaging algorithms are 2-D with only a few papers presenting 3-D reconstructions. For example, a 2-D Newton-type method using 5-parameters for inversion has been proposed and tested with a experimental SAM phantom [34]. A frequency-domain beamforming imaging algorithm with Bessel function has been applied to qualitative imaging of a realistic human head in 2-D [35]. This proposed method has been further applied to 3-D head imaging using a wearable EM cap with 16 planar antennas [36] and compared with 2-D results obtained by the polar sensitivity encoding method [37] that works on the principle of encoding S-parameters. A GN-type algorithm using the open source FreeFEM++ solver has been implemented for imaging a numerical head phantom, which solves the inverse problem by considering five \"sub-problems\" of cross-sections in parallel across five rings of the antenna array [38]. Recently, an FEM-based and FDTD-based iterative algorithm have been implemented on a single GPU card with Tikhonov regularization and a gradient-based inverse solver [4]. This work has performed 2-D and 3-D reconstructions with both a numerical and an anthropomorphic mannequin head phantom, using a matching medium with permittivity comparable to the brain material. 3-D imaging has also been proposed in [39], which combined FEM with TSVD for linear inversion. It is important to note that the complex structure of the head cannot be known a priori, resulting in a highly non-linear and ill-posed EM inverse scattering problem. Assuming that some prior information may be available, it is therefore important to investigate performance under different imaging scenarios. To this end, we have recently applied the DBIM with the fast iterative shrinkage/thresholding algorithm (FISTA) [40] to 2-D numerical brain phantoms with limited prior information.