Local SAR reduction based on channel-dependent Tikhonov parameters
نویسندگان
چکیده
Methods: This study relies solely on simulations of a transmit-array head coil at 7T. The coil consisted of 8 stripline dipoles distributed every 40degrees on a cylindrical surface of 27.6-cm diameter, leaving an open space in front of the patient’s eyes. An eight-anatomical structure human head model (provided by Aarkid, East Lothian, Scotland) was placed at the centre of the coil. The same head model with a slightly off-centered position was also considered (10°-rotation around the x and y-axes). Full-wave simulations with the finite element method (HFSS, Ansoft, Pittsburgh, PA), which take into account tuning to 297 Mhz, matching to 50-Ohm, and mutual coupling, provided the electric and magnetic field maps. Threeand five-spoke kspace trajectories [3] were designed for flip-angle (FA) homogenization in the spatial domain [4] with the local variable exchange method [5]. A 20°angle was targeted in a central slice of the brain using 700μs apodized sinc sub-pulses (time-bandwidth product equal to 4). For the purpose of local SAR reduction, the Tikhonov parameter originally introduced by Grissom et al. [4] was generalized in the form of a diagonal matrix, allowing coil element-dependent regularization. First an initial candidate waveform is obtained using the conventional scalar form of the Tikhonov parameter. Subsequently, coil-dependent Tikhonov parameters were iteratively optimized. During this optimization procedure, the 10-gram average SAR distribution was evaluated for the candidate waveform. Incrementing the Tikhonov parameter (+5%) associated with the coil element nearest to the spatial location of the maximum 10-gram SAR, a new candidate waveform is obtained. The procedure can then be stopped when SAR guidelines are reached or when negligible gain is perceived in local SAR-limitation with respect to FA-homogenization performance. In order to minimize computation time, the method was implemented in CUDA and performed on a GPU (GeForce 9600m, NVIDIA).
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