Computational and experimental evaluation of the Tic-Tac-Toe RF coil for 7 Tesla MRI

Abstract

A variety of 7 Tesla RF coil systems have been proposed to produce spin excitation (B1+ field) and MR image acquisition. Different groups have attempted to mitigate the challenges at high and ultra-high field MRI by proposing novel hardware and software solutions to obtain uniformly high spin excitation at acceptable RF absorption levels. In this study, we extensively compare the designs of two distributed-circuit based RF coils: the Tic-Tac-Toe (TTT) head coil and TEM head coil on multiple anatomically detailed head models and in-vivo. Bench measurements of s-parameters and experimental B1+ field distribution were obtained in volunteers and compared with numerical simulations. RF absorption, quantified by both average and peak SAR, and B1+ field intensity and homogeneity, calculated/measured in terms of maximum over minimum and coefficient of variation (CV) in the region of interest (ROI), are presented for both coils. A study of the RF consistency of both coils across multiple head models for different RF excitation strategies is also presented.

Improved 7 Tesla transmit field homogeneity with reduced electromagnetic power deposition using coupled Tic Tac Toe antennas

“…The Tx coil is based on the TTT antennas, previously described for foot/ankle 15 , breast 16,17 , and head imaging at 7T 11,12 . Briefly, one TTT panel is composed of eight squareshape transmission lines elements, made using 3D printed polycarbonate and 8 µm-thick copper sheets, connected to each other in a Tic Tac Toe fashion.…”

“…www.nature.com/scientificreports/ coils when compared with traditional RF coils. As a comparison, Krishnamurthy et al 12 reported the values of SAR efficiency of 1.27 µT/ W/kg and homogeneity of 27% for the TEM resonator, using the same simulation environment utilized in this work. Other studies investigated the SAR performance for several other coil designs [34][35][36][37] , however the simulations were performed in different software environments and often using different head models, which makes the comparison with this work challenging.…”

Neuroimaging of Small Vessel Disease in Late-Life Depression

“…The tic-tac-toe (TTT) RF system for 7 T head MRI is composed by a 16-channel transmit array (4 TTT elements located around the head) and a 32-channel receive insert, for optimal receiving performance. The TTT transmit array has been shown to produce a homogenous spin excitation at 7 T frequencies [42] and superior subject insensitiveness (low variations of the RF performance with different head shapes) [43]. It also presents lower global and local (SAR) when compared with other transmit coil designs [42, 43], increasing patient safety and minimizing the risks of temperature increase and tissue damage.…”

“…High resolution in vivo gradient echo (GRE) whole-brain acquisitions at 7 T, showing details of the brain structure and a comparison with histology. The images were acquired with the tic-tac-toe RF system [42, 43] and with the following GRE sequence parameters: TR = 1960, TE = 14, in-plane resolution 0.21 × 0.21 mm 2 , slice thickness of 1.5 mm, and 64 slices (114.9 mm) in total (20% gap between slices). In ( a ), selected slices showing the whole-brain acquisition; in ( b ), zoomed image showing the medullary draining veins throughout the deep white matter acquired without acceleration (18 min acquisitions); in ( c ), similar zoomed images acquired with acceleration factor (GRAPPA) 2 (9 min acquisition); in ( d ), the zoomed image in the cortical region, showing cortical microvessels (black arrows), without acceleration in the acquisition; in ( e ), similar slice showing the cortical regions and cortical microvessels (black arrows), acquired with GRAPPA 2; in ( f ), figure adapted from Ref.…”

Analysis of Hippocampal Subfields in Sickle Cell Disease Using Ultrahigh Field MRI

“…Subregions of the hippocampus are differentially affected, depending on the disease [26]. Recent advances in MRI techniques, such as 7 Tesla (T) MRI, have improved the spatial resolution and signal-to-noise ratio of MRI images [27]. These improvements enable more accurate volumetric analysis of the hippocampus subfields in in vivo studies, and improve automatic segmentation methods [28][29][30][31][32].…”