A 64-channel 3T array coil for accelerated brain MRI

Abstract

A 64-channel brain array coil was developed and compared to a 32-channel array constructed with the same coil former geometry to precisely isolate the benefit of the 2-fold increase in array coil elements. The constructed coils were developed for a standard clinical 3T MRI scanner and used a contoured head-shaped curved former around the occipital pole and tapered in at the neck to both improve sensitivity and patient comfort. Additionally, the design is a compact, split-former design intended for robust daily use. Signal-to-noise ratio and noise amplification (G-factor) for parallel imaging were quantitatively evaluated in human imaging and compared to a size and shape-matched 32-channel array coil. For unaccelerated imaging, the 64-channel array provided similar signal-to-noise ratio in the brain center to the 32-channel array and 1.3-fold more signal-to-noise ratio in the brain cortex. Reduced noise amplification during highly parallel imaging of the 64-channel array provided the ability to accelerate at approximately one unit higher at a given noise amplification compared to the sized-matched 32-channel array. For example, with a 4-fold acceleration rate, the central brain and cortical signal-to-noise ratio of the 64-channel array was 1.2- and 1.4-fold higher, respectively, compared to the 32-channel array. The characteristics of the coil are demonstrated in accelerated brain imaging.

Figure 1

Completed 64-channel (a) and 32-channel (b) array coils without covers. Both sized matched coil helmets are designed with a split-able anatomical shaped former design.

Figure 2

Mechanical implantation of the housing split to allow the loops on the two halves to be geometrically decoupled. The adjacent anterior loops are bent over the housing's rim structure to achieve critical overlap with their nearest neighbors on the posterior housing segment. Small windows in the posterior rim structure provide access for mechanical decoupling (a). The windows are covered in the final setup (b). A snap-in mechanism overlaps both portions (c).

Figure 3

Simulation geometries for time-domain finite element computations for the 64-channel (a) and 32-channel (b) coil. Position and loop shape of the elements were transferred from the hexagon/pentagon pattern from the 3D helmet model. The split-housing structure is incorporated into the loop array models (orange: posterior housing segment, blue: anterior housing segment). Loop shapes (e.g. bent loops for critical overlap between housing segments, or “potato-shaped” loops in the neck region) were carefully 3D modeled in order to obtain realistic simulated acceleration capabilities.

Figure 4

Circuit schematic for a coil pair of the 64-channel array coil. Each coil element consists of four (C₁-C₄) capacitors. The variable capacitor C₁ fine-tunes the coil element frequency. Where C₂ and C₃ are equally valued and provide a capacitive voltage divider at the coil output circuit. A detuning trap is formed around C₃ using a variable inductor L and a Diode D. A series capacitive matching (C₄) transforms the element impedance to 50 Ohm. After the preamplifiers the detected signals are down converted to an intermediate frequency and multiplexed onto a single coaxial output. The signals are later de-multiplexed back into individual channels in the receiver unit. (Values for a 64ch loop (65mm dia.): C₁≈14pF, C₂=C₃=47pF, C₄≈8pF, C₅=1nF, L≈36nH, L_RFC=3.3nH. Values for a 32ch loop (95mm dia.): C₁≈32pF, C_T32=27pF, C₂=C₃=33pF, C₄≈11pF, C₅=1nF, L≈48nH, L_RFC=3.3nH).

Figure 5

Noise correlation coefficient matrices generated from an acquisition without RF excitation. The averaged off-diagonal of the matrix was 9% (min: 0.1%, max: 38%) for the 64-channel array coil and 11% (min: 0.1%, max: 38%) for 32-channel coil.

Figure 6

In vivo SNR comparisons in a sagittal plane between the sized-matched 64-channel (a) and 32-channel (b) array coils. The SNR measurements show a 1.3-fold peripheral SNR increase in the brain and nearly the same SNR is achieved in the coil's center.

Figure 7

Measured SNR in non-accelerated (R=1) and 2D accelerated (R=3 to R=5) cases for the 64-channel coil (top row) 32-channel coil (bottom row) in a transversal mid-brain slice. The 64-channel coil increasingly outperforms the 32-channel coil with higher acceleration factors in both central and peripheral SNR. The scans were accelerated in anterior–posterior direction.

Figure 8

Relative SNR gain of the 64-channel coil over the 32-channel coil as a function of acceleration. The higher peripheral SNR of the 64-channel coil and the reduced G-factors, mainly obtained in the center of the 64-channel coil, show substantial better SNR performance in all brain areas with higher accelerated acquisitions compared to the 32-channel array coil.

Figure 9

Measured and simulated inverse G-factor maps of transverse in-vivo images and digital anatomical head model slices, respectively. The maps from the measured data were calculated using images from a PD-weighted FLASH sequence and additionally noise correlation information. The maps of simulated data were computed from the complex reception profile of the coil (B₁^-). The simulated data show slightly lower mean G-factors . Both methods for G-factors calculation show most favorable G-factors for the 64-channel array coil, roughly providing one additional unit of acceleration for a given noise amplification factor.

Figure 10

Inverse G-factor map comparison between the 64-channel and 32-channel coil, obtained from the accelerated simultaneous multi-slice acquisition with blipped-CAIPI. In-plane acceleration factor of R=2 and slice acceleration of R_sl=5 are used. The 64-channel array coil provides an average G-factor reduction of 10.7% across all five slices and a 21.2% noise amplification decrease of the peak G-factors, when compared to the 32-channel array.

Figure 11

Fully encoded sagittal 1-mm isotropic MPRAGE images with GRAPPA acceleration factor of R = 4 and an acquisition time of 3:38 minutes for whole brain coverage. The combination of reduced G-factors and improved cortical SNR of the 64-channel coil (a), translates to improved image quality when compared to the 32-channel coil (b). Images from both coils are obtained from the same subject.