Azma Mareyam1, John E Kirsch1,2, Ehsan Kazemivalipour1,2, Michele Scipioni1,2, Magdelena Suriano Allen1,3, Jeffrey Short1, Hammodi Almurani1, Ciprian Catana1,2, and Lawrence L Wald1,2,4
1Radiology, A.A.Martinos Center of Biomedical Imaging/Massachusetts General Hospital, Charlestown, MA, United States, 2Harvard Medical School, Boston, MA, United States, 3Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, United States, 4Harvard-MIT Division of Health Sciences Technology, Cambridge, MA, United States
Synopsis
Keywords: PET/MR, Parallel Imaging, MR-PET 7T array
Motivation: We are developing the Human Dynamic NeuroChemical Connectome, a high spatio-temporal resolution brain PET (HSTR-BrainPET) scanner integrated with a 7T MR system
Goal(s): To build a 16-channel PET-compatible RF coil with parallel transmit capability based on an 8-channel test array for high-resolution imaging.
Approach: We designed and built multi-channel transmit-receive coils with RF screen that fits inside the spherical geometry of the PET camera. Coil performance was characterized with and without PET modules present.
Results: The performance of the 8-channel coil was satisfactory. Preliminary tests with the 16-channel array showed some loss in sensitivity in the CP mode.
Impact: By designing a 16-channel transmit-receive
RF head coil and RF screen that both conform to the novel spherical geometry
PET camera, we can acquire high-quality MR data simultaneously with PET data
while also minimizing 511 keV photon attenuation.
Introduction
We are developing a high spatio-temporal resolution
brain PET (HSTR-BrainPET) scanner integrated with a 7T MR system. Our primary goal is to build a PET camera with an
order of magnitude higher sensitivity than existing MR-compatible PET devices
to allow investigators to seamlessly merge the dynamic functional capabilities
of PET and functional MRI on a comparable time scale [1]. Given the limited
space and the need to minimize 511 keV photon attenuation, we designed and
built an 8 and a 16-channel transmit-receive radio-frequency (RF) brain coil
and RF screen that conforms to the unique spherical design of the PET
detectors. The coil helmet is contoured
for comfort with eye cutouts to accommodate video goggles. It is connected to 16
individual transmit receive switches with preamplifiers. SNR and B1+ of the CP
mode were evaluated for the two arrays.Methods
Fig.1a shows the
main components of the spherical Brain-PET insert (Fig. 1b) arranged around a
partial sphere (32 cm inner Ø, with a 25 cm Ø front opening to allow the
positioning of the subject and a 9 cm Ø back opening) [2]. PET detection
efficiency is enhanced using 26 mm long LSO crystals and high-performance
readout electronics with DOI and TOF capabilities (Fig.1c). Figure 1d
shows the helmet former with RF shield along with an interface box housing TR switches
and preamps (Fig. 1g). The helmet (Fig. 1e) was sized to accommodate most adult
heads and 3D printed.
Coil
elements are arranged in an overlapped array with two rows of eight elements
each (16ch array) or a single row (8ch array). AWG18 wire loops with six equally spaced
capacitors was used to improve the unloaded-to-loaded Q and to minimize
attenuation by the PET camera. To eliminate cable current losses and have a
balanced feed at the coil we used 1:1 180º lumped element balun [3]. In
addition, cable traps were placed in front of the T/R switches/preamps in the
interface unit positioned outside the PET FOV.
The
design of the copper-clad Kapton (polyamide) slotted shield consists of two
layers of overlapping
conductive patches (PyraluxAC182500RY), with a thin layer of Kapton dielectric
between the patches to provide the necessary distributed capacitance to
complete the RF current paths at 300MHz while breaking eddy currents.
Data
were acquired on an FDA-approved 7T MRI scanner (MAGNETOM Terra, Siemens
Healthcare), (Fig. 1f) using an oil phantom to acquire B1+ maps (64x64 matrix,
FOV 192x192mm, slice thickness 10mm) to determine the correct magnitude and
phase for CP mode. SNR maps were obtained following the method of Kellman &
McVeigh [4] using the same oil phantom. Noise covariance was estimated from
thermal noise data using an anthropomorphic head phantom [5] and 3D ME MPRAGE
(acceleration 2, FOV 256x256x192mm, Resolution 1mm isotropic, TR 2.5s, ∆TE
1.86ms, 4 echoes, TI 1s, Flip angle 8 ⁰, bandwidth 650Hz/pixel) was acquired.
The 8-channel test array was used along with a single ring of the PET with 12
modules to compare B1+, B0, eddy current field compensation with and without
the presence of the PET gantry.Results
Bench
tests on each coil element showed a Q unloaded-to-loaded ratio of ~130/110.
S21, between loaded neighboring elements and the next neighboring elements ranged
from -18 dB to -11.5 dB. S11 reflections showed the elements tuned and matched
to 50 Ω. Fig. 2a, 2b shows the two 3D printed arrays with similar designs. Fig.
2c, 2d show the corrected and uncoupled individual magnitudes for the 8 and 16
elements of the two arrays to compute the CP mode. Fig. 2e, 2f shows the
average SNR 29.33 and 35.35 from the two arrays. Fig. 2g, 2h shows the noise
correlation matrix between channels for an average of 17.3% and 21% for the two
arrays. Fig. 3 shows a 3D ME MPRAGE. The same maps were acquired again with a
single PET ring and 12 modules turned on with the 8-channel test array, which
showed that there was minimal drop in the SNR and the B1+ map and had minimal effect on
static B0 field, B1+ field, and on the eddy current field.Discussions and Conclusion
A 16-channel transmit MR-PET array was
successfully built and tested. It shows some loss in sensitivity in CP mode due
to the presence of cable currents and interactions with 16 cables near each other,
compared to an 8 -channel test array where cable arrangement is further apart, while
RF performance was optimized with no cable interactions, a point to be addressed
and solved.Acknowledgements
This work was supported, in part, by BRAIN Initiative NIH-NIBIB &
NINDS grant 1U01EB029826-01.References
[1] Sander et al.
MRM 2015 73(6), [2] Scipioni et al. IEEE NSS 2023, [3] Rivera et al. (2011)
Proc ISMRM, [4] Kellman et al. MRM 2005, 54(6): 1439-47, [5] Graedel
et al. (2014) MRM