A 32-channel parallel transmit system add-on for 7T MRI
Purpose
A 32-channel parallel transmit (pTx) add-on for 7 Tesla whole-body imaging is presented. First results are shown for phantom and in-vivo imaging.
Methods
The add-on system consists of a large number of hardware components, including modulators, amplifiers, SAR supervision, peripheral devices, a control computer, and an integrated 32-channel transmit/receive body array. B1+ maps in a phantom as well as B1+ maps and structural images in large volunteers are acquired to demonstrate the functionality of the system. EM simulations are used to ensure safe operation.
Results
Good agreement between simulation and experiment is shown. Phantom and in-vivo acquisitions show a field of view of up to 50 cm in z-direction. Selective excitation with 100 kHz sampling rate is possible. The add-on system does not affect the quality of the original single-channel system.
Conclusion
The presented 32-channel parallel transmit system shows promising performance for ultra-high field whole-body imaging.
A 32-channel parallel transmit (pTx) add-on for 7 Tesla whole-body imaging is presented. First results are shown for phantom and in-vivo imaging.
Methods
The add-on system consists of a large number of hardware components, including modulators, amplifiers, SAR supervision, peripheral devices, a control computer, and an integrated 32-channel transmit/receive body array. B1+ maps in a phantom as well as B1+ maps and structural images in large volunteers are acquired to demonstrate the functionality of the system. EM simulations are used to ensure safe operation.
Results
Good agreement between simulation and experiment is shown. Phantom and in-vivo acquisitions show a field of view of up to 50 cm in z-direction. Selective excitation with 100 kHz sampling rate is possible. The add-on system does not affect the quality of the original single-channel system.
Conclusion
The presented 32-channel parallel transmit system shows promising performance for ultra-high field whole-body imaging.
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This work may be used under aCreative Commons Attribution 4.0 License (CC BY 4.0)
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