Gaspar Delso1, Mehdi Khalighi2, Dan Rettmann3, Michel Tohme4, and David Goldhaber4
1GE Healthcare, Zurich, Switzerland, 2GE Healthcare, Stanford, CA, United States, 3GE Healthcare, Rochester, MN, United States, 4GE Healthcare, Waukesha, WI, United States
Synopsis
One of the known potential
benefits of integrated positron emission and magnetic resonance (PET/MR)
scanners is the possibility of using MR data to monitor patient motion.
The present work evaluates
the performance of a new pulse sequence designed exclusively for PET/MR head
tracking. By assuming that all the required imaging data is provided by the PET
subsystem, the MR sequence can be optimized exclusively for the task of
providing accurate motion estimates over long periods of time (e.g. 30-60min).
Data management, SAR and noise concerns are therefore minimized.
Purpose
One of the known potential
benefits of integrated positron emission and magnetic resonance (PET/MR)
scanners is the possibility of using MR data to monitor patient motion.
However, motion monitoring
in currently available MR sequences is accessory to their imaging function.
Even when MR imaging is not required, the existing sequences with motion
monitoring capability will not lend themselves to run uninterrupted for long
periods of time, as often required by PET studies. Problems such as the
generation of large amounts of imaging data, unnecessary radio-frequency
exposure and patient discomfort due to continuous noise conditions will have to
be dealt with.
The present work evaluates
the performance of a new pulse sequence designed exclusively for PET/MR head
tracking. By assuming that all the required imaging data is provided by the PET
subsystem, the MR sequence can be optimized exclusively for the task of
providing accurate motion estimates over long periods of time (e.g. 30-60min).
Data management, SAR and noise concerns are therefore minimized.
Methods
The proposed sequence
borrows on the Prospective Motion Correction approach developed by White et al1.
This approach uses orthogonal spiral navigator acquisitions (TE 1ms, TR 16ms, FA 8deg,
BW 125kHz, FOV 32cm, matrix 2048x1, image size 128x128, ST 13mm) as the input of an extended Kalman
filter algorithm in charge of real-time motion estimation2. Contrary
to the typical implementation of sequences based on this correction method,
where the navigator pulses are integrated in a 3D SPGR or FSE framework, the
proposed sequence includes no imaging generation pulses, instead providing the
user with full control over the navigator timing properties.
In-vivo measurements of the motion estimation
performance were carried out on a SIGNA PET/MR system (GE Healthcare). These
included: long-term stability measurements, both under static and piecewise
static conditions; and accuracy measurements, using as reference 3D FSPGR
volumes (TE 1.2ms, TR 5.5ms, FA 15deg, BW 31.25kHz, FOV 24cm, matrix 120x120,
pixel size 2x2mm, ST 2mm) acquired immediately before
and after each motion estimation sequence. The Integrated Registration software
(Advantage Workstation, GE Healthcare) was used to compute the rigid
transformation between these volumes.
Results
The accuracy measurements
show good agreement between the motion estimates provided by the sequence and
the ground truth obtained by registering the FSPGR datasets. The measured
differences between these methods are summarized in Table 1. The motion
estimation algorithm was found to diverge in two series where a sudden large
motion caused the head tracking to lose lock. These series were excluded from further
analysis.
Short-term stability measurements on the static
intervals before and after patient motion show standard deviations below 0.2mm
for the translation parameters and below 0.3deg for the rotation parameters. Long-term
stability measurements yielded similar results after de-trending, with standard
deviations below 0.6mm for the translation parameters and below 0.5° for the
rotation parameters.
Discussion
The obtained accuracy and
stability values fall well within the acceptable range for PET motion
correction (the intrinsic spatial resolution of clinical PET systems being
typically >4mm). Trends and oscillatory patterns were patient-dependent,
which suggests they are not caused by the motion tracking algorithm. Long acquisition
times were possible without any kind of data management issues. SAR values were
negligible throughout the examination.
Ongoing work is aimed at increasing the lock and
re-capture range of the tracking algorithm. While this was not a relevant issue
in the MR imaging scenarios for which the algorithm was originally developed, sudden
large movement is likely to occur during long PET acquisitions.
Conclusion
The results obtained in this
study indicate that the proposed non-imaging sequence is suitable for MR-driven
PET motion correction in hybrid scanners. Further work is underway to improve
the tracking range.
Acknowledgements
No acknowledgement found.References
1. White
et al. Magn Reson Med. 2010; 63(1):91-105.
2.
Shankaranarayanan et al. Proc ISMRM 2008; Toronto (CA):214.