Megan E Poorman1,2, Rasim Boyacioglu3, William A Grissom4, Mark A Griswold3, and Kathryn E Keenan1
1Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, CO, United States, 2Department of Physics, University of Colorado Boulder, Boulder, CO, United States, 3Department of Radiology, Case Western Reserve Univeristy, Cleveland, OH, United States, 4Vanderbilt University Institute of Imaging Science, Nashville, TN, United States
Synopsis
Temperature monitoring in both
adipose and aqueous tissues is important for guidance of thermal therapies in
vivo. However, the proton resonant frequency shift for
thermometry is only reliable in aqueous tissues. Temperature mapping in adipose
has been explored using relaxation, but is limited by the accuracy and speed of
the method used. MR Fingerprinting provides a framework for mapping differences
due to multiple tissue properties simultaneously. This work explores adapting
the MRF framework to allow temperature changes to be mapped directly in all
tissue types, and simulates a dictionary update method that could offer
improved temporal resolution over standard MRF.
Introduction
Temperature mapping in both
adipose and aqueous tissue is important for guidance of thermal therapy in
vivo. The proton resonant frequency-shift (PRFS)1 is commonly used
to map temperature changes in aqueous tissues, but is unreliable in adipose
tissue. To overcome this barrier, some techniques rely on changes in T1
or T2 to monitor changes in temperature in adipose tissue2.
However, these methods require accurate and precise mapping of T1
and T2, which can be difficult to achieve in the high temporal
resolution (~3s) required for guidance of therapies. In this work, we propose
using Magnetic Resonance Fingerprinting (MRF)3 adapted for mapping
of temperature. MRF is a quantitative imaging method that is capable of mapping
multiple tissue properties simultaneously. However, conventional MRF acquisitions
are too slow to allow real-time monitoring (~40s). In this feasibility study,
we explore using an accelerated quadratic RF MRF technique (MRFqRF)4
to map temperature derived from changes in T1, T2, and B0 in both aqueous and adipose
phantoms. To overcome temporal resolution constraints and better inform
sequence design, we propose an evolving temperature dictionary reconstruction method
and explore its accuracy over a range of image SNRs and heating levels. Methods
Data Acquisition
A phantom (beef
muscle or pork fat) was placed within a water bath and in a 20-channel head coil at
isocenter of the MRI scanner. A laser ablation system (PhotoMedex, LaserPro980)
was used to locally heat the phantom via ablation probe placed within the
tissue. Fiber optic temperature probes (Luxtron, LumaSense Technologies) were
placed at the laser focal point and a distal site. An axial slice was chosen
that encompassed both the ablation probe and fiber optic probe tips to map
temperature in the hotspot. Baseline MRF data was acquired prior to
heating with the laser for 48s (1.5W CW). MRF data was acquired throughout
heating and during subsequent cooling to room temperature.
Pulse Sequence
MRFqRF was implemented on a 3T
MRI scanner (Skyra, Siemens) with 876 timepoints (Figure 1A, TR/TE = 11/2.2ms).
The off-resonance frequency was swept 2.88Hz/TR by applying quadratic phase to
the RF pulses. This acquisition increases sensitivity of MRFqRF to B0 changes5.
To increase T1 sensitivity, inversion pulses were applied after every 219
timepoints. This block of 876 timepoints was repeated continuously throughout
the experiment.
Conventional MRFqRF Reconstruction
Acquired MRFqRF data was reconstructed
with randomized SVD and matched to a dictionary constructed over a range of low
resolution T1/T2/B0 values (min:max 10:3000/2:500/-50:50).
Each block’s 876 timepoints was matched separately to the dictionary to reconstruct
T1, T2, and B0 maps (Figure 1A). Quadratic
interpolation of MRFqRF tissue property maps achieved fine property resolution.
Improved Temperature Reconstruction
To improve the temporal
resolution and sensitivity of the MRFqRF measurements, an evolving temperature
dictionary was created (Figure 1B). This dictionary consists of two
concatenated segments: the first segment is the baseline signal evolution (876
timepoints) with the matched T1, T2, and B0
values. The second segment is a shortened dynamic consisting of 219 timepoints
(2.6s) from the next MRF block, acquired during temperature change. This
shortened dynamic is fit to a temperature dictionary generated over a feasible
range of temperature changes (0°C to 40°C). This dictionary can be generated
on the fly and used to fit each subsequent dynamic. The accuracy and
sensitivity of this method was explored in simulation over a range of SNRs and temperature
changes. Results and Discussion
Changes in T1, T2,
and B0 generated from conventional MRFqRF reconstruction can be seen
in Figure 2. In the muscle phantom,
there is a change in resonant frequency with temperature, coinciding with the
laser output. However, the temporal resolution is insufficient to fully capture
the heating pattern. The adipose phantom exhibits no such frequency shift with
temperature, but T2 increases with the laser output. In both
phantoms the T1 and T2 reconstructions have large
variations through time due to noise, impeding the accuracy of this
reconstruction.
A representative temperature
dictionary from the proposed temperature reconstruction is shown in Figure 3. The
effect of B0 shifts in water and T1/T2 changes in fat are
visible in the dictionary as shifts in peak locations or changes in peak
magnitudes, respectively. The dictionary was generated in 0.41s on a 16GB RAM
computer and matching took 0.03s, implying that this method is feasible for online
temperature reconstruction.
Results from the simulated
heating experiment with temperature dictionary reconstruction are shown in Figure
4. For both muscle and fat, SNR of 1 is too low to accurately reconstruct
temperature. An SNR of 2.5 enables the method to imprecisely track dynamic
changes, while an SNR of 10 yields a precise and accurate reconstruction.
The sensitivity of this temperature
reconstruction method to a 1°C change in temperature is shown in
Figure 5. An SNR of >2.5 is needed to detect this level of change, which is
feasible with MRFqRF acquisitions. Conclusions
We explored MRF for
multi-contrast temperature mapping in multiple tissue types. The conventional
MRF T1, T2, and B0 maps show changes with
temperature in both tissue types, but are limited by temporal resolution and
SNR. The proposed temperature reconstruction has potential to overcome these
limitations, suggesting that a fast and accurate MRF thermometry method is
feasible. Acknowledgements
This work was supported by Siemens Healthcare and a cooperative grant between the University of Colorado and NIST. References
- Ishihara Y, Calderon A, et al. A precise and fast temperature mapping use water proton chemical shift. Magnetic Resonance in Medicine 1995 34:814-823
- Odéen H and Parker D. Non-Invasive Thermometry with Magnetic Resonance Imaging. Theory and Applications of Heat Transfer in Humans, Volume 1, 2018
- Ma D, Gulani V, et al. Magnetic resonance fingerprinting. Nature 2013 495:187-192
- Wang C, Coppo S, Mehta B, et al. Magnetic resonance fingerprinting with quadratic RF phase for measurement of T2* simultaneously with δf ,T1, and T2. Magnetic Resonance in Medicine 2019 81(3):1849-1862.
- Magnetic Resonance Fingerprinting with Quadratic RF Phase for Continuous Temperature Monitoring in Aqueous Tissues. Submitted to ISMRM 2020