Synopsis
Dual-pathway sequences have been
proposed to improve the temperature-to-noise-ratio (TNR) in MR thermometry. The
present work establishes how much improvement these sequences may bring for
various tissue types. Simulation results were validated against analytical
equations, phantom and in vivo human results. PSIF-FISP thermometry allowed TNR
improvements for kidney, pelvis, spleen or gray matter, and up to 2-3 fold
reductions in TR with 20% TNR gains were achievable. Further TNR benefits are
expected for heated tissues, due to heating-related changes in relaxation
rates. In other tissue types such as liver, muscle or pancreas improvements were
observed only for short TR settings.
PURPOSE
To find out how much of a boost in
temperature-to-noise-ratio (TNR) can be achieved with dual-pathway sequences,
as a function of tissue type.
INTRODUCTION
MR thermometry is a valuable
tool for monitoring thermal therapies. The most common approach is based on the
proton-resonant-frequency (PRF) shift of water (1-3). The phase maps required
for PRF thermometry are typically obtained using gradient-recalled echo (GRE) sequences. Dual-pathway unbalanced steady-state sequences have been proposed
instead to boost TNR (4). These sequences sample a ‘fast imaging with
steady-state free precession’ (FISP) signal late in the TR period and an
inverted-FISP (PSIF) signal early in TR (4). The acquisition is similar to that
of a dual-echo steady state (DESS) sequence (5), except for the order of the
PSIF and FISP echoes. The basic idea is that the temperature sensitivity of the
PSIF signal depends on (TR-TE) rather than TE, making FISP and PSIF signals natural
partners toward utilizing the entire TR period in a TNR-effective manner, with
one signal pathway sampled early and the other one late in TR.
However, the signal
intensity of the PSIF signal greatly varies according to parameter setting and
relaxation parameters, making it unclear how much of a TNR boost might be
obtained. Using a two-echo FISP sequence as a reference standard, whereby the
two FISP echoes have the same TE and bandwidth as the PSIF and FISP of the
tested dual-pathway sequence, TNR boosts were quantified here for a number of
different tissue types and parameter settings.
METHODS AND RESULTS
All in vivo and phantom experiments
performed here were aimed primarily at validating our simulation program. Whenever
possible, validation was also performed against analytical expressions. Once
validated, this software could then be used to test a wide array of tissue
types and imaging parameters.
Validation against analytical solutions
($$$R_2' = 0$$$): With
no reversible decay component, i.e., $$$R_2'$$$=0 and $$${T_2}^*$$$=$$$T_2$$$,
the analytical value for PSIF and FISP signal can be found in Refs (6-8).
Analytical and simulated results for the strength of both signal types,
normalized by $$$M_0$$$, were obtained over a wide range of $$$T_1$$$, $$$T_2$$$,
flip angle and TR settings. Analytical and simulated results showed
near-perfect agreement, see Fig. 1.
Validation against in-vivo and phantom experiments
($$$R_2' \neq 0$$$):
Following informed consent, abdominal imaging was performed on three subjects
and brain imaging on four subjects, on a 3 T system using the PSIF-FISP
sequence. Phantoms doped with varying concentrations of manganese sulfate (MnSO4)
were scanned as well. Predictions for the relative PSIF / FISP signal, as
obtained from our simulation program, were compared to their in vivo and
phantom counterparts (Fig. 2). Close agreement can be seen relative to the
identity line (Fig. 2a, b and c), and in Bland-Altman plots (Fig.
2d, e, f, g).
Validation of TNR values: Simulated signal levels for PSIF and
FISP pathways were converted into relative TNR, as compared to the dual-FISP
reference standard. Monte Carlo simulations, whereby various levels of noise
were added to data, were used to validate this conversion step from signal
levels to relative TNR. As shown in Fig. 3, near perfect agreement was obtained
between analytical and Monte Carlo values.
TNR-based recommendations: The purpose of Fig. 1-3 was to validate
our simulation program, while the purpose of Fig. 4-5 was to generate
recommendations on when/where to use PSIF-FISP for thermometry. Results are shown
in Fig. 4 for many different tissue types (9-14), over a wide range of TR and flip angle
settings. Green indicates a TNR boost while red indicates a TNR penalty for the
PSIF-FISP sequence. Contour plots were overlaid to show how well the reference
standard (dual-FISP) sequence performed as a function of TR and flip angle (‘100’
for maximum performance, contour ‘90’ indicates 90% of maximum performance,
etc.). Figure 5 plots the TNR boost achieved with a PSIF-FISP sequence as a
function of $$$T_1$$$ and $$$T_2$$$ (for
TR = 15 ms, flip angle = 60$$$^o$$$).
DISCUSSION AND CONCLUSION
As seen in Fig. 4, wherever the reference
standard dual-FISP sequence performs best (innermost contour) the PSIF-FISP
sequence has very little more to offer. However, whenever TR is shortened
enough, for faster imaging and/or improved motion robustness, the PSIF-FISP
sequence becomes advantageous (see green regions in Fig. 4 at short TR
settings). The PSIF-FISP sequence is generally better suited for tissues with
longer $$$T_1$$$ and $$$T_2$$$ values (e.g. kidney), and not so much otherwise
(e.g. liver). It is worth noting that as tissues are heated, both $$$T_1$$$ and
$$$T_2$$$ tend to increase, making PSIF-FISP sequences more desirable (Fig. 5).
Acknowledgements
Financial support from NIH grants
R25CA089017, R01CA149342, R01EB010195, R21EB019500 and P41EB015898 is duly
acknowledged.References
1. De
Poorter J, De Wagter C, De Deene Y, Thomsen C, Stahlberg F, Achten E.
Noninvasive MRI thermometry with the proton resonance frequency (PRF) method:
in vivo results in human muscle. Magn Reson Med 1995;33(1):74-81.
2. Ishihara
Y, Calderon A, Watanabe H, Okamoto K, Suzuki Y, Kuroda K, Suzuki Y. A precise
and fast temperature mapping using water proton chemical shift. Magn Reson Med
1995;34(6):814-823.
3. Peters
RD, Hinks RS, Henkelman RM. Ex vivo tissue-type independence in
proton-resonance frequency shift MR thermometry. Magn Reson Med
1998;40(3):454-459.
4. Madore
B, Panych LP, Mei CS, Yuan J, Chu R. Multipathway sequences for MR thermometry.
Magn Reson Med 2011;66(3):658-668.
5. Bruder H, Fischer H, Graumann R, Deimling
M. A new steady-state imaging sequence for simultaneous acquisition of two MR
images with clearly different contrasts. Magn Reson Med 1988;7(1):35-42
6. Hanicke
W, Vogel HU. An analytical solution for the SSFP signal in MRI. Magn Reson Med
2003;49(4):771-775.
7. Zur Y, Stokar S, Bendel P. An analysis of fast imaging sequences with steady-state transverse magnetization refocusing. Magn Reson Med 1988;6(2):175-193.
8. Gyngell ML. The steady-state signals in short-repetition-time sequences. Journal of Magnetic Resonance (1969) 1989;81(3):474-483.
9. Wansapura
JP, Holland SK, Dunn RS, Ball WS Jr. NMR relaxation times in the human brain at
3.0 tesla. J Magn Reson Imaging. 1999 Apr;9(4):531-8.
10. de
Bazelaire CM, Duhamel GD, Rofsky NM, Alsop DC. MR imaging relaxation times of
abdominal and pelvic tissues measured in vivo at 3.0 T: preliminary results.
Radiology. 2004 Mar;230(3):652-9.
11. Gold
GE, Han E, Stainsby J, Wright G, Brittain J, Beaulieu C. Musculoskeletal MRI at
3.0 T: relaxation times and image contrast. AJR Am J Roentgenol. 2004
Aug;183(2):343-51.
12. Stanisz
GJ, Odrobina EE, Pun J, Escaravage M, Graham SJ, Bronskill MJ, Henkelman RM.
T1, T2 relaxation and magnetization transfer in tissue at 3T. Magn Reson Med.
2005 Sep;54(3):507-12.
13. Rakow-Penner
R, Daniel B, Yu H, Sawyer-Glover A, Glover GH. Relaxation times of breast
tissue at 1.5T and 3T measured using IDEAL. J Magn Reson Imaging. 2006
Jan;23(1):87-91.
14. Wright
PJ, Mougin OE, Totman JJ, Peters AM, Brookes MJ, Coxon R, Morris PE, Clemence
M, Francis ST, Bowtell RW, Gowland PA. Water proton T1 measurements in brain
tissue at 7, 3, and 1.5 T using IR-EPI, IR-TSE, and MPRAGE: results and
optimization. MAGMA. 2008 Mar;21(1-2):121-30.