Xingwang Yong1, Shanshan Lu2, Yi-Cheng Hsu3, Yi Sun3, Dan Wu1, and Yi Zhang1
1Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China, 2The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China, 3MR Collaboration, Siemens Healthcare Ltd., Shanghai, China
Synopsis
Amide proton transfer (APT) imaging can detect
pH-related changes in ischemic stroke lesions. However, the widely-used
magnetization transfer ratio asymmetry (MTRasym) analysis method is
susceptible to various contamination sources. Here we improved the previous
extrapolated semisolid magnetization transfer reference (EMR) method by
numerical fitting of the EMR signal (NEMR) in the modified Bloch-McConnel
equation. The proposed NEMR method was
compared with the previous EMR method in Monte Carlo simulations, demonstrating
superior accuracy. Furthermore, the NEMR maps were compared with EMR and MTRasym
maps in nine ischemic stroke patients, yielding a better depiction of the
ischemic lesions.
INTRODUCTION
Amide proton transfer (APT) imaging, a subtype of
chemical exchange saturation transfer (CEST) imaging, has been successfully applied
to ischemic stroke, given its ability to probe tissue pH (1). Since the widely-used magnetization transfer ratio
asymmetry (MTRasym) analysis has contamination from multiple sources, several
methods have been proposed to obtain cleaner CEST signals by removing confounding
factors (2-4), among which the EMR (2) method generates an extrapolated semisolid
magnetization transfer reference signal to be subtracted from the experimental
z-spectrum. However, the EMR method is limited to a steady-state solution and
cannot take into account the RF pulse shape used for CEST saturation. To address these limitations, we incorporated the lineshape of semisolid
magnetization transfer (MT) pool into the Bloch-McConnell equation (5) and performed numerical fitting of the EMR signal (NEMR).
The proposed NEMR method was compared with the original EMR method in stroke
patients, demonstrating superior accuracy.THEORY
A two-pool system including water (pool a) and MT (pool b) subject
to CEST saturation can be described as: $$\begin{array}{l}\frac{{d{M_x}_b}}{{dt}}=-\Delta{\omega_b}{M_{yb}}-{R_{2b}}{M_{xb}}\\\frac{{d{M_y}_b}}{{dt}}=\Delta{\omega_b}{M_{xb}}-{R_{2b}}{M_{yb}}-{\omega_1}{M_{zb}}\\\frac{{d{M_{zb}}}}{{dt}}={\omega_1}{M_{yb}}-{R_{1b}}({M_z}_b-{M_{0b}})+{k_{ab}}{M_{za}}-{k_{ba}}{M_z}_b\end{array}$$
where $$$\Delta{\omega_b}$$$ is the frequency offset for pool b; R1b
and R2b are longitudinal and transverse relaxation rates of pool b,
respectively; kab is exchange rate from pool a to pool b and vice
versa. Because T2 of MT is on the microsecond level, Mxb and Myb
can be assumed to be in steady-state after millisecond-long saturation. Then Myb
is
$${M}_{yb}=-\frac{{{\omega_1}{R_{2b}}}}{{{{(\Delta{\omega_b})}^2}+{{({R_{2b}})}^2}}}{M}_{zb}$$
which can be rewritten as $$${\omega_1}{M_y}_b=-{R}_{rfb}{M_z}_b$$$ by defining $$${R}_{rfb}=\frac{{\omega_1^2{R_{2b}}}}{{{{(\Delta{\omega_b})}^2}+{{({R_{2b}})}^2}}}$$$
Thus Mzb can be
described as $$\frac{{d{M_{zb}}}}{{dt}}=-{R}_{rfb}{M}_{zb}-{R_{1b}}({M}_{zb}-{M_{0b}})+{k_{ab}}{M_{za}}-{k_{ba}}{M}_{zb}$$
Furthermore, Rrfb
can be represented by a lineshape function g(), $$${R}_{rfb}{\rm{=}}\omega_1^2{\pi}g(2\pi\Delta{\omega_b})$$$, where g(·) could be lorentzian, gaussian, or super
lorentzian (6). Hence, the lineshape of MT
pool is integrated into Bloch-McConnell equation (5). Furthermore, when solving
for Mzb numerically, the pulse shape of CEST satuation can be
considered. In contrast, the conventional EMR method is based on analytical
solution of the two-pool system that cannot take account of RF pulse shapes.METHODS
Monte Carlo simulation was performed
to compare the accuracy of EMR and NEMR. First, a ground-truth z-spectrum was
simulated using a four-pool model, including water, MT, amide, and NOE. Next,
this z-spectrum was fitted using EMR and NEMR, respectively. Then, relative
error of fitted parameters was calculated. This process was repeated 1000 times
by using different exchange parameters in the four-pool model.
Nine ischemic stroke patients were recruited, and were imaged
on a 3T Siemens Skyra scanner. In addition to anatomical FLAIR and
diffusion-weighted imaging (DWI) scans, CEST imaging was executed with frequency
offsets from -6 to 80 ppm (7), TR/TE=3000/7.2ms, slice thickness=5mm, and FOV=185x185mm2.
Depending on the status of the patients, CEST data were acquired using B1 power
of 1, 1.5, and 2uT fully or partially, with each acquisition taking 3.1 minutes.
CEST images were registered to the frame at 3.5ppm (8), and B0-correction was performed with dual-echo
gradient-echo images. Then EMR and NEMR fitting were performed voxelwise,
respectively, giving corresponding APT# and NOE# map.
Specifically, APT#=Zfit(3.5ppm)-Zexp(3.5ppm),
NOE#=Zfit(-3.5ppm)-Zexp(-3.5ppm), where Zfit
is the fitted z-spectrum by EMR or NEMR and Zexp is the
z-spectrum acquired experimentally. An experienced radiologist delineated an
ROI enclosing the whole ischemic lesion and drew a circular ROI in the
contralateral normal-appearing white matter. Signal contrast was defined as
difference of signal mean between these two ROIs. Paired t-test was conducted
to compare (a) the signal contrast between EMR and NEMR, and (b) the signal
contrast between 1uT, 1.5uT, and 2uT.RESULTS
As shown in Fig.
1, the Monte Carlo simulation revealed that NEMR yielded significantly
smaller relative errors than EMR (p<0.05). Fig. 2 shows the APT-weighted (APTw) image from MTRasym analysis
had poor contrast and was not able to reliably separate the ischemic lesion from
normal-appearing white matter. On the contrary, the APT# and NOE#
maps exhibited marked hypointensity in the ischemic lesion. Notably, the
ischemic lesion in APT# and NOE# images were better
depicted at 1uT than 1.5uT. Furthermore, the NEMR maps presented a better
contrast between the lesion and normal tissues than the EMR maps at a B1 level
of 1.5uT. In Fig. 3, quantitative analysis
revealed that both APT# (result not shown) and NOE# signals
at 1uT had significantly stronger contrast (p<0.05) than those at 2uT. Fig. 4 illustrates that NEMR had
significantly larger contrast (p<0.05) than EMR, and the NOE#
contrast was greater than APT#. Fig.
5 presents images from a representative case at 1uT, which again
demonstrated the superior contrast of NEMR maps to APTw images. Importantly, the
NOE# map showed a lesion that was not visible on the APT#
map.DISCUSSION and CONCLUSION
The EMR method assumes steady-state for both pools, which is often not valid, e.g. for saturation duration
<1sec. Differently, NEMR assumes steady-state for MT pool only, which is
essentially true. Besides, NEMR can incorporate the RF pulse shape during
fitting which EMR cannot. Thus NEMR is more accurate than EMR, as indicated by the
Monte Carlo simulation. Here, we demonstrated the superior performance of the
proposed NEMR method to the previous EMR method. Furthermore, our results
showed a B1 power of 1uT generated stronger contrast than 1.5uT and 2uT for
imaging ischemic stroke lesions. Finally, the NOE# maps yielded better
contrast than APT#. Acknowledgements
NSFC grant number: 81971605, 61801421. References
1. Zhou
J, Payen JF, Wilson DA, Traystman RJ, Van Zijl PCM. Using the amide proton
signals of intracellular proteins and peptides to detect pH effects in MRI.
Nature Medicine. Volume 9; 2003. p 1085-1090.
2. Heo
H-YY, Zhang Y, Jiang S, Lee D-HH, Zhou J. Quantitative assessment of amide
proton transfer (APT) and nuclear overhauser enhancement (NOE) imaging with
extrapolated semisolid magnetization transfer reference (EMR) signals: II.
Comparison of three EMR models and application to human brain glioma at.
Magnetic Resonance in Medicine. Volume 75; 2016. p 1630-1639.
3. Zaiß
M, Schmitt B, Bachert P. Quantitative separation of CEST effect from
magnetization transfer and spillover effects by Lorentzian-line-fit analysis of
z-spectra. Journal of Magnetic Resonance. Volume 211; 2011. p 149-155.
4. Zaiss
M, Xu J, Goerke S, Khan IS, Singer RJ, Gore JC, Gochberg DF, Bachert P. Inverse
Z-spectrum analysis for spillover-, MT-, and T1-corrected steady-state pulsed
CEST-MRI - application to pH-weighted MRI of acute stroke. NMR in Biomedicine.
Volume 27; 2014. p 240-252.
5. Desmond
KL, Stanisz GJ. Understanding quantitative pulsed CEST in the presence of MT.
Magnetic Resonance in Medicine. Volume 67; 2012. p 979-990.
6. Morrison
C, Mark Henkelman R. A Model for Magnetization Transfer in Tissues. Magnetic
Resonance in Medicine. Volume 33; 1995. p 475-482.
7. Heo
HY, Zhang Y, Jiang S, Lee DH, Zhou J. Quantitative assessment of amide proton
transfer (APT) and nuclear overhauser enhancement (NOE) imaging with
extrapolated semisolid magnetization transfer reference (EMR) signals: II.
Comparison of three EMR models and application to human brain glioma at 3
Tesla. Magn Reson Med 2016;75(4):1630-1639.
8. Zhang Y, Heo HY, Lee DH, Zhao X, Jiang S, Zhang
K, Li H, Zhou J. Selecting the reference image for registration of CEST series.
J Magn Reson Imaging 2016;43(3):756-761.