Hernan Jara1, Arnaud Guidon2, Lloyd Estkowski3, Jorge A Soto1, and Osamu Sakai1
1Radiology, Boston University, Boston, MA, United States, 2Global MR Applications and Workflow, GE Healthcare, Boston, MA, United States, 3Global MR Applications and Workflow, GE Healthcare, Menlo Park, CA, United States
Synopsis
Purpose: To
test the hypothesis that correlation time diffusion coefficient (DCT)
MRI is not be dependent of the main magnetic field strength B0. Methods: A heathy volunteer was scanned
(head) at 1.5T and 3.0T with the same diffusion mapping capable scans:
quadra-FSE for DCT mapping and DW-SE-sshEPI for DPFG
mapping. Results: Despite the B0
dependent shifts of the relaxation time (T1, T2)
histograms, the DCT, DPFG, and PD histograms do not
exhibited significant B0 shifts. Conclusion: The correlation time diffusion coefficient is
theoretically and experimentally a genuine physical property inherent to
biological tissue and independent of experimental variables including B0.Introduction
Quantitative MRI parameters include measures of 1) tissue
hydration –the proton densities (PD) of the mobile and the semisolid water pools--,
2) tissue micro- and macro-kinetics (diffusion coefficient, perfusion
parameters, and flow velocity), and 3) measures of spin interactions with the
microscopic magnetic milieu via the relaxation times (T1, T2,
T2*). Of these qMRI parameters, only the relaxation times depend on
the main magnetic field strength B0 and the others are purely
determined by the physical and physiological state of tissue. Accordingly, PD as
well as the diffusion coefficient, as measured by the Stejskal-Tanner
pulsed-field-gradient (PFG) experiment (1), are theoretically and experimentally
independent of B0. Correlation time diffusion is an alternative qMRI
technique for mapping the diffusion coefficient (2-4) --termed DCT to distinguish it from
DPFG-- that uses relaxometric (T1 and T2) and
proton density (PD) information (5-10). Hence, although DCT is
theoretically B0 independent, the question arises as to whether the
field independence holds in practice. To help elucidate this question, a
volunteer was scanned at 1.5T and 3.0T with the same multispectral qMRI
technique using the quadra-FSE pulse sequence and with the standard PFG
diffusion technique.
Methods
and Materials
The volunteer (male 35yo) was scanned at
3.0T (Discovery MR750, GE Healthcare, Waukesha, WI) and a week later at 1.5T (Optima
MR450w, GE Healthcare, Waukesha, WI). The imaging protocol included: a)
standard PFG diffusion-weighted scan using the DW single-shot EPI sequence, and
b) a newly developed pulse sequence termed herein quadra fast spin-echo
(quadra-FSE; see Figure 1), which
is multispectral in PD, T1, and T2 and therefore can be
used for DCT mapping. The DW-SE-sshEPI sequence was implemented with
TE=63.9ms and TR=8,000ms) and diffusion encoding along the three physical axes
(b=0 and 1,000s/mm2). The quadra-FSE pulse sequence is a
concatenation of two dual-echo fast spin-echo (DE-FSE) that are run
consecutively with equal parameters including pre-scan values, but with
different TR values and consequently different levels of T1
saturation. The key pulse sequence parameters are the two TRs (5,320 & 600ms)
and the two effective echo times (TE1eff=22.1 & TE2eff=99.6ms
with ETL=10). Four directly acquired images per slice are generated with
varying levels of T1- and T2-weighting; specifically
Sat1_E1, Sat1_E2, Sat2_E1, and Sat2_E2 (see Figure
1) and these images can be qMRI processed to generate coregistered
maps of PD, T1, and T2. In turn, these maps were used to
map DCT using equations derived from the microscopic theory of
relaxation. The directly acquired images of both scans were transferred to a
personal computer equipped with Mathcad (version 2001i, PTC,
Needham, MA) and qMRI processed with
model conforming algorithms to produce maps of PD, T1, T2,
DCT, and DPFG. These maps were segmented with a dual-clustering
algorithm leading to datasets representing the totality of the intracranial
tissues (white matter (WM), gray matter (GM), meninges, and cerebrospinal fluid
(CSF)). The resulting intracranial matter (ICM) segments were further processed
with a pixel counting algorithm to generate histograms representing the
individual distributions of each qMRI parameter.
Results
ICM T1
histograms representing all-intracranial tissues of the same volunteer scanned
at 1.5T (blue line) and 3.0T (red line) with the quadra-FSE pulse sequence are
shown in Figure 2. These are
fundamentally tri-modal histograms with well-defined spectral features representing
WM, GM, and CSF. Note that as is well known, the WM and GM T1values
at 3.0T are longer than at 1.5T by 15%-20%. Analogously, T2
histograms of the all-intracranial tissues of the same volunteer scanned at
1.5T (blue line) and 3.0T (red line) with the quadra-FSE pulse sequence are
shown in Figure 3. In this case
the spectral shift is in the opposite direction; the T2 values at
3.0T are
shorter than at 1.5T by 15%-20%. Despite the B0 dependent
shifts of the relaxation time histograms, the PD, DCT, and DPFG
histograms do not exhibited significant B0 shifts (see Figures 4 & 5).
Conclusion
These experiments performed on the same human subject
confirm the hypothesis that the physico-physiological qMRI parameters (PD, DCT,
and DPFG) are independent of the main magnetic field strength,
within experimental uncertainty. Most importantly, this study further confirms
the physical interpretation of DCT as a purely physical and B0-independent diffusion measure, despite the fact that it is calculated using the
relaxation times, which are B0 dependent.
Acknowledgements
This work was supported in part by a research grant from GE Healthcare.References
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