Alexey Samsonov1
1University of Wisconsin-Madison, Madison, WI, United States
Synopsis
Macromolecular proton
fraction (MPF) is an established myelin marker with confirmed clinical
relevance, but with sensitivity to technological variations such as B1 field errors. We propose a method to derive B1 map for MPF correction from MPF data itself. The method is based on standard two-pool MT formalism enhanced with improved relaxometry constraints.
Introduction
Macromolecular proton
fraction (MPF) is an established myelin marker with confirmed clinical
relevance in a variety of neurological disorders. One drawback of MPF mapping
is its substantial sensitivity to B1 field errors. While these can be measured by
dedicated calibration protocols such as actual flip angle imaging (AFI)1, it comes at the expense of significant
overhead (~3-4 min vs. ~7.5 min for MPF data collection)2. Recently, a possibility to estimate surrogate
B1 map from MPF data itself was suggested3. The empirical observation of the global linear
relationship between single-pool R1
and MPF in the absence of B1 errors was
discovered. This allowed approximating B1 map with sufficient accuracy after
learning calibration constants for a given acquisition protocol and scanner. In
this work, we propose alternative treatment of the problem within fast MPF
mapping framework, which is completely based on the two-pool MT model, thereby
avoiding uncertainties associated with single pool approximations. We
demonstrate that B1 estimation may be performed solely within the two-pool
model formalism using normative estimates of relaxation constants.Theory
Our method is based on the
recent findings that the use of underestimated values of longitudinal
relaxation rate of macromolecular (bound) protons (R1b), a fixed parameter of the model, results in incomplete
separation of macromolecular contributions from R1 of the free water protons (R1f),
thereby inducing the macromolecular contrast in the R1f maps4. Proper modeling with more accurate estimates
of R1b can enable the separation of
macromolecular effects from R1f maps,
which will make them consistent with the original model of this parameter
(given by a sum of relaxation rates of a pure saline and contributions from
paramagnetic substances5). The removal of macromolecular effects leads
to spatially homogeneous R1f, as
confirmed by results from our and other groups (Fig. 1). Here, we propose to use
this fact for B1 map estimation within fast MPF mapping framework. Namely,
instead of using B1 as a pre-measured map, we introduce it as a free parameter
into the model while constraining R1f to its normative value.Methods
Data Acquisition: Experiments were
performed on a 3.0T GE Discovery MR750 (Waukesha, WI) using an 8-channel head
array in two healthy and three multiple sclerosis (MS) subjects after informed
consent. The protocol included two SPGR and one MT-weighted SPGR measurement
with optimized Z-spectra sampling and additional model constraints for fast 3-point
MPF mapping as previously described6,7. Healthy volunteers
were scanned with 1x1x2 mm resolution in 18 min; MS subjects were scanned with
2x2 GRAPPA-accelerated 1.3 mm-3 isotropic in 7.5 min8. Additionally, a B1
map was collected with AFI1 as a reference. Additional
3T MPF mapping dataset from different system (Philips 3T) was obtained from https://www.macromolecularmri.org/.
Determination of Longitudinal
Relaxation Constraints: We formulated our R1b estimation (submitted as a separate abstract to ISMRM 2021)
using assumption that proper modeling interactions of free and bound protons with
optimal R1b value should eliminate
macromolecule-induced spatial variations from R1f, thereby minimizing the information content (as measured by histogram
entropy function9). The fit yielded average
value of R1b=6.5 s-1 across
all subjects scanned on GE system. Mean R1f
was 0.3045 s-1, which is consistent with previous studies3,4.
Processing: The MPF data were fit for proton density, MPF, and B1 error, while R1f and R1b were kept fixed to their
normative values. As the resulting raw B1 map is defined only in brain tissue,
its values in CSF and surrounding tissues were approximated using local polynomial
fit smoothing10 weighted with MPF to measure a confidence of
raw B1 estimation. Results
Figure 1 supports the use
of R1f from improved two-pool
modeling as a constrained parameter. Figure 2 illustrates the effect of
improper selection of R1b (including
previously proposed values for this parameter) on the calibrationless B1 field.
Figure 3 presents results of applying our method for correction of MPF errors.
Figure 4 demonstrates calibrationless B1 mapping on a different platform
(Philips 3T), initially supporting the independence of the estimated relaxometry
constants on the vendor.Discussion and Conclusions
We presented a calibrationless B1
mapping method, which derives from recent advances in two-pool MT modeling and is
inspired by improved estimation of longitudinal relaxation rate of
macromolecular protons. The method allows re-interpretation of the previous
empirical B1 mapping method3 within two-pool
model formalism, in which B1 mapping is performed as a standard two-pool model fit
with constrained relaxometry constants. The method is directly applicable only
in tissues that demonstrate MT effect. However, our results indicate that the
B1 map may be reasonably well approximated within other non-MT areas such as
CSF using standard assumption of B1 field smoothness. Another limitation is the
potential sensitivity of calibrationless B1 maps to local deviations of R1f
from its nominal value due to paramagnetic iron deposition in deep GM, which
will be the subject of future studies. Acknowledgements
The
work was supported by NIH (R01EB027087, R24NS104098) and GE Healthcare.References
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