Silvin P. Knight^{1}, James F. Meaney^{1}, and Andrew J. Fagan^{1,2}

^{1}National Centre for Advanced Medical Imaging (CAMI), St James Hospital / School of Medicine, Trinity College University of Dublin, Dublin 8, Ireland, ^{2}Department of Radiology, Mayo Clinic, Rochester, MN, United States

### Synopsis

A novel
anthropomorphic phantom test device was used to investigate the effects of temporal resolution (T_{res}), *B*_{1}^{+}-field non-uniformities, and pharmacokinetic
(PK) model fitting methods on the absolute accuracy and precision
of DCE-MRI measurements of the arterial input function (AIF), and resulting PK parameter estimates.
Optimizing the T_{res} was found to reduce the maximum errors in PK
parameter estimation from ~47% to ~20%. By correcting
for *B*_{1}^{+}-field non-uniformities these
errors were further reduced to ~7%.
Using a linear rather than non-linear version of the standard Tofts model further increased the
accuracy and precision of PK parameter estimations.

**Introduction**

The arterial
input function (AIF) is a measure of change in contrast agent (CA)
concentration over time in a blood vessel feeding the tissue of interest, and
is critical to the retrieval of pharmacokinetic (PK) perfusion parameters using
the standard Tofts model in quantitative DCE-MRI

^{[1]}. However, to date a method
has been lacking which allows for repeated acquisitions of a known ‘ground
truth’, physiologically-relevant AIF, in an anthropomorphic environment which
replicates the challenges faced in abdominal / pelvic imaging. This has hampered a comprehensive
investigation into the absolute accuracy and precision of AIF measurements made
using various DCE-MRI approaches. This shortcoming is addressed in the
present study, wherein a completely characterized phantom device was
used to quantify the effects of temporal resolution (T

_{res}),

*B*_{1}^{+}-field
non-uniformities, and PK model fitting regime on the accuracy and precision of
AIF measurements and resulting PK parameter estimates

^{ [2]}.

**Methods**

In order to
establish a physiologically-relevant curve-shape for the AIF used herein,
prostate DCE data from an earlier in vivo study was used (n=32), with data
acquired with a T_{res}
of 3.1s using a 3T scanner (*Achieva*,
Philips, Netherlands) and 8-channel detector coil coupled with an endorectal
coil (3D-SPGR, TR/TE=5.5/2.0ms, α=15°, FOV=256x256x60mm^{3},
voxels=1x1x6mm^{3}, SENSE
factor=2). A ground truth AIF was then established for the phantom
system based on this patient-derived AIF using repeated measurements from a
highly precise optical imaging system (5 intra-/ 5 inter-session) ^{[2]}. The ground truth AIF was then
repeatedly measured (5 intra-/ 5 inter-session) using the scanner with
a 32-channel detector coil (3D-SPGR, TR/TE=3.5/1.6ms, α=23°, FOV=224x224x18mm^{3}, voxels=1x1x6mm^{3})
simultaneously with either ‘healthy’ or ‘tumor’ prostate tissue
CA-concentration-time curves, with T_{res} = [1.2 - 30.6s]. *B*_{1}^{+} maps were
also acquired using a dual-steady-state sequence with the same geometry and
spatial resolution as above and with: TR_{1}/TR_{2}/TE =
30ms/150ms/2ms, α=60°.
The data were analyzed with and without voxel-wise flip-angle correction
(VFAC) applied, with root mean square errors (RMSE) and concordance
correlation coefficient (CCC) values calculated between the MR-measured and
ground truth AIFs, as well as PK modelling
performed using linear
and non-linear forms of the standard Tofts PK model ^{[1, 3]}, with all results compared against
precisely known ground truth values.**Results**

Figure 1 shows the population-averaged AIF established from
32 patient datasets, along with the ground truth AIF established from repeated
optical measurements. Optical
intra-/inter-session measurements gave high repeatability for the AIF produced
at the phantom device, with all CCC values >0.995 (95% confidence intervals = [0.992,0.999]) and %RMSE ≤1.1%.
For DCE phantom measurements, the
actual flip-angle at the ROIs where measurements were performed differed from
the set value by between -29% and -33% for all experiments, which led to large
underestimations in the derived Gd concentrations, as illustrated in Figure 2(a),
with correspondingly low CCC values (minimum CCC = 0.42, and %RMSE of up to
14%). These errors were greatly reduced
when VFAC was applied to the data, as illustrated in Figure 2(b), with a
corresponding gain in the CCC (>0.83) and %RMSE (<8%) values. Large errors in PK parameter estimations of up to 47% were
found when an inappropriate T

_{res} was used and no VFAC performed; by
optimizing the T

_{res} these errors reduced to ~20%, and by applying VFAC these
errors were further decreased to ≤7% (as illustrated in Figures 3). Additionally,
the use of a linear PK modelling fitting regime, rather than non-linear, almost
doubled the accuracy of certain parameter estimations, increased intra-/inter-session
precision by up to a further 4%, and relaxed the dependence of the accuracy of
the results on the T

_{res} used.

**Discussion*** *

These
results demonstrate the significant effect that T

_{res} and

*B*_{1}^{+}-field non-uniformities
can have on DCE-MRI measurements, and thus underpins the importance of
controlling these parameters by careful acquisition sequence optimization,

*B*_{1}^{+}-field mapping,
and the use of an appropriate data pre-processing regime to correct for flip-angle
deviations. These results also
demonstrated that a further appreciable gain in PK modelling accuracy and
precision can be achieved through the optimization of the PK model fitting
regime.

**Conclusion**

The results of this study provide quantitative insight into the effects
of T_{res}, *B*_{1}^{+}-field non-uniformities, and the applied PK model-fitting strategy on the accuracy and
precision of DCE-MRI measurements. This
type of quantitative phantom-based approach can be used to optimize and
validate new and existing DCE acquisition protocols, ensuring consistent
measurement accuracy, and offers the
prospect of increasing the sensitivity and specificity of DCE-MRI, possibly leading to a
standardization in the way DCE-MRI is performed, and thus a wider acceptance of
the technique for use in routine clinical examinations.### Acknowledgements

Supported by Irish Cancer Society Research Scholarship
CRS13KNI### References

[1]
Tofts PS, Brix G, Buckley DL, Evelhoch JL, Henderson E, Knopp MV, et al.
Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI
of a diffusable tracer: standardized quantities and symbols. J Magn Reson
Imaging. 1999;10:223-32.

[2]
Knight SP, Browne JE, Meaney JF, Smith DS, Fagan AJ. A novel anthropomorphic
flow phantom for the quantitative evaluation of prostate DCE-MRI acquisition
techniques. Phys Med Biol. 2016;61:7466-83.

[3]
Murase K. Efficient method for calculating kinetic parameters using T1-weighted
dynamic contrast-enhanced magnetic resonance imaging. Magn Reson Med.
2004;51:858-62