Download Feasibility of using limited‐population‐based arterial input function

Magnetic Resonance in Medicine 59:1183–1189 (2008)
Feasibility of Using Limited-Population-Based Arterial
Input Function for Pharmacokinetic Modeling of
Osteosarcoma Dynamic Contrast-Enhanced MRI Data
Ya Wang,1 Wei Huang,1–3* David M. Panicek,2,3 Lawrence H. Schwartz,2,3 and
Jason A. Koutcher1– 4
For clinical dynamic contrast-enhanced (DCE) MRI studies, it is
often not possible to obtain reliable arterial input function (AIF)
in each measurement. Thus, it is important to find a representative AIF for pharmacokinetic modeling of DCE-MRI data when
individual AIF (Ind-AIF) measurements are not available. A total
of 16 patients with osteosarcomas in the lower extremity (knee
region) underwent multislice DCE-MRI. Reliable Ind-AIFs were
obtained in five patients with a contrast injection rate of 2 cc/s
and another five patients with a 1 cc/s injection rate. Average
AIF (Avg-AIF) for each injection rate was constructed from the
corresponding five Ind-AIFs. For each injection rate there are
no statistically significant differences between pharmacokinetic parameters of the five patients derived with Ind-AIFs and
Avg-AIF. There are no statistically significant changes in pharmacokinetic parameters of the 16 patients when the two AvgAIFs were applied in kinetic modeling. The results suggest that
it is feasible, as well as practical, to use a limited-populationbased Avg-AIF for pharmacokinetic modeling of osteosarcoma
DCE-MRI data. Further validation with a larger population
and multiple regions is desirable. Magn Reson Med 59:
1183–1189, 2008. © 2008 Wiley-Liss, Inc.
Key words: dynamic contrast-enhanced MRI; arterial input
function; osteosarcoma; Ktrans; pharmakinetic modeling
There has been increasing interest in the T1-weighted dynamic contrast-enhanced (DCE) MRI method for the study
of many different tumor types, using the Gd (III) chelate
contrast agents (1). There are generally three approaches
for analyzing DCE-MRI signal time courses: 1) qualitative
subjective assessment of curve shape, such as wash-out,
plateau, and persistent; 2) empirical quantitation, such as
maximum slope and percent signal intensity change; and
3) analytical pharmacokinetic modeling. The latter is more
sophisticated, and also the more desirable. Unlike the first
two approaches, analytical modeling of DCE-MRI data ex-
1Department of Medical Physics, Memorial Sloan-Kettering Cancer Center,
New York, New York, USA.
2Department of Radiology, Memorial Sloan-Kettering Cancer Center, New
York, New York, USA.
3Department of Radiology, Weill Medical College of Cornell University, New
York, New York, USA.
4Department of Medicine, Memorial Sloan-Kettering Cancer Center, New
York, New York, USA.
Grant sponsor: National Cancer Institute/National Institutes of Health; Grant
number: 1 R01 CA104754.
Ya Wang and Wei Huang contributed equally to this study.
*Correspondence to: Wei Huang, PhD, Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY
10021. E-mail: [email protected]
Received 30 January 2007; revised 23 July 2007; accepted 6 September
2007.
DOI 10.1002/mrm.21432
Published online in Wiley InterScience (www.interscience.wiley.com).
© 2008 Wiley-Liss, Inc.
tracts pharmacokinetic parameters that should be independent of data acquisition details, contrast agent dose
and injection rate, magnetic field strength, and vendor
platform, etc. This improves DCE-MRI study reproducibility and enables meaningful comparison of results from
different imaging sites where different DCE-MRI protocols
are employed. The extracted pharmacokinetic parameters
are usually variants of: Ktrans, a rate constant for contrast
agent plasma/interstitium transfer, and ve, the interstitial
space volume fraction (the putative contrast agent distribution volume). These parameters have been used for cancer diagnosis (2– 4) and monitoring effects of antiangiogenic therapies (5,6).
A characteristic aspect of pharmacokinetic modeling of
DCE-MRI signal time course is the requirement for an
arterial input function (AIF). The absolute accuracy of the
pharmacokinetic parameters, Ktrans and ve, depends on the
AIF accuracy (7,8). Ideally, individually measured AIF
should be used for kinetic modeling of the corresponding
tissue DCE-MRI data in each experiment (3,7,9). However,
for patient studies that are conducted in clinical settings, it
is often not possible to obtain reliable AIF measurement in
each DCE-MRI examination, due to either data acquisition
constraints, such as excitation volume coverage and image
slice angulation, or lack of a visible artery that is anatomically adjacent to the tissue of interest. One solution to
such problem is to generate a population-based average
AIF for kinetic modeling of DCE-MRI data when an individual AIF is not obtainable. One recent study (10) shows
that use of a population-averaged AIF reduces variability
and improves reproducibility of DCE-MRI pharmacokinetic model parameters.
Using a semiquantitative approach, we reported that the
histogram amplitude of the initial slope of DCE-MRI signal
time course correlates significantly with necrosis percentage of osteogenic and Ewing sarcoma, which is an important indicator of the effectiveness of chemotherapy (11). In
determination of AIF for absolute quantitation of Ktrans and
ve, it is often not possible to perform reliable AIF measurement in each individual osteosarcoma DCE-MRI experiment. Also, the contrast agent injection rate may not be
consistent between studies because of variations in location and size of intravenous (IV) catheters that are already
in place before patients undergo DCE-MRI studies. In this
preliminary study, we sought to assess the feasibility of
using an average AIF obtained from a limited population
of osteosarcoma patients for kinetic modeling of DCE-MRI
data of a larger population, as well as to assess the effects
of different injection rates (1 cc/s and 2 cc/s) on determination of pharmacokinetic parameters.
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FIG. 1. a: A postcontrast sagittal image extracted from a multislice DCE-MRI acquisition, showing an osteosarcoma in the distal femur and
the adjacent femoral artery. The AIF data points were obtained from the red ROI placed within the artery. b: An AIF plot (plasma Gd-DTPA
concentration time course). The contrast washout phase was fitted with a biexponential decay function.
MATERIALS AND METHODS
Patients
Prior to definitive surgery, 16 patients (mean age ⫽
16 years, range ⫽ 10 –29 years) with osteosarcomas in the
lower extremity underwent a routine clinical MRI protocol, in which a DCE-MRI scan was added for the purpose
of evaluating the efficacy of chemotherapy in inducing
tumor necrosis. The DCE-MRI study was conducted under
an Internal Review Board–approved protocol, and the
written consent was obtained from each patient prior to
the DCE-MRI scan.
Data Acquisition
All the MRI studies were performed with a 1.5T GE Excite
system (General Electric Medical Systems, Milwaukee, WI,
USA). An extremity knee coil was used for RF transmission and reception. Before the DCE-MRI study was conducted, a standard clinical MRI exam was performed
through the tumor. Axial T1-weighted and fat-suppressed
fast spin-echo T2-weighted images were obtained with as
small a field of view (FOV) as possible. Longitudinal (coronal and/or sagittal) T1-weighted and fat-suppressed fast
spin-echo T2-weighted images through the entire bone
were also obtained with a small FOV. These were followed
by proton density MRI and the T1-weighted DCE-MRI
study in the sagittal direction, and then by postcontrast
axial fat-suppressed T1-weighted MRI. For DCE-MRI data
acquisition, a fast multiplanar spoiled gradient echo sequence was employed with flip angle (␣) ⫽ 30°, TE ⫽
2.9 ms, TR ⫽ 7.5–9.0 ms, FOV ⫽ 20 –24 cm, and matrix
size ⫽ 256 ⫻ 128 zero filled to 256 ⫻ 256 during image
reconstruction. The entire tumor was imaged with eight to
11 sagittal slices of 10 –12-mm thickness and zero gap. The
total DCE-MRI acquisition time was about 5–7 min with
7–10 s temporal resolution and 30 – 60 time course data
points. At the beginning of the sixth image set (data point)
acquisition, gadopentrate dimeglumine (Gd-DTPA) con-
trast agent (Magnevist; Berlex Laboratories, Wayne, NJ,
USA) at a dose of 0.1 mmol/kg was administered intravenously with a rate of 1 cc/s or 2 cc/s by a MR-compatible
programmable power injector (Spectris; Medrad, Indianola, PA, USA). Besides the MRI examination during the
visit, a patient usually also underwent other clinical procedures and often arrived at the MRI suite with the IV
catheter already in place. The injection rate was determined according to the location and the size of the IV
catheter. Proton density images were acquired for the purpose of determining the longitudinal relaxation rate constant, R1, for each DCE-MRI data point, using the same
pulse sequence with ␣ ⫽ 30°, TE ⫽ 2.0 ms, TR ⫽ 350 ms,
and DCE-MRI-matching slice number, thickness, and location.
DCE-MRI Data Analysis
For AIF determination, whenever a femoral artery adjacent
to the tumor was clearly visible in the DCE-MRI image
series, a manually drawn region of interest (ROI) was
placed within the artery and the signal time course was
obtained. Figure 1a shows the relative locations of the
knee region osteosarcoma and the femoral artery of a patient, as well as the ROI placement (in red) for AIF sampling. Due to the nature of multislice acquisition for DCEMRI and angulation of the sagittal slices, the femoral artery
was not always clearly visible, depending on the relative
locations of the osteosarcoma and the artery. Reliable ROI
signal time courses were obtained in only five patients
with 1 cc/s contrast injections and five patients with 2 cc/s
injections.
To obtain the AIF, the signal time course needs to be
converted to plasma contrast concentration time course.
Assuming TE ⬍⬍ T2, the signal intensity (S) of a spoiled
gradient echo sequence is given by (12):
1 ⫺ exp共 ⫺ TR/T1兲
S ⫽ S 0 sin ␣
,
1 ⫺ cos ␣ exp共 ⫺ TR/T1兲
[1]
AIF for Osteosarcoma DCE-MRI
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where S0 is a constant proportional to the proton density of
the sample. By comparing the S values of the artery ROI
from the DCE and proton density images, R1 (⬅ 1/T1) for
each time point of the DCE series can be theoretically
derived using Eq. [1]. To correct for possible errors in T1
calculation likely caused by imperfect slice profile, a calibration curve of signal intensity ratio of T1-weighted image over proton density image vs. T1 was constructed using
a method introduced by Parker et al. (13). A total of 12 agar
gel phantoms doped with various concentrations of GdDTPA were imaged with the same pulse sequence and
acquisition parameters as those used for DCE and proton
density MRI. The T1 values for each phantom were first
measured using an inversion-recovery spectroscopy sequence, covering a range of 105 to 2224 ms. The 12 data
points were empirically fitted with a biexponential function with offset (13) to generate the calibration curve. The
artery ROI R1 values for the DCE image series were obtained from the calibration curve and then converted to
Gd-DTPA concentrations using the following linear equation:
R 1 ⫽ r1 䡠 Cp共t兲 ⫹ R10 ,
[2]
where Cp(t) is the arterial plasma Gd-DTPA concentration
at time t, r1 is the contrast agent relaxivity, which was
taken to be 4.1 s–1(mmol/liter)–1 at 1.5T (14), and R10 is the
precontrast R1. The derived Cp(t) time course was fitted
with a biexponential decay function in the washout phase
(15) to generate the AIF. Figure 1b shows an AIF from a
DCE-MRI study with a 2 cc/s contrast injection rate. By
simple averaging of the five individual AIFs (Ind-AIFs) at
each injection rate (1 cc/s and 2 cc/s) with peak height
aligned, average AIFs (Avg-AIFs) for the two injection
rates were obtained.
For the tumor tissue DCE-MRI time course data, an ROI
was manually drawn circumscribing the contrast-enhanced tumor for signal intensity measurement. The signal
intensity time course was converted to R1 time course
using the signal ratio of the T1-weighted DCE image over
the proton density image and the T1 calibration curve, and
subsequently converted to tumor tissue– contrast agent
concentration, Ct(t), time course with Eq. [2] by substituting Cp(t) with Ct(t). An in-house IDL (version 6.0; Research
Systems, Boulder, CO, USA) program based on the Toft’s
(16) model was used to fit the Ct(t) time course for the
extraction of the Ktrans and ve parameters, as shown in the
following Kety-Schmidt type of rate law equation:
FIG. 2. Average AIFs for 1 cc/s and 2 cc/s contrast injection rates
obtained from five individually sampled AIFs of the corresponding
injection rates, respectively.
data. Exclusion of the vp parameter may cause significant
errors, however, when there is less contrast extravasation,
such as when Ktrans ⬍ 0.001 min–1. For each DCE MRI data
set of this study, since the arrival of AIF peak amplitude
preceded the apparent rise of tumor tissue signal intensity,
defined as signal intensity rising more than one standard
deviation (SD) of the signal intensities of the five precontrast injection baseline data points, only the biexponential
function-fitted AIF washout phase was used for Ct(t) curve
fitting with time zero in Eq. [3] set as the time of AIF peak
amplitude. Both Ind-AIFs (on the 10 corresponding patient
data sets) and Avg-AIFs (on all 16 patient data sets) were
used for the pharmacokinetic modeling analyses of the
tumor tissue time course data, which were performed for
the ROI, as well as each image pixel within the ROI. For
the latter approach, histogram analyses (11) of the pixel
Ktrans and ve were performed and the median values of
these parameters were calculated. For the purpose of this
study, only the image slice that included the center portion of the tumor was used for data analysis.
Student’s paired t-test was used to evaluate differences
in pharmacokinetic parameters resulted from the use of
Ind- and Avg-AIFs of the same contrast injection rate, as
well as differences resulted from the use of Avg-AIFs of the
two injection rates.
RESULTS
t
冕
C t共t兲 ⫽ Ktrans
Cp共t⬘兲exp共 ⫺ Ktransve⫺1 共t ⫺ t⬘兲兲dt⬘.
[3]
0
The addition of the term that includes plasma volume
fraction (vp), vpCp(t), is ignored on the right side of this
equation. Li et al. (17) have recently shown that when
there is sufficient contrast agent extravasation from plasma
to interstitium, such as in tumor tissue, the Ktrans and ve
parameters are adequate for kinetic modeling of DCE-MRI
Figure 2 shows the biexponential function-fitted Avg-AIFs
for 2 cc/s (solid line) and 1 cc/s (dotted line) contrast
injection rates, respectively. The two Avg-AIFs have almost the same maximum Gd-DTPA concentration, with
minor wash-out shape mismatch. Figure 3 shows scatter
plots of tumor tissue ROI Ktrans parameters (Fig. 3a) and
median values of Ktrans histograms (Fig. 3b) obtained from
kinetic modeling with the Ind-AIFs and the Avg-AIF for
the five patients who had a 2 cc/s contrast injection rate.
The straight lines connect the data points from the same
patient. There are no statistically significant differences
between Ktrans parameters derived with Ind-AIFs and those
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FIG. 3. Scatter plots of Ktrans parameters obtained from single-image slice pharmacokinetic modeling analyses of five DCE-MRI studies (five
patients) with 2 cc/s contrast injection rate. Kinetic analyses using the individually measured AIF (Ind-AIF) and the average AIF (Avg-AIF)
were performed for each study: (a) tumor tissue ROI analysis; (b) median value of histogram analysis of pixel Ktrans parameters within the
ROI. The straight lines connect data points from the same study.
derived with the Avg-AIF (P ⫽ 0.45 for ROI Ktrans and 0.37
for histogram median Ktrans, paired t-test). The comparisons between the Ktrans and ve parameters obtained with
the Ind-AIFs and those obtained with the Avg-AIFs for
both injection rates are summarized in Table 1. At either
injection rate, no significant changes in Ktrans and ve parameters occurred when the Avg-AIF was used for kinetic
modeling. Figure 4 displays the representative graphs for a
patient who had a 2 cc/s contrast injection, showing pixel
Ktrans (Fig. 4a) and ve (Fig. 4b) values within the tumor
tissue ROI obtained with the Avg-AIF plotted against those
obtained with the Ind-AIF. There were a total of 1940
pixels within the ROI. Both plots demonstrate significant
linear correlations (P ⬍ 0.0001) with the slope values close
to one (1.048 for Ktrans and 1.064 for ve). Similar results
were obtained from the other nine patients with reliable
Ind-AIF measurements at either injection rate. This indicates that the use of Avg-AIF works equally well for both
ROI and pixel-by-pixel data analyses.
Figure 5 shows scatter plots of tumor ROI Ktrans parameters (Fig. 5a) and median values of Ktrans histograms (Fig.
5b) obtained from kinetic modeling with the two Avg-AIFs
for all 16 patients. There are no statistically significant
differences between the two sets of Ktrans parameters (P ⫽
0.92 for ROI Ktrans and 0.86 for histogram median Ktrans)
and ve parameters (plots not shown here. P ⫽ 0.74 for ROI
ve and 0.79 for histogram median ve).
DISCUSSION AND CONCLUSIONS
Previous studies (3,7,9) suggest that AIF should be individually monitored if accurate kinetic modeling of DCEMRI time course data is desired. However, for clinical
DCE-MRI studies, because of data acquisition constraints
or lack of a visible artery adjacent to the tissue of interest
(TOI), it is not practical to measure individual AIF for each
DCE-MRI scan. Therefore, it is important to find a reasonable AIF substitute for pharmacokinetic modeling when
individual AIF measurement is not achievable. Works by
Yankeelov et al. (18,19) and Walker-Samuel et al. (20,21)
show that if a reliable AIF is not available, a reference
region model is a reasonable alternative for measuring
DCE-MRI kinetics. In this model, however, literature values of Ktrans and ve have to be assigned to a reference tissue
and the assumption that the TOI and the reference tissue
share the same AIF has to be made. The TOI Ktrans and ve
parameters can be extracted by comparing the curve
shapes of the TOI and the reference tissue. Another study
by Yang et al. (22) proposed a similar approach using a
double-reference method, in which two reference tissue
Table 1
Comparisons of Ktrans and ve Obtained with Ind-AIF and Avg-AIF*
Contrast injection rate
2 cc/s
Ktrans
(min⫺1)
ROI
Histogram median Ktrans (min⫺1)
ROI ve
Histogram median ve
1 cc/s
Ind-AIF
Avg-AIF
Ind-AIF
Avg-AIF
0.87 ⫾ 0.55
0.57 ⫾ 0.31
0.58 ⫾ 0.17
0.59 ⫾ 0.19
0.76 ⫾
0.61 ⫾ 0.35b
0.59 ⫾ 0.14e
0.61 ⫾ 0.17f
0.61 ⫾ 0.56
0.55 ⫾ 0.53
0.50 ⫾ 0.24
0.50 ⫾ 0.23
0.66 ⫾ 0.66c
0.52 ⫾ 0.48d
0.49 ⫾ 0.26g
0.50 ⫾ 0.27h
0.46a
*Mean ⫾ SD; Student’s paired t-test for Ktrans and ve values obtained with Ind-AIF and Avg-AIF at each injection rate: aP ⫽ 0.45, bP ⫽ 0.37,
cP ⫽ 0.56, dP ⫽ 0.65, eP ⫽ 0.93, fP ⫽ 0.48, gP ⫽ 0.69, hP ⫽ 0.88.
AIF for Osteosarcoma DCE-MRI
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FIG. 4. Scatter plots of pixel (a) Ktrans and (b) ve parameters obtained from single-image slice pharmacokinetic modeling analyses of the
DCE-MRI data from one patient with a 2 cc/s contrast injection. The Ktrans and ve parameters extracted with the average AIF (Avg-AIF) are
plotted against those extracted with the individually measured AIF (Ind-AIF). The analyzed pixels (N ⫽ 1940) are within the tumor tissue ROI.
The solid straight lines represent linear correlations, while the dashed ones are lines of identity.
regions are assumed to have the same pharmacokinetic
parameters and share the same AIF with the TOI. The AIF
is extracted by comparing the curve shapes of the two
reference tissues and then used for kinetic modeling of the
TOI. The advantage of these two reference methods is that
a direct AIF measurement is not required. However, both
methods require assumptions that do not necessarily reflect the actual tissue pharmacokinetic characteristics, and
may introduce errors in kinetic modeling. Through direct
AIF measurement, our preliminary results suggest that it is
reasonable, as well as practical, to use a limited-population-based Avg-AIF for pharmacokinetic modeling of osteosarcoma DCE-MRI data from a large population when it
is not possible to measure Ind-AIF in each patient. This
finding is consistent with that of a recent study (10), which
demonstrates improved reproducibility in DCE-MRI kinetic modeling using a population-averaged AIF. Limitedpopulation-averaged AIF was also used in a breast DCEMRI study (4) for data analysis of each acquisition, though
comparison of extracted pharmacokinetic parameters resulted from the use of Avg-AIF and Ind-AIFs were not
described. The Avg-AIF approach probably works the best
for the extremity region that is far away from the heart,
with the dispersion effects reducing intersubject differences in Ind-AIF. For anatomical regions closer to the
heart, such as head, neck, and breast, the use of Avg-AIF
may cause errors in kinetic analysis due to large intersubject differences in Ind-AIF. It is important to note that the
results of this study were obtained under the conditions
where the osteosarcomas were located in the knee area and
FIG. 5. Scatter plots of Ktrans parameters of all 16 patients obtained from pharmacokinetic modeling analyses using the average AIFs
(Avg-AIFs) for 1 cc/s and 2 cc/s injection rates: (a) tumor tissue ROI analysis; (b) median value of histogram analysis of pixel Ktrans
parameters within the ROI.
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the AIF data samplings were all performed in the femoral
artery. Our experience has shown that AIFs sampled from
the femoral artery for DCE-MRI data analysis of tumors
located in the knee area are not similar to those sampled
from different arteries adjacent to osteosarcomas in other
anatomical regions, such as the ankle (Huang W, Wang Y,
Koutcher JA., unpublished results). Therefore, an Avg-AIF
used for knee area osteosarcoma DCE-MRI data analysis
should not be used for the same purpose for ankle area
osteosarcoma or tumors in other anatomical regions. We
are currently evaluating AIFs in different anatomic regions
(Dave A, Lee N, Stambuk H, Wang Y, Huang W, Koutcher
JA., unpublished results). In Ind-AIF measurements from a
major artery, partial volume effects are largely avoided by
selecting the image slice that contains the central portion
of the artery and placing the ROI for AIF sampling well
within the artery. Despite the use of short TR values employed in the spoiled gradient echo type sequence, the
in-flow effects are minimized because the image slice for
AIF measurement is usually located near the central position of the multislice acquisition volume and the ROI for
AIF sampling is placed as far away from the edge of the
FOV as possible.
Clinical requirement for diagnosis often dictates large
imaging spatial coverage of the TOI and high image inplane resolution, which result in poorer temporal resolution for DCE-MRI acquisition. It is reported by Roberts et
al. (23) that insufficient data sampling (poor temporal resolution) of the single-bolus AIF may lead to large errors in
kinetic modeling of DCE-MRI data. Our approach of using
limited-population-based Avg-AIF provides opportunity
to improve accuracy of quantitative analysis of osteosarcoma DCE-MRI data that were acquired with poorer temporal resolution, but otherwise with the same data acquisition scheme and contrast injection setup as the current
study.
The Avg-AIFs of 1 cc/s and 2 cc/s contrast injection rates
in the femoral artery in the knee area have similar peak
amplitude and decay curve shape, and the use of each for
kinetic modeling of all 16 patient data sets does not seem
to cause significant changes in derived pharmacokinetic
parameters. This may be due to the small difference in the
injection rate. Following bolus contrast administration
with 2 cc/s injection rate (mostly in a peripheral vein) or
with 1 cc/s injection rate (mostly in a central vein), the
dispersion effects may be to such an extent, that by the
time the Gd-DTPA contrast agent reaches the part of femoral artery in the knee area, the differences between 1-cc/s
and 2 cc/s injection rates may not be detectable in AIF data
sampling. It is quite possible that a very different AIF
curve shape will be generated if a significantly higher
contrast agent injection rate is employed. For this osteosarcoma DCE-MRI study, only the 1 cc/s and 2 cc/s injection rates are employed clinically depending on the location and size of IV catheter. The findings in this work
indicate that Avg-AIFs derived from 1 cc/s and 2 cc/s
contrast injection rates may be used interchangeably for
kinetic modeling of DCE-MRI data when individual AIF
measurement is not feasible. For quality control purposes,
the contrast agent delivery process should be standardized
for the DCE MRI study, such as the use of a power injector,
the timing of injection, the volume of saline flush, etc.
Wang et al.
With the limited study population of 16 patients, the AvgAIF of each injection rate was obtained from only five
Ind-AIFs. Further validation of the Avg-AIF approach with
a larger population and in multiple regions is desirable.
In this study, a biexponential decay function was used
to fit the AIF data points. In a DCE-MRI study of the knee
in children with juvenile rheumatoid arthritis (24), the AIF
data points were sampled from a popliteal artery and fitted
with three different functions: triexponential, gamma-variate plus biexponential, and biexponential. Statistically significant differences in extracted pharmacokinetic parameters were found between the biexponential fitting and the
other two fittings, suggesting that choice of curve fitting
function for AIF data points can cause systemic errors.
However, the ultimate goal of our osteosarcoma DCE-MRI
study is to assess the efficacy of chemotherapy as a longitudinal study prior to definitive surgery. As long as the
biexponential function is consistently used for AIF curve
fitting, any systematic error that might be introduced by
the selection of such function should not impede the longitudinal comparisons of changes in pharmacokinetic parameters that are caused by chemotherapy treatment.
ACKNOWLEDGMENTS
We thank Drs. Amita Dave and Yousef Mazaheri Tehrani
for providing the data for the T1 calibration curve, and Ms.
Melissa Potuzak for managing the patient data base and the
IRB protocol.
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