Jenny Chen1, Mari Hagiwara1, Artem Mikheev1, Henry Rusinek1, Jean Logan1, Elcin Zan1, and Sungheon Gene Kim1
1Department of Radiology, NYU School of Medicine, New York, NY, United States
Synopsis
Detecting malignant lymph nodes in the neck remains a challenge such
that most patients still have to consider aggressive treatment including nodal
dissection. In this study, we used positron emission tomography with
18F-fluorodeoxyglucose (18F-FDG PET), dynamic contrast enhanced
magnetic resonance imaging (DCE-MRI), and diffusion weighted imaging (DWI) to
assess head and neck cancer cases and consider the feasibility of using all
three imaging methods in synergy to assess the regional lymph nodes.
Introduction
Accurate identification of nodal metastasis is a crucial step for
treatment planning and evaluation of therapy response in head and neck cancer
(HNC). Dynamic contrast enhanced (DCE)-MRI has been used to measure blood
flow-related parameters (perfusion, permeability, vascular volume, etc.), in
order to assess tissue microenvironment in cancerous regions. 18F-fluorodeoxyglucose
(FDG) PET provides visualization of cancer cells which has abnormally high
rates of glucose metabolism while diffusion weighted imaging (DWI) provides
visualization of tumor lesions with restrict diffusion. This study aims to use
all three imaging methods by obtaining quantitative DCE-MR and FDG PET kinetic
parameters and apparent diffusion coefficient (ADC) to assess the nodal
metastasis in HNC.Methods
This study recruited HNC patients (n=23) who were scheduled for node
dissection surgery. Within one week before their planned surgery, each patient had
one research PET/MR scan. This was done with a whole body 3T PET/MR scanner
(Biograph mMR, Siemens Healthcare). Using the conventional neck lymph node
levels, the locations of removed lymph nodes were noted during the dissection
surgery so multi-parametric MRI measures can be compared to pathology by lymph
node levels.
The PET scan started 1 minute before the injection of PET tracer
(10 mCi of 18F-FDG) and was acquired dynamically for 60 minutes. The
attenuation correction map was generated using T1-weighted Dixon gradient-echo
sequence water and fat images and was used to reconstruct 127 axial slices with
344x344 matrix at 2x2x2 mm3 voxel size on the vendor platform.
Fractional uptake value (ki) of tumor regions of interest (ROIs)
were estimated using the two compartment model 1 in FireVoxel (https://wp.nyu.edu/firevoxel) with the
individual specific arterial input function (AIF) automatically generated using
the principal component analysis (PCA) method 2.
During PET acquisition, DCE-MRI scan was acquired simultaneously
using a golden-angle radial 3D gradient echo sequence with TR/TE = 3.6/1.7 ms,
FA=10deg, Res=1x1x2mm. The baseline was acquired for 1 minute followed by
injection of a dose of GD-DTPA (1mM/kg body weight) at 1mL/s into an
antecubital vein then a saline flush, after which the scan continued for 9
minutes. The dynamic images were reconstructed using the GRASP method 3.
Plasma volume fraction (vp), interstitial volume fraction (ve), and
permeability-surface area product (PS) of ROIs were estimated for each ROI
using two-compartment model 4 in FireVoxel with the arterial input function
(AIF) automatically generated using PCA 2.
DWI data were
also acquired during the PET acquisition using a twice-refocused spin echo
sequence with echo planar readout and
b-values ranging from 200 to 800; a total of 10 images including the b0 image. Apparent
diffusion coefficient (ADC) image was generated using FireVoxel.
Due to long scan time, there were inevitable motion and
misalignment between datasets, so image coregistration across and within
modalities is essential to making sure ROIs cover the same areas in each image.
DCE-MRI images were coregistered to a specific frame after the baseline which
is referred to as the fixed frame using SimpleITK’s SimpleElastix (https://simpleelastix.github.io/) with
parameters from its database (http://elastix.bigr.nl/wiki/index.php/Par0048). DWI volumes
were registered to the fixed DCE-MRI frame using SimpleITK to reslice DWI to
match DCE-MRI voxel dimensions and to register each DWI with metric as mutual
information, optimizer as regular step gradient descent, and interpolator and
transform as b-spline. PET images were registered to the fixed DCE-MRI using
FireVoxel.Results
Figure 1 shows an example of a case with PET and DWI images aligned to the
DCE-MRI fixed frame. Representative PET and DCE-MRI data and their kinetic
model fits are shown in Figure 2. The pharmacokinetic model analysis of dynamic
PET data shows that the fractional uptake in metastatic regions is
significantly higher (p<0.001) (Fig.3). In DCE-MRI there was statistical
significance in vp where the mean of vp is lower in
metastatic nodes than that of normal nodes (p=0.0384). Additionally, ve
and PS are both significantly lower in metastatic nodes (p<0.001). DWI data show
that there is no statistical significance for higher 10th percentile and median
diffusivity in metastatic areas. However, there is a significantly higher 90th
percentile diffusivity in metastatic nodes (p=0.0234). The results are
summarized in Table 1.Discussion and Conclusion
The kinetic parametric data from PET and DCE along with ADC from DWI
were successfully obtained and used to show how individual parameters can be
used to classify metastatic nodes. Future studies can further explore the
reproducibility of these results and obtain voxel-by-voxel parametric maps for measurement
of tumor heterogeneity and their association with histological properties.Acknowledgements
NIH grants R21CA188217, R01CA160620, R01CA219964, UG3CA228699References
-
Sokoloff, Louis, et al. "The
[14C] deoxyglucose method for the measurement of local cerebral glucose
utilization: theory, procedure, and normal values in the conscious and
anesthetized albino rat." Journal of neurochemistry 28.5 (1977):
897-916.
- Sanz‐Requena R, Prats‐Montalban JM,
Marti‐Bonmati L, et al. Automatic individual arterial input functions
calculated from PCA outperform manual and population‐averaged approaches
for the pharmacokinetic modeling of DCE‐MR images. J Magn Reson Imaging.
2015;42(2):477‐487.
- Feng L, Grimm R, Block KT,
Chandarana H, Kim S, Xu J, Axel L, Sodickson DK, Otazo R. Golden-angle
radial sparse parallel MRI: combination of compressed sensing, parallel
imaging, and golden-angle radial sampling for fast and flexible dynamic
volumetric MRI. Magn Reson Med 2014;72(3):707-717.
- Flouri, D. , Lesnic, D. and
Sourbron, S. P. (2016), Fitting the two‐compartment model in DCE‐MRI by
linear inversion. Magn. Reson. Med., 76: 998-1006.