Sudhanya Chatterjee1, Dattesh D Shanbhag1, Venkata Veerendranadh Chebrolu1, Uday Patil1, Sandeep N Gupta2, Moonjung Hwang 3, Jeong Hee Yoon4, Jeong Min Lee4, and Rakesh Mullick1
1GE Global Research, Bangalore, India, 2GE Global Research, Niskayuna, NY, United States, 3GE Healthcare, Seoul, Korea, Republic of, 4Seoul National University Hospital, Seoul, Korea, Republic of
Synopsis
Main aim of this research is
to investigate a source separation based approach to remove
noise from true signal, while maintaining original tissue enhancement
signature. It is based on the hypothesis that there exists overlapping temporal
information in the DCE-MRI data, which if identified, can be used for filtering
noise out of the true concentration data. We demonstrate the utility of source separation
and subsequent weight estimation methodology to filter “noise” from DCE
concentration data and impact on the pK model parameters in liver DCE-MRI.Purpose
Dynamic
contrast enhancement (DCE) MRI has been used to study tumor microvasculature in
terms of parametric measures such as blood flow and rate of leakage [1]. In
practice, to maintain high temporal resolution and derive relevant parameters accurately
(e.g. wash-in rate), SNR of DCE-MRI acquisition is compromised and can be
worsened by fast imaging artifacts (e.g. streaking due to under-sampling) [2]. Consequently,
pharmaco-kinetic (PK) model [1] fit to DCE data is poor and results in
“pixelated” maps even within a homogenous tissue. Moreover, if such DCE temporal
data is used for subtraction from baseline, it can obscure a lesion and reduce
clinical confidence in the results. Previously, an ICA based source separation
method was investigated for removal of under-sampling artifacts [2]. One
problem with generic ICA based methods is that they can result in negative
source component(s), which can be difficult to interpret physiologically and more
importantly result in loss of useful information from remaining components. Since
DCE images are typically fit to PK model and shape correlated to tumor
malignancy [8], it is essential that all sources are non-negative and
repetitive to ensure physiological plausibility while maintaining the shape of
DCE curves. One source separation method which can mathematically satisfy these
conditions is Convex Analysis of Mixtures of Nonnegative Sources (CAMNS) [3,4].
In this work we demonstrate the utility of CAMNS and subsequent weight
estimation methodology to filter “noise” from DCE concentration data and its
impact on the PK model parameters in liver
DCE-MRI.
Methods
MRI Data: MRI data for this study was obtained on 1.5T GE SIGNA
EXCITE and GE Signa HDxt system (GEHC) for patients with liver fibrosis (N =2)
and tumor (N =1). The liver-DCE protocol was: 3D SPGR sequence, TE/TR =
1.12/4.8 to 1.3/4.5 ms , FA = 15-30°, FOV = 400 to 460 mm2, 32 to 82
bolus volumes. DCE
signal data was converted into concentration units using the baseline images
and fixed tissue T
1 (1.5T = 550 ms, 3T = 800ms) and used for
CAMNS based filtering. Physiologically implausible voxels were removed.
CAMNS based filtering: CAMNS algorithm
was used to identify underlying sources
of DCE-MRI concentration data. Three sources were assumed: vascular, leakage
and noise [5]. Once sources are
obtained, a constrained optimization problem is solved to calculate weights
corresponding to the sources as follows: If data is $$$ X \varepsilon \Re^ {m x n} $$$ , (m
= bolus phases, n = no. of voxels) and p-sources $$$ s
\varepsilon \Re^{m x p} $$$, weights w calculated by solving $$$
\min_{w} \left \| X-ws \right \| $$$ such that w $$$
\geqslant 0 $$$.
Components corresponding
to
jth source
are obtained by multiplying
jth row of
w with
jth column of
s. Thus,
we reconstruct 4D-data as: Data
4D, filtered = $$$ \sum_{\forall i }^{ } component_i $$$ where
is the
componenti corresponding to the
ith source. It
should be noted that weight estimation is done over the entire FOV, including
those voxels which were removed because of being implausible or extremely noisy
from DCE standpoint.
PK Fitting: The
original 4D concentration data and post CAMNS filtered concentration data were
both fit using a dual input (aorta and portal vein), single compartment Materne
model [6].
Coefficient
of determination (
R2) metric
was computed at each voxel to assess goodness-of-fit before and after CAMNS
based filtering. The methodology was implemented using functionality in ITK [7].
Results
Figure 1 shows a representative liver tumor case.
Notice that extremely noisy voxels (Figure 1B) have some DCE relevant shape, but
nevertheless cannot be filtered using direct filtering methods. Sources from
CAMNS (Figure 1C) are used to estimate source weights (Figure 1D). Figure 2
demonstrates efficacy of CAMNS approach in filtering DCE curves with
different wash-in and wash-out characteristics [8]. Notice that important characteristics
such as bolus arrival time, initial upslope and wash-out characteristics are
well preserved by CAMNS based filtering. We did notice that in voxels
which represent AIF shape (sharp peak), there was a tendency to smoothen the peak (Figure 2c is good
example). Further investigation indicated that number of AIF–like voxels was
very less in data provided to CAMNS (over-aggressive pre-filtering was responsible).
Figure 3 demonstrates that R
2 metric
for PK fit on CAMNs filtered data (Figure 3D) is much higher (0.99) as compared
to R
2 obtained with original concentration data (~0.7) (Figure 3C). Figure 3B (arrow) also demonstrates
how some of missing K
trans signatures are highlighted post
filtering.
Conclusion
CAMNS based filtering improves accuracy of DCE-MRI quantification and will enhance confidence of clinicians in DCE data and PK maps.
Acknowledgements
No acknowledgement found.References
[1]. Tofts, PS et.al., JMRI, 10, no. 3 (1999): 223-232.
[2]. Martel AL, Magn Reson Med 59:874–884, 2008
[3]. IEEE TRANSACTIONS ON SIGNAL
PROCESSING, VOL. 56, NO. 10, PART 2, PP. 5120-5134, OCT. 2008
[4]. Palomar, Daniel P., and Yonina C. Eldar. Convex
optimization in signal processing and communications. Cambridge university
press, 2010.
[5].
Li Chen, IEEE
TRANSACTIONS ON MEDICAL IMAGING, VOL. 30, NO. 12, DECEMBER 2011
[6]. Materne R, Magnetic Resonance in Medicine 47:135–142
(2002)
[7]. www.itk.org
[8]. Johnson, Linda M., Baris Turkbey, William D. Figg, and
Peter L. Choyke. "Multiparametric MRI in prostate cancer management."
Nature Reviews Clinical Oncology 11, no. 6 (2014): 346-353.