Dominick Jon Romano1, Qihao Zhang1, Ilhami Kovanlikaya2, Gloria Chia-Yi Chiang2, Pascal Spincemaille2, and Yi Wang3
1Biomedical Engineering, Cornell University, New York, NY, United States, 2Radiology, Weill Cornell Medical College, New York, NY, United States, 3Radiology, Cornell University, New York, NY, United States
Synopsis
A recently proposed perfusion analysis method, Quantitative
Transport Mapping (QTM), was applied in malignant Glioma (grade III and IV) to
obtain the flow speed map. Pathological lesions were separated into
DCE-Enhancing, Darkening, and total affected ROIs. The average speed scalar for
each ROI suggests that DCE-Enhancing tumor presents with significantly
increased speed while non-enhancing tumor does not experience a speed change.
Introduction
Perfusion
weighted imaging such as PET, DCE-CT, and DCE-MRI have become an important
component in the diagnosis diseases such as cancer (1). Quantifying perfusion parameters from the image maps
can potentially provide researchers and clinicians with tissue specific
information (1-3).
Gliomas are of particular interest, and are partially graded based upon
T2-FLAIR and DCE-MRI findings (2). Higher grade gliomas, called glioblastomas, are
characterized by a DCE-enhancing ring and
a non-enhancing surrounding region (4).
The observed DCE enhancement may likely be due to angiogenesis — vascular
formation due to hypoxic and growth signaling which is typical of tumors and more
pronounced for aggressive gliomas (5). In turn,
increased vascular density may induce greater flow speeds, defined as the magnitude of vascular velocity, in these regions] (6). To investigate this matter, QTM was applied to
obtain flow speed maps of the whole brain. ROI selection based on DCE
enhancement and tumor morphology allowed to split the pathological ROI into
Enhancing, non-enhancing, and total tumor regions. Our results show that
DCE-Enhancing glioma regions have significantly higher flow speeds compared to
contralateral normal appearing white matter regions. The non-enhancing regions
did not experience a change in flow speed.
Theory and Methods
The
tracer concentration is derived from the DCE-MRI intensity assuming a linear
relationship $$$c(\bf{r},t) \propto I(\bf{r},t)$$$ (7). The time-resolved concentration map was then
processed using QTM. This consists of fitting the concentration to the
transport equation in the convection dominant regime (7):
$$\partial_{r}c(\bf{r},t)=-\nabla\cdot(c(\bf{r},t)\cdot\bf{u}(\bf{r})) [1]$$
Where $$$\partial_{t}$$$ is the time derivative, $$$\nabla$$$, is the gradient operator with the following representation in cartesian coordinates ($$$\nabla \to (\partial_x,\partial_y,\partial_z)$$$) $$$c(\bf{r},t)$$$ is the tracer concentration spatiotemporal scalar field at voxel position $$$\bf{r} = (r^{x}, r^{y}, r^{z})$$$ encloeds in the volume $$$\omega \to (N_{x},N_{y},N_{z})$$$ along $$$(x,y,z)$$$ and time index $$$t\in [1,2,...,N_{t}-1]$$$ with $$$N_{t}$$$ as the number of frames in the acquisition the voxel velocity field is $$$\bf{u}(\bf{r})$$$ Eq.
1 is implemented
using finite differences. The resulting linear system is solved as
an optimization problem that is regularized using total variation with the
regularization parameter$$$\lambda = 10^{-4}$$$ chosen
according to the L-curve method as detailed in a previous QTM study (7):
$$\bf{u}=argmin_{\bf{u}} \sum_{t=1}^{N_{t} - 1}|\partial_{t}c+\nabla \cdot (c\bf{u})^{2}_{2}+\lambda |\nabla \bf{u}|_{1}| [2]$$
The speed scalar map is then the vector magnitude of the velocity, $$$u \equiv |\bf{u}|$$$ DCE-MRI images of high grade (III-IV) glioma patients (n=15)
were processed with QTM. For each case, an experienced radiologist selected total, enhancing, and non-enhancing tumor regions
along with a control region contralateral to the tumor (Figure 1). The
enhancing regions correspond to relatively hyperintense patterns post-contrast.
Non-enhancing regions are characterized by little to no enhancement observed in
the tumor region. Lastly, the control is defined as healthy tissue
contralateral to the tumor region. The speed was averaged over these regions and used for
statistical testing.
(:p<0.05, :p<0.01, :p<0.001) Results
Figure 2 shows that the entire tumor ROI displayed a
significantly higher average flow speed compared to the control. Furthermore,
flow speeds in DCE-enhancing regions were significantly higher compared to the
control, while non-enhancing speeds could not be distinguished from the control
distribution (Figure 2a, c). This result indicates that DCE-enhancing and non-enhancing
tumor regions have distinct flow speeds. To further investigate this idea, we
directly compare the enhancing and non-enhancing pathology. We found that the
enhancing region produced significantly higher flow speeds compared to non-enhancing
flow speeds (Figure 2b,c, p<0.001), indicating that a total tumor ROI
average of flow speed is not justified. DCE-Enhancing ROI averages had
significantly higher ROI speed averages compared to the total tumor regions
speed (Figure 2a,b, p<0.001). Meanwhile, DCE-
non-enhancing region speeds were significantly lower
than the total tumor region flow speed (Figure 3). These results suggest that
DCE-enhancing and non-enhancing tumor regions have differing biofluid properties.Discussion and Conclusion
Angiogenesis
is characteristic of high-grade gliomas (5,8).
The literature has reported increased blood flow (9) and perfusion in gliomas (2). Our observation of increased blood flow at
DCE-brightening further supports that gliomas change tissue perfusion as
detailed in previous reports and simulations (10,11). Interestingly, the DCE-non-enhancing region
experienced no change in flow speed magnitude. This is likely due to the
peripheral invasion and edema areas where nascent growth factor signals have
not yet taken effect. Our results suggest that blood flow speed is distinct to
DCE-enhancing regions, providing further insight into the interpretation of
glioma DCE-MRI images. Acknowledgements
No acknowledgement found.References
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