Dynamic Contrast-enhanced CT of Head and Neck Tumors

Rationale and Objectives

To evaluate the interchangeability of perfusion parameters between two software packages for the postprocessing of dynamic contrast-enhanced (DCE) computed tomographic images of head and neck tumors.

Materials and Methods

DCE computed tomographic images of 75 patients with head and neck tumors were postprocessed using a software package based on the maximum-slope approach and Patlak analysis, as well as a software package with deconvolution-based analysis incorporating an adiabatic approximation of tissue homogeneity (ATH) model. The evaluated perfusion parameters included blood flow ( F ), blood volume ( v ), and permeability–surface area product ( PS ). Region-of-interest (ROI) analysis of the tumors and the metastatic lymph nodes was performed. The perfusion parameters were compared using the Wilcoxon matched-pairs test and Bland-Altman plots.

Results

One hundred fifty-two ROIs of tumors and nodes were outlined and analyzed. Moderate to good correlations were demonstrated between the various perfusion values ( r = 0.56–0.72, P < .0001). The Wilcoxon test revealed a significant difference between the two methods ( P < .001), with the F , v , and PS values obtained using the maximum-slope approach and Patlak analysis higher than those obtained using deconvolution-based analysis with the assumptions of the ATH model. The Bland-Altman plots for F and v values revealed a proportionality trend with outliers, which were strongly associated with the magnitudes of the parameters. Analysis of the PS values did not show any systematic bias.

Conclusion

There were significant differences in the perfusion parameters obtained using the two software packages, and thus, these parameters are not directly interchangeable.

Since the proposal of dynamic contrast-enhanced (DCE) computed tomography (CT) to assess tissue perfusion in the late 1970s, developments in multislice computed tomographic technology have allowed DCE CT to achieve better coverage as well as higher temporal (approaching subsecond) and spatial (submillimeter) resolutions. Furthermore, advances in antiangiogenesis therapy for tumors have drawn more attention to the use of DCE CT to generate functional images of tumors, to guide therapeutic decisions, and to monitor treatment effects.

Commercially available software packages for deriving first-pass parameters using DCE CT often attempt to compute summary parameters, such as the area under the curve and time to peak, whereas other, more theoretical approaches are based on assumptions such as an initial time interval with no venous outflow, a single pass of tracer with no recirculation, and no efflux of tracer back into the capillaries ( ). More recently, deconvolution approaches that derive physiologic parameters from the impulse residue function, R ( t ), of the tissue have been increasingly used ( ). The numerical deconvolution method has been the method of choice in several studies reported recently in the literature on head and neck perfusion imaging ( ). However, numerical deconvolution is very sensitive to noise and thus requires regularization, which imposes assumptions on the noise present or the desirable shape of R ( t ) ( ). The tracer extravasation parameters, which are strongly associated with the phenomenon of neoangiogenesis, are currently assessed using two distinct approaches in commercial software packages: (1) Patlak analysis and (2) deconvolution analysis on the basis of an adiabatic approximation of tissue homogeneity (ATH) model ( ).

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Materials and methods

Patients

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Perfusion Computed Tomographic Imaging Protocol

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Postprocessing of the Perfusion Computed Tomographic Data

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Statistical Analysis

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Results

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Blood Flow

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Blood Volume

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Permeability Values

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Discussion

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Appendix

Theoretical Background

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Deconvolution Analysis Based on the Central Volume Principle and an ATH Model

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Q(t)=FCa(t)⊗R(t)=Ca(t)⊗FR(t), Q

(

)

(

)

⊗

(

)

(

)

⊗

(

where ⊗ is the convolution operator.

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E=1−e−PSF. E

−

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Maximum-Slope Model and Patlak Graphic Analysis

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Q(t)=F∫t0Ca(t)dt−F∫t0Cv(t)dt, Q

(

)

∫

(

)

−

∫

(

)

where F is blood flow to the tissue. Assuming time t to be less than the minimum transit time needed for the contrast medium to traverse the tissue region and that all of the injected contrast medium will remain within the tissue (with no venous outflow), it is obvious that

dQ(t)dt=FCa(t), d

(

)

(

from which F can be calculated as

F=dQ(t)dtCa(t), F

(

)

(

)

or equivalently as

F=maximum initial slope ofQ(t)peak height ofCa(t). F

maximum initial slope of

(

)

peak height of

(

)

Q ( t ) and C a ( t ) can be estimated from the tissue and arterial tissue density curves, respectively ( ).

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Q(t)=VbCa(t)+K∫t0Ca(u)du. Q

(

)

(

)

∫

(

)

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References

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Dynamic Contrast-enhanced CT of Head and Neck Tumors

Rationale and Objectives

Materials and Methods

Results

Conclusion

Materials and methods

Patients

Perfusion Computed Tomographic Imaging Protocol

Postprocessing of the Perfusion Computed Tomographic Data

Statistical Analysis

Results

Blood Flow

Blood Volume

Permeability Values

Discussion

Appendix

Theoretical Background

Deconvolution Analysis Based on the Central Volume Principle and an ATH Model

Maximum-Slope Model and Patlak Graphic Analysis

References

Further Reading

Abstracts of Funded National Institutes of Health Grants

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