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Delta-projection Imaging on Contrast-enhanced Ultrasound to Quantify Tumor Microvasculature and Perfusion

Rationale and Objectives

The aim of this study was to assess the Δ-projection image processing technique for visualizing tumor microvessels and for quantifying the area of tissue perfused by them on contrast-enhanced ultrasound images.

Materials and methods

The Δ-projection algorithm was implemented to quantify perfusion by tracking the running maximum of the difference (Δ) between the contrast-enhanced ultrasound image sequence and a baseline image. Twenty-five mice with subcutaneous K1735 melanomas were first imaged with contrast-enhanced grayscale and then with minimum-exposure contrast-enhanced power Doppler (minexCPD) ultrasound. Delta-projection images were reconstructed from the grayscale images and then used to evaluate the evolution of tumor vascularity during the course of contrast enhancement. The extent of vascularity (ratio of the perfused area to the tumor area) for each tumor was determined quantitatively from Δ-projection images and compared to the extent of vascularity determined from contrast-enhanced power Doppler images. Delta-projection and minexCPD measurements were compared using linear regression analysis.

Results

Delta-projection was successfully performed in all 25 cases. The technique allowed the dynamic visualization of individual blood vessels as they filled in real time. Individual tumor blood vessels were distinctly visible during early image enhancement. Later, as an increasing number of blood vessels were filled with the contrast agent, clusters of vessels appeared as regions of perfusion, and the identification of individual vessels became difficult. Comparisons were made between the perfused area of tumors in Δ-projections and in minexCPD images. The Δ-projection perfusion measurements were correlated linearly with minexCPD.

Conclusion

Delta-projection visualized tumor vessels and enabled the quantitative assessment of the tumor area perfused by the contrast agent.

Vascular targeting has evolved into an attractive approach for cancer therapy. Disruption of the tumor vasculature deprives the neoplasm of oxygen and eventually leads to tumor cell death. Several promising vascular disrupting agents (VDAs), including pharmaceutical ( ) and non-pharmaceutical ( ) agents, are currently being investigated in different phases of clinical trials. Such VDA evaluation requires an accurate method for the direct assessment of tumor vascularity in response to treatment. Measurement of tissue microvessel density by tumor biopsy is a potential approach for monitoring therapy. However, repeated assessment is often not possible, because of the invasive nature of the technique, and the procedure is prone to sampling error due to the small sample size. The destruction of blood vessels by the probing biopsy needle could further complicate the assessment. The direct visualization of tumor vasculature by imaging is an attractive alternative for assessing VDAs because it can be performed repeatedly, and it provides a global view of the change in tumor vascularity, either across an anatomic plane or over the entire tumor volume.

Diagnostic ultrasound is one of the most commonly used modalities for cancer imaging. The equipment is portable and is widely available in large and small imaging centers. It provides both high spatial and temporal resolution, and the Doppler mode enables the direct visualization of tumor blood vessels. Currently, clinical scanners allow the visualization of blood vessels that are 0.2 to 0.5 mm in diameter, but to image smaller blood vessels similar to those targeted by VDAs, ultrasound contrast agents must be used. Various ultrasound techniques have been proposed that use contrast agents in combination with power Doppler ( ) or grayscale imaging (fundamental ( ), harmonic ( ), or subharmonic ( )). Each approach has its strengths and weaknesses.

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Materials and Methods

Animal and Imaging Studies

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Description of the Δ-Projection Imaging Algorithm

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Figure 1, Diagram of Δ-projection imaging. (a) The running maximum grayscale difference between the i th ( a i ) and baseline ( a 0 ) images of the time series of contrast-enhanced images is projected onto the baseline image as pseudocolor to construct the Δ-projection image sequence d n . (b) The Δ-projection calculation is repeated on each image to form a time sequence of projection images.

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Image Analysis for Δ-Projection and minexCPD

Delta-projection imaging

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PAF(Δ)=100×(nΔ/N). PAF(Δ)

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MinexCPD imaging

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PAF(D)=100×(nD/N). PAF(D)

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Comparison Between Δ-Projection and minexCPD

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Results

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Figure 2, Time course of Δ-projection images showing increasing tumor vascularity. Colored regions represent vascular regions enhanced by the inflow of contrast agents. Images (a) – (e) are the images at 0, 5, 6, 7, and 18 seconds after a bolus injection of contrast agent. The bars on the right side of each panel represent 1 mm. There is a mild increase in echogenicity from the center of the tumor to the periphery (a) . The bright echo at the bottom of the panels is the reflection from the tumor-air interface distal to the imaging transducer. Blood vessels of 250 μm in diameter ( open arrows ) or smaller become visible in the image (b, c) . The branching network of tumor blood vessels becomes increasingly complex with the inflow of more contrast agent (d, e) . The total area of perfusion in (e) is 90%. Visually, although most of the area is covered by colored pixels, the interior has regions dotted with non-colored pixels showing lack of perfusion.

Figure 3, The time evolution of the tumor area enhanced by the inflow of contrast agent. Time points marked by arrows ( a )–( e ) correspond to images shown in Figure 2 at 0, 5, 6, 7, and 18 seconds after a bolus injection of contrast. The tumor area perfused by the contrast agent increases with time, then levels off after maximum perfusion is achieved. The plateau of the curve corresponds to the total area of perfusion.

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Figure 4, Visual comparison of tumor perfusion imaged by Δ-projection and by minimum-exposure contrast-enhanced power Doppler (minexCPD). (a) Delta-projection image of a mouse tumor; (b) minexCPD image of the mouse tumor shown in (a) ; (c) and (d) number the correlated regions of vascularity from the Δ-projection and minexCPD images in (a) and (b) . Eight different regions designated by the numbers 1 to 8 in the Δ-projection image (c) correspond to regions 1' to 8' in the minexCPD image (d) . Although there are some differences, each tumor perfusion region (eg, regions 1 and 1') is highly correlated in the 2 images. The total percentage area of flow was 63.9% by Δ projection and 59.3% by minexCPD.

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Figure 5, Graph of the correlation between percentage area of flow (PAF) determined from Δ-projection and from minimum-exposure contrast-enhanced power Doppler (minexCPD) imaging. The solid line is the linear regression fit of the data. The dotted line is the line of identity with unit slope. The PAF(Δ) and PAF(D) data fit closely to the linear model, with a slope ( m ) of 0.95 and a regression coefficient ( R ) of 0.94 ( R 2 = 0.88). The slope of 0.95 < 1 indicates that the PAF estimated by Δ projection was 5% lower than that estimated by minexCPD.

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Discussion

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