J Acoust Soc Am. 2011 Jun;129(6):3756-67. doi: 10.1121/1.3578459.

Improved signal processing to detect cancer by ultrasonic molecular imaging of targeted nanoparticles.

Hughes M, Marsh J, Lanza G, Wickline S, McCarthy J, Wickerhauser V, Maurizi B, Wallace K.

Source

Cardiovascular Division, Washington University School of Medicine, Campus Box 8215, 4320 Forest Park Avenue, St. Louis, Missouri 63108, USA. domenico.stanzial@cnr.it

Abstract

In several investigations of molecular imaging of angiogenic neovasculature using a targeted contrast agent, Renyi entropy [I(f)(r)] and a limiting form of Renyi entropy (I(f,∞)) exhibited significantly more sensitivity to subtle changes in scattering architecture than energy-based methods. Many of these studies required the fitting of a cubic spline to backscattered waveforms prior to calculation of entropy [either I(f)(r) or I(f,∞)]. In this study, it is shown that the robustness of I(f,∞) may be improved by using a smoothing spline. Results are presented showing the impact of different smoothing parameters. In addition, if smoothing is preceded by low-pass filtering of the waveforms, further improvements may be obtained.

PMID:: 21682399; [PubMed - indexed for MEDLINE]
PMCID:: PMC3143678

Free PMC Article

Images from this publication.See all images (17)Free text

Figure 1

A diagram of the apparatus used to acquire backscattered RF data, in vivo, (A) a typical ultrasonic gray scale image together with (B) a histologically stained section of the tumor indicating portions where

-targeted nanoparticles nanoparticles could adhere, and (C) a further enlarged section indicating more precisely the location of viable target sites (red by

staining).

Improved signal processing to detect cancer by ultrasonic molecular imaging of targeted nanoparticles

J Acoust Soc Am. 2011 June;129(6):3756-3767.

Figure 2

Subsets (middle portion:

) of smoothing spline

images based on unfiltered (top row) and low-pass filtered (bottom row) RF obtained from a MDA435-implanted mouse injected with

-targeted nanoparticles. (A) Smoothing parameter

, (B)

, (C)

, (D)

. The RF used to construct these images was acquired immediately after injection.

Improved signal processing to detect cancer by ultrasonic molecular imaging of targeted nanoparticles

J Acoust Soc Am. 2011 June;129(6):3756-3767.

Figure 3

Subsets (middle portion:

) of smoothing spline

images based on unfiltered (top row) and low-pass filtered (bottom row) RF obtained from a MDA435-implanted mouse injected with

-targeted nanoparticles. (A) Smoothing parameter

, (B)

, (C)

, (D)

The RF used to construct these images was acquired immediately after injection.

Improved signal processing to detect cancer by ultrasonic molecular imaging of targeted nanoparticles

J Acoust Soc Am. 2011 June;129(6):3756-3767.

Figure 4

Effects of smoothing parameter S on smoothed spline output for representative segments of unfiltered RF data.

Improved signal processing to detect cancer by ultrasonic molecular imaging of targeted nanoparticles

J Acoust Soc Am. 2011 June;129(6):3756-3767.

Figure 5

Effects of smoothing parameter S on smoothed spline output for low-pass filtered versions of the same RF data shown in Fig. 4.

Improved signal processing to detect cancer by ultrasonic molecular imaging of targeted nanoparticles

J Acoust Soc Am. 2011 June;129(6):3756-3767.

Figure 6

Effects of smoothing parameter S on smoothed spline output for unfiltered RF data. Left column: Comparison of log magnitude of Fourier transforms of smoothed (i.e.,

) and unsmoothed (i.e.,

) data from the left columns of Fig. 4. Right column: Plots of the unsmoothed log magnitude minus the smoothed log magnitude.

Improved signal processing to detect cancer by ultrasonic molecular imaging of targeted nanoparticles

J Acoust Soc Am. 2011 June;129(6):3756-3767.

Figure 7

Same comparison as Fig. 6 but applied to the low-pass filtered data of, Fig. 5.

Improved signal processing to detect cancer by ultrasonic molecular imaging of targeted nanoparticles

J Acoust Soc Am. 2011 June;129(6):3756-3767.

Figure 8

Analysis steps for

images from a single mouse. Step 1: the cumulative distribution function (CDF) of all 12 images aquired from the animal are computed. Step 2: The CDF is used to segment the image into targeted (inside the blue boundaries) and non-targeted (outside) segments according to pixel value being above or below the threshold level (44% in this case). Step 3: The mean value,

, of

is computed for each “targeted region. Step 4: These are used to calculate

for each time point in the study. This procedure is repeated for all mice in each group. An example of the resulting averages at each time point for two of the groups studied (using the 44% analysis threshold) is shown in Fig. 10, where the subscript i has been suppressed in

Improved signal processing to detect cancer by ultrasonic molecular imaging of targeted nanoparticles

J Acoust Soc Am. 2011 June;129(6):3756-3767.

Figure 9

Closeup of thresholded smoothing spline

images. (Left) Image based on backscattered RF obtained from MDA435 tumor implanted mouse (same RF data used in Figs. 2345) immediately after injection of

-targeted nanoparticles. (Right) Image based on backscattered RF obtained from the same region 60 min after injection, “colorized” so that all pixels in the bottom 44% of the histogram (constructed using the 0-60 min

images obtained from this mouse) are red.

Improved signal processing to detect cancer by ultrasonic molecular imaging of targeted nanoparticles

J Acoust Soc Am. 2011 June;129(6):3756-3767.

Figure 10

(Color online) Average time course of

images obtained from the five MDA435-implanted (left, top) and five non-implanted mice (right, top) injected with

-targeted nanoparticles, five MDA435-implanted (left, middle),and five non-implanted mice (right, middle) injected with non-targeted nanoparticles, and five MDA435-implanted (left, bottom) and five non-implanted mice (right, bottom) injected with saline. These data were obtained for the CDF threshold set to include the lower 44% of pixel values in the

from each of the images in each group. Standard error bars for each group are also shown.

Improved signal processing to detect cancer by ultrasonic molecular imaging of targeted nanoparticles

J Acoust Soc Am. 2011 June;129(6):3756-3767.

Figure 11

(Color online) Average time course of

images obtained from the five MDA435-implanted mice injected with

-targeted nanoparticles and the corresponding confidence ratios (c) as a function of time post-injection. The left panel is the same panel appearing in the top right column of Fig. 10.

Improved signal processing to detect cancer by ultrasonic molecular imaging of targeted nanoparticles

J Acoust Soc Am. 2011 June;129(6):3756-3767.

Figure 12

Confidence panel for

-targeted nanoparticle group processed using optimal smoothing splines applied to low-pass filtered RF data. This image is composed of data like that shown in the right side of Fig. 11.

Improved signal processing to detect cancer by ultrasonic molecular imaging of targeted nanoparticles

J Acoust Soc Am. 2011 June;129(6):3756-3767.

Figure 13

Confidence panel stack comprised of confidence panels for all groups processed using optimal smoothing splines applied to low-pass filtered RF data. This panel is comprised of confidence summary images like that shown in Fig. 12.

Improved signal processing to detect cancer by ultrasonic molecular imaging of targeted nanoparticles

J Acoust Soc Am. 2011 June;129(6):3756-3767.

Figure 14

Confidence panel summary composed of confidence panel stacks for all groups, masked at successively greater confidence levels (labels A–F defined in Fig. 13). The left most (unmasked) stack of this image is shown in Fig. 13 and presents confidence panels in the same order. Also indicated is a conservatively chosen range of analysis thresholds (44%–62%) that produce an “extensive” region of confidence ratios, c, having an absolute value greater than 4 only for the tumor-implanted group injected with