978-1-4244-7000-6/11/$26.00 ©2011 IEEE

HIGH-RESOLUTION QUANTITATIVE ULTRASOUND IMAGING FOR SOFT TISSUE CLASSIFICATION

Ahmed M. Mahmoud, and Osama M. Mukdadi Bunyen Teng, and S. Jamal Mustafa

West Virginia University West Virginia University Department of Mechanical and Aerospace

Engineering Morgantown, WV 26506

Center for Cardiovascular and Respiratory Sciences

Morgantown, WV 26506 Sam.mukdadi@mail.wvu.edu

ABSTRACT

Mouse models have been widely used in cardiovascular research when investigating the progression or treatment of various diseases. It is always challenging to find non-invasive tools for early detection of diseases. This led us to the development of small animal models for diagnostic imaging techniques, such as high-frequency quantitative ultrasound imaging. This work describes the development of an ultrasound tissue classification technique that could be used to quantitatively assess atherosclerotic plaques in mouse heart. Signal and image processing of radiofrequency ultrasound data have been performed in time, frequency, and wavelet domains to quantitatively assess the severity of atherosclerosis in an APOE-KO mouse model fed with high fat diet. Multiple in vitro experiments were conducted, and ultrasound images of high contrast-resolution were obtained using parameters from various domains. For example, images reconstructed using the time variance (T���, and frequency skewness (PDskew) parameters showed high-contrast resolutions of approximately 17.95±2.98 dB, and 9.67±0.24 dB (n=4) between normal tissue and atherosclerotic lesions. The technique is integrated with a custom-designed high-frequency ultrasound imaging system and is able to provide parametric images of high contrast and special resolution down to 24 microns.

Keywords- Atherosclerosis; Quantitative Ultrasound; Image and Signal Processing.

1. INTRODUCTION

The availability of well-defined genetic strains and the ability to create transgenic and knockout mice make these models extremely significant tools in different kinds of research. For example, genetically modified mice play a powerful role in understanding the molecular mechanisms and pathogenesis of human cardiovascular diseases like human atherosclerosis [1]. The APOE-KO (“knockout”) mice are commonly used model in this kind of research [2]. However, these studies require high-resolution quantitative imaging techniques to assess the anatomy and functionality of the cardiovascular system in this small animal.

High-frequency ultrasound (>30 MHz), or ultrasound biomicroscopy (UBM), is one of the imaging techniques that have

been recently developed to assess the cardiovascular function in small animals [3]. However, few of these techniques could reach the challenging resolution required to fully describe the mice vasculature due to its small size, which requires a spatial resolution of 50 μm or less [4]. Turnbull et al. [5] first used a high-frequency ultrasound system to study mutant phenotyping in mouse embryo. Few years later, Srinivasan et al. [6] were able to monitor the development of mouse embryonic heart by using a noninvasive 40 MHz UBM system built for imaging through the uterus. Then, further developments were conducted by Foster et al. [7] to design an improved prototype scanner.

Currently, commercial UBM systems for small animal imaging are available (Vevo 770 and Vevo 2100, Visualsonics Inc., Toronto, ON, Canada). However; for monitoring atherosclerosis disease progression, it requires an imaging system that provides an accurate, precise, and quantitative assessment of the site of an atherosclerotic plaque undergoing treatment. Such quantitative tissue classification techniques of atherosclerotic plaques are not available in the abovementioned systems.

Several tissue classification techniques were described to diagnose atherosclerotic plaques in human arteries using intravascular ultrasound imaging (IVUS) [8, 9]. These techniques extract some parameters from ultrasound radiofrequency (RF) data, in particular from the spectral response, to reconstruct virtual histology maps for the quantitative assessment. In this work, our main goal is to incorporate tissue classification techniques with our high-frequency ultrasound imaging system [4] to quantitatively image and assess atherosclerotic plaques in mouse via reconstructing high-resolution parametric images. In addition, novel parameters are investigated and described in time, frequency, and wavelet domains. In the following sections, details about the ultrasound system, signal/image processing, and experimental procedures are described.

2. METHODS

2.1. Ultrasound Imaging System

This tissue classification technique was integrated and tested with a high-frequency ultrasound imaging system for small animals developed in our laboratory [4]. The system was tested using various ultrasound transducers at wide range of frequencies (18-120 MHz). In this work, two ultrasound transducers of approximately 30 MHz and 60 MHz in center frequency were used (Olympus NDT Inc., Waltham, MA, USA). These

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Figure 1. Schematic diagram describing the main steps of the

parametric imaging algorithm

transducers can cover a wide range of frequencies (18-81 MHz). During scanning, a high-performance PC synchronizes a two-axis positioning system of 1 μm resolution with data acquisition to collect ultrasound signals on-the-fly during the transducer movement down to 10 �m apart. Received RF signals were digitized and saved using a sampling rate of 200 MHz as a 14-bits word.

A custom user-friendly computer graphical user interface (GUI) was designed with Microsoft Visual C++ for control and data acquisition. Data were then transferred to Matlab7.1 (The MathWorks, Inc., Natick, MA, USA) for post processing and image reconstruction. To obtain a high-resolution B-mode ultrasound images, several signal and image processing algorithms were applied to the high-frequency echo signals. These procedures were described and tested via both phantom and in vitro studies [4, 10]. This ultrasound system provides access to ultrasound data at all stages including RF and envelop data, and B-mode images, which is essential for the tissue classification technique described below.

2.2. Tissue Classification

Figure 1 describes the main procedures of the tissue classification technique discussed in this paper. First, a region of interest (ROI) is segmented using the high-resolution B-mode image. The ROI is then extracted from RF ultrasound data after beamforming and divided into 2D kernels of small size, such as 0.3 mm × 0.15 mm. In order to reconstruct the parametric images, a vector Si,j is generated for each spatial location (i,j) within the ultrasound image. This vector is reconstructed from the neighborhood of pixel Pi,j in kernel Ki,j. Vector Si,j that corresponds to the pixel Pi,j can be defined as:

���� ��� ������ ��� ������ ��� ����� ������ �, (1) where n+1 and m+1 are the kernel length and width in pixels, respectively. Vectors are reconstructed from RF ultrasound data after beamforming for each spatial location within the ROI. These vectors could be considered as 1D signals that contain embedded information about the neighborhood characteristics and are used to

extract various parameters in time, frequency, and wavelet domains.

In time domain, the envelope of the signal is first detected, normalized using echo from a perfect reflector, and then time parameters are calculated. These time parameters (TPs) include the root mean square (RMS) value, variance�����, skewness (skew), kurtosis (kurt), and the ratio between the sample standard deviation (�� and the mean (�), which is named the time ratio (TR) along this paper. Statistical parameters such as skew, kurt, and TR have been used before for tissue characterization of atherosclerotic plaques by intravascular ultrasound radiofrequency and showed high sensitivity for lipid core detection [9]. RMS is a statistical measure of the magnitude of varying quantity and is evaluated as:

��� �� ���� �������������� ��!� � , (2)

where x is the sample number in signal S. Skewness is a measure of the symmetry and kurtosis is a measure of the “peakedness” of the distribution and are calculated as:

"#$% &'����()*) , (3)

#+,- &'����(.*. , (4) where E is the expected value. In addition to these parameters, Shannon entropy is calculated as a sensitive parameter to the energy distribution within a ROI, or S in ultrasound imaging [11]. Non-normalized Shannon entropy is used and calculated as:

/��� 0� �1�1 234��1��� (5) where S is the signal and Sx the coefficients of S in an orthonormal basis [12]. Since E(S) gives a negative value, we have incorporated a negative sign and modified the name to Ent, a positive parameter.

The second set of parameters is extracted from the frequency response of signal S. Kawasaki et al. [8] have performed several studies and reported the usefulness of using intravascular ultrasound integrated backscatter (IB) images to characterize atherosclerotic plaques in human arteries. These are parametric images reconstructed using the mean power of the ultrasound backscattered spectral data. Our team has reported that other parameters such as the variance ���� and the maximum amplitude of the power spectral density could be used in addition to the mean power [10]. Similar procedures are followed where Burg algorithm is used to estimate the power spectral density (PSD) from signal S for each spatial location. This power density PDi,j is estimated using Burg autoregressive (AR) prediction model for the signal (Si,j). It fits an AR linear prediction filter model of the order specified for the input signal by minimizing the errors using least square method. Then, PD is calculated from the frequency response of the prediction model. The AR filter parameters are constrained to satisfy the Levinson-Durbin recursion [13, 14]. In this work, the AR spectrum was calculated using a fast Fourier transform (FFT) of 512 length and a 12th order AR model after applying hamming window on the time signal. After estimating

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(a) (b)

(c) (d) (e)

(f) (g) (h) Figure 2. Approximately matched images for a short axis slice of

20-week normal mouse heart. Images show no evidence of atherosclerotic plaque in the aortic root. a) Histology section

with H & E staining with 1mm grid, b) ultrasound B-mode image, and c-h) ultrasound parametric image reconstructed using time variance (T���, time ratio (TR), frequency mean power (PDavg), frequency skewness (PDskew), wavelet RMS

(Wrms), and wavelet ratio (WR), respectively.

the PDi,j using Matlab “pburg” function, it was subtracted from the PD of a perfect reflector (PDref) as advised in [9]. Note that Hilbert window was applied to the signals before calculating the PD to avoid the spectral leakage. Then, a set of frequency parameters (FPs) are extracted including the maximum power (PDmax), the mean power (PDavg), the variance of power spectrum (PDvar), and its skewness (PDskew).

The last set of parameters is extracted from the approximation coefficients of wavelet Daubechies 3 (db3). This kind of wavelet decomposition is widely used and has been previously used in ultrasound imaging decomposition [15]. The wavelet approximation signal Wi,j is evaluated by applying Matlab “dwt” function on the time signal Si,j after normalization. In this study, the detail coefficients of wavelet have not been used to avoid the uncertainties of high frequency noise. Wavelet parameters (WPs) such as the RMS of the approximation signal (Wrms) and the ratio between the standard deviation (W�) and mean (5), that we call wavelet ratio (WR), have been used in this pilot study. However in the future, other parameters such as the skewness and kurtosis could be evaluated and several level of decomposition could be investigated.

After calculating the value of each parameter in different domains, it replaces the center pixel/pixels corresponding to its kernel within the ROI. These procedures are repeated for all kernels to form a parametric image using a single parameter.

2.3. In Vitro Experiments

In vitro experiments were conducted to investigate the efficacy of the system on reconstructing high-resolution B-mode and parametric images for real objects. Studies were performed to image isolated APOE-KO mouse hearts (20 week old n=4 with atherosclerosis) and its wild type control (C57BL/6J, 20 week old). These animals were part of the colony maintained by Dr. Mustafa at the Health Science Center (HSC), West Virginia University (WVU), Morgantown, WV. All mice procedures were performed and monitored by trained personnel and according to the protocols approved by the WVU Animal Care and Use committee.

Hearts were placed near the transducer focus on a thin layer of silicone gel lining in a glass plate and forming the reference echo. RF signals were collected using a separation distances down to 10 μm between adjacent signals. More than fifty ultrasound scans 100 μm apart in the elevation were acquired for the short axis of each mouse heart covering the upper part including the aortic root. After ultrasound scanning, hearts were sliced to obtain histology sections including the aortic root with H&E staining.

3. RESULTS AND DISCUSSION

To assess the system resolution experimentally, B-mode images for an 8 μm-in-diameter tungsten wire immersed in water were acquired. The experimental values for the axial and lateral resolutions were approximately measured to be 24 μm and 123 μm, respectively, using the 60 MHz ultrasound transducer.

The B-mode images of the short axis scans were compared to histology sections to render approximately the best-matched couple for each heart. Then, the ROI was determined using B-mode images, and then parametric images were reconstructed using raw ultrasound data after focusing. Figure 2 shows different

images approximately matched for a short axis section in the heart of 20-weeks normal mouse. Similar structures were observed in the histology section (Fig. 2(a)) and the B-mode image in Fig. 2(b). These structures include the aortic root in the middle between the right and left ventricles with no signs of atherosclerotic plaques. As an example, two parameters from each domain were used to reconstruct high-resolution parametric images including T��� TR, PDavg, PDskew, Wrms, and WR using dynamic ranges of 45 dB, 22 dB, 25 dB, 18 dB, 3.25 dB, 20 dB, respectively. These ranges were selected based on the parameters’ values for optimum visualization, and the results obtained in our preliminary experiments (n=4). Fig. 2(c-h), show parametric images reconstructed using T��� TR, PDavg, PDskew, Wrms, and WR, respectively. These images show minor changes in the dB values (low contrast) at all sites for all parameters. On the other hand, major changes in the dB values of all parameters (high contrast) were observed in the parametric images shown in Fig. 3(c-h) for the same parameters and dynamic ranges. These images were reconstructed for a short axis section in diseased heart of 20-weeks APOE-KO mice. Lesions of atherosclerotic plaques were diagnosed, particularly in the aortic root where the yellow arrows point in Fig. 3(a).

It was observed experimentally for n=4 that T��� TR, PDavg, PDskew, Wrms, and WR could achieve minimum contrast resolutions of approximately 17.95±2.98 dB, 9.53±0.91 dB, 13.14±0.59 dB, 9.67±0.24 dB, 1.27±0.14 dB, and 7.67±0.88 dB between normal tissue and lesions of atherosclerotic plaques using the above mentioned dynamic ranges. While these values of

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(a) (b)

(c) (d) (e)

(f) (g) (h) Figure 3. Approximately matched images for a short axis slice of

20-week APOE-KO mouse heart. Images show evidence of atherosclerotic plaque in the aortic root where the yellow arrows point. a) Histology section with H & E staining with 1mm grid, b) ultrasound B-mode image, and c-h) ultrasound parametric image

reconstructed using time variance (T���, time ratio (TR), frequency mean power (PDavg), frequency skewness (PDskew),

wavelet RMS (Wrms), and wavelet ratio (WR), respectively.

contrast resolutions were very low between water and normal heart tissue of approximately 3.02±2.58 dB, 1.39±0.91 dB, 0.08±0.12 dB, 0±0.11 dB, 0.18±.09 dB, 1.93±1.03 dB, respectively, which could be seen in Fig. 2(c-h).

4. CONCLUSIONS

An ultrasound tissue classification technique for atherosclerotic plaques in mouse is described. This technique extracts various parameters from time, frequency, and wavelet domains to reconstruct ultrasound parametric images. This technique was integrated with our custom high-frequency ultrasound imaging system, which achieved a spatial resolution of 24 μm. Parametric images were reconstructed in various domains and were matched with histology and succeeded to quantitatively assess lesions of atherosclerotic plaques. For example, images reconstructed using the time variance (T���, and frequency skewness (PDskew) parameters showed high-contrast resolutions of approximately 17.95±2.98 dB, and 9.67±0.24 dB (n=4) between normal tissue and atherosclerotic lesions. This technique could be used with any ultrasound machine providing RF accessibility.

5. ACKNOWLEDGMENT

This work was supported by NIH-DE019561 to OMM and in part by NIH-HL027339 and HL094447 to SJM.

6. REFERENCES

[1] A. Daugherty, "Mouse models of atherosclerosis," American Journal of the Medical Sciences, vol. 323, pp. 3-10, Jan 2002.

[2] J. C. Russell and S. D. Proctor, "Small animal models of cardiovascular disease: tools for the study of the roles of metabolic syndrome, dyslipidemia, and atherosclerosis," Cardiovascular Pathology, vol. 15, pp. 318-330, Nov-Dec 2006.

[3] O. Aristizabal, D. A. Christopher, F. S. Foster, and D. H. Turnbull, "40-MHz echocardiography scanner for cardiovascular assessment of mouse embryos," Ultrasound in Medicine and Biology, vol. 24, pp. 1407-1417, Nov 1998.

[4] A. M. Mahmoud, D. H. Cortes, S. J. Mustafa, and O. M. Mukdadi, "High frequency precise ultrasound imaging system to assess mouse hearts and blood vessels," in Proceedings of the ASME 2008 Summer Bioengineering Conference, FL, 2008.

[5] D. H. Turnbull, T. S. Bloomfield, H. S. Baldwin, F. S. Foster, and A. L. Joyner, "Ultrasound Backscatter Microscope Analysis of Early Mouse Embryonic Brain-Development," Proceedings of the National Academy of Sciences of the United States of America, vol. 92, pp. 2239-2243, Mar 14 1995.

[6] S. Srinivasan, H. S. Baldwin, O. Aristizabal, L. Kwee, M. Labow, M. Artman, and D. H. Turnbull, "Noninvasive, in utero imaging of mouse embryonic heart development with 40-MHz echocardiography," Circulation, vol. 98, pp. 912-918, Sep 1 1998.

[7] F. S. Foster, M. Y. Zhang, Y. Q. Zhou, G. Liu, J. Mehi, E. Cherin, K. A. Harasiewicz, B. G. Starkoski, L. Zan, D. A. Knapik, and S. L. Adamson, "A new ultrasound instrument for in vivo microimaging of mice," Ultrasound in Medicine and Biology, vol. 28, pp. 1165-1172, Sep 2002.

[8] M. Kawasaki, H. Takatsu, T. Noda, K. Sano, Y. Ito, K. Hayakawa, K. Tsuchiya, M. Arai, K. Nishigaki, G. Takemura, S. Minatoguchi, T. Fujiwara, and H. Fujiwara, "In vivo quantitative tissue characterization of human coronary arterial plaques by use of integrated backscatter intravascular ultrasound and comparison with angioscopic findings," Circulation, vol. 105, pp. 2487-2492, May 28 2002.

[9] N. Komiyama, G. J. Berry, M. L. Kolz, A. Oshima, J. A. Metz, P. Preuss, A. F. Brisken, M. P. Moore, P. G. Yock, and P. J. Fitzgerald, "Tissue characterization of atherosclerotic plaques by intravascular ultrasound radiofrequency signal analysis: An in vitro study of human coronary arteries," American Heart Journal, vol. 140, pp. 565-574, Oct 2000.

[10] A. M. Mahmoud, B. Teng, S. J. Mustafa, and O. M. Mukdadi, "High-Frequency Tissue Classification of Atherosclerotic Plaques in an APOE-KO Mouse Model Using Spectral Analysis," in Proceedings of the ASME International Mechanical Engineering Congress and Exposition (IMECE), 2009, pp. 505-508.

[11] A. M. Mahmoud, O. M. Mukdadi, and Y. M. Kadah, "Entropy Based Phase Aberration Correction Technique in Ultrasound Imaging," in Proceedings of the ASME Summer Bioengineering Conference, Keystone, Colorado, USA, 2007.

[12] R. R. Coifman and M. V. Wickerhauser, "Entropy-Based Algorithms for Best Basis Selection," IEEE Transactions on Information Theory, vol. 38, pp. 713-718, Mar 1992.

[13] S. L. Marple, "Digital Spectral Analysis," Prentice-Hall, Ed., ed Englewood Cliffs, NJ, 1987.

[14] P. Stoica and R. L. Moses, Introduction to Spectral Analysis: Prentice-Hall, 1997.

[15] G. Cincotti, G. Loi, and M. Pappalardo, "Frequency decomposition and compounding of ultrasound medical images with wavelet packets," IEEE Transactions on Medical Imaging, vol. 20, pp. 764-771, Aug 2001.

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