Advanced spectral analyses for real-time automatic echographic tissue-typing of simulated tumor masses at different compression stages

Automatic Echographic Detection of Halloysite Clay Nanotubes in a Low Concentration Range

Aim of this work was to investigate the automatic echographic detection of an experimental drug delivery agent, halloysite clay nanotubes (HNTs), by employing an innovative method based on advanced spectral analysis of the corresponding "raw" radiofrequency backscatter signals. Different HNT concentrations in a low range (5.5-66 × 1010 part/mL, equivalent to 0.25-3.00 mg/mL) were dispersed in custom-designed tissue-mimicking phantoms and imaged through a clinically-available echographic device at a conventional ultrasound diagnostic frequency (10 MHz). The most effective response (sensitivity = 60%, specificity = 95%), was found at a concentration of 33 × 1010 part/mL (1.5 mg/mL), representing a kind of best compromise between the need of enough particles to introduce detectable spectral modifications in the backscattered signal and the necessity to avoid the losses of spectral peculiarity associated to higher HNT concentrations. Based on theoretical considerations and quantitative comparisons with literature-available results, this concentration could also represent an optimal concentration level for the automatic echographic detection of different solid nanoparticles when employing a similar ultrasound frequency. Future dedicated studies will assess the actual clinical usefulness of the proposed approach and the potential of HNTs for effective theranostic applications.

Application of wavelet techniques for cancer diagnosis using ultrasound images: A Review

Ultrasound is an important and low cost imaging modality used to study the internal organs of human body and blood flow through blood vessels. It uses high frequency sound waves to acquire images of internal organs. It is used to screen normal, benign and malignant tissues of various organs. Healthy and malignant tissues generate different echoes for ultrasound. Hence, it provides useful information about the potential tumor tissues that can be analyzed for diagnostic purposes before therapeutic procedures. Ultrasound images are affected with speckle noise due to an air gap between the transducer probe and the body. The challenge is to design and develop robust image preprocessing, segmentation and feature extraction algorithms to locate the tumor region and to extract subtle information from isolated tumor region for diagnosis. This information can be revealed using a scale space technique such as the Discrete Wavelet Transform (DWT). It decomposes an image into images at different scales using low pass and high pass filters. These filters help to identify the detail or sudden changes in intensity in the image. These changes are reflected in the wavelet coefficients. Various texture, statistical and image based features can be extracted from these coefficients. The extracted features are subjected to statistical analysis to identify the significant features to discriminate normal and malignant ultrasound images using supervised classifiers. This paper presents a review of wavelet techniques used for preprocessing, segmentation and feature extraction of breast, thyroid, ovarian and prostate cancer using ultrasound images.

Differentiation of Breast Lesions by Use of HyperSPACE: Hyper-Spectral Analysis for Characterization in Echography

Early diagnosis represents the cornerstone in breast cancer control. Ultrasound is still a valid tool because of its low invasiveness, reduced costs and reduced risk of harm, but better exploitation of its potential is necessary to extract information on tissue features. The proposed method, HyperSPACE (hyper-spectral analysis for characterization in echography), which processes the ultrasonic radiofrequency signal in an N-dimension spectral hyperspace to define several characteristic parameters of the tissue under investigation, was used with the aim of differentiating two types of breast lesion: infiltrating ductal carcinoma and fibroadenoma. The analyzed data set consisted of 2000 radiofrequency frames related to 200 sections of pathologic breast nodules: 104 infiltrating ductal carcinomas and 96 fibroadenomas. The algorithm was trained on single radiofrequency frames related to 50 sections (26 carcinomas, 24 fibroadenomas) to recognize the two pathologies considered, and all the radiofrequency frames related to the other 150 sections were classified, yielding a sensitivity of 92.2%, specificity of 93%, positive predictive value of 93.2% and negative predictive value of 91%. The results were compared with those of RULES (radiofrequency ultrasonic local estimators), a processing method set developed by our group and used by other researchers in clinical and laboratory environments.

Analysis of laser speckle contrast images variability using a novel empirical mode decomposition: comparison of results with laser Doppler flowmetry signals variability

Laser Doppler flowmetry (LDF) and laser speckle contrast imaging (LSCI) have emerged as noninvasive optical modalities to monitor microvascular blood flow. Many studies proposed to extract physiological information from LDF by analyzing signals variability. By opposition, such analyses for LSCI data have not been conducted yet. We propose to analyze LSCI variability using a novel data-driven method: the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). CEEMDAN is suitable for nonlinear and nonstationary data and leads to intrinsic mode functions (IMFs). It is based on the ensemble empirical mode decomposition (EEMD) which relies on empirical mode decomposition (EMD). In our work the average frequencies of LSCI IMFs given by CEEMDAN are compared with the ones given by EMD and EEMD. Moreover, LDF signals acquired simultaneously to LSCI data are also processed with CEEMDAN, EMD and EEMD. We show that the average frequencies of IMFs given by CEEMDAN depend on the signal-to-noise ratio (SNR) used in the computation but, for a given SNR, the average frequencies found for LSCI are close to the ones obtained for LDF. By opposition, EEMD leads to IMFs with frequencies that do not vary much when the SNR level is higher than a threshold. The new CEEMDAN algorithm has the advantage of achieving a complete decomposition with no error in the reconstruction but our study suggests that further work is needed to gain knowledge in the adjustment of the added noise level. CEEMDAN, EMD and EEMD are data-driven methods that can provide a better knowledge of LSCI.

The clinical utility of FibroScan(®) as a noninvasive diagnostic test for liver disease

An important aspect of managing chronic liver disease is assessing for evidence of fibrosis. Historically, this has been accomplished using liver biopsy, which is an invasive procedure associated with risk for complications and significant sampling and observer error, limiting the accuracy for determination of fibrosis stage. Hence, several serum biomarkers and imaging methods for noninvasive assessment of liver fibrosis have been developed. In this article, we review the current literature on an important noninvasive imaging modality to measure tissue elastography (FibroScan^®). This ultrasound-based technique is now increasingly available in many countries and has been shown to be a reliable and safe noninvasive means of assessing disease severity in chronic liver disease of varying etiology.