Simona Granchi

FEMMINA: a fast echographic multiparametric multi-imaging novel apparatus

The aim of the work is to present a novel apparatus for experimental activity in research of new methods for studying material and biological tissue with ultrasound. FEMMINA (Fast Echographic Multiparametric Multi Imaging Novel Apparatus) is a hardware and software platform dedicated to ultrasonic signal and image processing. It uses the radiofrequency signal for multiparametric calculation and presentation with a multiprocessing digital architecture. The architecture is designed to be modular, expandable and aimed at embracing different ultrasonic investigation techniques. It provides a multianalysis and interactive image system not only for clinical usage but also for all applications where a high image rate production is required. In order to obtain an efficient interactive system, it was also necessary to realize a fast signal processing, as well as to implement a fast visualization tool for managing multiple images. The platform is completely programmable, and for a specific application it can be on-line reconfigured in dependence of the parameters that, from time to time, must be evaluated. Spectral images of biological in-vitro and in-vivo tissue, obtained through the Discrete Wavelet Transform (DWT) by using new processing algorithms are presented as preliminary applications of the platform, as well as vector images for blood flow 2-D Doppler investigation. Innovative blood images, obtained without using the Doppler effect, and novel vector distribution images for blood velocity are reported.

RADIOFREQUENCY REAL TIME PROCESSING: ULTRASONIC SPECTRAL IMAGES AND VECTOR DOPPLER INVESTIGATION

Novel radio frequency processing techniques for biological tissue characterization with ultrasound are presented in order to improve the diagnostic power of ultrasonic echographic systems. Spectral images of biological “in-vitro” and “in-vivo” tissue, obtained through the Discrete Wavelet Packet Transform (DWPT) are presented, as well as velocity vector maps of blood flow obtained with 2-D Doppler investigation. The implementation of the Discrete Wavelet Packet Transform through a digital filter produces, for each acquired frame, real time spectral maps, in different frequency bands. Multi-parametric images are composed by merging these maps using a dedicated “balance image fusion” algorithm. New blood images, obtained without using the Doppler effect, for “in-vivo” and “in-vitro” experiments are presented based on the exploitation of non-linear ultrasound-medium interaction effects [1,2,3,4]. The proposed spectral processing procedure seems to be suitable to perform tissue characterization. Pathological portions inside tissue could be detected thanks to their different echo frequency content which in turns is determined by linear and non-linear ultrasonicmedium interaction. The target of future clinical applications is to investigate the potential of the procedure as a “virtual biopsy”. Vector Doppler multi parametric images are obtained with compound measurements of Doppler shifts along different directions, and superimposing the resulting 2-D velocity vector maps to the conventional morphological B-mode representation [5,6,7] The results, here presented, were produced by employing a hardware and software platform dedicated to ultrasonic signal and image processing [8,9,10]. The radiofrequency signal for multi parametric calculation and presentation with a multiprocessing digital architecture was used. This platform provides a multi analysis and

Clinical test of RULES (RULES: radiofrequency ultrasonic local estimators)

An echographic method for differentiating pathological regions in biological tissue was proposed previously (Masotti, L. et al., Proc. IEEE Ultrason. Symp., p.1030-3, 2003). The method, named RULES (Biagi, E. et al., Italian Patent FI2003A000254, 2003; Italian Patent FI2002A34, 2002; European Patent 03425118.1, 2003; US Patent 10/383674, 2003), is based on radiofrequency (RF) echographic signal processing. It permits spectral parameters related to the organization and mechanical properties of investigated tissue to be extracted. Spectral images are produced through a processing procedure, based on the discrete wavelet packet transform. A hardware-software platform, FEMMINA (fast echographic multiparameter multi image novel apparatus) (Scabia, M. et al., IEEE Trans. UFFC, vol.49, no.10, p.1444-51, 2002), for real-time signal and image processing was employed in order to test the diagnostic applicability of the method. The results for breast tumor detection and characterization are presented together with the results from a clinical investigation on the prostate, where the RULES features are employed for localizing pathological zones in order to guide bioptical sampling.

Real Time Images of Local Ultrasonic Spectral Parameters for Tissue Differentation Through Wavelet Transform

In this work we propose a novel echographic method to investigate biological tissue internal organization and consequently to differentiate pathological regions. The method is based on time-frequency processing of the radiofrequency echographic signal. Our analysis correlates spectral parameters to actual organization and mechanical properties of investigated tissue and, clinically, to physical properties of tissue derived from histology.

Tissue characterization in echographic spectral hyperspace: Breast pathologies differentiation

Spectral processing procedure on RadioFrequency (RF) echographic signals is proposed for detecting and characterizing mammary pathologies in order to improve echographic diagnosis on breast cancer that is the second leading cause of cancer death among women. The spectral content of each RF track of a frame is decomposed in N-subband obtained by a bank of filters derived from Morlet Wavelet. The proposed processing procedure works in a N-dimensional spectral hyperspace. Different biological structure can be differentiated by their position in the hyperspace. A Clustering technique is employed to detect the typical spatial distributions. The algorithm is developed in two phases: Training step and Classification step. In the first one, a set of patients are selected and only Regions Of Interest (ROI) are processed to define the suitable Clusters. The Classifications phase, which operates on entire frame, is applied over all patients. The method is amplitude independent and moreover it is capable to compensate for different frequency responses of ultrasonic transducers.

Ultrasonic Images of Tissue Local Power Spectrum by Means of Wavelet Packets for Prostate Cancer Detection

The aim of this work is to present a novel apparatus for experimental activity in research where a high frequency signal must be acquired and processed in real-time, and represented through a multi-image visualization tool. The proposed apparatus is a hardware and software platform dedicated to signal and/or image processing and fast data visualization. Currently the system is employed for studying new algorithms for biological tissue ultrasound investigation 1,2. Real-time operation mode designates clinical environment as its elective application because fast data processing is a necessary prerequisite in order to evaluate the on-line diagnostic performance of different investigation methods. Furthermore, for research purposes, the possibility to have simultaneous views of different ultrasonic parameters is essential for an efficient analysis, verification and modelling of the specific ultrasound-media interaction phenomenon .

Real Time Processing of the Radiofrequency Echo Signal for On-Line Spectral Maps

In this work we presented a hardware-software platform, which was designed to be expandable, modular, and adaptable to any other system which needs to produce significant and easily readable real time images. Such platform, being completely programmable, can also be reconfigured in dependence of the object being investigated.

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.

Carotid plaque tissue differentiation based on radiofrequency echographic signal local spectral content (RULES: Radiofrequency Ultrasonic Local Estimators)

An echographic method is proposed in order to detect and differentiate carotid vessel plaques. The method based on a novel spectral processing procedure for the radiofrequency echo signal is named RULES (radiofrequency ultrasonic local estimators). It allows the extraction of local spectral parameters related to the organization and mechanical properties of an investigated tissue region. Spectral images are produced through a processing procedure, based on a statistical analysis of radiofrequency signal spectral coefficients, calculated with the discrete wavelet packet transform. For each organ, the regions of the investigated tissue, which exhibit the same distribution of the spectral coefficients, were considered homogeneous and were put in correspondence with local tissue condition (healthy or with pathology), as derived from histological analysis.

Clinical Experimentation of FEMMINA* and RULES+ for Prostate and Breast Tumor Detection

An echographic method for differentiating pathological regions in biological tissue is presented. The method, named RULES, permits to extract spectral parameters related to the organization and mechanical properties of investigated tissue. The results for breast tumor detection and characterization will be presented in this work together with the results coming from the clinical experimentation on prostate to guide the bioptical sampling