Full Wavefield Analysis and Damage Imaging Through Compressive Sensing in Lamb Wave Inspections.

One of the main challenges faced by the structural health monitoring community is acquiring and processing huge sets of acoustic wavefield data collected from sensors, such as scanning laser Doppler vibrometers or ultrasonic scanners. In fact, extracting information that allows the estimation of the damage condition of a structure can be a time-consuming process. This paper presents a damage detection and localization technique based on a compressive sensing algorithm, which significantly allows us to reduce the acquisition time without losing in detection accuracy. The proposed technique exploits the sparsity of the wavefield in different representation domains, such as those spanned by wave atoms, curvelets, and Fourier exponentials to recover the full wavefield and, at the same time, to infer the damage location, based on comparison between the wavefield reconstructions produced by the different representation domains. The procedure is applied to three different setups related to an aluminum plate with a notch, a glass fiber reinforced polymer plate with a notch, and a composite plate with a delamination. The results show that the technique can be applied in a variety of structural components to reduce acquisition time and achieve high performance in defect detection and localization by removing up to 80% of the Nyquist sampling grid.

Machine learning of the spatio-temporal characteristics of echocardiographic deformation curves for infarct classification.

The aim of this study was to analyze the whole temporal profiles of the segmental deformation curves of the left ventricle (LV) and describe their interrelations to obtain more detailed information concerning global LV function in order to be able to identify abnormal changes in LV mechanics. The temporal characteristics of the segmental LV deformation curves were compactly described using an efficient decomposition into major patterns of variation through a statistical method, called Principal Component Analysis (PCA). In order to describe the spatial relations between the segmental traces, the PCA-derived temporal features of all LV segments were concatenated. The obtained set of features was then used to build an automatic classification system. The proposed methodology was applied to a group of 60 MRI-delayed enhancement confirmed infarct patients and 60 controls in order to detect myocardial infarction. An average classification accuracy of 87% with corresponding sensitivity and specificity rates of 89% and 85%, respectively was obtained by the proposed methodology applied on the strain rate curves. This classification performance was better than that obtained with the same methodology applied on the strain curves, reading of two expert cardiologists as well as comparative classification systems using only the spatial distribution of the end-systolic strain and peak-systolic strain rate values. This study shows the potential of machine learning in the field of cardiac deformation imaging where an efficient representation of the spatio-temporal characteristics of the segmental deformation curves allowed automatic classification of infarcted from control hearts with high accuracy.

Model-based compressive sensing for damage localization in Lamb wave inspection.

Compressive sensing (CS) has emerged as a potentially viable technique for the efficient compression and analysis of high-resolution signals that have a sparse representation in a fixed basis. In this work, we have developed a CS approach for ultrasonic signal decomposition suitable to achieve high performance in Lamb-wave-based defect detection procedures. In the proposed approach, a CS algorithm based on an alternating minimization (AM) procedure is adopted to extract the information about both the system impulse response and the reflectivity function. The implemented tool exploits the dispersion compensation properties of the warped frequency transform as a means to generate the sparsifying basis for the signal representation. The effectiveness of the decomposition task is demonstrated on synthetic signals and successfully tested on experimental Lamb waves propagating in an aluminum plate. Compared with available strategies, the proposed approach provides an improvement in the accuracy of wave propagation path length estimation, a fundamental step in defect localization procedures.

Guided wave expansion in warped curvelet frames.

Lamb wave testing for structural health monitoring (SHM) often relies on analysis of wavefields recorded through scanning laser Doppler vibrometers (SLDVs) or ultrasonic scanners. Damage detection and characterization with these techniques requires isolation of defect-induced reflections in the wavefield from the injected wave packet and from scattering events associated with structural features such as boundaries, rivets, joints, etc. This is a challenging task when dealing with complex structures and multimodal, dispersive propagation regimes, whereby various wave contributions in both the time/space and the frequency/wavenumber domain overlap. A new mathematical tool named warped curvelet frames (WCFs) is proposed to effectively decompose the recorded wavefields. The presented technique results from the combination of two operators, i.e., the curvelet transform (CT) and the warped frequency transform (WFT). The CT provides an optimally sparse representation of nondispersive wave propagators. Combining the CT with the WFT allows for a flexible analysis of multimodal wave propagation in dispersive media. Exploiting the spatial and temporal localization of curvelets, as well as the spectro-temporal adaptation of the analysis frame to the characteristics of each propagating mode, provided by frequency warping, a convenient decomposition of guided waves is achieved and relevant contributions can be effectively isolated. The proposed approach is validated through dedicated simulations and further tested experimentally to demonstrate the effectiveness of the method in separating guided wave modes corresponding to acoustic events in close spatial proximity.

A retrospective study to reduce prostate biopsy cores by a real time interactive tool.

OBJECTIVE: Prostate carcinoma (PCa) is one of the most frequent neoplasms, with more than 110.000 new cases/year in Europe. As PCa is not clearly demonstrable at transrectal ultrasound (TRUS), guidelines on TRUS guided biopsy suggest to perform a random tissue sampling (at least 8-12 "cores" depending on gland volume). Although accuracy grows with core number, patient discomfort and adverse event probability grow as well. Thus it would be worth to aim to reduce the number of prostate biopsy cores without loss of diagnostic accuracy. MATERIALS AND METHODS: A retrospective study was performed to evaluate the feasibility of an improved version of a rtCAB tool developed at DEIS (University of Bologna) for the reduction of prostate biopsy cores. rtCAB is an innovative processing technique which enhances TRUS video stream by a live false color overlay image that helps the physician to perform the biopsy by guiding the sampling into target zones. In order to train rtCAB, a monocentric, single operator prostate gland adenocarcinoma database has been built. The database enlists 81 patients, for a total of 743 prostate byoptic (PBx) cores and 14860 ROI. For each patient we collected age, PSA levels, digital rectal examination (DRE) findings, presence or absence of focal lesions, and prostate volume. During TRUS, raw ultrasound data were acquired and associated to each PBx core. For each core we collected both the radio frequency (RF) signal and the histological outcome. RESULTS: The whole system was optimized for reducing the number of false positives while preserving an acceptable number of false negatives. Comparing to a classical PBx approach (8-12 cores), the estimated positive predictive value (PPV) of our method increased from 25% to 40%, with an overall sensitivity of 85%. CONCLUSIONS: Preliminary results show that the proposed tool can provide real-time feedback to the operator during TRUS. Sensitivity and PPV values suggest that a reduction of almost 50% the number of biopsy cores without losing in diagnostic accuracy is feasible. A prospective study is needed to further confirm these preliminary retrospective results.

Predictive deconvolution and hybrid feature selection for computer-aided detection of prostate cancer.

Computer-aided detection (CAD) schemes are decision making support tools, useful to overcome limitations of problematic clinical procedures. Trans-rectal ultrasound image based CAD would be extremely important to support prostate cancer diagnosis. An effective approach to realize a CAD scheme for this purpose is described in this work, employing a multi-feature kernel classification model based on generalized discriminant analysis. The mutual information of feature value and tissue pathological state is used to select features essential for tissue characterization. System-dependent effects are reduced through predictive deconvolution of the acquired radio-frequency signals. A clinical study, performed on ground truth images from biopsy findings, provides a comparison of the classification model applied before and after deconvolution, showing in the latter case a significant gain in accuracy and area under the receiver operating characteristic curve.