Saba Adabi

Double-Stage Delay Multiply and Sum Beamforming Algorithm: Application to Linear-Array Photoacoustic Imaging

Photoacoustic imaging (PAI) is an emerging medical imaging modality capable of providing high spatial resolution of Ultrasound (US) imaging and high contrast of optical imaging. Delay-and-Sum (DAS) is the most common beamforming algorithm in PAI. However, using DAS beamformer leads to low resolution images and considerable contribution of off-axis signals. A new paradigm namely delay-multiply-and-sum (DMAS), which was originally used as a reconstruction algorithm in confocal microwave imaging, was introduced to overcome the challenges in DAS. DMAS was used in PAI systems and it was shown that this algorithm results in resolution improvement and sidelobe degrading. However, DMAS is still sensitive to high levels of noise, and resolution improvement is not satisfying. Here, we propose a novel algorithm based on DAS algebra inside DMAS formula expansion, double stage DMAS (DS-DMAS), which improves the image resolution and levels of sidelobe, and is much less sensitive to high level of noise compared to DMAS. The performance of DS-DMAS algorithm is evaluated numerically and experimentally. The resulted images are evaluated qualitatively and quantitatively using established quality metrics including signal-to-noise ratio (SNR), full-width-half-maximum (FWHM) and contrast ratio (CR). It is shown that DS-DMAS outperforms DAS and DMAS at the expense of higher computational load. DS-DMAS reduces the lateral valley for about 15 dB and improves the SNR and FWHM better than 13% and 30%, respectively. Moreover, the levels of sidelobe are reduced for about 10 dB in comparison with those in DMAS.

Large-scale analysis of dielectric and mechanical properties of pavement using GPR and LFWD

Over the last few years ground-penetrating radar (GPR) has proved to be an effective instrument for pavement applications spanning from physical to geometrical inspections of roads. In this paper, the new challenge of inferring mechanical properties of road pavements and materials from their dielectric characteristics was investigated. A pulsed GPR system with ground-coupled antennas, 600 MHz and 1600 MHz center frequencies of investigation, was used over a 4 m×30 m test site with a flexible pavement structure. A spacing of 0.40 m between the GPR acquisition tracks was considered both longitudinally and transversely in order to configure a square regular grid mesh of 836 nodes. Accordingly, the Youngs modulus of elasticity was measured on each grid node using light falling weight deflectometer (LFWD). Therefore, a semi-empirical model for predicting strength properties of pavement was developed by comparing the observed elastic modulus and the electromagnetic response of substructure on each grid node. A good agreement between observed and modeled values was found, thereby showing great promises for large-scale mechanical inspections of pavements using GPR.

Optical Coherence Tomography Technology and Quality Improvement Methods for Optical Coherence Tomography Images of Skin: A Short Review

Optical coherence tomography (OCT) delivers 3-dimensional images of tissue microstructures. Although OCT imaging offers a promising high-resolution method, OCT images experience some artifacts that lead to misapprehension of tissue structures. Speckle, intensity decay, and blurring are 3 major artifacts in OCT images. Speckle is due to the low coherent light source used in the configuration of OCT. Intensity decay is a deterioration of light with respect to depth, and blurring is the consequence of deficiencies of optical components. In this short review, we summarize some of the image enhancement algorithms for OCT images which address the abovementioned artifacts.

An overview of methods to mitigate artifacts in optical coherence tomography imaging of the skin

Optical coherence tomography (OCT) of skin delivers three‐dimensional images of tissue microstructures. Although OCT imaging offers a promising high‐resolution modality, OCT images suffer from some artifacts that lead to misinterpretation of tissue structures. Therefore, an overview of methods to mitigate artifacts in OCT imaging of the skin is of paramount importance. Speckle, intensity decay, and blurring are three major artifacts in OCT images. Speckle is due to the low coherent light source used in the configuration of OCT. Intensity decay is a deterioration of light with respect to depth, and blurring is the consequence of deficiencies of optical components.

Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms

Currently, diagnosis of skin diseases is based primarily on the visual pattern recognition skills and expertise of the physician observing the lesion. Even though dermatologists are trained to recognize patterns of morphology, it is still a subjective visual assessment. Tools for automated pattern recognition can provide objective information to support clinical decision-making. Noninvasive skin imaging techniques provide complementary information to the clinician. In recent years, optical coherence tomography (OCT) has become a powerful skin imaging technique. According to specific functional needs, skin architecture varies across different parts of the body, as do the textural characteristics in OCT images. There is, therefore, a critical need to systematically analyze OCT images from different body sites, to identify their significant qualitative and quantitative differences. Sixty-three optical and textural features extracted from OCT images of healthy and diseased skin are analyzed and, in conjunction with decision-theoretic approaches, used to create computational models of the diseases. We demonstrate that these models provide objective information to the clinician to assist in the diagnosis of abnormalities of cutaneous microstructure, and hence, aid in the determination of treatment. Specifically, we demonstrate the performance of this methodology on differentiating basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) from healthy tissue.

Functional photoacoustic tomography for neonatal brain imaging: developments and challenges

Transfontanelle ultrasound imaging (TFUSI) is a routine diagnostic brain imaging method in infants who are born prematurely, whose skull bones have not completely fused together and have openings between them, so-called fontanelles. Open fontanelles in neonates provide acoustic windows, allowing the ultrasound beam to freely pass through. TFUSI is used to rule out neurological complications of premature birth including subarachnoid hemorrhage (SAH), intraventricular (IVH), subependimal (SEPH), subdural (SDH) or intracerebral (ICH) hemorrhages, as well as hypoxic brain injuries. TFUSI is widely used in the clinic owing to its low cost, safety, accessibility, and noninvasive nature. Nevertheless, the accuracy of TFUSI is limited. To address several limitations of current clinical imaging modalities, we develop a novel transfontanelle photoacoustic imaging (TFPAI) probe, which, for the first time, should allow for non-invasive structural and functional imaging of the infant brain. In this study, we test the feasibility of TFPAI for detection of experimentally-induced intra ventricular and Intraparenchymal hemorrhage phantoms in a sheep model with a surgically-induced cranial window which will serve as a model of neonatal fontanelle. This study is towards using the probe we develop for bedside monitoring of neonates with various disease conditions and complications affecting brain perfusion and oxygenation, including apnea, asphyxia, as well as for detection of various types of intracranial hemorrhages (SAH, IVH, SEPH, SDH, ICH).

An intelligent speckle reduction algorithm for optical coherence tomography images

Optical Coherence Tomography (OCT) offers three dimensional images of tissue microstructures. Although OCT imaging offers a promising high resolution method, due to the low coherent light source used in the configuration of OCT, OCT images suffers from an artefact called, speckle. Speckle deteriorates the image quality and effects image analysis algorithm such as segmentation and pattern recognition. We present a novel and intelligent speckle reduction algorithm to reduce speckle based on an ensemble framework of Multi-Layer Perceptron (MLP) neural networks. We tested the algorithm on images of retina obtained from a spectrometer-based Fourier-domain OCT system operating at 890 nm, and observed considerable improvement in the signal-to-noise ratio and contrast of the images.

Mitigation of Speckle Noise in Optical Coherence Tomograms

Optical Coherence Tomography (OCT) is a promising high-resolution imaging technique that works based on low coherent interferometry. However, like other low coherent imaging modalities, OCT suffers from an artifact called, speckle. Speckle reduces the detectability of diagnostically relevant features in the tissue. Retinal optical coherence tomograms are of a great importance in detecting and diagnosing eye diseases. Different hardware or software based techniques are devised in literatures to mitigate speckle noise. The ultimate aim of any software-based despeckling technique is to suppress the noise part of speckle while preserves the information carrying portion of that. In this chapter, we reviewed the most prominent speckle reduction methods for OCT images to date and then present a novel and intelligent speckle reduction algorithm to reduce speckle in OCT images of retina, based on an ensemble framework of Multi-Layer Perceptron (MLP) neural networks.

A novel dermo-epidermal localization algorithm for swept source OCT images of human skin

Optical coherence tomography (OCT) is a noninvasive diagnostic method that offers a view into the superficial layers of the skin in vivo in real-time. OCT delivers morphological images of microstructures within the skin. Epidermal thickness in OCT images is of paramount importance, since dermo-epidermal junction (DEJ) location alteration is the start of several skin abnormalities. Due to the presence of speckle noise, devising an algorithm for locating DEJ in the OCT images is challenging. In this study we propose a semi-automatic DEJ detection algorithm based on graph theory that is resistant to speckle. In this novel approach we use attenuation map as a complementary feature compared to the previous methods that are mainly based on the intensity information. The method is based on converting border segmentation problem to the shortest path problem using graph theory. To smooth borders, we introduced a thinning fuzzy system enabling closer match to manual segmentation. Subsequently, an averaged A-scan analysis is performed to obtain the mean epidermal thickness. The DEJ detection method is performed on 96 B-Scan OCT skin images taken from different sites of body of healthy individuals. The results are evaluated based on several expert’s visual analysis.

Speckle reduction of OCT images using an adaptive cluster-based filtering

Optical coherence tomography (OCT) has become a favorable device in the dermatology discipline due to its moderate resolution and penetration depth. OCT images however contain grainy pattern, called speckle, due to the broadband source that has been used in the configuration of OCT. So far, a variety of filtering techniques is introduced to reduce speckle in OCT images. Most of these methods are generic and can be applied to OCT images of different tissues. In this paper, we present a method for speckle reduction of OCT skin images. Considering the architectural structure of skin layers, it seems that a skin image can benefit from being segmented in to differentiable clusters, and being filtered separately in each cluster by using a clustering method and filtering methods such as Wiener. The proposed algorithm was tested on an optical solid phantom with predetermined optical properties. The algorithm was also tested on healthy skin images. The results show that the cluster-based filtering method can reduce the speckle and increase the signal-to-noise ratio and contrast while preserving the edges in the image.