Optical coherence tomography imaging of melanoma skin cancer.

No consensus guidelines exist on the use of optical coherence tomography (OCT) for diagnosis of cutaneous melanoma. The objectives of this review are to provide a descriptive review of the literature on characteristics of cutaneous melanomas seen on high-definition OCT (HD-OCT), speckle variance OCT (SV-OCT), and conventional OCT and to compare their diagnostic ability with that of histopathology. A review of PubMed and Google Scholar identified all available literature on OCT in melanoma skin cancer that included all in vivo and ex vivo studies on human or human tissues and excluded all studies on non-human subjects or animal studies. Two hundred nine abstracts were considered for evaluation, 31 abstracts were selected for manuscript review, and 14 abstracts were included that met all criteria. Diagnoses of MIS and MM using HD-OCT and SV-OCT were consistently reported to correlate with histopathology. However, accuracy of diagnosis using conventional OCT varied. Most authors agreed that it was difficult to differentiate MM from benign nevi using conventional OCT. HD-OCT, SV-OCT, and conventional OCT show promise for visualizing cutaneous melanoma. The use of OCT in diagnosis of melanoma is rarely reported in the literature. There is a need to increase and standardize reporting of OCT for diagnosis of cutaneous melanoma.

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

BACKGROUND: 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. METHOD: Two speckle reduction methods (one based on artificial neural network and one based on spatial compounding), an attenuation compensation algorithm (based on Beer-Lambert law) and a deblurring procedure (using deconvolution), are described. Moreover, optical properties extraction algorithm based on extended Huygens-Fresnel (EHF) principle to obtain some additional information from OCT images are discussed. RESULTS: In this short overview, we summarize some of the image enhancement algorithms for OCT images which address the abovementioned artifacts. The results showed a significant improvement in the visibility of the clinically relevant features in the images. The quality improvement was evaluated using several numerical assessment measures. CONCLUSION: Clinical dermatologists benefit from using these image enhancement algorithms to improve OCT diagnosis and essentially function as a noninvasive optical biopsy.

Swept-Source Optical Coherence Tomography-Supervised Biopsy.

BACKGROUND: Currently, only skin biopsy can provide definitive histological confirmation for the diagnosis of skin diseases. To improve the diagnostic accuracy and to assist the dermatologist, various imaging techniques have been added to the examination of skin. Among all these techniques, the recent advances in optical coherence tomography (OCT) have made it possible to image the skin up to 2 millimeters in depth. OBJECTIVE: To testify the feasibility of OCT imaging in skin biopsy, the authors investigated the OCT imaging for real-time visualization of needle insertion and punch biopsy techniques in both a tissue phantom and biological tissue. MATERIALS AND METHODS: A swept-source OCT with 1,305-nm central wavelength was used in this study. The euthanized mouse was used for real-time visualization of needle insertion. A gelatin phantom with India ink was used to demonstrate the punch biopsy using OCT. RESULTS: Optical coherence tomography can provide guidance for skin injections as well as real-time imaging to assist in the performance of punch biopsy. CONCLUSION: Optical coherence tomography holds potential not only as a diagnostic tool in dermatology. It can also allow for visualization for more accurate drug delivery, and noninvasively assess the response to treatment.

High-resolution photoacoustic tomography of resting-state functional connectivity in the mouse brain.

The increasing use of mouse models for human brain disease studies presents an emerging need for a new functional imaging modality. Using optical excitation and acoustic detection, we developed a functional connectivity photoacoustic tomography system, which allows noninvasive imaging of resting-state functional connectivity in the mouse brain, with a large field of view and a high spatial resolution. Bilateral correlations were observed in eight functional regions, including the olfactory bulb, limbic, parietal, somatosensory, retrosplenial, visual, motor, and temporal regions, as well as in several subregions. The borders and locations of these regions agreed well with the Paxinos mouse brain atlas. By subjecting the mouse to alternating hyperoxic and hypoxic conditions, strong and weak functional connectivities were observed, respectively. In addition to connectivity images, vascular images were simultaneously acquired. These studies show that functional connectivity photoacoustic tomography is a promising, noninvasive technique for functional imaging of the mouse brain.

Noninvasive photoacoustic computed tomography of mouse brain metabolism in vivo.

We have demonstrated the feasibility of imaging mouse brain metabolism using photoacoustic computed tomography (PACT), a fast, noninvasive and functional imaging modality with optical contrast and acoustic resolution. Brain responses to forepaw stimulations were imaged transdermally and transcranially. 2-NBDG, which diffuses well across the blood-brain-barrier, provided exogenous contrast for photoacoustic imaging of glucose response. Concurrently, hemoglobin provided endogenous contrast for photoacoustic imaging of hemodynamic response. Glucose and hemodynamic responses were quantitatively decoupled by using two-wavelength measurements. We found that glucose uptake and blood perfusion around the somatosensory region of the contralateral hemisphere were both increased by stimulations, indicating elevated neuron activity. While the glucose response area was more homogenous and confined within the somatosensory region, the hemodynamic response area had a clear vascular pattern and spread wider than the somatosensory region. Our results demonstrate that 2-NBDG-enhanced PACT is a promising tool for noninvasive studies of brain metabolism.

Wide-field two-dimensional multifocal optical-resolution photoacoustic-computed microscopy.

Optical-resolution photoacoustic microscopy (OR-PAM) is an emerging technique that directly images optical absorption in tissue at high spatial resolution. To date, the majority of OR-PAM systems are based on single-focused optical excitation and ultrasonic detection, limiting the wide-field imaging speed. While 1D multifocal OR-PAM (1D-MFOR-PAM) has been developed, the potential of microlens and transducer arrays has not been fully realized. Here we present the development of 2D multifocal optical-resolution photoacoustic-computed microscopy (2D-MFOR-PACM), using a 2D microlens array and a full-ring ultrasonic transducer array. The 10 mm×10 mm microlens array generates 1800 optical foci within the focal plane of the 512-element transducer array, and raster scanning the microlens array yields optical-resolution photoacoustic images. The system has improved the in-plane resolution of a full-ring transducer array from ≥100 to 29 µm and achieved an imaging time of 36 s over a 10 mm×10 mm field of view. In comparison, the 1D-MFOR-PAM would take more than 4 min to image over the same field of view. The imaging capability of the system was demonstrated on phantoms and animals both ex vivo and in vivo.

Vibration analysis of healthy skin: toward a noninvasive skin diagnosis methodology.

Several noninvasive imaging techniques have been developed to monitor the health of skin and enhance the diagnosis of skin diseases. Among them, skin elastography is a popular technique used to measure the elasticity of the skin. A change in the elasticity of the skin can influence its natural frequencies and mode shapes. We propose a technique to use the resonant frequencies and mode shapes of the skin to monitor its health. Our study demonstrates how the resonant frequencies and mode shapes of skin can be obtained using numerical and experimental analysis. In our study, natural frequencies and mode shapes are obtained via two methods: (1) finite element analysis: an eigensolution is performed on a finite element model of normal skin, including stratum corneum, epidermis, dermis, and subcutaneous layers and (2) digital image correlation (DIC): several in-vivo measurements have been performed using DIC. The experimental results show a correlation between the DIC and FE results suggesting a noninvasive method to obtain vibration properties of the skin. This method can be further examined to be eventually used as a method to differentiate healthy skin from diseased skin. Prevention, early diagnosis, and treatment are critical in helping to reduce the incidence, morbidity, and mortality associated with skin cancer; thus, making the current study significant and important in the field of skin biomechanics.

Optical Radiomic Signatures Derived from Optical Coherence Tomography Images Improve Identification of Melanoma.

The current gold standard for clinical diagnosis of melanoma is excisional biopsy and histopathologic analysis. Approximately 15-30 benign lesions are biopsied to diagnose each melanoma. In addition, biopsies are invasive and result in pain, anxiety, scarring, and disfigurement of patients, which can add additional burden to the health care system. Among several imaging techniques developed to enhance melanoma diagnosis, optical coherence tomography (OCT), with its high-resolution and intermediate penetration depth, can potentially provide required diagnostic information noninvasively. Here, we present an image analysis algorithm, "optical properties extraction (OPE)," which improves the specificity and sensitivity of OCT by identifying unique optical radiomic signatures pertinent to melanoma detection. We evaluated the performance of the algorithm using several tissue-mimicking phantoms and then tested the OPE algorithm on 69 human subjects. Our data show that benign nevi and melanoma can be differentiated with 97% sensitivity and 98% specificity. These findings suggest that the adoption of OPE algorithm in the clinic can lead to improvements in melanoma diagnosis and patient experience. SIGNIFICANCE: This study describes a noninvasive, safe, simple-to-implement, and accurate method for the detection and differentiation of malignant melanoma versus benign nevi.

Cluster-based filtering framework for speckle reduction in OCT images.

Optical coherence tomography (OCT) has become a popular modality in the dermatology discipline due to its moderate resolution and penetration depth. OCT images, however, contain a grainy pattern called speckle. To date, a variety of filtering techniques have been introduced to reduce speckle in OCT images. However, further improvement is required to reduce edge smoothing and the deterioration of small structures in OCT images after despeckling. In this manuscript, we present a novel cluster-based speckle reduction framework (CSRF) that consists of a clustering method, followed by a despeckling method. Since edges are borders of two adjacent clusters, the proposed framework leaves the edges intact. Moreover, the multiplicative speckle noise could be modeled as additive noise in each cluster. To evaluate the performance of CSRF and demonstrate its generic nature, a clustering method, namely k-means (KM), and, two pixelwise despeckling algorithms, including Lee filter (LF) and adaptive Wiener filter (AWF), are used. The results indicate that CSRF significantly improves the performance of despeckling algorithms. These improvements are evaluated on healthy human skin images in vivo using two numerical assessment measures including signal-to-noise ratio (SNR), and structural similarity index (SSIM).

Intravascular imaging in neuroendovascular surgery: a brief review.

Intravascular imaging has significantly contributed to the advancement of interventional cardiology. Intravascular ultrasound and optical coherence tomography have facilitated decision-making and interventional strategies in management of coronary artery lesions. Yet, applications of these modalities are limited in cerebrovascular practice. With the momentum in advancement of neuroendovascular interventions and techniques for treatment of strokes, cerebrovascular atherosclerotic diseases, aneurysms and vascular malformations, there is a need for the development of high-resolution platforms that can safely be used in cerebrovascular system, and to meet the imaging requirements in the field. In this brief review, we aim to discuss current and emerging intravascular imaging modalities and explore their potentials in field of neuroendovascular surgery.

Linear-array photoacoustic imaging using minimum variance-based delay multiply and sum adaptive beamforming algorithm.

In photoacoustic imaging, delay-and-sum (DAS) beamformer is a common beamforming algorithm having a simple implementation. However, it results in a poor resolution and high sidelobes. To address these challenges, a new algorithm namely delay-multiply-and-sum (DMAS) was introduced having lower sidelobes compared to DAS. To improve the resolution of DMAS, a beamformer is introduced using minimum variance (MV) adaptive beamforming combined with DMAS, so-called minimum variance-based DMAS (MVB-DMAS). It is shown that expanding the DMAS equation results in multiple terms representing a DAS algebra. It is proposed to use the MV adaptive beamformer instead of the existing DAS. MVB-DMAS is evaluated numerically and experimentally. In particular, at the depth of 45 mm MVB-DMAS results in about 31, 18, and 8 dB sidelobes reduction compared to DAS, MV, and DMAS, respectively. The quantitative results of the simulations show that MVB-DMAS leads to improvement in full-width-half-maximum about 96%, 94%, and 45% and signal-to-noise ratio about 89%, 15%, and 35% compared to DAS, DMAS, MV, respectively. In particular, at the depth of 33 mm of the experimental images, MVB-DMAS results in about 20 dB sidelobes reduction in comparison with other beamformers.

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.

Learnable despeckling framework for optical coherence tomography images.

Optical coherence tomography (OCT) is a prevalent, interferometric, high-resolution imaging method with broad biomedical applications. Nonetheless, OCT images suffer from an artifact called speckle, which degrades the image quality. Digital filters offer an opportunity for image improvement in clinical OCT devices, where hardware modification to enhance images is expensive. To reduce speckle, a wide variety of digital filters have been proposed; selecting the most appropriate filter for an OCT image/image set is a challenging decision, especially in dermatology applications of OCT where a different variety of tissues are imaged. To tackle this challenge, we propose an expandable learnable despeckling framework, we call LDF. LDF decides which speckle reduction algorithm is most effective on a given image by learning a figure of merit (FOM) as a single quantitative image assessment measure. LDF is learnable, which means when implemented on an OCT machine, each given image/image set is retrained and its performance is improved. Also, LDF is expandable, meaning that any despeckling algorithm can easily be added to it. The architecture of LDF includes two main parts: (i) an autoencoder neural network and (ii) filter classifier. The autoencoder learns the FOM based on several quality assessment measures obtained from the OCT image including signal-to-noise ratio, contrast-to-noise ratio, equivalent number of looks, edge preservation index, and mean structural similarity index. Subsequently, the filter classifier identifies the most efficient filter from the following categories: (a) sliding window filters including median, mean, and symmetric nearest neighborhood, (b) adaptive statistical-based filters including Wiener, homomorphic Lee, and Kuwahara, and (c) edge preserved patch or pixel correlation-based filters including nonlocal mean, total variation, and block matching three-dimensional filtering.

Neonatal brain resting-state functional connectivity imaging modalities.

Infancy is the most critical period in human brain development. Studies demonstrate that subtle brain abnormalities during this state of life may greatly affect the developmental processes of the newborn infants. One of the rapidly developing methods for early characterization of abnormal brain development is functional connectivity of the brain at rest. While the majority of resting-state studies have been conducted using magnetic resonance imaging (MRI), there is clear evidence that resting-state functional connectivity (rs-FC) can also be evaluated using other imaging modalities. The aim of this review is to compare the advantages and limitations of different modalities used for the mapping of infants' brain functional connectivity at rest. In addition, we introduce photoacoustic tomography, a novel functional neuroimaging modality, as a complementary modality for functional mapping of infants' brain.

Granular Cell Tumor Imaging Using Optical Coherence Tomography.

BACKGROUND: Granular cell tumor (GCT) is a relatively uncommon tumor that may affect the skin. The tumor can develop anywhere on the body, although it is predominately seen in oral cavities and in the head and neck regions. Here, we present the results of optical coherence tomography (OCT) imaging of a large GCT located on the abdomen of a patient. We also present an analytical method to differentiate between healthy tissue and GCT tissues. MATERIALS AND METHODS: A multibeam, Fourier domain, swept source OCT was used for imaging. The OCT had a central wavelength of 1305 ± 15 nm and lateral and axial resolutions of 7.5 and 10 µm, respectively. Qualitative and quantitative analyses of the tumor and healthy skin are reported. RESULTS: Abrupt changes in architectures of the dermal and epidermal layers in the GCT lesion were observed. These architectural changes were not observed in healthy skin. DISCUSSION: To quantitatively differentiate healthy skin from tumor regions, an optical attenuation coefficient analysis based on single-scattering formulation was performed. The methodology introduced here could have the capability to delineate boundaries of a tumor prior to surgical excision.

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.

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.

Prediction of individual differences in risky behavior in young adults via variations in local brain structure.

In recent years the problem of how inter-individual differences play a role in risk-taking behavior has become a much debated issue. We investigated this problem based on the well-known balloon analog risk task (BART) in 48 healthy subjects in which participants inflate a virtual balloon opting for a higher score in the face of a riskier chance of the balloon explosion. In this study, based on a structural Voxel Based Morphometry (VBM) technique we demonstrate a significant positive correlation between BART score and size of the gray matter volume in the anterior insula in riskier subjects. Although the anterior insula is among the candidate brain areas that were involved in the risk taking behavior in fMRI studies, here based on our structural data it is the only area that was significantly related to structural variation among different subjects.