Speckle-free, near-infrared portable confocal microscope

Handheld cross-polarised microscope for imaging individual pigmented cells in human skin in vivo

We present the development of a simple, handheld cross-polarised microscope (CPM) and demonstration of imaging individual pigmented cells in human skin in vivo. In the CPM device, the cross-polarised detection approach is used to reduce the specular reflection from the skin surface and preferentially detect multiply-scattered light. The multiply-scattered light works as back illumination from within the tissue towards the skin surface, and superficial pigment such as intraepidermal melanin absorbs some spectral bands of the multiply-scattered light and cast coloured shadows. Since the light that interacted with the superficial pigment only needs to travel a short distance before it exits the skin surface, microscopic details of the pigment can be preserved. The CPM device uses a water-immersion objective lens with a high numerical aperture to image the microscopic details with minimal spherical aberrations and a small depth of focus. Preliminary results from a pilot study of imaging skin lesions in vivo showed that the CPM device could reveal three-dimensional distribution of pigmented cells and intracellular distribution of pigment. Co-registered CPM and reflectance confocal microscopy images showed good correspondence between dark, brown cells in CPM images and bright, melanin-containing cells in reflectance confocal microscopy images.

Spectrally encoded dual-mode interferometry with orthogonal scanning

Spectrally encoded confocal microscopy (SECM) is a high-speed reflectance confocal microscopy technique. Here, we present a method to integrate optical coherence tomography (OCT) and SECM for complementary imaging by adding orthogonal scanning to the SECM configuration. The co-registration of SECM and OCT is automatic, as all system components are shared in the same order, eliminating the need for additional optical alignment. The proposed multimode imaging system is compact and cost-effective while providing the benefits of imaging aiming and guidance. Furthermore, speckle noise can be suppressed by averaging the speckles generated by shifting the spectral-encoded field in the direction of dispersion. Using a near infrared (NIR) card and a biological sample, we demonstrated the capability of the proposed system by showing SECM imaging at depths of interest guided by the OCT in real time and speckle noise reduction. Interfaced multimodal imaging of SECM and OCT was implemented at a speed of approximately 7 frames/s using fast-switching technology and GPU processing.

Confocal Microscopy for Diagnosis and Management of Cutaneous Malignancies: Clinical Impacts and Innovation

Cutaneous malignancies are common malignancies worldwide, with rising incidence. Most skin cancers, including melanoma, can be cured if diagnosed correctly at an early stage. Thus, millions of biopsies are performed annually, posing a major economic burden. Non-invasive skin imaging techniques can aid in early diagnosis and save unnecessary benign biopsies. In this review article, we will discuss in vivo and ex vivo confocal microscopy (CM) techniques that are currently being utilized in dermatology clinics for skin cancer diagnosis. We will discuss their current applications and clinical impact. Additionally, we will provide a comprehensive review of the advances in the field of CM, including multi-modal approaches, the integration of fluorescent targeted dyes, and the role of artificial intelligence for improved diagnosis and management.

Low-cost, chromatic confocal endomicroscope for cellular imaging in vivo

We have developed a low-cost, chromatic confocal endomicroscope (CCE) that can image a cross-section of the tissue at cellular resolution. In CCE, a custom miniature objective lens was used to focus different wavelengths into different tissue depths. Therefore, each tissue depth was encoded with the wavelength. A custom miniature spectrometer was used to spectrally-disperse light reflected from the tissue and generate cross-sectional confocal images. The CCE prototype had a diameter of 9.5 mm and a length of 68 mm. Measured resolution was high, 2 µm and 4 µm for lateral and axial directions, respectively. Effective field size was 468 µm. Preliminary results showed that CCE can visualize cellular details from cross-sections of the tissue in vivo down to the tissue depth of 100 µm.

High-speed label-free confocal microscopy of Caenorhabditis elegans with near infrared spectrally encoded confocal microscopy

Caenorhabditis elegans (C. elegans) is an optically transparent nematode that shares many gene orthologs and homologs with humans. C. elegans are widely used in large populations for genetic studies relevant to human biology and disease. Success of such studies frequently relies on the ability to image C. elegans structure at high-resolution and high-speed. In this manuscript, we report on the feasibility and suitability of a high-speed variant of reflectance confocal microscopy, known as spectrally encoded confocal microscopy (SECM), for label-free imaging of C. elegans. The developed system utilizes near-infrared illumination in conjunction with refractive and diffractive optics to instantaneously image a confocal image line at a speed of up to 147 kHz with lateral and axial resolutions of 2µm and 10µm, respectively. Our imaging results from wild-type C. elegans and four mutant strains (MT2124, MT1082, CB61, and CB648) demonstrate the ability of SECM in revealing the overall geometry, key internal organs, and mutation-induced structural variations, opening the door for downstream integration of SECM in microfluidic platforms for high throughput structural imaging of C. elegans.

Deep Learning-Based Denoising in High-Speed Portable Reflectance Confocal Microscopy

BACKGROUND AND OBJECTIVE

Portable confocal microscopy (PCM) is a low-cost reflectance confocal microscopy technique that can visualize cellular details of human skin in vivo. When PCM images are acquired with a short exposure time to reduce motion blur and enable real-time 3D imaging, the signal-to-noise ratio (SNR) is decreased significantly, which poses challenges in reliably analyzing cellular features. In this paper, we evaluated deep learning (DL)-based approach for reducing noise in PCM images acquired with a short exposure time.

STUDY DESIGN/MATERIALS AND METHODS

Content-aware image restoration (CARE) network was trained with pairs of low-SNR input and high-SNR ground truth PCM images obtained from 309 distinctive regions of interest (ROIs). Low-SNR input images were acquired from human skin in vivo at the imaging speed of 180 frames/second. The high-SNR ground truth images were generated by registering 30 low-SNR input images obtained from the same ROI and summing them. The CARE network was trained using the Google Colaboratory Pro platform. The denoising performance of the trained CARE network was quantitatively and qualitatively evaluated by using image pairs from 45 unseen ROIs.

RESULTS

CARE denoising improved the image quality significantly, increasing similarity with the ground truth image by 1.9 times, reducing noise by 2.35 times, and increasing SNR by 7.4 dB. Banding noise, prominent in input images, was significantly reduced in CARE denoised images. CARE denoising provided quantitatively and qualitatively better noise reduction than non-DL filtering methods. Qualitative image assessment by three confocal readers showed that CARE denoised images exhibited negligible noise more often than input images and non-DL filtered images.

CONCLUSIONS

Results showed the potential of using a DL-based method for denoising PCM images obtained at a high imaging speed. The DL-based denoising method needs to be further trained and tested for PCM images obtained from disease-suspicious skin lesions.