Full-field Optical Coherence Tomography Research Articles

Time-domain full-field optical coherence tomography with a digital defocus correction

Time-domain full-field optical coherence tomography (TD-FF-OCT) is an interferometric technique capable of acquiring high-resolution images deep within the biomedical tissue, utilizing a spatially and temporally incoherent light source. However, optical aberrations, such as sample defocus, can degrade the image quality, thereby limiting the achievable imaging depth. Here we demonstrate that the sample defocus within a highly scattering medium can be digitally corrected over a wide defocus range if the optical path lengths in the sample and reference arms are matched. We showcase the application of digital defocus correction on both reflective and scattering samples, effectively compensating digitally for up to 1 mm of defocus.

Abstract PO1-07-08: Value of high resolution full field optical coherence tomography and dynamic cell imaging (D-FFOCT) for one-stop rapid diagnosis breast clinic

Abstract BACKGROUND: Full field optical coherence tomography combined to dynamic cell imaging (D-FFOCT) is a new, simple to use, nondestructive, quick technique than can provide sufficient spatial resolution to mimic histopathological analysis. The objective of this study was to evaluate diagnostic performance of D-FFOCT for one-stop rapid diagnosis breast clinic. METHODS: D-FFOCT was applied to fresh untreated breast and nodes biopsies. Four different readers (senior and junior radiologist, surgeon and pathologist) analyzed the samples without knowing final histological diagnosis or ACR classification. The results were compared to conventional processing and staining (Hematoxylin-eosin). RESULTS: A total of 217 biopsies were performed on 152 patients. There were 144 breast biopsies and 61 lymph nodes with 101 infiltrative cancers (49,27%), 99 benign lesions (48,29%), 3 ductal in situ carcinoma (1,46%) and 2 atypias (0,98%). The diagnostic performances results were as follow: sensitivity: 77% [0.7;0.82], specificity: 64% [0.58;0.71], PPV: 74% [0.68;0.78] and NPV: 75% [0.72;0.78]. A large images atlas was created as well as a diagnosis algorithm from the readers experience. CONCLUSION: D-FFOCT provides interesting results defining malignancy on fresh breast and nodes samples. By training with the diagnosis algorithm and the images atlas or by using machine learning, radiologists could obtain even better outcomes allowing quick detection of breast cancer and lymph node involvement. diagnosis algorithm Figure 1 Images in DCI and FFOCT Two infiltrating cancer diagnosed on pathology (A,B). Focusing on cells (C). Normal galactophoric duct (D). Adenofibroma (E) Citation Format: Alexis Simon, Yasmina Badachi, Jacques Ropers, Isaura Laurent, Lida Dong, Elisabeth Da Maia, Agnes Bourcier, Geoffroy Canlorbe, Catherine Uzan. Value of high resolution full field optical coherence tomography and dynamic cell imaging (D-FFOCT) for one-stop rapid diagnosis breast clinic [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-07-08.

Abstract PO5-07-05: Deep learning can diagnose axillary lymph node metastases on optical virtual histologic images in breast cancer patients during surgery

Abstract Background: Reliable identification of axillary lymph node (ALN) involvement in patients with breast cancer allows for definitive axillary dissection at the time of the initial surgery, thus avoiding the need for a separate axillary surgery. However, conventional intraoperative ALN diagnostic methods are time-consuming and labor-intensive and can result in tissue destruction. Dynamic full field optical coherence tomography, also called dynamic cell imaging (DCI), has been developed and validated to offer rapid and label-free histologic approximations of metastatic and non-metastatic ALNs. In this study, we aim to optimize the diagnostic pipeline with an automated approach and present the results of using a deep learning (DL) algorithm with DCI to predict ALN status intraoperatively in patients with breast cancer. Methods: Breast cancer patients who required ALN staging were enrolled prospectively in this study. DCI was applied to bisected fresh lymph nodes in a non-destructive manner, and the specimens were subsequently sent for histopathological examination, regarded as the gold standard for comparison. A DL model was trained and fine-tuned on over 80,000 DCI images, and the results were mapped to slide level to predict ALN diagnosis. Results: Total 607 DCI slides of ALNs with 112,852 cropped patches were included in the study. The DL model was trained and validated on a dataset containing 481 slides and tested on an independent testing dataset with 126 slides. In the test set, the DL algorithm yielded accuracy for prediction of ALN status, with sensitivity and specificity of 91.9% and 95.5% and an area under the receiver operating characteristic curve (AUC) of 0.937 (95% confidence interval [CI]: 0.912-0.957) at slide level. Conclusion: These results demonstrate that the integration of DCI with DL is rapid, reduces labor requirements and minimizes tissue destruction. Meanwhile, this algorithm had high classification accuracy to predict the metastatic burden of ALNs for patients with breast cancer. Citation Format: Shuwei Zhang, Houpu Yang, Jin Zhao, Shu Wang. Deep learning can diagnose axillary lymph node metastases on optical virtual histologic images in breast cancer patients during surgery [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-07-05.

Influence of static and dynamic ocular aberrations on full-field optical coherence tomography for in vivo high-resolution retinal imaging.

Under spatially incoherent illumination, time-domain full-field optical coherence tomography (FFOCT) offers the possibility to achieve in vivo retinal imaging at cellular resolution over a wide field of view. Such performance is possible, albeit there is the presence of ocular aberrations even without the use of classical adaptive optics. While the effect of aberrations in FFOCT has been debated these past years, mostly on low-order and static aberrations, we present, for the first time to our knowledge, a method enabling a quantitative study of the effect of statistically representative static and dynamic ocular aberrations on FFOCT image metrics, such as SNR, resolution, and image similarity. While we show that ocular aberrations can decrease FFOCT SNR and resolution by up to 14 dB and fivefold, we take advantage of such quantification to discuss different possible compromises between performance gain and adaptive optics complexity and speed, to optimize both sensor-based and sensorless FFOCT high-resolution retinal imaging.

Training With Uncertain Annotations for Semantic Segmentation of Basal Cell Carcinoma From Full-Field OCT Images.

Semantic segmentation of basal cell carcinoma (BCC) from full-field optical coherence tomography (FF-OCT) images of human skin has received considerable attention in medical imaging. However, it is challenging for dermatopathologists to annotate the training data due to OCT's lack of color specificity. Very often, they are uncertain about the correctness of the annotations they made. In practice, annotations fraught with uncertainty profoundly impact the effectiveness of model training and hence the performance of BCC segmentation. To address this issue, we propose an approach to model training with uncertain annotations. The proposed approach includes a data selection strategy to mitigate the uncertainty of training data, a class expansion to consider sebaceous gland and hair follicle as additional classes to enhance the performance of BCC segmentation, and a self-supervised pre-training procedure to improve the initial weights of the segmentation model parameters. Furthermore, we develop three post-processing techniques to reduce the impact of speckle noise and image discontinuities on BCC segmentation. The mean Dice score of BCC of our model reaches 0.503±0.003, which, to the best of our knowledge, is the best performance to date for semantic segmentation of BCC from FF-OCT images.

Potential rapid intraoperative cancer diagnosis using dynamic full-field optical coherence tomography and deep learning: A prospective cohort study in breast cancer patients

An intraoperative diagnosis is critical for precise cancer surgery. However, traditional intraoperative assessments based on hematoxylin and eosin (H&E) histology, such as frozen sections, are time-, resource-, and labor-intensive, and involve specimen-consuming concerns. Here, we report a near-real-time automated cancer diagnosis workflow for breast cancer that combines dynamic full-field optical coherence tomography (D-FFOCT), a label-free optical imaging method, and deep learning for bedside tumor diagnosis during surgery. To classify the benign and malignant breast tissues, we conducted a prospective cohort trial. In the modeling group (n =182), D-FFOCT images were captured from April 26 to June 20, 2018, encompassing 48 benign lesions, 114 invasive ductal carcinoma (IDC), 10 invasive lobular carcinoma, 4 ductal carcinoma in situ (DCIS), and 6 rare tumors. Deep learning model was built up and fine-tuned in 10,357 D-FFOCT patches. Subsequently, from June 22 to August 17, 2018, independent tests (n = 42) were conducted on 10 benign lesions, 29 IDC, 1 DCIS, and 2 rare tumors. The model yielded excellent performance, with an accuracy of 97.62%, sensitivity of 96.88% and specificity of 100%; only one IDC was misclassified. Meanwhile, the acquisition of the D-FFOCT images was non-destructive and did not require any tissue preparation or staining procedures. In the simulated intraoperative margin evaluation procedure, the time required for our novel workflow (approximately 3 min) was significantly shorter than that required for traditional procedures (approximately 30 min). These findings indicate that the combination of D-FFOCT and deep learning algorithms can streamline intraoperative cancer diagnosis independently of traditional pathology laboratory procedures.

Feasibility of High-Cellular-Resolution Full-Field, Artificial-Intelligence-Assisted, Real-Time Optical Coherence Tomography in the Evaluation of Vitiligo: A Prospective Longitudinal Follow-Up Study.

Vitiligo, a psychologically distressing pigmentary disorder characterized by white depigmented patches due to melanocyte loss, necessitates non-invasive tools for early detection and treatment response monitoring. High-cellular-resolution full-field optical coherence tomography (CRFF-OCT) is emerging in pigmentary disorder assessment, but its applicability in vitiligo repigmentation after tissue grafting remains unexplored. To investigate the feasibility of CRFF-OCT for evaluating vitiligo lesions following tissue grafting, our investigation involved ten vitiligo patients who underwent suction blister epidermal grafting and laser ablation at a tertiary center between 2021 and 2022. Over a six-month period, clinical features, dermoscopy, and photography data were recorded. Utilizing CRFF-OCT along with artificial intelligence (AI) applications, repigmentation features were captured and analyzed. The CRFF-OCT analysis revealed a distinct dark band in vitiligo lesion skin, indicating melanin loss. Grafted areas exhibited melanocytes with dendrites around the epidermal-dermal junction and hair follicles. CRFF-OCT demonstrated its efficacy in the early detection of melanocyte recovery and accurate melanin quantification. This study introduces CRFF-OCT as a real-time, non-invasive, and in vivo evaluation tool for assessing vitiligo repigmentation, offering valuable insights into pigmentary disorders and treatment responses.

Optics and quantitative assessment of corneal transparency

While some optical properties of cornea like its stromal birefringence had been known since the early nineteenth century, theories explaining its transparency remained inconclusive until the publication of David Maurice' emblematic paper in 1957 Maurice made a model calculation of light scattering by a single collagen fibril as a “dielectric needle”. The volume density of the fibrils being known, Maurice could demonstrate that in the absence of any order among the fibrils the cornea would be opaque. Maurice postulated the hexagonal structure which since has been confirmed by electron microscopy. In modern terms, the order among the collagen fibrils within a stromal collagen lamella is described by a “structure factor” in the radiative transfer equation. This term corresponds to a description of the structure in reciprocal space and is thus equivalent to the representation of crystalline lattices in the first Brillouin zone used for structural modelling in crystallography.In a recent study we further analysed the collagen structure of healthy and oedematous corneas on a nanometric level observed by transmission electron microscopy (TEM). By automated image analysis we could retrieve the so‐called “pair correlation function” and calculate light attenuation within the corresponding cornea. Our findings show that the 1/e light penetration depth within healthy cornea could be calculated as 7300 ± 800 μm, which explains well the transparency of healthy cornea. However, the photon mean free path of oedematous cornea turned out to be from two to three times that value. In oedematous cornea, the “dilution” effect of the scatters is in fact stronger than the degradation of transparency by the perturbation of the structure. The actual loss of transparency in oedematous cornea may therefore be attributed to scattering processes at the micrometric level induced by the augmented optical contrast at the sites of the keratocytes between the collagen lamellae.Perturbations in corneal transparency do not only hamper vision, they also impact optical imaging methods on cornea. This, in turn, permits to study the transparency properties of the corneal tissue. One of the main research interests of our group is to analyse corneal images recorded by optical coherence tomography (OCT) and to extract quantitative parameters like photon mean path length and the fraction of coherently transmitted light.Our partners could show that in the light scattering regime, a term in the radiation transfer equation may be neglected and the attenuation of the full field OCT signal in the volume of the tissue reduces to a Lambert–Beer law. We developed a data analysis algorithm based on Bayesian inference to extract the attenuation coefficient and used it for the analysis of ex vivo corneal high‐resolution FF‐OCT images.In a second step, we developed an image processing algorithm for clinical OCT images removing typical artefacts and compensating for inhomogeneous illumination and corneal curvature. We presently are able to extract corneal transparency parameters from routine clinical OCT images and to provide post‐op follow‐up after keratoplasty or other surgical interventions.

Optical tomography in a single camera frame using fringe-encoded deep-learning full-field OCT.

Optical coherence tomography is a valuable tool for in vivo examination thanks to its superior combination of axial resolution, field-of-view and working distance. OCT images are reconstructed from several phases that are obtained by modulation/multiplexing of light wavelength or optical path. This paper shows that only one phase (and one camera frame) is sufficient for en face tomography. The idea is to encode a high-frequency fringe patterns into the selected layer of the sample using low-coherence interferometry. These patterns can then be efficiently extracted with a high-pass filter enhanced via deep learning networks to create the tomographic full-field OCT view. This brings 10-fold improvement in imaging speed, considerably reducing the phase errors and incoherent light artifacts related to in vivo movements. Moreover, this work opens a path for low-cost tomography with slow consumer cameras. Optically, the device resembles the conventional time-domain full-field OCT without incurring additional costs or a field-of-view/resolution reduction. The approach is validated by imaging in vivo cornea in human subjects. Open-source and easy-to-follow codes for data generation/training/inference with U-Net/Pix2Pix networks are provided to be used in a variety of image-to-image translation tasks.

Contributed Session I: Human cone response models for optoretinography with FF-SS-OCT and adaptive optics.

Recent work has shown that human rod and cone outer segments (ROS and COS, respectively) deform in response to visual stimuli. In phase-based optoretinography (ORG) the phases of backscattered light from the inner/outer segment junction (IS/OS) and the COS/ROS tips (COST/ROST), is measured, which allows observation of stimulus-evoked, nanometer-scale changes in the OS length. In this work, we used a full-field swept-source OCT with AO that allowed up to kHz volume rates. ORG responses were recorded in two healthy volunteers, with photopigment bleaching levels in the range of 1-60 %, and modeled using an exponential sum. The proposed harmonic oscillator-based response model allowed us to describe the shape of the cone's ORG responses by amplitudes of deflection and relaxation times. Our preliminary results show that responses to complex stimuli were consistent with photopigment availability, which in the context of the consensus theory that adaptation in cones is mediated by photopigment suggests that the ORG may be a useful way to probe light adaptation in cones. The development of simple quantitative parameters describing the ORG response should benefit future clinical applications and help to track the progress of blinding diseases.

Bond-selective full-field optical coherence tomography.

Optical coherence tomography (OCT) is a label-free, non-invasive 3D imaging tool widely used in both biological research and clinical diagnosis. Conventional OCT modalities can only visualize specimen tomography without chemical information. Here, we report a bond-selective full-field OCT (BS-FF-OCT), in which a pulsed mid-infrared laser is used to modulate the OCT signal through the photothermal effect, achieving label-free bond-selective 3D sectioned imaging of highly scattering samples. We first demonstrate BS-FF-OCT imaging of 1 µm PMMA beads embedded in agarose gel. Next, we show 3D hyperspectral imaging of up to 75 µm of polypropylene fiber mattress from a standard surgical mask. We then demonstrate BS-FF-OCT imaging on biological samples, including cancer cell spheroids and C. elegans. Using an alternative pulse timing configuration, we finally demonstrate the capability of BS-FF-OCT on imaging a highly scattering myelinated axons region in a mouse brain tissue slice.

Value of high-resolution full-field optical coherence tomography and dynamic cell imaging for one-stop rapid diagnosis breast clinic.

Full-field optical coherence tomography combined with dynamic cell imaging (D-FFOCT) is a new, simple-to-use, nondestructive, quick technique that can provide sufficient spatial resolution to mimic histopathological analysis. The objective of this study was to evaluate diagnostic performance of D-FFOCT for one-stop rapid diagnosis breast clinic. Dynamic full-field optical coherence tomography was applied to fresh, untreated breast and nodes biopsies. Four different readers (senior and junior radiologist, surgeon, and pathologist) analyzed the samples without knowing final histological diagnosis or American College of Radiology classification. The results were compared to conventional processing and staining (hematoxylin-eosin). A total of 217 biopsies were performed on 152 patients. There were 144 breast biopsies and 61 lymph nodes with 101 infiltrative cancers (49.27%), 99 benign lesions (48.29%), 3 ductal in situ carcinoma (1.46%), and 2 atypias (0.98%). The diagnostic performance results were as follow: sensitivity: 77% [0.7;0.82], specificity: 64% [0.58;0.71], PPV: 74% [0.68;0.78], and NPV: 75% [0.72;0.78]. A large image atlas was created as well as a diagnosis algorithm from the readers' experience. With 74% PPV and 75% NPV, D-FFOCT is not yet ready to be used in clinical practice to identify breast cancer. This is mainly explained by the lack of experience and knowledge of this new technic by the four lectors. By training with the diagnosis algorithm and the image atlas, radiologists could have better outcomes allowing quick detection of breast cancer and lymph node involvement. Deep learning could also be used, and further investigation will follow.

Dynamic full-field optical coherence tomography module adapted to commercial microscopes allows longitudinal in vitro cell culture study

Dynamic full-field optical coherence tomography (D-FFOCT) has recently emerged as a label-free imaging tool, capable of resolving cell types and organelles within 3D live samples, whilst monitoring their activity at tens of milliseconds resolution. Here, a D-FFOCT module design is presented which can be coupled to a commercial microscope with a stage top incubator, allowing non-invasive label-free longitudinal imaging over periods of minutes to weeks on the same sample. Long term volumetric imaging on human induced pluripotent stem cell-derived retinal organoids is demonstrated, highlighting tissue and cell organization processes such as rosette formation and mitosis as well as cell shape and motility. Imaging on retinal explants highlights single 3D cone and rod structures. An optimal workflow for data acquisition, postprocessing and saving is demonstrated, resulting in a time gain factor of 10 compared to prior state of the art. Finally, a method to increase D-FFOCT signal-to-noise ratio is demonstrated, allowing rapid organoid screening.

High-resolution full-field optical coherence tomography microscope for the evaluation of freshly excised skin specimens during Mohs surgery: A feasibility study.

Histopathology for tumor margin assessment is time-consuming and expensive. High-resolution full-field optical coherence tomography (FF-OCT) images fresh tissues rapidly at cellular resolution and potentially facilitates evaluation. Here, we define FF-OCT features of normal and neoplastic skin lesions in fresh ex vivo tissues and assess its diagnostic accuracy for malignancies. For this, normal and neoplastic tissues were obtained from Mohs surgery, imaged using FF-OCT, and their features were described. Two expert OCT readers conducted a blinded analysis to evaluate their diagnostic accuracies, using histopathology as the ground truth. A convolutional neural network was built to distinguish and outline normal structures and tumors. Of the 113 tissues imaged, 95 (84%) had a tumor (75 basal cell carcinomas [BCCs] and 17 squamous cell carcinomas [SCCs]). The average reader diagnostic accuracy was 88.1%, with a sensitivity of 93.7%, and a specificity of 58.3%. The artificial intelligence (AI) model achieved a diagnostic accuracy of 87.6 ± 5.9%, sensitivity of 93.2 ± 2.1%, and specificity of 81.2 ± 9.2%. A mean intersection-over-union of 60.3 ± 10.1% was achieved when delineating the nodular BCC from normal structures. Limitation of the study was the small sample size for all tumors, especially SCCs. However, based on our preliminary results, we envision FF-OCT to rapidly image fresh tissues, facilitating surgical margin assessment. AI algorithms can aid in automated tumor detection, enabling widespread adoption of this technique.

Modeling multiply scattered light in full-field optical coherence tomographic imaging of biological tissues

In this work, we have constructed a handheld full-field optical coherence tomography (FFOCT) system with high spatial resolution, and the Michelson interferometer (served as compensating optical path) and Fizeau interferometer (served as detection) were mounted in series in this system. The expressions of the multiple scattered light decomposed as the sum of incoherent and coherent contribution were derived based on the diffusion equation and mutual coherence function, and the variations of resolution with imaging depth were derived by using the model of multiple scattering. Experiments were conducted on samples composed of the USAF 1951 resolution target covered by the onion slice and human finger-skin slice, respectively, to validate and evaluate the accuracy of our model. A method of image restoration is then presented in which the relation between the resolution and measuring depth and the simulated PSF in the absence of multiple scattering are employed. With the help of this method the lateral resolution of 2.2 μm and 3.9 μm were separately achieved in the onion slice (measuring depth of 64 μm) and human finger-skin slice (measuring depth of 60 μm), and more tiny structures in the en face image of human finger-skin and moth wings were visible. In addition, a novel method for estimating the values of mean free path and anisotropy of scattering medium was proposed.

Comparative analysis of full-field OCT and optical transmission tomography.

This work compares two tomographic imaging technologies, time-domain full-field optical coherence tomography (FFOCT) working in reflection and optical transmission tomography (OTT), using a new optical setup that combines both. We show that, due to forward-scattering properties, the axial sectioning and contrast in OTT can be optimized by tuning illumination. The influence of sample scattering and thickness are discussed. We illustrate the comparison of the two methods in static (morphology) and dynamic (metabolic contrast) regimes using cell cultures, tissues and entire organisms emphasizing the advantages of both approaches.

Dynamic full-field optical coherence tomography for live-cell imaging and growth-phase monitoring in Aspergillus fumigatus.

The diagnosis of cutaneous manifestations of deep mycoses relies on both histopathological and direct examinations. Yet, the current diagnostic criteria cannot prevent missed cases, including invasive aspergillosis, which requires the development of a novel diagnostic approach and imaging tools. We recently introduced the use of dynamic full-field optical coherence tomography (D-FF-OCT) in fungal diagnostics with a definition approaching that of conventional microscopy and the ability to return metabolic information regarding different fungal species. The present work focuses on subcellular dynamics and live-cell imaging of Aspergillus fumigatus with D-FF-OCT to follow the fungal growth stages. The A. fumigatus ATCC 204305 quality-control strain was used for all imaging experiments, following incubation times varying between 24 and 72h at 30°C in a humidified chamber on Sabouraud dextrose agar. Fungal growth was subsequently monitored with D-FF-OCT for up to 5h at room temperature and following the pharmacological stress of either voriconazole, amphotericin B, or caspofungin gradient concentration. D-FF-OCT images allow not only the visualization of intracellular trafficking of vacuoles but also an evolving dynamic segmentation of conidiophores depending on the chronological development and aging of the hyphae or the effect of antifungal treatment. The same applies to conidial heads, with the most intense D-FF-OCT signal coming from vesicles, revealing a changing dynamic within a few hours only, as well as complete extinction following subsequent drying of the Sabouraud dextrose agar. These results provide additional data on the ability of D-FF-OCT to monitor some of the main life cycle processes, dynamics, and intracellular trafficking of vacuoles in A. fumigatus, with or without the effect of pharmacological stress. Such complementary metabolic information could help both clinicians and microbiologists in either mechanistic studies toward experimental mycology or the development of a potential D-FF-OCT-guided diagnosis of superficial fungal infections.

Adaptability of melanocytes post ultraviolet stimulation in patients with melasma.

Dynamic in vivo changes in melanin in melasma lesions after exposure to ultraviolet (UV) irradiation have not been described. To determine whether melasma lesions and nearby perilesions demonstrated different adaptive responses to UV irradiation and whether the tanning responses were different among different locations on face. We collected sequential images from real-time cellular resolution full-field optical coherence tomography (CRFF-OCT) at melasma lesions and perilesions among 20 Asian patients. Quantitative and layer distribution analyses for melanin were performed using a computer-aided detection (CADe) system that utilizes spatial compounding-based denoising convolutional neural networks. The detected melanin (D) is melanin with a diameter >0.5 µm, among which confetti melanin (C) has a diameter of >3.3 µm and corresponds to a melanosome-rich package. The calculated C/D ratio is proportional to active melanin transportation. Before UV exposure, melasma lesions had more detected melanin (p = 0.0271), confetti melanin (p = 0.0163), and increased C/D ratio (p = 0.0152) in the basal layer compared to those of perilesions. After exposure to UV irradiation, perilesions have both increased confetti melanin (p = 0.0452) and the C/D ratio (p = 0.0369) in basal layer, and this effect was most prominent in right cheek (p = 0.030). There were however no significant differences in the detected, confetti, or granular melanin areas before and after exposure to UV irradiation in melasma lesions in all the skin layers. Hyperactive melanocytes with a higher baseline C/D ratio were noted in the melasma lesions. They were "fixed" on the plateau and were not responsive to UV irradiation regardless of the location on face. Perilesions retained adaptability with a dynamic response to UV irradiation, in which more confetti melanin was shed, mainly in the basal layer. Therefore, aggravating effect of UV on melasma was mainly due to UV-responsive perilesions rather than lesions.

Fractal-based aberration-corrected full-field OCT.

The Kolmogorov turbulence model has been validated as a quantitative 3D light scattering model of the inhomogeneous refraction index of biological tissue using full-field OCT (FF-OCT). A fractal-based computational compensation approach was proposed for correcting of depth-resolved aberrations with volumetric FF-OCT. First, the power-spectral density spectrum of the index inhomogeneities was measured by radial Fourier transformation of volumetric data. The spectrum's shape indicates the spatial correlation function and can be quantified as the fractal dimension of tissue. The defocusing correction matrix was built by applying fractal-based analysis as an image quality metric. For comparison, tissue-induced in-depth aberration models were built by phase compensation. After digital aberration correction of FF-OCT images, it enables extracting the temporal contrast indicating the sample dynamics in onion in mitosis and ex vivo mouse heart during delayed neuronal death. The proposed fractal-based contrast augmented images show subcellular resolution recording of dynamic scatters of the growing-up onion cell wall and some micro activities. In addition, low-frequency chamber and high-frequency cardiac muscle fibers from ex vivo mouse heart tissue. Therefore, the depth-resolved changes in fractal parameters may be regarded as a quantitative indicator of defocus aberration compensation. Also the enhanced temporal contrast in FF-OCT has the potential to be a label-free, non-invasive, and three-dimensional imaging tool to investigate sub-cellular activities in metabolism studies.

Time-domain full-field optical coherence tomography with digital confocal line scanning.

Full-field optical coherence tomography (FF-OCT) is a camera-based interferometric microscopy technique that can image deep in tissue with high spatial resolution. However, the absence of confocal gating leads to suboptimal imaging depth. Here, we implement digital confocal line scanning in time-domain FF-OCT by exploiting the row-by-row detection feature of a rolling-shutter camera. A digital micromirror device (DMD) is used in conjunction with the camera to produce synchronized line illumination. An improvement in the SNR by an order of magnitude is demonstrated on a sample of a US Air Force (USAF) target mounted behind a scattering layer.