Fiberscopic pattern removal for optimal coverage in 3D bladder reconstructions of fiberscope cystoscopy videos.

Purpose: In the current clinical standard of care, cystoscopic video is not routinely saved because it is cumbersome to review. Instead, clinicians rely on brief procedure notes and still frames to manage bladder pathology. Preserving discarded data via 3D reconstructions, which are convenient to review, has the potential to improve patient care. However, many clinical videos are collected by fiberscopes, which are lower cost but induce a pattern on frames that inhibit 3D reconstruction. The aim of our study is to remove the honeycomb-like pattern present in fiberscope-based cystoscopy videos to improve the quality of 3D bladder reconstructions. Approach: Our study introduces an algorithm that applies a notch filtering mask in the Fourier domain to remove the honeycomb-like pattern from clinical cystoscopy videos collected by fiberscope as a preprocessing step to 3D reconstruction. We produce 3D reconstructions with the video before and after removing the pattern, which we compare with a metric termed the area of reconstruction coverage (ARC), defined as the surface area (in pixels) of the reconstructed bladder. All statistical analyses use paired t-tests. Results: Preprocessing using our method for pattern removal enabled reconstruction for all (n=5) cystoscopy videos included in the study and produced a statistically significant increase in bladder coverage (p=0.018). Conclusions: This algorithm for pattern removal increases bladder coverage in 3D reconstructions and automates mask generation and application, which could aid implementation in time-starved clinical environments. The creation and use of 3D reconstructions can improve documentation of cystoscopic findings for future surgical navigation, thus improving patient treatment and outcomes.

SpecReFlow: an algorithm for specular reflection restoration using flow-guided video completion.

Purpose: Specular reflections (SRs) are highlight artifacts commonly found in endoscopy videos that can severely disrupt a surgeon's observation and judgment. Despite numerous attempts to restore SR, existing methods are inefficient and time consuming and can lead to false clinical interpretations. Therefore, we propose the first complete deep-learning solution, SpecReFlow, to detect and restore SR regions from endoscopy video with spatial and temporal coherence. Approach: SpecReFlow consists of three stages: (1) an image preprocessing stage to enhance contrast, (2) a detection stage to indicate where the SR region is present, and (3) a restoration stage in which we replace SR pixels with an accurate underlying tissue structure. Our restoration approach uses optical flow to seamlessly propagate color and structure from other frames of the endoscopy video. Results: Comprehensive quantitative and qualitative tests for each stage reveal that our SpecReFlow solution performs better than previous detection and restoration methods. Our detection stage achieves a Dice score of 82.8% and a sensitivity of 94.6%, and our restoration stage successfully incorporates temporal information with spatial information for more accurate restorations than existing techniques. Conclusions: SpecReFlow is a first-of-its-kind solution that combines temporal and spatial information for effective detection and restoration of SR regions, surpassing previous methods relying on single-frame spatial information. Future work will look to optimizing SpecReFlow for real-time applications. SpecReFlow is a software-only solution for restoring image content lost due to SR, making it readily deployable in existing clinical settings to improve endoscopy video quality for accurate diagnosis and treatment.

Introduction to the special feature: amplify Black voices in optics and photonics.

This multi-journal special issue highlights the work of Black scientists and engineers in optics and photonics to accomplish the goal of engaging the entire optics and photonics community and bring awareness to the quality of their research and contributions to the field.

Label-Free Optical Technologies to Enhance Noninvasive Endoscopic Imaging of Early-Stage Cancers.

White light endoscopic imaging allows for the examination of internal human organs and is essential in the detection and treatment of early-stage cancers. To facilitate diagnosis of precancerous changes and early-stage cancers, label-free optical technologies that provide enhanced malignancy-specific contrast and depth information have been extensively researched. The rapid development of technology in the past two decades has enabled integration of these optical technologies into clinical endoscopy. In recent years, the significant advantages of using these adjunct optical devices have been shown, suggesting readiness for clinical translation. In this review, we provide an overview of the working principles and miniaturization considerations and summarize the clinical and preclinical demonstrations of several such techniques for early-stage cancer detection. We also offer an outlook for the integration of multiple technologies and the use of computer-aided diagnosis in clinical endoscopy. Expected final online publication date for the Annual Review of Analytical Chemistry, Volume 17 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Enhancing the image quality of blue light cystoscopy through green-hue correction and fogginess removal.

Blue light cystoscopy (BLC) is a guideline-recommended endoscopic tool to detect bladder cancer with high sensitivity. Having clear, high-quality images during cystoscopy is crucial to the sensitive, efficient detection of bladder tumors; yet, important diagnostic information is often missed or poorly visualized in images containing illumination artifacts or impacted by impurities in the bladder. In this study, we introduce computational methods to remove two common artifacts in images from BLC videos: green hue and fogginess. We also evaluate the effect of artifact removal on the perceptual quality of the BLC images through a survey study and computation of Blind/Referenceless Image Spatial Quality Evaluator scores on the original and enhanced images. We show that corrections and enhancements made to cystoscopy images resulted in a better viewing experience for clinicians during BLC imaging and reliably restored lost tissue features that were important for diagnostics. Incorporating these enhancements during clinical and OR procedures may lead to more comprehensive tumor detection, fewer missed tumors during TURBT procedures, more complete tumor resection and shorter procedure time. When used in off-line review of cystoscopy videos, it may also better guide surgical planning and allow more accurate assessment and diagnosis.

Multiparameter interferometric polarization-enhanced imaging differentiates carcinoma in situ from inflammation of the bladder: an ex vivo study.

Significance: Successful differentiation of carcinoma in situ (CIS) from inflammation in the bladder is key to preventing unnecessary biopsies and enabling accurate therapeutic decisions. Current standard-of-care diagnostic imaging techniques lack the specificity needed to differentiate these states, leading to false positives. Aim: We introduce multiparameter interferometric polarization-enhanced (MultiPIPE) imaging as a promising technology to improve the specificity of detection for better biopsy guidance and clinical outcomes. Approach: In this ex vivo study, we extract tissue attenuation-coefficient-based and birefringence-based parameters from MultiPIPE imaging data, collected with a bench-top system, to develop a classifier for the differentiation of benign and CIS tissues. We also analyze morphological features from second harmonic generation imaging and histology slides and perform imaging-to-morphology correlation analysis. Results: MultiPIPE enhances specificity to differentiate CIS from benign tissues by nearly 20% and reduces the false-positive rate by more than four-fold over clinical standards. We also show that the MultiPIPE measurements correlate well with changes in morphological features in histological assessments. Conclusions: The results of our study show the promise of MultiPIPE imaging to be used for better differentiation of bladder inflammation from flat tumors, leading to a fewer number of unnecessary procedures and shorter operating room (OR) time.

SmartOCT: smartphone-integrated optical coherence tomography.

Smartphone devices have seen unprecedented technical innovation in computational power and optical imaging capabilities, making them potentially invaluable tools in scientific imaging applications. The smartphone's compact form-factor and broad accessibility has motivated researchers to develop smartphone-integrated imaging systems for a wide array of applications. Optical coherence tomography (OCT) is one such technique that could benefit from smartphone-integration. Here, we demonstrate smartOCT, a smartphone-integrated OCT system that leverages built-in components of a smartphone for detection, processing and display of OCT data. SmartOCT uses a broadband visible-light source and line-field OCT design that enables snapshot 2D cross-sectional imaging. Furthermore, we describe methods for processing smartphone data acquired in a RAW data format for scientific applications that improves the quality of OCT images. The results presented here demonstrate the potential of smartphone-integrated OCT systems for low-resource environments.

DiffuserSpec: spectroscopy with Scotch tape.

Computational spectroscopy breaks the inherent one-to-one spatial-to-spectral pixel mapping of traditional spectrometers by multiplexing spectral data over a given sensor region. Most computational spectrometers require components that are complex to design, fabricate, or both. DiffuserSpec is a simple computational spectrometer that uses the inherent spectral dispersion of commercially available diffusers to generate speckle patterns that are unique to each wavelength. Using Scotch tape as a diffuser, we demonstrate narrowband and broadband spectral reconstructions with 2-nm spectral resolution over an 85-nm bandwidth in the near-infrared, limited only by the bandwidth of the calibration dataset. We also investigate the effect of spatial sub-sampling of the 2D speckle pattern on resolution performance.

Depth-resolved extraction of optical attenuation for glaucoma assessment in clinical settings: a pilot study.

We assessed the ability of the optical attenuation coefficient (AC) to detect early-stage glaucoma with two AC estimation algorithms: retinal layer intensity ratio (LIR) and depth-resolved confocal (DRC). We also introduced new depth-dependent AC parameters for retinal nerve fiber layer assessment. Optical coherence tomography B-scans were collected from 44 eyes of age-similar participants with eye health ranging from healthy to severe glaucoma, including glaucoma suspect patients. Mean AC values estimated from the DRC method are comparable to ratio-extracted values (p > 0.5 for all study groups), and the depth-dependent ACDRC parameters enhance the utility of the AC for detection of early-stage glaucoma.

A multi-channel smartphone-based spectroscopic system for high-throughput biosensing in low-resource settings.

Primary healthcare centers (PHC) are the first point of contact for people in low-resource settings, and laboratory services play a critical role in early diagnosis of any disease. In recent years, several smartphone-based spectroscopic systems have been demonstrated to translate lab-confined healthcare applications into point-of-care environments to improve their accessibility. Due to constraints, such as the low availability of skilled personnel and consumables in a PHC, batch processing would be ideal for a large number of samples. Therefore, high-throughput and multi-channel detection is equally critical as affordability and portability. To date, most point-of-care systems are designed to perform a single type of analysis at a time. Herein, we introduce a smartphone-based spectroscopic system based on the use of line-beam illumination to achieve high-throughput sensing (15 channels simultaneously) within a 3d-printed microfluidic device. We also developed a smartphone application to process the spectral data and provide the results in real-time. Bland-Altman analysis revealed that the proposed device performs similarly to a laboratory spectrophotometer. The availability of the developed system will enable detection of multiple samples rapidly in low-resource settings with the existing limited manpower and infrastructures. The fast turnaround time may eventually help in timely diagnosis of patients during situations of high sample load, such as during disease outbreaks.

Birefringent tissue-mimicking phantom for polarization-sensitive optical coherence tomography imaging.

SIGNIFICANCE: Tissue birefringence is an important parameter to consider when designing realistic, tissue-mimicking phantoms. Options for suitable birefringent materials that can be used to accurately represent tissue scattering are limited. AIM: To introduce a method of fabricating birefringent tissue phantoms with a commonly used material-polydimethylsiloxane (PDMS)-for imaging with polarization-sensitive optical coherence tomography (PS-OCT). APPROACH: Stretch-induced birefringence was characterized in PDMS phantoms made with varying curing ratios, and the resulting phantom birefringence values were compared with those of biological tissues. RESULTS: We showed that, with induced birefringence levels up to 2.1 × 10 - 4, PDMS can be used to resemble the birefringence levels in weakly birefringent tissues. We demonstrated the use of PDMS in the development of phantoms to mimic the normal and diseased bladder wall layers, which can be differentiated by their birefringence levels. CONCLUSIONS: PDMS allows accurate control of tissue scattering and thickness, and it exhibits controllable birefringent properties. The use of PDMS as a birefringent phantom material can be extended to other birefringence imaging systems beyond PS-OCT and to mimic other organs.

Cost-Efficient Video Synthesis and Evaluation for Development of Virtual 3D Endoscopy.

OBJECTIVE: 3D reconstruction of the shape and texture of hollow organs captured by endoscopy is important for the diagnosis and surveillance of early and recurrent cancers. Better evaluation of 3D reconstruction pipelines developed for such applications requires easy access to extensive datasets and associated ground truths, cost-efficient and scalable simulations of a range of possible clinical scenarios, and more reliable and insightful metrics to assess performance. METHODS: We present a computer-aided simulation platform for cost-effective synthesis of monocular endoscope videos and corresponding ground truths that mimic a range of potential settings and situations one might encounter during acquisition of clinical endoscopy videos. Using cystoscopy of the bladder as model case, we generated an extensive dataset comprising several synthesized videos of a bladder phantom. We then introduce a novel evaluation procedure to reliably assess an individual 3D reconstruction pipeline or to compare different pipelines. RESULTS: To illustrate the use of the proposed platform and evaluation procedure, we use the aforementioned dataset and ground truths to evaluate a proprietary 3D reconstruction pipeline (CYSTO3D) for bladder cystoscopy videos and compared it with a general-purpose 3D reconstruction pipeline (COLMAP). The evaluation results provide insight into the suggested clinical acquisition protocol and several potential areas for refinement of the pipeline to improve future performance. CONCLUSION: Our work proposes an endoscope video synthesis and reconstruction evaluation toolset and presents experimental results that illustrate usage of the toolset to efficiently assess performance and reveal possible problems of any given 3D reconstruction pipeline, to compare different pipelines, and to provide technically or clinically actionable insights.

Recurrence monitoring for ovarian cancer using a cell phone-integrated paper device to measure the ovarian cancer biomarker HE4/CRE ratio in urine.

Ovarian cancer has a poor cure rate and rates of relapse are high. Current recurrence detection is limited by non-specific methods such as blood testing and ultrasound. Based on reports that human epididymis four (HE4) / creatinine (CRE) ratios found in urine are elevated in ovarian cancers, we have developed a paper-based device that combines lateral flow technology and cell phone analysis to quantitatively measure HE4/CRE. Surrogate samples were used to test the performance over clinically expected HE4/CRE ratios. For HE4/CRE ratios of 2 to 47, the percent error was found to be 16.0% on average whether measured by a flatbed scanner or cell phone. There was not a significant difference between the results from the cell phone or scanner. Based on published studies, error in this method was less than the difference required to detect recurrence. This promising new tool, with further development, could be used at home or in low-resource settings to provide timely detection of ovarian cancer recurrence.

Anti-Black racism in academia and what you can do about it.

The experiences of Black scientists and engineers reveal that science is not a meritocracy. Here is a list of recommendations to combat anti-Black racism in academic institutions.

Low-Cost, Volume-Controlled Dipstick Urinalysis for Home-Testing.

Dipstick urinalysis provides quick and affordable estimations of multiple physiological conditions but requires good technique and training to use accurately. Manual performance of dipstick urinalysis relies on good human color vision, proper lighting control, and error-prone, time-sensitive comparisons to chart colors. By automating the key steps in the dipstick urinalysis test, potential sources of error can be eliminated, allowing self-testing at home. We describe the steps necessary to create a customizable device to perform automated urinalysis testing in any environment. The device is cheap to manufacture and simple to assemble. We describe the key steps involved in customizing it for the dipstick of choice and for customizing a mobile phone app to analyze the results. We demonstrate its use to perform urinalysis and discuss the critical measurements and fabrication steps necessary to ensure robust operation. We then compare the proposed method to the dip-and-wipe method, the gold standard technique for dipstick urinalysis.

Smartphone-based optical spectroscopic platforms for biomedical applications: a review [Invited].

Rapid advancements in smartphone technology have enabled the integration of many optical detection techniques that leverage the embedded functional components and software platform of these sophisticated devices. Over the past few years, several research groups have developed high-resolution smartphone-based optical spectroscopic platforms and demonstrated their usability in different biomedical applications. Such platforms provide unprecedented opportunity to develop point-of-care diagnostics systems, especially for resource-constrained environments. In this review, we discuss the development of smartphone systems for optical spectroscopy and highlight current challenges and potential solutions to improve the scope for their future adaptability.

Optical Technologies for Improving Healthcare in Low-Resource Settings: introduction to the feature issue.

This feature issue of Biomedical Optics Express presents a cross-section of interesting and emerging work of relevance to optical technologies in low-resource settings. In particular, the technologies described here aim to address challenges to meeting healthcare needs in resource-constrained environments, including in rural and underserved areas. This collection of 18 papers includes papers on both optical system design and image analysis, with applications demonstrated for ex vivo and in vivo use. All together, these works portray the importance of global health research to the scientific community and the role that optics can play in addressing some of the world's most pressing healthcare challenges.

Review of methods and applications of attenuation coefficient measurements with optical coherence tomography.

The optical attenuation coefficient (AC), an important tissue parameter that measures how quickly incident light is attenuated when passing through a medium, has been shown to enable quantitative analysis of tissue properties from optical coherence tomography (OCT) signals. Successful extraction of this parameter would facilitate tissue differentiation and enhance the diagnostic value of OCT. In this review, we discuss the physical and mathematical basis of AC extraction from OCT data, including current approaches used in modeling light scattering in tissue and in AC estimation. We also report on demonstrated clinical applications of the AC, such as for atherosclerotic tissue characterization, malignant lesion detection, and brain injury visualization. With current studies showing AC analysis as a promising technique, further efforts in the development of methods to accurately extract the AC and to explore its potential use for more extensive clinical applications are desired.

Automatically Determining the Confocal Parameters From OCT B-Scans for Quantification of the Attenuation Coefficients.

The attenuation coefficient is a relevant biomarker for many diagnostic medical applications. Recently, the Depth-Resolved Confocal (DRC) technique was developed to automatically estimate the attenuation coefficients from Optical Coherence Tomography (OCT) data with pixel-level resolution. However, DRC requires that the confocal function parameters (i.e., focal plane location and apparent Rayleigh range) be known a priori. In this paper, we present the autoConfocal algorithm: a simple, automatic method for estimating those parameters directly from OCT imagery when the focal plane is within the sample. We present autoConfocal+DRC results on phantom data, ex-vivo biological tissue data, and in-vivo clinical data.