Ultracompact fluorescence smartphone attachment using built-in optics for protoporphyrin-IX quantification in skin

Smartphone-based fluorescence imaging systems have the potential to provide convenient quantitative image guidance at the point of care. However, common approaches have required the addition of complex optical attachments, which reduce translation potential. In this study, a simple clip-on attachment appropriate for fluorescence imaging of protoporphyrin-IX (PpIX) in skin was designed using the built-in light source and ultrawide camera sensor of a smartphone. Software control for image acquisition and quantitative analysis was developed using the 10-bit video capability of the phone. Optical performance was characterized using PpIX in liquid tissue phantoms and endogenously produced PpIX in mice and human skin. The proposed system achieves a very compact form factor (\u3c30 cm3) and can be readily fabricated using widely available low-cost materials. The limit of detection of PpIX in optical phantoms was \u3c10 nM, with good signal linearity from 10 to 1000 nM (R2 \u3e0.99). Both murine and human skin imaging verified that in vivo PpIX fluorescence was detected within 1 hour of applying aminolevulinic acid (ALA) gel. This ultracompact handheld system for quantification of PpIX in skin is well-suited for dermatology clinical workflows. Due to its simplicity and form factor, the proposed system can be readily adapted for use with other smartphone devices and fluorescence imaging applications. Hardware design and software for the system is made freely available on GitHub (https://github.com/optmed/CompactFluorescenceCam)

Developing diagnostic assessment of breast lumpectomy tissues using radiomic and optical signatures

High positive margin rates in oncologic breast-conserving surgery are a pressing clinical problem. Volumetric X-ray scanning is emerging as a powerful ex vivo specimen imaging technique for analyzing resection margins, but X-rays lack contrast between non-malignant and malignant fibrous tissues. In this study, combined micro-CT and wide-field optical image radiomics were developed to classify malignancy of breast cancer tissues, demonstrating that X-ray/optical radiomics improve malignancy classification. Ninety-two standardized features were extracted from co-registered micro-CT and optical spatial frequency domain imaging samples extracted from 54 breast tumors exhibiting seven tissue subtypes confirmed by microscopic histological analysis. Multimodal feature sets improved classification performance versus micro-CT alone when adipose samples were included (AUC = 0.88 vs. 0.90; p-value = 3.65e−11) and excluded, focusing the classification task on exclusively non-malignant fibrous versus malignant tissues (AUC = 0.78 vs. 0.85; p-value = 9.33e−14). Extending the radiomics approach to high-dimensional optical data—termed “optomics” in this study—offers a promising optical image analysis technique for cancer detection. Radiomic feature data and classification source code are publicly available

3D printing fluorescent material with tunable optical properties

The 3D printing of fluorescent materials could help develop, validate, and translate imaging technologies, including systems for fluorescence-guided surgery. Despite advances in 3D printing techniques for optical targets, no comprehensive method has been demonstrated for the simultaneous incorporation of fluorophores and fine-tuning of absorption and scattering properties. Here, we introduce a photopolymer-based 3D printing method for manufacturing fluorescent material with tunable optical properties. The results demonstrate the ability to 3D print various individual fluorophores at reasonably high fluorescence yields, including IR-125, quantum dots, methylene blue, and rhodamine 590. Furthermore, tuning of the absorption and reduced scattering coefficients is demonstrated within the relevant mamalian soft tissue coefficient ranges of 0.005–0.05 mm−1 and 0.2–1.5 mm−1, respectively. Fabrication of fluorophore-doped biomimicking and complex geometric structures validated the ability to print feature sizes less than 200 μm. The presented methods and optical characterization techniques provide the foundation for the manufacturing of solid 3D printed fluorescent structures, with direct relevance to biomedical optics and the broad adoption of fast manufacturing methods in fluorescence imaging

Tools to build consensus and accelerate clinical translation of intraoperative fluorescence imaging

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Scatter signatures in SFDI data enable breast surgical margin delineation via ensemble learning

Margin assessment in gross pathology is becoming feasible as various explanatory deep learning-powered methods are able to obtain models for macroscopic textural information, tissue microstructure, and local surface optical properties. Unfortunately, each different method seems to lack enough diagnostic power to perform an adequate classification on its own. This work proposes using several separately trained deep convolutional networks, and averaging their responses, in order to achieve a better margin assessment. Qualitative leave-one-out cross-validation results are discussed for a cohort of 70 samples.Spanish Ministry of Science, Innovation and Universities (FIS2010-19860, TEC2016-76021-C2-2-R), Spanish Ministry of Economy, Industry and Competitiveness and Instituto de Salud Carlos III (DTS17-00055, DTS15- 00238), Instituto de Investigación Valdecilla (INNVAL16/02, INNVAL18/23), Spanish Ministry of Education, Culture, and Sports (FPU16/05705)

Automated surgical margin assessment in breast conserving surgery using SFDI with ensembles of self-confident deep convolutional networks

With an adequate tissue dataset, supervised classification of tissue optical properties can be achieved in SFDI images of breast cancer lumpectomies with deep convolutional networks. Nevertheless, the use of a black-box classifier in current ex vivo setups provides output diagnostic images that are inevitably bound to show misclassified areas due to inter- and intra-patient variability that could potentially be misinterpreted in a real clinical setting. This work proposes the use of a novel architecture, the self-introspective classifier, where part of the model is dedicated to estimating its own expected classification error. The model can be used to generate metrics of self-confidence for a given classification problem, which can then be employed to show how much the network is familiar with the new incoming data. A heterogenous ensemble of four deep convolutional models with self-confidence, each sensitive to a different spatial scale of features, is tested on a cohort of 70 specimens, achieving a global leave-one-out cross-validation accuracy of up to 81%, while being able to explain where in the output classification image the system is most confident.Spanish Ministry of Science, Innovation and Universities (FIS2010-19860, TEC2016-76021-C2-2-R), Spanish Ministry of Economy, Industry and Competitiveness and Instituto de Salud Carlos III (DTS17-00055, DTS15- 00238), Instituto de Investigación Valdecilla (INNVAL16/02, INNVAL18/23), Spanish Ministry of Education, Culture, and Sports (FPU16/05705)

Modeling and synthesis of breast cancer optical property signatures with generative models

Is it possible to find deterministic relationships between optical measurements and pathophysiology in an unsupervised manner and based on data alone? Optical property quantification is a rapidly growing biomedical imaging technique for characterizing biological tissues that shows promise in a range of clinical applications, such as intraoperative breast-conserving surgery margin assessment. However, translating tissue optical properties to clinical pathology information is still a cumbersome problem due to, amongst other things, inter- and intrapatient variability, calibration, and ultimately the nonlinear behavior of light in turbid media. These challenges limit the ability of standard statistical methods to generate a simple model of pathology, requiring more advanced algorithms. We present a data-driven, nonlinear model of breast cancer pathology for real-time margin assessment of resected samples using optical properties derived from spatial frequency domain imaging data. A series of deep neural network models are employed to obtain sets of latent embeddings that relate optical data signatures to the underlying tissue pathology in a tractable manner. These self-explanatory models can translate absorption and scattering properties measured from pathology, while also being able to synthesize new data. The method was tested on a total of 70 resected breast tissue samples containing 137 regions of interest, achieving rapid optical property modeling with errors only limited by current semi-empirical models, allowing for mass sample synthesis and providing a systematic understanding of dataset properties, paving the way for deep automated margin assessment algorithms using structured light imaging or, in principle, any other optical imaging technique seeking modeling. Code is available.This work was supported in part by the National Cancer Institute, US National Institutes of Health, under grants R01 CA192803 and F31 CA196308, by the Spanish Ministry of Science and Innovation under grant FIS2010-19860, by the Spanish Ministry of Science, Innovation and Universities under grants TEC2016-76021-C2-2-R and PID2019-107270RB-C21, by the Spanish Minstry of Economy, Industry and Competitiveness and Instituto de Salud Carlos III via DTS17-00055, by IDIVAL under grants INNVAL 16/02, and INNVAL 18/23, and by the Spanish Ministry of Education, Culture, and Sports with PhD grant FPU16/05705, as well as FEDER funds

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

Sub-surface fluorescence time-of-flight imaging using a large format SPAD sensor

Classical fluorescence guided surgery uses long laser pulses to excite fluorescent tracers, only providing the practitioner with 2D information. In this work, we develop and test a 3D imaging technique using LiDAR technology to sense target tissue depths up to 5 mm below the surface. Imaging through highly scattering medium like tissues implies that temporal information used for 3D reconstruction is convolved with diffuse dynamics of the medium. Using diffuse reflectance as an indicator of the medium’s optical properties, correction of the time-of-flight is made possible. Time-of-flight fluorescence imaging, captures intensity as well as temporal profile of fluorescence excited by ultra-short pulses