Lens Free Holographic Imaging for Urinary Tract Infection Screening.

OBJECTIVE: The diagnosis of urinary tract infection (UTI) currently requires precise specimen collection, handling infectious human waste, controlled urine storage, and timely transportation to modern laboratory equipment for analysis. Here we investigate holographic lens free imaging (LFI) to show its promise for enabling automatic urine analysis at the patient bedside. METHODS: We introduce an LFI system capable of resolving important urine clinical biomarkers such as red blood cells, white blood cells, crystals, and casts in 2 mm thick urine phantoms. RESULTS: This approach is sensitive to the particulate concentrations relevant for detecting several clinical urine abnormalities such as hematuria and pyuria, linearly correlating to ground truth hemacytometer measurements with R 2 = 0.9941 and R 2 = 0.9973, respectively. We show that LFI can estimate E. coli concentrations of 10 3 to 10 5 cells/mL by counting individual cells, and is sensitive to concentrations of 10 5 cells/mL to 10 8 cells/mL by analyzing hologram texture. Further, LFI measurements of blood cell concentrations are relatively insensitive to changes in bacteria concentrations of over seven orders of magnitude. Lastly, LFI reveals clear differences between UTI-positive and UTI-negative urine from human patients. CONCLUSION: LFI is sensitive to clinically-relevant concentrations of bacteria, blood cells, and other sediment in large urine volumes. SIGNIFICANCE: Together, these results show promise for LFI as a tool for urine screening, potentially offering early, point-of-care detection of UTI and other pathological processes.

Speckle illumination SFDI for projector-free optical property mapping.

Spatial frequency domain imaging can map tissue scattering and absorption properties over a wide field of view, making it useful for clinical applications such as wound assessment and surgical guidance. This technique has previously required the projection of fully characterized illumination patterns. Here, we show that random and unknown speckle illumination can be used to sample the modulation transfer function of tissues at known spatial frequencies, allowing the quantitative mapping of optical properties with simple laser diode illumination. We compute low- and high-spatial frequency response parameters from the local power spectral density for each pixel and use a lookup table to accurately estimate absorption and scattering coefficients in tissue phantoms, in vivo human hand, and ex vivo swine esophagus. Because speckle patterns can be generated over a large depth of field and field of view with simple coherent illumination, this approach may enable optical property mapping in new form-factors and applications, including endoscopy.

Rapid tissue oxygenation mapping from snapshot structured-light images with adversarial deep learning.

SIGNIFICANCE: Spatial frequency-domain imaging (SFDI) is a powerful technique for mapping tissue oxygen saturation over a wide field of view. However, current SFDI methods either require a sequence of several images with different illumination patterns or, in the case of single-snapshot optical properties (SSOP), introduce artifacts and sacrifice accuracy. AIM: We introduce OxyGAN, a data-driven, content-aware method to estimate tissue oxygenation directly from single structured-light images. APPROACH: OxyGAN is an end-to-end approach that uses supervised generative adversarial networks. Conventional SFDI is used to obtain ground truth tissue oxygenation maps for ex vivo human esophagi, in vivo hands and feet, and an in vivo pig colon sample under 659- and 851-nm sinusoidal illumination. We benchmark OxyGAN by comparing it with SSOP and a two-step hybrid technique that uses a previously developed deep learning model to predict optical properties followed by a physical model to calculate tissue oxygenation. RESULTS: When tested on human feet, cross-validated OxyGAN maps tissue oxygenation with an accuracy of 96.5%. When applied to sample types not included in the training set, such as human hands and pig colon, OxyGAN achieves a 93% accuracy, demonstrating robustness to various tissue types. On average, OxyGAN outperforms SSOP and a hybrid model in estimating tissue oxygenation by 24.9% and 24.7%, respectively. Finally, we optimize OxyGAN inference so that oxygenation maps are computed ∼10 times faster than previous work, enabling video-rate, 25-Hz imaging. CONCLUSIONS: Due to its rapid acquisition and processing speed, OxyGAN has the potential to enable real-time, high-fidelity tissue oxygenation mapping that may be useful for many clinical applications.

GANPOP: Generative Adversarial Network Prediction of Optical Properties From Single Snapshot Wide-Field Images.

We present a deep learning framework for wide-field, content-aware estimation of absorption and scattering coefficients of tissues, called Generative Adversarial Network Prediction of Optical Properties (GANPOP). Spatial frequency domain imaging is used to obtain ground-truth optical properties at 660 nm from in vivo human hands and feet, freshly resected human esophagectomy samples, and homogeneous tissue phantoms. Images of objects with either flat-field or structured illumination are paired with registered optical property maps and are used to train conditional generative adversarial networks that estimate optical properties from a single input image. We benchmark this approach by comparing GANPOP to a single-snapshot optical property (SSOP) technique, using a normalized mean absolute error (NMAE) metric. In human gastrointestinal specimens, GANPOP with a single structured-light input image estimates the reduced scattering and absorption coefficients with 60% higher accuracy than SSOP while GANPOP with a single flat-field illumination image achieves similar accuracy to SSOP. When applied to both in vivo and ex vivo swine tissues, a GANPOP model trained solely on structured-illumination images of human specimens and phantoms estimates optical properties with approximately 46% improvement over SSOP, indicating adaptability to new, unseen tissue types. Given a training set that appropriately spans the target domain, GANPOP has the potential to enable rapid and accurate wide-field measurements of optical properties.

Wide-field optical property mapping and structured light imaging of the esophagus with spatial frequency domain imaging.

As the incidence of esophageal adenocarcinoma continues to rise, there is a need for improved imaging technologies with contrast to abnormal esophageal tissues. To inform the design of optical technologies that meet this need, we characterize the spatial distribution of the scattering and absorption properties from 471 to 851 nm of eight resected human esophagi tissues using Spatial Frequency Domain Imaging. Histopathology was used to categorize tissue types, including normal, inflammation, fibrotic, ulceration, Barrett's Esophagus and squamous cell carcinoma. Average absorption and reduced scattering coefficients of normal tissues were 0.211 ± 0.051 and 1.20 ± 0.18 mm-1 , respectively at 471 nm, and both values decreased monotonically with increasing wavelength. Fibrotic tissue exhibited at least 68% larger scattering signal across all wavelengths, while squamous cell carcinoma exhibited a 36% decrease in scattering at 471 nm. We additionally image the esophagus with high spatial frequencies up to 0.5 mm-1 and show strong reflectance contrast to tissue treated with radiation. Lastly, we observe that esophageal absorption and scattering values change by an average of 9.4% and 2.7% respectively over a 30 minute duration post-resection. These results may guide system design for the diagnosis, prevention and monitoring of esophageal pathologies.