arXiv subjects

Sunil Bhatt

Publications and source records attributed to Sunil Bhatt.

4 recordsLinked to original sources

arXiv2022

Multimodal Optical Techniques in Pre-Clinical Evaluation of Oral Cancer: Fluorescence Imaging and Spectroscopic Devices

Objective: Survival rate of oral squamous cell carcinoma (OSCC) patients is very poor and can be improved using highly sensitive, specific and accurate techniques. Autofluorescence and fluorescence techniques are very sensitive and useful in cancer screening. Furthermore, fluorescence spectroscopy is directly linked with molecular levels of human tissue and can be used as quantitative tool for cancer detection. Materials and Methods: Here, we report development of multi-modal autofluorescence and fluorescence imaging and spectroscopic (MAF-IS) smartphone-based systems for fast and real time oral cancer screening. Fluorescence-autofluorescence images and spectroscopic datasets shows significant change in oral cancer and normal tissue in terms of fluorescence-intensity, spectral-shape, and red-shift respectively. Results: In this study, total 68 samples (33 cancerous and 35 normal) of 18 OSCC patients and 13 patients of precancerous tissues (dysplasia and fibrosis) are screened. Main remarkable finding of the study is presence of three peaks viz ~636 nm, ~680 nm and ~705 nm with decrease in intensity around 450 nm ~ 520 nm in OSCC in case of autofluorescence. Another finding is red shift in fluorescence spectroscopy of OSCC, dysplasia and fibrosis from normal which is 6.59+-4.54 nm, 3+-4.78 nm and 1.5+-0.5 nm respectively and can be used as cancer marker in real-time screening. Finally, support vector machine (SVM) based classifier is applied for classification of OSCC tissue from normal tissue. The average sensitivity, specificity and accuracy are found as 88.89% ,100 %, and 95%, respectively. Conclusion: Autofluorescence and fluorescence-based imaging and spectroscopy is used for pre-clinical screening of different oral lesions.

physics.med-ph↗

arXiv2022

Single-shot multispectral quantitative phase imaging using deep neural network

Multi-spectral quantitative phase imaging (MS-QPI) is a cutting-edge label-free technique to determine the morphological changes, refractive index variations and spectroscopic information of the specimens. The bottleneck to implement this technique to extract quantitative information, is the need of more than two measurements for generating MS-QPI images. We propose a single-shot MS-QPI technique using highly spatially sensitive digital holographic microscope assisted with deep neural network (DNN). Our method first acquires the interferometric datasets corresponding to multiple wavelengths ({\lambda}=532, 633 and 808 nm used here). The acquired datasets are used to train generative adversarial network (GAN) to generate multi-spectral quantitative phase maps from a single input interferogram. The network is trained and validated on two different samples, the optical waveguide and a MG63 osteosarcoma cells. Further, validation of the framework is performed by comparing the predicted phase maps with experimentally acquired and processed multi-spectral phase maps. The current MS-QPI+DNN framework can further empower spectroscopic QPI to improve the chemical specificity without complex instrumentation and color-cross talk.

physics.optics↗

arXiv2020

High-resolution single-shot phase-shifting interference microscopy using deep neural network for quantitative phase imaging of biological samples

White light phase-shifting interference microscopy (WL-PSIM) is a prominent technique for high-resolution quantitative phase imaging (QPI) of industrial and biological specimens. However, multiple interferograms with accurate phase-shifts are essentially required in WL-PSIM for measuring the accurate phase of the object. Here, we present single-shot phase-shifting interferometric techniques for accurate phase measurement using filtered white light phase-shifting interference microscopy (F-WL-PSIM) and deep neural network (DNN). The methods are incorporated by training the DNN to generate 1) four phase-shifted frames and 2) direct phase from a single interferogram. The training of network is performed on two different samples i.e., optical waveguide and MG63 osteosarcoma cells. Further, performance of F-WL-PSIM+DNN framework is validated by comparing the phase map extracted from network generated and experimentally recorded interferograms. The current approach can further strengthen QPI techniques for high-resolution phase recovery using a single frame for different biomedical applications.

eess.IV↗

arXiv2020

High space-bandwidth in quantitative phase imaging using partially spatially coherent optical coherence microscopy and deep neural network

Quantitative phase microscopy (QPM) is a label-free technique that enables to monitor morphological changes at subcellular level. The performance of the QPM system in terms of spatial sensitivity and resolution depends on the coherence properties of the light source and the numerical aperture (NA) of objective lenses. Here, we propose high space-bandwidth QPM using partially spatially coherent optical coherence microscopy (PSC-OCM) assisted with deep neural network. The PSC source synthesized to improve the spatial sensitivity of the reconstructed phase map from the interferometric images. Further, compatible generative adversarial network (GAN) is used and trained with paired low-resolution (LR) and high-resolution (HR) datasets acquired from PSC-OCM system. The training of the network is performed on two different types of samples i.e. mostly homogenous human red blood cells (RBC) and on highly heterogenous macrophages. The performance is evaluated by predicting the HR images from the datasets captured with low NA lens and compared with the actual HR phase images. An improvement of 9 times in space-bandwidth product is demonstrated for both RBC and macrophages datasets. We believe that the PSC-OCM+GAN approach would be applicable in single-shot label free tissue imaging, disease classification and other high-resolution tomography applications by utilizing the longitudinal spatial coherence properties of the light source.

physics.bio-ph↗