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

Investigation on morphological and molecular fingerprints of penguin brain using label-free optical imaging and spectroscopic techniques

The morphology and molecular study of the penguin brain are crucial to define its survival in the extreme conditions of Antarctica. The present study focusses on extracting different optical parameters of the penguin brain using label-free optical imaging and spectroscopic techniques. In label-free optical imaging, we have used quantitative phase imaging, which provides morphological information about the neurons in brain tissue, giving the quantitative phase value of 5 to 20 radians corresponding to the 8 µm tissue section. In label-free spectroscopic techniques, we have used autofluorescence and Raman spectroscopy. Autofluorescence spectroscopy provides molecular information about nicotinamide dinucleotide, flavins, lipofuscins, and porphyrins in the brain's spectral range of 420 nm to 700 nm. Raman spectroscopy provides multiple peaks associated with different molecules in the brain; among them, few signals are observed at approximately 1305 cm-1, 1448 cm-1, and 1661 cm-1, which correspond to vibrational modes indicative of vibrational features within lipids and protein structures, as well as the presence of amide groups within brain tissue constituents. All these techniques provide the microscopic and molecular fingerprint of the penguin brain, which can be useful for understanding penguin's anatomical, physiological, and social behavior.

A novel approach in cancer diagnosis: integrating holography microscopic medical imaging and deep learning techniques-challenges and future trends

Medical imaging is pivotal in early disease diagnosis, providing essential insights that enable timely and accurate detection of health anomalies. Traditional imaging techniques, such as Magnetic Resonance Imaging (MRI), Computer Tomography (CT), ultrasound, and Positron Emission Tomography (PET), offer vital insights into three-dimensional structures but frequently fall short of delivering a comprehensive and detailed anatomical analysis, capturing only amplitude details. Three-dimensional holography microscopic medical imaging provides a promising solution by capturing the amplitude (brightness) and phase (structural information) details of biological structures. In this study, we investigate the novel collaborative potential of Deep Learning (DL) and holography microscopic phase imaging for cancer diagnosis. The study comprehensively examines existing literature, analyzes advancements, identifies research gaps, and proposes future research directions in cancer diagnosis through the integrated Quantitative Phase Imaging (QPI) and DL methodology. This novel approach addresses a critical limitation of traditional imaging by capturing detailed structural information, paving the way for more accurate diagnostics. The proposed approach comprises tissue sample collection, holographic image scanning, preprocessing in case of imbalanced datasets, and training on annotated datasets using DL architectures like U-Net and Vision Transformer(ViT's). Furthermore, sophisticated concepts in DL, like the incorporation of Explainable AI (XAI) techniques, are suggested for comprehensive disease diagnosis and identification. The study thoroughly investigates the advantages of integrating holography imaging and DL for precise cancer diagnosis. Additionally, meticulous insights are presented by identifying the challenges associated with this integration methodology.

Resolution-enhanced quantitative phase imaging of blood platelets using a generative adversarial network

We developed a new method to enhance the resolution of blood platelet aggregates imaged via quantitative phase imaging (QPI) using a Pix2Pix generative adversarial network (GAN). First, 1 µm polystyrene beads were imaged with low- and high-resolution QPI, to train the GAN model and validate its applicability. Testing on the polystyrene beads demonstrated a mean error of 4.14% in the generated high-resolution optical-path-delay values compared to the optically acquired ones. Next, blood platelets were collected with low- and high-resolution QPI, and a deep neural network was trained to predict the high-resolution platelet optical-path-delay profiles using the low-resolution profiles, achieving a mean error of 7.01% in the generated high-resolution optical-path-delay values compared to the optically acquired ones. These results highlight the potential of the method in enhancing QPI resolution of cell aggregates without the need for sophisticated optical equipment and optical system modifications for high-resolution microscopy, allowing for better understanding of platelet-related disorders and conditions such as thrombocytopenia and thrombocytosis.

Compact Linnik-type hyperspectral quantitative phase microscope for advanced classification of cellular components

Hyperspectral quantitative phase microscopy (HS-QPM) involves the acquisition of phase images across narrow spectral bands, which enables wavelength-dependent study of different biological samples. In the present work, a compact Linnik-type HS-QPM system is developed to reduce the instability and complexity associated with conventional HS-QPM techniques. The use of a single objective lens for both reference and sample arms makes the setup compact. The capabilities of the system are demonstrated by evaluating the HS phase map of both industrial and biological specimens. Phase maps of exfoliated cheek cells at different wavelengths are stacked to form a HS phase cube, adding spectral dimensionality to spatial phase distribution. Analysis of wavelength response of different cellular components are performed using principal component analysis to identify dominant spectral features present in the HS phase dataset. Findings of the study emphasize on the efficiency and effectiveness of HS-QPM for advancing cellular characterization in biomedical research and clinical applications.

Multimodal fluorescence imaging and spectroscopic techniques for oral cancer screening: a real-time approach

The survival rate of oral squamous cell carcinoma (OSCC) patients is very poor, but it can be improved using highly sensitive, specific, and accurate techniques. Autofluorescence and fluorescence techniques are very sensitive and helpful in cancer screening; being directly linked with the molecular levels of human tissue, they can be used as a quantitative tool for cancer detection. Here, we report the development of multi-modal autofluorescence and fluorescence imaging and spectroscopic (MAF-IS) smartphone-based systems for fast and real-time oral cancer screening. MAF-IS system is indigenously developed and offers the advantages of being a low-cost, handy, non-contact, non-invasive, and easily operable device that can be employed in hospitals, including low-resource settings. In this study, we report the results of 43 individuals with 28 OSCC and 15 oral potentially malignant disorders (OPMDs), i.e., epithelial dysplasia and oral submucous fibrosis, using the developed devices. We observed a red shift in fluorescence emission spectrain vivo. We found red-shift of 7.72 ± 6 nm, 3 ± 4.36 nm, and 1.33 ± 0.47 nm in the case of OSCC, epithelial dysplasia, and oral submucous fibrosis, respectively, compared to normal. The results were compared with histopathology and found to be consistent. Further, the MAF-IS system provides results in real-time with higher accuracy and sensitivity compared to devices using a single modality. Our system can achieve an accuracy of 97% with sensitivity and specificity of 100% and 94.7%, respectively, even with a smaller number of patients (28 patients of OSCC). The proposed MAF-IS device has great potential for fast screening and diagnosis of oral cancer in the future.

Single-shot multispectral quantitative phase imaging of biological samples using deep learning

Multispectral quantitative phase imaging (MS-QPI) is a high-contrast label-free technique for morphological imaging of the specimens. The aim of the present study is to extract spectral dependent quantitative information in single-shot using a highly spatially sensitive digital holographic microscope assisted by a deep neural network. There are three different wavelengths used in our method: λ=532, 633, and 808 nm. The first step is to get the interferometric data for each wavelength. The acquired datasets are used to train a generative adversarial network to generate multispectral (MS) quantitative phase maps from a single input interferogram. The network was trained and validated on two different samples: the optical waveguide and MG63 osteosarcoma cells. Validation of the present approach is performed by comparing the predicted MS phase maps with numerically reconstructed (F T+T I E) phase maps and quantifying with different image quality assessment metrices.

Point-of-care devices based on fluorescence imaging and spectroscopy for tumor margin detection during breast cancer surgery: Towards breast conservation treatment

OBJECTIVE

Fluorescence-based methods are highly specific and sensitive and have potential in breast cancer detection. Simultaneous fluorescence imaging and spectroscopy during intraoperative procedures of breast cancer have great advantages in detection of tumor margin as well as in classification of tumor to healthy tissues. Intra-operative real-time confirmation of breast cancer tumor margin is the aim of surgeons, and therefore, there is an urgent need for such techniques and devices which fulfill the surgeon's priorities.

METHODS

In this article, we propose the development of fluorescence-based smartphone imaging and spectroscopic point-of-care multi-modal devices for detection of invasive ductal carcinoma in tumor margin during removal of tumor. These multimodal devices are portable, cost-effective, noninvasive, and user-friendly. Molecular level sensitivity of fluorescence process shows different behavior in normal, cancerous and marginal tissues. We observed significant spectral changes, such as, red-shift, full-width half maximum (FWHM), and increased intensity as we go towards tumor center from normal tissue. High contrast in fluorescence images and spectra are also recorded for cancer tissues compared to healthy tissues. Preliminary results for the initial trial of the devices are reported in this article.

RESULTS

A total 44 spectra from 11 patients of invasive ductal carcinoma (11 spectra for invasive ductal carcinoma and rest are normal and negative margins) are used. Principle component analysis is used for the classification of invasive ductal carcinoma with an accuracy of 93%, specificity of 75% and sensitivity of 92.8%. We obtained an average 6.17 ± 1.66 nm red shift for IDC with respect to normal tissue. The red shift and maximum fluorescence intensity indicates p < 0.01. These results described here are supported by histopathological examination of the same sample.

CONCLUSION

In the present manuscript, simultaneous fluorescence-based imaging and spectroscopy is accomplished for the classification of IDC tissues and breast cancer margin detection.

Multimodal on-chip nanoscopy and quantitative phase imaging reveals the nanoscale morphology of liver sinusoidal endothelial cells

Visualization of three-dimensional (3D) morphological changes in the subcellular structures of a biological specimen is a major challenge in life science. Here, we present an integrated chip-based optical nanoscopy combined with quantitative phase microscopy (QPM) to obtain 3D morphology of liver sinusoidal endothelial cells (LSEC). LSEC have unique morphology with small nanopores (50-300 nm in diameter) in the plasma membrane, called fenestrations. The fenestrations are grouped in discrete clusters, which are around 100 to 200 nm thick. Thus, imaging and quantification of fenestrations and sieve plate thickness require resolution and sensitivity of sub-100 nm along both the lateral and the axial directions, respectively. In chip-based nanoscopy, the optical waveguides are used both for hosting and illuminating the sample. The fluorescence signal is captured by an upright microscope, which is converted into a Linnik-type interferometer to sequentially acquire both superresolved images and phase information of the sample. The multimodal microscope provided an estimate of the fenestration diameter of 119 ± 53 nm and average thickness of the sieve plates of 136.6 ± 42.4 nm, assuming the constant refractive index of cell membrane to be 1.38. Further, LSEC were treated with cytochalasin B to demonstrate the possibility of precise detection in the cell height. The mean phase value of the fenestrated area in normal and treated cells was found to be 161 ± 50 mrad and 109 ± 49 mrad, respectively. The proposed multimodal technique offers nanoscale visualization of both the lateral size and the thickness map, which would be of broader interest in the fields of cell biology and bioimaging.