Recent applications of near-infrared spectroscopy in cancer diagnosis and therapy

Applying virtual sample generation and ensemble modeling for improving the spectral diagnosis of cancer

Cancer diagnosis plays a key role in facilitating treatment and improving survival rates of patients. The combination of near-infrared (NIR) spectroscopy with data-driven algorithms offers a rapid and cost-effective approach for such a task. Due to the limitations of objective cases, the number of tumor samples is usually smaller, and the resulting dataset exhibit the issues of class imbalance, which has a more serious impact on the performance of diagnostic models. To deal with class imbalance and improve the sensitivity, this work investigates the feasibility of NIR spectroscopy combined with virtual sample generation (VSG) as well as ensemble strategy for developing diagnostic models. Based on preliminary experiment, several learning algorithms such as discriminant analysis (DA) and partial least square-discriminant analysis (PLS-DA) are screened out as algorithms for constructing prediction models. Three algorithms of VSG including synthetic minority oversampling technique (SMOTE), Borderline-SMOTE and adaptive synthetic sampling (ADASYN) are used for experiment. A fixed sample subset composed of 27 cancer samples and 54 normal samples are hold out as the test set. Three training sets containing 5, 10, 25 minority class samples and 54 majority class samples are used for model development. The experimental result indicates that overall, with PLS-DA algorithm, all VSG approaches can significantly improve the sensitivity of cancer diagnosis for all cases of training sets with different minority samples, but ADASYN performs the best. It reveals that the integration of NIR, PLS-DA, and ADASYN is a promising tool package for developing diagnosis methods.

Plasma-based near-infrared spectroscopy for early diagnosis of lung cancer

Lung cancer (LC) continues to be a leading death cause in China, primarily due to late diagnosis. This study aimed to evaluate the effectiveness of using plasma-based near-infrared spectroscopy (NIRS) for LC early diagnosis. A total of 171 plasma samples were collected, including 73 healthy controls (HC), 73 LC, and 25 benign lung tumors (B). NIRS was utilized to measure the spectra of samples. Pre-processing methods, including centering and scaling, standard normal variate, multiplicative scatter correction, Savitzky-Golay smoothing, Savitzky-Golay first derivative, and baseline correction were applied. Subsequently, 4 machine learning (ML) algorithms, including partial least squares (PLS), support vector machines (SVM), gradient boosting machine, and random forest, were utilized to develop diagnostic models using train set data. Then, the predictive performance of each model was evaluated using test set samples. The study was conducted in 5 comparisons as follows: LC and HC, LC and B, B and HC, the diseased group (D) and HC, as well as LC, B and HC. Among the 5 comparisons, SVM consistently generated the best performance with a certain pre-processing method, achieving overall accuracy of 1.0 (kappa: 1.0) in the comparisons of LC and HC, B and HC, as well as D and HC. Pre-processing was identified as a crucial step in developing ML models. Interestingly, PLS demonstrated remarkable stability and relatively high predictive performance across the 5 comparisons, even though it did not achieve the top results like SVM. However, none of these algorithms were able to effectively distinguish B from LC. These findings indicate that the combination of plasma-based NIRS with ML algorithms is a rapid, non-invasive, effective, and economical method for LC early diagnosis.

Advancements in medical research: Exploring Fourier Transform Infrared (FTIR) spectroscopy for tissue, cell, and hair sample analysis

BACKGROUND

Fourier Transform Infrared (FTIR) spectroscopy has emerged as a powerful analytical tool in medical research, offering non-invasive and precise examination of the molecular composition of biological samples. The primary objective of this review is to underscore the benefits of FTIR spectroscopy in medicinal research, emphasizing its ability to delineate molecular fingerprints and assist in the identification of biochemical structures and key peaks in biological samples.

METHODS

This review comprehensively explores the diverse applications of FTIR spectroscopy in medical investigations, with a specific focus on its utility in analyzing tissue, cells, and hair samples. Various sources, including Google Scholar, PubMed, WorledCat and Scopus, were utilized to conduct this comprehensive literature review.

RESULTS

Recent advancements showcase the versatility of FTIR spectroscopy in elucidating cellular and molecular processes, facilitating disease diagnostics, and enabling treatment monitoring. Notably, FTIR spectroscopy has found significant utility in clinical assessment, particularly in screening counterfeit medicines, owing to its user-friendly operation and minimal sample preparation requirements. Furthermore, customs officials can leverage this technique for preliminary analysis of suspicious samples.

CONCLUSION

This review aims to bridge a gap in the literature and serve as a valuable resource for future research endeavors in FTIR spectroscopy within the medical domain. Additionally, it presents fundamental concepts of FTIR spectroscopy and spectral data interpretation, highlighting its utility as a tool for molecular analysis using Mid-Infrared (MIR) radiation.

Near-infrared spectroscopy as a novel method of ex vivo bladder cancer tissue characterisation

OBJECTIVE

To evaluate near-infrared (NIR) spectroscopy in differentiating between benign and malignant bladder pathologies ex vivo immediately after resection, including the grade and stage of malignancy.

PATIENTS AND METHODS

A total of 355 spectra were measured on 71 bladder specimens from patients undergoing transurethral resection of bladder tumour (TURBT) between April and August 2022. Scan time was 5 s, undertaken using a portable NIR spectrometer within 10 min from excision. Specimens were then sent for routine histopathological correlation. Machine learning models were applied to the spectral dataset to construct diagnostic algorithms; these were then tested for their ability to predict the histological diagnosis of each sample using its NIR spectrum.

RESULTS

A two-group algorithm comparing low- vs high-grade urothelial cancer demonstrated 97% sensitivity, 99% specificity, and the area under the receiver operating characteristic curve (AUC) was 0.997. A three-group algorithm predicting stages Ta vs T1 vs T2 achieved 97% sensitivity, 92% specificity, and the AUC was 0.996.

CONCLUSIONS

This first study evaluating the diagnostic potential of NIR spectroscopy in urothelial cancer shows that it can be accurately used to assess tissue in an ex vivo setting immediately after TURBT. This offers point-of-care assessment of bladder pathology, with potential to influence the extent of resection, reducing both the need for re-resection where invasive disease may be suspected, and also the potential for complications where extent of diagnostic resection can be limited. Further studies utilising fibre-optic probes offer the potential for in vivo assessment.

Non-invasive assessment of periodontal inflammation by continuum-removal hemodynamic spectral indices

BACKGROUND

Hyperspectral techniques have aroused great interest in non-invasively measuring periodontal tissue hemodynamics. However, current studies mainly focused on three typical inflammation stages (healthy, gingivitis and periodontitis) and practical approaches for using optical spectroscopy for early and precisely detection of periodontal inflammation at finer disease stages have not been well studied.

METHODS

This study provided novel spectroscopic insights into periodontitis at different stages of disease, and developed six simple but physically meaning hemodynamic spectral indices (HSIs) including four spectral absorption depths of oxyhemoglobin ( D HbO 2 ), deoxyhemoglobin ( D Hb ), total hemoglobin ( t Hb ) and tissue water ( D water ), and two normalized difference indices of oxyhemoglobin( N D HbO 2 I ) and deoxyhemoglobin ( N D Hb I ) from continuum-removal spectra (400-1700 nm) of periodontal tissue collected from 47 systemically healthy subjects over different severities from healthy, gingivitis, slight, moderate to severe periodontitis for early and precision diagnostics of periodontitis. Typical statistical analyses were conducted to explore the effectiveness of the proposed HSIs.

RESULTS

D Hb and t Hb exerted significant increasing trends as inflammation progressed, whereas D HbO 2 exhibited significant difference (P < 0.05) from the healthy sites only at moderate and severe periodontitis and D water presented unstable sensitives to disease severity. By contrast, N D HbO 2 I and N D Hb I showed more steadily downward trends as severity increased, and demonstrated the highest correlations with clinical gold standard parameters. Particularly, the proposed normalized HSIs ( N D HbO 2 I and N D Hb I ) yielded high correlations of - 0.49 and - 0.44 with probing depth, respectively, far outperforming results achieved by previous studies. The performances of the HSIs were also confirmed using the periodontal therapy group.

CONCLUSIONS

These results indicated great potentials of combination optical spectroscopy and smart devices to non-invasively probe periodontitis at earlier stages using the simple and practical HSIs. Trial registration This study was retrospectively registered in the Chinese Clinical Trial Registry on October 24, 2021, and the clinical registration number is ChiCTR2100052306.

Early osteoarthritis diagnosis based on near-infrared spectroscopy combined with aquaphotomics

Osteoarthritis (OA) is the most common joint disease and the leading cause of disability in elderly individuals. Despite rapid advances in imaging techniques, early OA diagnosis remains a clinical challenge. In the present study, the feasibility of early OA diagnosis was explored via near-infrared spectroscopy (NIRS) combined with aquaphotomics. Synovial fluid samples from 65 cases of OA categorized as mild, moderate, and severe according to theKellgrenandLawrence classification criteria were analyzed via NIRS. The 1st overtone of water (1300-1600 nm) was considered as the research object for an aquaphotomics model, and aquagrams of the mild, moderate, and severe OA cases were generated using 12 water absorption patterns for early OA diagnosis.The aquaphotomics results exhibited clear differences in the region of 1300-1500 nm, and the number of hydrogen bonds of different water species (1412,1424, 1482, and 1496 nm) evidently correlated with OA occurrence and development. With OA progression, the absorption intensity of water molecules without hydrogen bonds (1412 nm/1424 nm) became stronger, while the absorption intensity of water molecules with four hydrogen bonds (1482 nm/1496 nm) decreased.These results together reveal that the established accurate and rapid early OA diagnosis model based on NIRS combined with aquaphotomics is effective and feasible, and that the number of hydrogen bonds can be used as a biomarker for early OA diagnosis.

Near-infrared spectroscopy and machine learning algorithms for rapid and non-invasive detection of Trichuris

BACKGROUND

Trichuris trichiura (whipworm) is one of the most prevalent soil transmitted helminths (STH) affecting 604-795 million people worldwide. Diagnostic tools that are affordable and rapid are required for detecting STH. Here, we assessed the performance of the near-infrared spectroscopy (NIRS) technique coupled with machine learning algorithms to detect Trichuris muris in faecal, blood, serum samples and non-invasively through the skin of mice.

METHODOLOGY

We orally infected 10 mice with 30 T. muris eggs (low dose group), 10 mice with 200 eggs (high dose group) and 10 mice were used as the control group. Using the NIRS technique, we scanned faecal, serum, whole blood samples and mice non-invasively through their skin over a period of 6 weeks post infection. Using artificial neural networks (ANN) and spectra of faecal, serum, blood and non-invasive scans from one experiment, we developed 4 algorithms to differentiate infected from uninfected mice. These models were validated on mice from a second independent experiment.

PRINCIPAL FINDINGS

NIRS and ANN differentiated mice into the three groups as early as 2 weeks post infection regardless of the sample used. These results correlated with those from concomitant serological and parasitological investigations.

SIGNIFICANCE

To our knowledge, this is the first study to demonstrate the potential of NIRS as a diagnostic tool for human STH infections. The technique could be further developed for large scale surveillance of soil transmitted helminths in human populations.

Transmission infrared micro-spectroscopic study of individual human hair

Significance

Understanding the optical transmission property of human hair, especially in the infrared regime, is vital in physical, clinical, and biomedical research. However, the majority of infrared spectroscopy on human hair is performed in the reflection mode, which only probes the absorptance of the surface layer.

Aim

The direct transmission spectrum of individual hair without horizontal cut offers a rapid and non-destructive test of the hair cortex but is less investigated experimentally due to the small size and strong absorption of the hair.

Approach

In this work, we conduct a transmission infrared micro-spectroscopic study on individual human hair with the help of Fourier-transform infrared microscope experimentally. Its high spatial resolution of infrared micro-spectroscopy further allows the comparison among different regions of hair. The geometry effect of the internal hair structure is also quantified using the finite-element simulation, which supports the experimental results.

Results

By utilizing direct measurements of the transmission spectrum using a Fourier-transform infrared microscope, the human hair is found to display prominent band filtering behavior. In a case study of adult-onset Still's disease, the corresponding infrared transmission exhibits systematic variations of spectral weight as the disease evolves.

Conclusions

Our work implies that the variation of spectral weight may relate to the disordered microscopic structure variation of the hair cortex during an inflammatory attack. Our work reveals the potential of hair infrared transmission spectrum in tracing the variation of hair cortex retrospectively.

Exploring the Steps of Infrared (IR) Spectral Analysis: Pre-Processing, (Classical) Data Modelling, and Deep Learning

Infrared (IR) spectroscopy has greatly improved the ability to study biomedical samples because IR spectroscopy measures how molecules interact with infrared light, providing a measurement of the vibrational states of the molecules. Therefore, the resulting IR spectrum provides a unique vibrational fingerprint of the sample. This characteristic makes IR spectroscopy an invaluable and versatile technology for detecting a wide variety of chemicals and is widely used in biological, chemical, and medical scenarios. These include, but are not limited to, micro-organism identification, clinical diagnosis, and explosive detection. However, IR spectroscopy is susceptible to various interfering factors such as scattering, reflection, and interference, which manifest themselves as baseline, band distortion, and intensity changes in the measured IR spectra. Combined with the absorption information of the molecules of interest, these interferences prevent direct data interpretation based on the Beer-Lambert law. Instead, more advanced data analysis approaches, particularly artificial intelligence (AI)-based algorithms, are required to remove the interfering contributions and, more importantly, to translate the spectral signals into high-level biological/chemical information. This leads to the tasks of spectral pre-processing and data modeling, the main topics of this review. In particular, we will discuss recent developments in both tasks from the perspectives of classical machine learning and deep learning.

Diagnostic and monitoring applications using Near infrared (NIR) Spectroscopy in cancer and other diseases

Early cancer diagnosis plays a critical role in improving treatment outcomes and increasing survival rates for certain cancers. NIR spectroscopy offers a rapid and cost-effective approach to evaluate the optical properties of tissues at the microvessel level and provides valuable molecular insights. The integration of NIR spectroscopy with advanced data-driven algorithms in portable instruments has made it a cutting-edge technology for medical applications. NIR spectroscopy is a simple, non-invasive and affordable analytical tool that complements expensive imaging modalities such as functional magnetic resonance imaging, positron emission tomography and computed tomography. By examining tissue absorption, scattering, and concentrations of oxygen, water, and lipids, NIR spectroscopy can reveal inherent differences between tumor and normal tissue, often revealing specific patterns that help stratify disease. In addition, the ability of NIR spectroscopy to assess tumor blood flow, oxygenation, and oxygen metabolism provides a key paradigm for its application in cancer diagnosis. This review evaluates the effectiveness of NIR spectroscopy in the detection and characterization of disease, particularly in cancer, with or without the incorporation of chemometrics and machine learning algorithms. The report highlights the potential of NIR spectroscopy technology to significantly improve discrimination between benign and malignant tumors and accurately predict treatment outcomes. In addition, as more medical applications are studied in large patient cohorts, consistent advances in clinical implementation can be expected, making NIR spectroscopy a valuable adjunct technology for cancer therapy management. Ultimately, the integration of NIR spectroscopy into cancer diagnostics promises to improve prognosis by providing critical new insights into cancer patterns and physiology.

Near-infrared spectroscopy for structural bone assessment

Aims

Disorders of bone integrity carry a high global disease burden, frequently requiring intervention, but there is a paucity of methods capable of noninvasive real-time assessment. Here we show that miniaturized handheld near-infrared spectroscopy (NIRS) scans, operated via a smartphone, can assess structural human bone properties in under three seconds.

Methods

A hand-held NIR spectrometer was used to scan bone samples from 20 patients and predict: bone volume fraction (BV/TV); and trabecular (Tb) and cortical (Ct) thickness (Th), porosity (Po), and spacing (Sp).

Results

NIRS scans on both the inner (trabecular) surface or outer (cortical) surface accurately identified variations in bone collagen, water, mineral, and fat content, which then accurately predicted bone volume fraction (BV/TV, inner R² = 0.91, outer R² = 0.83), thickness (Tb.Th, inner R² = 0.9, outer R² = 0.79), and cortical thickness (Ct.Th, inner and outer both R² = 0.90). NIRS scans also had 100% classification accuracy in grading the quartile of bone thickness and quality.

Conclusion

We believe this is a fundamental step forward in creating an instrument capable of intraoperative real-time use.

Cite this article: Bone Jt Open 2023;4(4):250–261.

Malaria absorption peaks acquired through the skin of patients with infrared light can detect patients with varying parasitemia

To eliminate malaria, scalable tools that are rapid, affordable, and can detect patients with low parasitemia are required. Non-invasive diagnostic tools that are rapid, reagent-free, and affordable would also provide a justifiable platform for testing malaria in asymptomatic patients. However, non-invasive surveillance techniques for malaria remain a diagnostic gap. Here, we show near-infrared Plasmodium absorption peaks acquired non-invasively through the skin using a miniaturized hand-held near-infrared spectrometer. Using spectra from the ear, these absorption peaks and machine learning techniques enabled non-invasive detection of malaria-infected human subjects with varying parasitemia levels in less than 10 s.

In silico NIR spectroscopy - A review. Molecular fingerprint, interpretation of calibration models, understanding of matrix effects and instrumental difference

Quantum mechanical calculations are routinely used as a major support in mid-infrared (MIR) and Raman spectroscopy. In contrast, practical limitations for long time formed a barrier to developing a similar synergy between near-infrared (NIR) spectroscopy and computational chemistry. Recent advances in theoretical methods suitable for calculation of NIR spectra opened the pathway to modeling NIR spectra of various molecules. Accurate theoretical reproduction of NIR spectra of molecules reaching the size of long-chain fatty acids was accomplished so far. In silico NIR spectroscopy, where the spectra are calculated ab initio, provides substantial improvement in our understanding of the overtones and combination bands that overlap in staggering numbers and create complex lineshape typical for NIR spectra. This improves the comprehension of the spectral information enabling access to rich and detail molecular footprint, essential for fundamental research and useful in routine analysis by NIR spectroscopy and chemometrics. This review article summarizes the most recent accomplishments in the emerging field with examples of simulated NIR spectra of molecules reaching long-chain fatty acids and polymers. In addition to detailed NIR band assignments and new physical insights, simulated spectra enable innovative support in applications. Understanding of the difference in the performance observed between miniaturized NIR spectrometers and chemical interpretation of the chemometric models are noteworthy here. These new elements integrated into NIR spectroscopy framework enable a knowledge-based design of the analysis with comprehension of the processed chemical information.

Protein nanoparticles directed cancer imaging and therapy

Cancer has been a serious threat to human health. Among drug delivery carriers, protein nanoparticles are unique because of their mild and environmentally friendly preparation methods. They also inherit desired characteristics from natural proteins, such as biocompatibility and biodegradability. Therefore, they have solved some problems inherent to inorganic nanocarriers such as poor biocompatibility. Also, the surface groups and cavity of protein nanoparticles allow for easy surface modification and drug loading. Besides, protein nanoparticles can be combined with inorganic nanoparticles or contrast agents to form multifunctional theranostic platforms. This review introduces representative protein nanoparticles applicable in cancer theranostics, including virus-like particles, albumin nanoparticles, silk protein nanoparticles, and ferritin nanoparticles. It also describes the common methods for preparing them. It then critically analyzes the use of a variety of protein nanoparticles in improved cancer imaging and therapy.

Shedding light on neuroscience: Two decades of functional near-infrared spectroscopy applications and advances from a bibliometric perspective

Functional near-infrared spectroscopy (fNIRS) is a noninvasive optical brain-imaging technique that detects changes in hemoglobin concentration in the cerebral cortex. fNIRS devices are safe, silent, portable, robust against motion artifacts, and have good temporal resolution. fNIRS is reliable and trustworthy, as well as an alternative and a complement to other brain-imaging modalities, such as electroencephalography or functional magnetic resonance imaging. Given these advantages, fNIRS has become a well-established tool for neuroscience research, used not only for healthy cortical activity but also as a biomarker during clinical assessment in individuals with schizophrenia, major depressive disorder, bipolar disease, epilepsy, Alzheimer's disease, vascular dementia, and cancer screening. Owing to its wide applicability, studies on fNIRS have increased exponentially over the last two decades. In this study, scientific publications indexed in the Web of Science databases were collected and a bibliometric-type methodology was developed. For this purpose, a comprehensive science mapping analysis, including top-ranked authors, journals, institutions, countries, and co-occurring keywords network, was conducted. From a total of 2310 eligible documents, 6028 authors and 531 journals published fNIRS-related papers, Fallgatter published the highest number of articles and was the most cited author. University of Tübingen in Germany has produced the most trending papers since 2000. USA was the most prolific country with the most active institutions, followed by China, Japan, Germany, and South Korea. The results also revealed global trends in emerging areas of research, such as neurodevelopment, aging, and cognitive and emotional assessment.

Early Diagnosis of Type 2 Diabetes Based on Near-Infrared Spectroscopy Combined With Machine Learning and Aquaphotomics

Early diagnosis is important to reduce the incidence and mortality rate of diabetes. The feasibility of early diagnosis of diabetes was studied via near-infrared spectra (NIRS) combined with a support vector machine (SVM) and aquaphotomics. Firstly, the NIRS of entire blood samples from the population of healthy, pre-diabetic, and diabetic patients were obtained. The spectral data of the entire spectra in the visible and near-infrared region (400–2,500 nm) were used as the research object of the qualitative analysis. Secondly, several preprocessing steps including multiple scattering correction, variable standardization, and first derivative and second derivative steps were performed and the best pretreatment method was selected. Finally, for the early diagnosis of diabetes, models were established using SVM. The first overtone of water (1,300–1,600 nm) was used as the research object for an aquaphotomics model, and the aquagram of the healthy group, pre-diabetes, and diabetes groups were drawn using 12 water absorption patterns for the early diagnosis of diabetes. The results of SVM showed that the highest accuracy was 97.22% and the specificity and sensitivity were 95.65 and 100%, respectively when the pretreatment method of the first derivative was used, and the best model parameters were c = 18.76 and g = 0.008583.The results of the aquaphotomics model showed clear differences in the 1,400–1,500 nm region, and the number of hydrogen bonds in water species (1,408, 1,416, 1,462, and 1,522 nm) was evidently correlated with the occurrence and development of diabetes. The number of hydrogen bonds was the smallest in the healthy group and the largest in the diabetes group. The suggested reason is that the water matrix of blood changes with the worsening of blood glucose metabolic dysfunction. The number of hydrogen bonds could be used as biomarkers for the early diagnosis of diabetes. The result show that it is effective and feasible to establish an accurate and rapid early diagnosis model of diabetes via NIRS combined with SVM and aquaphotomics.

NIR/photoacoustic imaging of multitype gallbladder cancer using carboxyl/amino functionalized polymer dots

Gallbladder cancer has high incidence and mortality and a low early diagnosis rate and requires rapid and efficient diagnosis. Herein, carboxyl/amino functionalized polymer dots (Pdots) were designed to enhance cellular internalization and tumor accumulation. The prepared Pdots were 40-50 nm in diameter, contained no toxic metal, exhibited long circulation time and high stability, and produced strong NIR emission and photoacoustic signals. Different cellular uptake and distribution of functionalized Pdots in eight gallbladder cell lines were quantitatively investigated using flow cytometry and super-resolution microscopy. In vivo NIR fluorescence imaging showed that the functional Pdots had high accumulation in the tumor after 30 minutes of injection and remained there for up to 6 days. In addition, photoacoustic imaging found that the abundant blood vessels around the tumor microenvironment and Pdots entered the tumor through the blood vessels. Furthermore, a high heterogeneity of vascular networks was visualized in real-time and high resolution by probe-based confocal laser endomicroscopy imaging. These results offer a new avenue for the development of functional Pdots as a probe for multi-modal and multi-scale imaging of gallbladder cancer in small animals.

Synergy Effect of Combined Near and Mid-Infrared Fibre Spectroscopy for Diagnostics of Abdominal Cancer

Cancers of the abdominal cavity comprise one of the most prevalent forms of cancers, with the highest contribution from colon and rectal cancers (12% of the human population), followed by stomach cancers (4%). Surgery, as the preferred choice of treatment, includes the selection of adequate resection margins to avoid local recurrences due to minimal residual disease. The presence of functionally vital structures can complicate the choice of resection margins. Spectral analysis of tissue samples in combination with chemometric models constitutes a promising approach for more efficient and precise tumour margin identification. Additionally, this technique provides a real-time tumour identification approach not only for intraoperative application but also during endoscopic diagnosis of tumours in hollow organs. The combination of near-infrared and mid-infrared spectroscopy has advantages compared to individual methods for the clinical implementation of this technique as a diagnostic tool.

Potential of infrared microscopy to differentiate between dementia with Lewy bodies and Alzheimer's diseases using peripheral blood samples and machine learning algorithms

SIGNIFICANCE

Accurate and objective identification of Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) is of major clinical importance due to the current lack of low-cost and noninvasive diagnostic tools to differentiate between the two. Developing an approach for such identification can have a great impact in the field of dementia diseases as it would offer physicians a routine objective test to support their diagnoses. The problem is especially acute because these two dementias have some common symptoms and characteristics, which can lead to misdiagnosis of DLB as AD and vice versa, mainly at their early stages.

AIM

The aim is to evaluate the potential of mid-infrared (IR) spectroscopy in tandem with machine learning algorithms as a sensitive method to detect minor changes in the biochemical structures that accompany the development of AD and DLB based on a simple peripheral blood test, thus improving the diagnostic accuracy of differentiation between DLB and AD.

APPROACH

IR microspectroscopy was used to examine white blood cells and plasma isolated from 56 individuals: 26 controls, 20 AD patients, and 10 DLB patients. The measured spectra were analyzed via machine learning.

RESULTS

Our encouraging results show that it is possible to differentiate between dementia (AD and DLB) and controls with an ∼86  %   success rate and between DLB and AD patients with a success rate of better than 93%.

CONCLUSIONS

The success of this method makes it possible to suggest a new, simple, and powerful tool for the mental health professional, with the potential to improve the reliability and objectivity of diagnoses of both AD and DLB.

Optics Based Label-Free Techniques and Applications in Brain Monitoring

Functional near-infrared spectroscopy (fNIRS) has been utilized already around three decades for monitoring the brain, in particular, oxygenation changes in the cerebral cortex. In addition, other optical techniques are currently developed for in vivo imaging and in the near future can be potentially used more in human brain research. This paper reviews the most common label-free optical technologies exploited in brain monitoring and their current and potential clinical applications. Label-free tissue monitoring techniques do not require the addition of dyes or molecular contrast agents. The following optical techniques are considered: fNIRS, diffuse correlations spectroscopy (DCS), photoacoustic imaging (PAI) and optical coherence tomography (OCT). Furthermore, wearable optical brain monitoring with the most common applications is discussed.

Open-source python module for automated preprocessing of near infrared spectroscopic data

Near infrared spectroscopy (NIRS) is an analytical technique for determining the chemical composition or structure of a given sample. For several decades, NIRS has been a frequently used analysis tool in agriculture, pharmacology, medicine, and petrochemistry. The popularity of NIRS is constantly growing as new application areas are discovered. Contrary to mid infrared spectral region, the absorption bands in near infrared spectral region are often non-specific, broad, and overlapping. Analysis of NIR spectra requires multivariate methods which are highly subjective to noise arising from instrumentation, scattering effects, and measurement setup. NIRS measurements are also frequently performed outside of a laboratory which further contributes to the presence of noise. Therefore, preprocessing is a critical step in NIRS as it can vastly improve the performance of multivariate models. While extensive research regarding various preprocessing methods exists, selection of the best preprocessing method is often determined through trial-and-error. A more powerful approach for optimizing preprocessing in NIRS models would be to automatically compare a large number of preprocessing techniques (e.g., through grid-search or hyperparameter tuning). To enable this, we present, nippy, an open-source Python module for semi-automatic comparison of NIRS preprocessing methods (available at https://github.com/uef-bbc/nippy). We provide here a brief overview of the capabilities of nippy and demonstrate the typical usage through two examples with public datasets.

VIS-NIR spectral signature and quantitative analysis of HeLa and DU145 cell line

Cancer is increasing in incidence and the leading cause of death worldwide. Controlling and reducing cancer requires early detection and technique to accurately detect and quantify predictive biomarkers. Optical spectroscopy has shown promising non-destructive ability to display distinctive spectral characteristics between cancerous and normal tissues from different part of human organ. Nonetheless, not many information is available on spectroscopic properties of cancer cell lines. In this research, the visible-near infrared (VIS-NIR) absorbance spectroscopy measurement of cultured cervical cancer (HeLa) and prostate cancer cells (DU145) lines has been performed to develop spectral signature of cancer cells and to generate algorithm to quantify cancer cells. Spectroscopic measurement on mouse skin fibroblast (L929) was also taken for comparative purposes. In visible region, the raw cells' spectra do not produce any noticeable peak absorbance that provides information on color because the medium used for cells is colorless and transparent. NIR wavelength between 950 and 975 nm exhibit significant peak due to water absorbance by the medium. Development of spectral signature for the cells through the application of regression technique significantly enhances the diverse characteristics between L929, HeLa and DU145. The application of multiple linear regression allows high measurement accuracy of the cells with coefficient of determination above 0.94.

FTIR bio-spectroscopy scattering correction using natural biological characteristics of different cell lines

Fourier transform infrared (FTIR) spectroscopy is a well-known method of analysis, with various applications, including promising potential for analyzing biological samples. In the bio-spectroscopy of cells, Mie scattering may increase, which then causes spectral distortion, due to the similarity of cell size with the IR medium-wavelength. These changes make the spectrum unreliable. In previous scattering elimination studies, questionable estimations were considered. For instance, all cells were considered as spherical objects or cell size was estimated randomly. In an attempt to provide the best equation based on the natural existence of cells for the FTIR Mie scattering correction, we examined the actual biological data of cells - as opposed to those yielded from mathematical manipulations. So five biological factors: cell size, shape, granularity, circularity, and edge irregularities, for each cell line were considered as factors which cause scattering. For measuring cell size, roundness and edge irregularity, microscopy images were obtained and processed. For evaluating cell line granularity, flow cytometry was used. Finally, by including these factors, an algorithm was designed. To assess the accuracy of the proposed algorithm, the trypsinized cell spectrum was considered as the high scattering spectrum. Cells were also cultured on a MirrIR slide, and their ATR-FTIR spectrum was considered as the minimum scattering spectrum. The algorithm using the abovementioned five characteristics was used for 13 different cell lines, and in some cases the corrected spectrum demonstrated more than 97% resemblance with the ATR spectra of the same cells. A comparison between the results of this algorithm with the Bassan et al. (2017) algorithm for scattering correction that is freely available on the Internet was then conducted on two different cell lines, clearly showing the advantages of our algorithm, in terms of accuracy and precision. Therefore, this method can be viewed as a more suitable solution for scattering correction in cell investigations.

Spatially-enhanced time-domain NIRS for accurate determination of tissue optical properties

A multivariate method integrating time and space resolved techniques of near-infrared spectroscopy is proposed for simultaneously retrieving the absolute quantities of optical absorption and scattering properties in tissues. The time-domain feature of photon migration is advantageously constrained and regularized by its spatially-resolved amplitude patterns in the inverse procedure. Measurements of tissue-mimicking phantoms with various optical properties are analyzed with Monte-Carlo simulations to validate the method performance. The uniqueness, stability, and uncertainty of the method are discussed.

Non-invasive analysis of actinic keratosis using a cold stimulation and near-infrared spectroscopy

Non-melanoma skin cancers are the most common tumor in the Caucasian population, and include actinic keratosis (AK), which is considered an early form of in-situ squamous cell carcinoma (SCC). Currently the only way to monitor lesion progression (i.e., from AK to invasive SCC) is through an invasive bioptic procedure. Near-infrared spectroscopy (NIRS) is a non-invasive technique that studies haemoglobin (oxygenated haemoglobin, O2Hb, and deoxygenated haemoglobin, HHb) relative concentration variations. The objective of this study is to evaluate if AKs present a different vascular response when compared to healthy skin using time and frequency parameters extracted from the NIRS signals. The NIRS signals were acquired on the AKs and a healthy skin area of patients (n=53), with the same acquisition protocol: baseline signals (1.5 min), application of ice pack near lesion (1.5 min), removal of ice pack and acquisition of vascular recovery (1.5 min). We calculated 18 features to evaluate if the vascular response was different in the two cases (i.e., healthy skin and AK lesions). By applying the multivariate analysis of variance (MANOVA), a statistically significant difference is found in the O2Hb and HHb after the stimulus application. This shows how the NIRS technique can give important vascular information that could help the diagnosis of a lesion and the evaluation of its progression. Overall, the obtained results encourage us to look further into the study of the skin lesions and their progression with NIRS signals.

Changing landscape of optical imaging in skeletal metastases

Optical imaging is an emerging strategy for in vitro and in vivo visualization of the molecular mechanisms of cancer over time. An increasing number of optical imaging contrast agents and techniques have been developed in recent years specifically for bone research and skeletal metastases. Visualizing molecular processes in relation to bone remodeling in metastasized cancers provides valuable information for understanding disease mechanisms and monitoring expression of primary molecular targets and therapeutic efficacy. This review is intended to provide an overview of tumor-specific and non-specific contrast agents in the first near-infrared window (NIR-I) window from 650 nm to 950 nm that can be used to study functional and structural aspects of skeletal remodeling of cancer in preclinical animal models. Near-infrared (NIR) optical imaging techniques, specifically NIR spectroscopy and photoacoustic imaging, and their use in skeletal metastases will also be discussed. Perspectives on the promises and challenges facing this exciting field are then given.

Synergy of Fluorescence and Near-Infrared Spectroscopy in Detection of Colorectal Cancer

BACKGROUND

Colorectal cancer is one of the most common malignancies worldwide. There is an urgent need for simple and fast methods to improve tumor detection in the diagnostic and intraoperative setting to avoid complications and provide objective information in distinguishing malignant and benign colorectal tissue. Optical spectroscopy methods have recently shown a great potential for this discrimination in different organs.

MATERIALS AND METHODS

In this pilot study, fluorescence emission spectra (excitation: 473 nm) and diffuse reflectance spectra (DRS) of normal and tumor tissues from resected colorectal cancer specimen were measured using fiber optical probes in an ex vivo setting, and the data were subjected to multivariate analysis.

RESULTS

Substantial spectral differences were found in the fluorescence and DRS spectra of colorectal cancer tissue in comparison to benign tissue. The diagnostic potential of a multimode optical system combining both spectroscopic methods was investigated by mathematical combination. Compared with the individual techniques, a higher sensitivity of the joint DRS-fluorescence optical system in the discrimination between malignant and benign colorectal tissue could be observed.

CONCLUSIONS

In the pilot study presented herein, a quick and reliable method to differentiate malignant and benign colorectal tissue ex vivo with different spectroscopic techniques using spectral fiber probes could be established. Joint fluorescence and near-infrared spectroscopy had a higher sensitivity in tissue discrimination and showed to be a promising combination of two spectroscopic methods. Further studies using the synergic effect of fluorescence and DRS spectroscopy are needed to transfer these findings into the in vivo situation.

In vivo and in vitro application of near-infrared fiber optic probe for Ehrlich carcinoma distinction: Towards the development of real-time tumor margins assessment tool

This report describes a full-scale experiment on intradermal Ehrlich carcinoma (EC) differentiation in mouse model using NIR spectroscopy in diffuse reflectance mode and chemometric data processing. EC is widely used as an experimental tumor model due to its resemblance with human undifferentiated epithelial tumors and can be applied as a preclinical testing in order to verify the capability of NIR spectroscopy to distinguish cancer from healthy tissues before a clinical research with an aim of creating a new analytical tool for on-line intraoperative tumor margins assessment. The study consists of five steps of NIR spectra measurements: in vivo on the early stage of carcinoma growth; in vivo on the advanced stage of carcinoma growth; in vivo during the surgery; in vitro study of the post-operative materials stored in formalin; in vitro study of the post-operative materials stored in paraffin. It was shown that reliable tumor differentiation with a compact optic fiber probe was possible in all these cases. The classification models were built on two data sets, obtained during in vivo and in vitro measurements; both of them demonstrated 100% specificity and sensitivity.

Ultrafast and Noninvasive Long-Term Bioimaging with Highly Stable Red Aggregation-Induced Emission Nanoparticles

Strongly red luminescent and water-soluble probes are very important for studying biological events and processes. Fluorescent nanoparticles (NPs) built from the aggregation-induced emission luminogen (AIEgen) and amphipathic polymeric matrixes have been considered as promising candidates for bioimaging. However, AIE NPs with long-wavelength absorption suitable for in vivo application are still scarce. In this work, three AIE-active red-emissive BODIPY derivatives with long-wavelength absorption were rationally designed and synthesized. Then three NPs based on these AIEgens exhibit bright red photoluminescence with high fluorescence quantum yield in aqueous media. These NPs uniformly dispersed in water and showed excellent stability and good biocompatibility. They can be readily internalized by HeLa cells, and the staining process is performed by simply shaking the culture with cells for just a few seconds at room temperature, which indicates an ultrafast and easy-to-operate staining protocol. More importantly, long-term tracing in living cells and mouse over 15 days is successfully achieved. The strong fluorescence signals, ultrafast staining procedure, and long-term tracing abilities indicate that these AIE NPs hold great potential for monitoring biological processes.

Muscle oxygenation dynamics in response to electrical stimulation as measured with near-infrared spectroscopy: A pilot study

Neuromuscular electrical stimulation (NMES) is used for preventing muscle atrophy and improving muscle strength in patients and healthy people. However, the current intensity of NMES is usually set at a level that causes the stimulated muscles to contract. This typically causes pain. Quantifying the instantaneous changes in muscle microcirculation and metabolism during NMES before muscle contraction occurs is crucial, because it enables the current intensity to be optimally tuned, thereby reducing the NMES-induced muscle pain and fatigue. We applied near-infrared spectroscopy (NIRS) to measure instantaneous tissue oxygenation and deoxygenation changes in 43 healthy young adults during NMES at 10, 15, 20, 25, 30, and 35 mA. Having been stabilized at the NIRS signal baseline, the tissue oxygenation and total hemoglobin concentration increased immediately after stimulation in a dose-dependent manner (P < 0.05) until stimulation was stopped at the level causing muscle contraction without pain. Tissue deoxygenation appeared relatively unchanged during NMES. We conclude that NIRS can be used to determine the optimal NMES current intensity by monitoring oxygenation changes.

SVM Optimization for Brain Tumor Identification Using Infrared Spectroscopic Samples

The work presented in this paper is focused on the use of spectroscopy to identify the type of tissue of human brain samples employing support vector machine classifiers. Two different spectrometers were used to acquire infrared spectroscopic signatures in the wavenumber range between 1200⁻3500 cm^-1. An extensive analysis was performed to find the optimal configuration for a support vector machine classifier and determine the most relevant regions of the spectra for this particular application. The results demonstrate that the developed algorithm is robust enough to classify the infrared spectroscopic data of human brain tissue at three different discrimination levels.

Scattering-independent glucose absorption measurement using a spectrally resolved reflectance setup with specialized variable source-detector separations

We report a novel approach for the accurate measurement of glucose absorption in turbid media using a spectrally resolved reflectance setup. Our proposed reflectance setup with specialized variable source-detector separations enables scattering-independent absorption measurement, which is critical to in vivo long-term glucose concentration monitoring. Starting from the first-order approximation of the radiative transfer equation (RTE), we developed a scattering-independent glucose absorption measurement method and then evaluated this approach by Monte Carlo simulations as well as tissue-mimicking phantom studies in which glucose concentration was accurately measured. Our study demonstrates the potential of our proposed scattering-independent absorption measurement technique as an effective tool to quantify glucose levels in turbid media, which is an important step towards future in vivo long-term glucose concentration monitoring in human subjects.

Early diagnosis of Alzheimer's disease using infrared spectroscopy of isolated blood samples followed by multivariate analyses

Alzheimer's disease (AD) is the most common cause of dementia, particularly in the elderly. The disease is characterized by cognitive decline that typically starts with insidious memory loss and progresses relentlessly to produce global impairment of all higher cortical functions. Due to better living conditions and health facilities in developed countries, which result in higher overall life spans, these countries report upward trends of AD among their populations. There are, however, no specific diagnostic tests for AD and clinical diagnosis is especially difficult in the earliest stages of the disease. Early diagnosis of AD is frequently subjective and is determined by physicians (generally neurologists, geriatricians, and psychiatrists) depending on their experience. Diagnosing AD requires both medical history and mental status testing. Having trouble with memory does not mean you have AD. AD has no current cure, but treatments for symptoms are available and research continues. In this study, we investigated the potential of infrared microscopy to differentiate between AD patients and controls, using Fourier transform infrared (FTIR) spectroscopy of isolated blood components. FTIR is known as a quick, safe, and minimally invasive method to investigate biological samples. For this goal, we measured infrared spectra from white blood cells (WBCs) and plasma taken from AD patients and controls, with the consent of the patients or their guardians. Applying multivariate analysis, principal component analysis (PCA) followed by linear discriminant analysis (LDA), it was possible to differentiate among the different types of mild, moderate, and severe AD, and the controls, with 85% accuracy when using the WBC spectra and about 77% when using the plasma spectra. When only the moderate and severe stages were included, an 83% accuracy was obtained using the WBC spectra and about 89% when using the plasma spectra.

Combining random forest and 2D correlation analysis to identify serum spectral signatures for neuro-oncology

Fourier transform infrared (FTIR) spectroscopy has long been established as an analytical technique for the measurement of vibrational modes of molecular systems. More recently, FTIR has been used for the analysis of biofluids with the aim of becoming a tool to aid diagnosis. For the clinician, this represents a convenient, fast, non-subjective option for the study of biofluids and the diagnosis of disease states. The patient also benefits from this method, as the procedure for the collection of serum is much less invasive and stressful than traditional biopsy. This is especially true of patients in whom brain cancer is suspected. A brain biopsy is very unpleasant for the patient, potentially dangerous and can occasionally be inconclusive. We therefore present a method for the diagnosis of brain cancer from serum samples using FTIR and machine learning techniques. The scope of the study involved 433 patients from whom were collected 9 spectra each in the range 600-4000 cm(-1). To begin the development of the novel method, various pre-processing steps were investigated and ranked in terms of final accuracy of the diagnosis. Random forest machine learning was utilised as a classifier to separate patients into cancer or non-cancer categories based upon the intensities of wavenumbers present in their spectra. Generalised 2D correlational analysis was then employed to further augment the machine learning, and also to establish spectral features important for the distinction between cancer and non-cancer serum samples. Using these methods, sensitivities of up to 92.8% and specificities of up to 91.5% were possible. Furthermore, ratiometrics were also investigated in order to establish any correlations present in the dataset. We show a rapid, computationally light, accurate, statistically robust methodology for the identification of spectral features present in differing disease states. With current advances in IR technology, such as the development of rapid discrete frequency collection, this approach is of importance to enable future clinical translation and enables IR to achieve its potential.

Flexible Transient Optical Waveguides and Surface-Wave Biosensors Constructed from Monocrystalline Silicon

Optical technologies offer important capabilities in both biological research and clinical care. Recent interest is in implantable devices that provide intimate optical coupling to biological tissues for a finite time period and then undergo full bioresorption into benign products, thereby serving as temporary implants for diagnosis and/or therapy. The results presented here establish a silicon-based, bioresorbable photonic platform that relies on thin filaments of monocrystalline silicon encapsulated by polymers as flexible, transient optical waveguides for accurate light delivery and sensing at targeted sites in biological systems. Comprehensive studies of the mechanical and optical properties associated with bending and unfurling the waveguides from wafer-scale sources of materials establish general guidelines in fabrication and design. Monitoring biochemical species such as glucose and tracking physiological parameters such as oxygen saturation using near-infrared spectroscopic methods demonstrate modes of utility in biomedicine. These concepts provide versatile capabilities in biomedical diagnosis, therapy, deep-tissue imaging, and surgery, and suggest a broad range of opportunities for silicon photonics in bioresorbable technologies.

Three-Dimensional Highly Sensitive Diffusion Reflection-Based Imaging Method for the in Vivo Localization of Atherosclerosis Plaques Following Gold Nanorods Accumulation

In this work, we present a novel, simple, and highly accurate three-dimensional (3D) diffusion reflection (DR) imaging system and method for the detection of accumulation sites of gold nanorods (GNRs) within the tissue. GNRs are intensively used for diagnosis purposes of varied diseases, mainly because of their ability to well absorb visible light, which introduces them as terrific contrast agents in various imaging and theranostics methods. Lately, these GNRs unique absorption properties have served in DR intensity-based measurements, suggesting a novel diagnostic tool, DR-GNRs. In this paper, we show a new measurement system and method for DR, based on its radial collection from the tissue. These radial measurements enabled a unique 3D presentation of the DR-GNR, introducing the dimensions ρ for the radius, θ for the angle, and Γ for the reflected intensity. On the basis of the diffusion model, which enables to correlate between the sample's optical properties and its reflectance, a unique, radial map is presented. This map introduces the slopes of the DR curves in each measured angle, which are linearly correlated with the tissue's optical properties and with the GNRs concentrations within the tissue, thus enables the exact radial localization of the GNRs in the sample. We show the detection of macrophage accumulation in tissue-like phantoms, as well as the localization of unstable plaques in hyperlipidemic mice, in vivo. This highly accurate, powerful technology paves the way toward a real-time detection method that can be successfully integrated in the rapid increasing field of personalized medicine.

Effects of isoflurane anesthesia on physiological parameters in murine subcutaneous tumor allografts measured via diffuse reflectance spectroscopy

Diffuse reflectance spectroscopy (DRS) has been used in murine studies to quantify tumor perfusion and therapeutic response. These studies frequently use inhaled isoflurane anesthesia, which depresses the respiration rate and results in the desaturation of arterial oxygen saturation, potentially affecting tissue physiological parameters. However, there have been no controlled studies quantifying the effect of isoflurane anesthesia on DRS-derived physiological parameters of murine tissue. The goal of this study was to perform DRS on Balb/c mouse (n = 10) tissue under various anesthesia conditions to quantify effects on tissue physiological parameters, including total hemoglobin concentration, tissue oxygen saturation, oxyhemoglobin and reduced scattering coefficient. Two independent variables were manipulated including metabolic gas type (pure oxygen vs. medical air) and isoflurane concentration (1.5 to 4.0%). The 1.5% isoflurane and 1 L/min oxygen condition most closely mimicked a no-anesthesia condition with oxyhemoglobin concentration within 89% ± 19% of control. The time-dependent effects of isoflurane anesthesia were tested, revealing that anesthetic induction with 4.0% isoflurane can affect DRS-derived physiological parameters up to 20 minutes post-induction. Finally, spectroscopy with and without isoflurane anesthesia was compared for colon tumor Balb/c-CT26 allografts (n = 5) as a representative model of subcutaneous murine tumor allografts. Overall, isoflurane anesthesia yielded experimentally-induced depressed oxyhemoglobin, and this depression was both concentration and time dependent. Investigators should understand the dynamic effects of isoflurane on tissue physiological parameters measured by DRS. These results may guide investigators in eliminating, limiting, or managing anesthesia-induced physiological changes in DRS studies in mouse models.

Optical signature of nerve tissue-Exploratory ex vivo study comparing optical, histological, and molecular characteristics of different adipose and nerve tissues

Background

During several anesthesiological procedures, needles are inserted through the skin of a patient to target nerves. In most cases, the needle traverses several tissues—skin, subcutaneous adipose tissue, muscles, nerves, and blood vessels—to reach the target nerve. A clear identification of the target nerve can improve the success of the nerve block and reduce the rate of complications. This may be accomplished with diffuse reflectance spectroscopy (DRS) which can provide a quantitative measure of the tissue composition. The goal of the current study was to further explore the morphological, biological, chemical, and optical characteristics of the tissues encountered during needle insertion to improve future DRS classification algorithms.

Methods

To compare characteristics of nerve tissue (sciatic nerve) and adipose tissues, the following techniques were used: histology, DRS, absorption spectrophotometry, high‐resolution magic‐angle spinning nuclear magnetic resonance (HR‐MAS NMR) spectroscopy, and solution 2D ¹³C‐¹H heteronuclear single‐quantum coherence spectroscopy. Tissues from five human freshly frozen cadavers were examined.

Results

Histology clearly highlights a higher density of cellular nuclei, collagen, and cytoplasm in fascicular nerve tissue (IFAS). IFAS showed lower absorption of light around 1200 nm and 1750 nm, higher absorption around 1500 nm and 2000 nm, and a shift in the peak observed around 1000 nm. DRS measurements showed a higher water percentage and collagen concentration in IFAS and a lower fat percentage compared to all other tissues. The scattering parameter (b) was highest in IFAS. The HR‐MAS NMR data showed three extra chemical peak shifts in IFAS tissue.

Conclusion

Collagen, water, and cellular nuclei concentration are clearly different between nerve fascicular tissue and other adipose tissue and explain some of the differences observed in the optical absorption, DRS, and HR‐NMR spectra of these tissues. Some differences observed between fascicular nerve tissue and adipose tissues cannot yet be explained but may be helpful in improving the discriminatory capabilities of DRS in anesthesiology procedures. Lasers Surg. Med. 50:948–960, 2018. © 2018 The Authors. Lasers in Surgery and Medicine Published by Wiley Periodicals, Inc.

Multi-distance diffuse optical spectroscopy with a single optode via hypotrochoidal scanning

Frequency-domain diffuse optical spectroscopy (FD-DOS) is an established technique capable of determining optical properties and chromophore concentrations in biological tissue. Most FD-DOS systems use either manually positioned, handheld probes or complex arrays of source and detector fibers to acquire data from many tissue locations, allowing for the generation of 2D or 3D maps of tissue. Here, we present a new method to rapidly acquire a wide range of source-detector (SD) separations by mechanically scanning a single SD pair. The source and detector fibers are mounted on a scan head that traces a hypotrochoidal pattern over the sample that, when coupled with a high-speed FD-DOS system, enables the rapid collection of dozens of SD separations for depth-resolved imaging. We demonstrate that this system has an average error of 4±2.6% in absorption and 2±1.8% in scattering across all SD separations. Additionally, by linearly translating the device, the size and location of an absorbing inhomogeneity can be determined through the generation of B-scan images in a manner conceptually analogous to ultrasound imaging. This work demonstrates the potential of single optode diffuse optical scanning for depth resolved visualization of heterogeneous biological tissues at near real-time rates.

Hydrogen peroxide degradable conjugated polymer nanoparticles for fluorescence and photoacoustic bimodal imaging

A hydrogen peroxide degradable fluorescence/photoacoustic dual-modality contrast agent is prepared via in situ Sonogashira polymerization for cellular imaging.

Method for accurate registration of tissue autofluorescence imaging data with corresponding histology: a means for enhanced tumor margin assessment

An important step in establishing the diagnostic potential for emerging optical imaging techniques is accurate registration between imaging data and the corresponding tissue histopathology typically used as gold standard in clinical diagnostics. We present a method to precisely register data acquired with a point-scanning spectroscopic imaging technique from fresh surgical tissue specimen blocks with corresponding histological sections. Using a visible aiming beam to augment point-scanning multispectral time-resolved fluorescence spectroscopy on video images, we evaluate two different markers for the registration with histology: fiducial markers using a 405-nm CW laser and the tissue block's outer shape characteristics. We compare the registration performance with benchmark methods using either the fiducial markers or the outer shape characteristics alone to a hybrid method using both feature types. The hybrid method was found to perform best reaching an average error of 0.78±0.67  mm. This method provides a profound framework to validate diagnostical abilities of optical fiber-based techniques and furthermore enables the application of supervised machine learning techniques to automate tissue characterization.