Mid-infrared Spectroscopic Imaging Research Articles

Rapid Hyperspectral Photothermal Mid-Infrared Spectroscopic Imaging from Sparse Data for Gynecologic Cancer Tissue Subtyping.

Ovarian cancer detection has traditionally relied on a multistep process that includes biopsy, tissue staining, and morphological analysis by experienced pathologists. While widely practiced, this conventional approach suffers from several drawbacks: it is qualitative, time-intensive, and heavily dependent on the quality of staining. Mid-infrared (MIR) hyperspectral photothermal imaging is a label-free, biochemically quantitative technology that, when combined with machine learning algorithms, can eliminate the need for staining and provide quantitative results comparable to traditional histology. However, this technology is slow. This work presents a novel approach to MIR photothermal imaging that enhances its speed by an order of magnitude. This method resolves the longstanding trade-off between imaging resolution and data collection speed, enabling the reconstruction of high-quality, high-resolution images from undersampled data sets and achieving a 10X improvement in data acquisition time. We assessed the performance of our sparse imaging methodology using a variety of quantitative metrics, including mean squared error (MSE), structural similarity index (SSIM), and tissue subtype classification accuracies, employing both random forest and convolutional neural network (CNN) models, accompanied by Receiver Operating Characteristic (ROC) curves. Our statistically robust analysis, based on data from 100 ovarian cancer patient samples and over 65 million data points, demonstrates the method's capability to produce superior image quality and accurately distinguish between different gynecological tissue types with segmentation accuracy exceeding 95%. Our work demonstrates the feasibility of integrating rapid MIR hyperspectral photothermal imaging with machine learning in enhancing ovarian cancer tissue characterization, paving the way for quantitative, label-free, automated histopathology.

Cervical Cancer Tissue Analysis Using Photothermal Midinfrared Spectroscopic Imaging.

Hyperspectral photothermal mid-infrared spectroscopic imaging (HP-MIRSI) is an emerging technology with promising applications in cervical cancer diagnosis and quantitative, label-free histopathology. This study pioneers the application of HP-MIRSI to the evaluation of clinical cervical cancer tissues, achieving excellent tissue type segmentation accuracy of over 95%. This achievement stems from an integrated approach of optimized data acquisition, computational data reconstruction, and the application of machine learning algorithms. The results are statistically robust, drawing from tissue samples of 98 cervical cancer patients and incorporating over 40 million data points. Traditional cervical cancer diagnosis methods entail biopsy, staining, and visual evaluation by a pathologist. This process is qualitative, subject to variations in staining and subjective interpretations, and requires extensive tissue processing, making it costly and time-consuming. In contrast, our proposed alternative can produce images comparable to those from histological analyses without the need for staining or complex sample preparation. This label-free, quantitative method utilizes biochemical data from HP-MIRSI and employs machine-learning algorithms for the rapid and precise segmentation of cervical tissue subtypes. This approach can potentially transform histopathological analysis by offering a more accurate and label-free alternative to conventional diagnostic processes.

Label-Free Monitoring of Coculture System Dynamics: Probing Probiotic and Cancer Cell Interactions via Infrared Spectroscopic Imaging.

Evaluating the dynamic interaction of microorganisms and mammalian cells is challenging due to the lack of suitable platforms for examining interspecies interactions in biologically relevant coculture conditions. In this work, we demonstrate the interaction between probiotic bacteria (Lactococcus lactis and Escherichia coli) and A498 human cancer cells in vitro, utilizing a hydrogel-based platform in a label-free manner by infrared spectroscopy. The L. lactis strain recapitulated in the compartment system secretes polypeptide molecules such as nisin, which has been reported to trigger cell apoptosis. We propose a mid-infrared (IR) spectroscopic imaging approach to monitor the variation of biological components utilizing kidney cells (A498) as a model system cocultured with bacteria. We characterized the biochemical composition (i.e., nucleic acids, protein secondary structures, and lipid conformations) label-free using an unbiased measurement. Several IR spectral features, including unsaturated fatty acids, β-turns in protein, and nucleic acids, were utilized to predict cellular response. These features were then applied to establish a quantitative relationship through a multivariate regression model to predict cellular dynamics in the coculture system to assess the effect of nisin on A498 kidney cancer cells cocultured with bacteria. Overall, our study sheds light on the potential of using IR spectroscopic imaging as a label-free tool to monitor complex microbe-host cell interactions in biological systems. This integration will enable mechanistic studies of interspecies interactions with insights into their underlying physiological processes.

Leveraging mid-infrared spectroscopic imaging and deep learning for tissue subtype classification in ovarian cancer.

Mid-infrared spectroscopic imaging (MIRSI) is an emerging class of label-free techniques being leveraged for digital histopathology. Modern histopathologic identification of ovarian cancer involves tissue staining followed by morphological pattern recognition. This process is time-consuming and subjective and requires extensive expertise. This paper presents the first label-free, quantitative, and automated histological recognition of ovarian tissue subtypes using a new MIRSI technique. This optical photothermal infrared (O-PTIR) imaging technique provides a 10× enhancement in spatial resolution relative to prior instruments. It enables sub-cellular spectroscopic investigation of tissue at biochemically important fingerprint wavelengths. We demonstrate that the enhanced resolution of sub-cellular features, combined with spectroscopic information, enables reliable classification of ovarian cell subtypes achieving a classification accuracy of 0.98. Moreover, we present a statistically robust analysis from 78 patient samples with over 60 million data points. We show that sub-cellular resolution from five wavenumbers is sufficient to outperform state-of-the-art diffraction-limited techniques with up to 235 wavenumbers. We also propose two quantitative biomarkers based on the relative quantities of epithelia and stroma that exhibit efficacy in early cancer diagnosis. This paper demonstrates that combining deep learning with intrinsic biochemical MIRSI measurements enables quantitative evaluation of cancerous tissue, improving the rigor and reproducibility of histopathology.

Optical photothermal infrared spectroscopy and discrete wavenumber imaging for high content screening of single cells.

Major drawbacks of direct mid-infrared spectroscopic imaging of single cells in an aqueous buffer are strong water absorption, low resolution typically above 10 μm, and Mie scattering effects. This study demonstrates how an indirect detection principle can overcome these drawbacks using the optical photothermal infrared (O-PTIR) technique for high-resolution discrete wavenumber imaging and fingerprint spectroscopy of cultivated cells as a model system in a simple liquid sample chamber. The O-PTIR spectra of six leukemia- and cancer-derived cell lines showed main IR bands near 1648, 1547, 1447, 1400, 1220, and 1088 cm-1. Five spectra of approximately 260 single cells per cell type were averaged, the O-PTIR data set was divided into leukemia-derived cells (THP-1, HL 60, Jurkat, and Raji) and cancer cells (HeLa and HepaRG), and partial least squares linear discriminant analysis (PLS-LDA) was applied in the spectral range 800-1800 cm-1 to train three classification models. A leukemia versus cancer cell model showed an accuracy of 90.0%, the HeLa versus HepaRG cell model had an accuracy of 95.4%, and the model for the distinction of leukemia cells had an accuracy of 75.4%. IR bands in linear discriminants (LDs) of the models were correlated with second derivative spectra that resolved more than 25 subbands. The IR and second derivative spectra of proteins, DNA, RNA and lipids were collected as references to confirm band assignments. O-PTIR images of single cells at a 200 nm step size were acquired at 1086, 1548, and 1746 cm-1 to visualize the nucleic acid, protein, and lipid distribution, respectively. Variations in subcellular features and in the lipid-to-protein and nucleic acid-to-protein ratios were identified that were consistent with biomolecular information in LDs. In conclusion, O-PTIR can provide high-quality spectra and images with submicron resolution of single cells in aqueous buffers that offer prospects in high-content screening applications.

Polarization Sensitive Photothermal Mid-Infrared Spectroscopic Imaging of Human Bone Marrow Tissue.

Collagen quantity and integrity play an important role in understanding diseases such as myelofibrosis (MF). Label-free mid-infrared spectroscopic imaging (MIRSI) has the potential to quantify collagen while minimizing the subjective variance observed with conventional histopathology. Infrared (IR) spectroscopy with polarization sensitivity provides chemical information while also estimating tissue dichroism. This can potentially aid MF grading by revealing the structure and orientation of collagen fibers. Simultaneous measurement of collagen structure and biochemical properties can translate clinically into improved diagnosis and enhance our understanding of disease progression. In this paper, we present the first report of polarization-dependent spectroscopic variations in collagen from human bone marrow samples. We build on prior work with animal models and extend it to human clinical biopsies with a practical method for high-resolution chemical and structural imaging of bone marrow on clinical glass slides. This is done using a new polarization-sensitive photothermal mid-infrared spectroscopic imaging scheme that enables sample and source independent polarization control. This technology provides 0.5µm spatial resolution, enabling the identification of thin (≈1µm) collagen fibers that were not separable using Fourier Transform Infrared (FT-IR) imaging in the fingerprint region at diffraction-limited resolution ( ≈ 5µm). Finally, we propose quantitative metrics to identify fiber orientation from discrete band images (amide I and amide II) measured under three polarizations. Previous studies have used a pair of orthogonal polarization measurements, which is insufficient for clinical samples since human bone biopsies contain collagen fibers with multiple orientations. Here, we address this challenge and demonstrate that three polarization measurements are necessary to resolve orientation ambiguity in clinical bone marrow samples. This is also the first study to demonstrate the ability to spectroscopically identify thin collagen fibers (≈1µm diameter) and their orientations, which is critical for accurate grading of human bone marrow fibrosis.

Multi-modal image sharpening in fourier transform infrared (FTIR) microscopy.

Mid-infrared Spectroscopic Imaging (MIRSI) provides spatially-resolved molecular specificity by measuring wavelength-dependent mid-infrared absorbance. Infrared microscopes use large numerical aperture objectives to obtain high-resolution images of heterogeneous samples. However, the optical resolution is fundamentally diffraction-limited, and therefore wavelength-dependent. This significantly limits resolution in infrared microscopy, which relies on long wavelengths (2.5 μm to 12.5 μm) for molecular specificity. The resolution is particularly restrictive in biomedical and materials applications, where molecular information is encoded in the fingerprint region (6 μm to 12 μm), limiting the maximum resolving power to between 3 μm and 6 μm. We present an unsupervised curvelet-based image fusion method that overcomes limitations in spatial resolution by augmenting infrared images with label-free visible microscopy. We demonstrate the effectiveness of this approach by fusing images of breast and ovarian tumor biopsies acquired using both infrared and dark-field microscopy. The proposed fusion algorithm generates a hyperspectral dataset that has both high spatial resolution and good molecular contrast. We validate this technique using multiple standard approaches and through comparisons to super-resolved experimentally measured photothermal spectroscopic images. We also propose a novel comparison method based on tissue classification accuracy.

Superimposing interferogram method using a multi-slit array to enhance sensitivity and interference definition of spatial-phase-shift interferometers

We propose a superimposing interferogram method to enhance the sensitivity and interference definition of spatial-phase-shift interferometers. The proposed method uses a multi-slit array as a redesigned field stop for the spatial-phase-shift interferometer. The widths of the apertural and blocked areas correspond to half the wavelength of the fringe pattern on the imaging plane and determine the wavelength that improves the interference definition while eliminating those wavelengths that worsen it, thus improving the interference definition of the interferogram. Additionally, to improve the interferometer’s sensitivity, several apertural areas were aligned to increase the amount of incident light. We performed spectroscopic measurements and confirmed improvements in both the sensitivity and the interference definition using a prototype of a bean-sized mid-infrared spectroscopic imager based on a one-shot Fourier spectroscope and a prototype multi-slit array. Although mid-infrared light is absorbed well by water and its intensity is thus weak for detection using the array sensors of cameras, mid-infrared spectroscopic imagers that incorporate our proposed method can be used for biomedical measurements of samples containing water.

Characterization of colorectal mucus using infrared spectroscopy: a potential target for bowel cancer screening and diagnosis

AbstractBiological materials presenting early signs of cancer would be beneficial for cancer screening/diagnosis. In this respect, the suitability of potentially exploiting mucus in colorectal cancer was tested using infrared spectroscopy in combination with statistical modeling. Twenty-six paraffinized colon tissue biopsy sections containing mucus regions from 20 individuals (10 normal and 16 cancerous) were measured using mid-infrared spectroscopic imaging. A digital de-paraffinization, followed by cluster analysis driven digital color-coded multi-staining segmented the infrared images into various histopathological features such as epithelium, connective tissue, stroma, and mucus regions within the tissue sections. Principal component analysis followed by supervised linear discriminant analysis was carried out on pure mucus and epithelial spectra from normal and cancerous regions of the tissue. For the mucus-based classification, a sensitivity of 96%, a specificity of 83%, and an area under the curve performance of 95% was obtained. For the epithelial tissue-based classification, a sensitivity of 72%, a specificity of 88%, and an area under the curve performance of 89% was obtained. The mucus spectral profiles further showed contributions indicative of glycans including that of sialic acid changes between these pathology groups. The study demonstrates that infrared spectroscopic analysis of mucus discriminates colorectal cancers with high sensitivity. This concept could be exploited to develop screening/diagnostic approaches complementary to histopathology.Mucus was tested as a potential biological material for screening/early diagnosis of colorectal cancer using infrared spectroscopy. Based on a digital histopathology and statistical modeling approach, cancerous and non-cancerous samples were classified with an area under the curve performance of 95% based on mucus spectral profiles, indicating changes in the glycan component of mucins.

All-digital histopathology by infrared-optical hybrid microscopy

Optical microscopy for biomedical samples requires expertise in staining to visualize structure and composition. Midinfrared (mid-IR) spectroscopic imaging offers label-free molecular recording and virtual staining by probing fundamental vibrational modes of molecular components. This quantitative signal can be combined with machine learning to enable microscopy in diverse fields from cancer diagnoses to forensics. However, absorption of IR light by common optical imaging components makes mid-IR light incompatible with modern optical microscopy and almost all biomedical research and clinical workflows. Here we conceptualize an IR-optical hybrid (IR-OH) approach that sensitively measures molecular composition based on an optical microscope with wide-field interferometric detection of absorption-induced sample expansion. We demonstrate that IR-OH exceeds state-of-the-art IR microscopy in coverage (10-fold), spatial resolution (fourfold), and spectral consistency (by mitigating the effects of scattering). The combined impact of these advances allows full slide infrared absorption images of unstained breast tissue sections on a visible microscope platform. We further show that automated histopathologic segmentation and generation of computationally stained (stainless) images is possible, resolving morphological features in both color and spatial detail comparable to current pathology protocols but without stains or human interpretation. IR-OH is compatible with clinical and research pathology practice and could make for a cost-effective alternative to conventional stain-based protocols for stainless, all-digital pathology.

Automated Osteosclerosis Grading of Clinical Biopsies Using Infrared Spectroscopic Imaging.

Osteosclerosis and myefibrosis are complications of myeloproliferative neoplasms. These disorders result in excess growth of trabecular bone and collagen fibers that replace hematopoietic cells, resulting in abnormal bone marrow function. Treatments using imatinib and JAK2 pathway inhibitors can be effective on osteosclerosis and fibrosis; therefore, accurate grading is critical for tracking treatment effectiveness. Current grading standards use a four-class system based on analysis of biopsies stained with three histological stains: hematoxylin and eosin (H&E), Masson's trichrome, and reticulin. However, conventional grading can be subjective and imprecise, impacting the effectiveness of treatment. In this Article, we demonstrate that mid-infrared spectroscopic imaging may serve as a quantitative diagnostic tool for quantitatively tracking disease progression and response to treatment. The proposed approach is label-free and provides automated quantitative analysis of osteosclerosis and collagen fibrosis.

Relating strain hardening moduli in high density polyethylene copolymers to anisotropic molecular deformation using infrared spectroscopic imaging

Understanding and establishing the relationship between the microstructure of high density polyethylene (HDPE) resins and their mechanical behavior is critical in designing new products and catalysts to produce these polymers. Recent advancement in the mid-infrared (IR) spectroscopic imaging techniques combined with intuitive mathematical modelling can provide a powerful tool in solving this industry-wide long-standing challenge. In this work, IR spectral anisotropy methods coupled with the Crystal Tie Mesh (CTM) model were used to examine the effects of various resin architectures (monomodal and bimodal) on the bulk strain hardening moduli in cold drawn HDPE samples. Using a polarized laser for IR imaging, the related stress strain profiles of local strain gradients (100% sample strain) were modelled using a modified Eyring algorithm. A clear correlating trend was found between a resulting structural parameter in this algorithm (β) and the bulk strain hardening moduli for all samples studied. Results show that the more stable the molecular network (e.g., the lamella segments connected via bridging entanglements and tie molecules), the less strain is observed (i.e. higher β values) when a load is applied. The observed structural effects on the β parameter are consistent with previous results obtained using the CTM model that views the system to be a type of cross linked network formed by multiple bridging and entangled tie chains through common crystalline lamella.

Parametric standing wave generation of a shallow reflection plane in a nonrigid sample for use in a noninvasive blood glucose monitor.

.When monitoring a moist sample using mid-infrared spectroscopy, its thickness must be to avoid light absorption from the water. Therefore, we propose an ultrasonic-assisted mid-infrared spectroscopic imaging method that can generate a reflection plane at a depth of from the surface of the sample by creating an ultrasonic standing wave. A frequency of 10 MHz is required to obtain an optical path length of in biological samples. However, because biological samples generally have high compressibility, attenuation of ultrasonic waves at this frequency is significant. We use agar as a biological phantom and observe that a reflection plane is generated inside by ultrasonic standing waves using optical coherence tomography. It is found that when the sample is vibrated with an 800-kHz ultrasonic wave, a reflection plane is generated at a depth shallower than the theoretically predicted value. We believe that the reflection plane is generated by parametric standing waves, which are based on parametric effect. We detect the waveform distortion using an acoustic emission sensor and confirm the higher harmonics that generate the observed reflection plane using a fast Fourier transform.

Selecting optimal features from Fourier transform infrared spectroscopy for discrete-frequency imaging.

Tissue histology utilizing chemical and immunohistochemical labels plays an important role in biomedicine and disease diagnosis. Recent research suggests that mid-infrared (IR) spectroscopic imaging may augment histology by providing quantitative molecular information. One of the major barriers to this approach is long acquisition time using Fourier-transform infrared (FTIR) spectroscopy. Recent advances in discrete frequency sources, particularly quantum cascade lasers (QCLs), may mitigate this problem by allowing selective sampling of the absorption spectrum. However, DFIR imaging only provides a significant advantage when the number of spectral samples is minimized, requiring a priori knowledge of important spectral features. In this paper, we demonstrate the use of a GPU-based genetic algorithm (GA) using linear discriminant analysis (LDA) for DFIR feature selection. Our proposed method relies on pre-acquired broadband FTIR images for feature selection. Based on user-selected criteria for classification accuracy, our algorithm provides a minimal set of features that can be used with DFIR in a time-frame more practical for clinical diagnosis.

Spectroscopic imaging of biomaterials and biological systems with FTIR microscopy or with quantum cascade lasers

Spectroscopic imaging of biomaterials and biological systems has received increased interest within the last decade because of its potential to aid in the detection of disease using biomaterials/biopsy samples and to probe the states of live cells in a label-free manner. The factors behind this increased attention include the availability of improved infrared microscopes and systems that do not require the use of a synchrotron as a light source, as well as the decreasing costs of these systems. This article highlights the current technical challenges and future directions of mid-infrared spectroscopic imaging within this field. Specifically, these are improvements in spatial resolution and spectral quality through the use of novel added lenses and computational algorithms, as well as quantum cascade laser imaging systems, which offer advantages over traditional Fourier transform infrared systems with respect to the speed of acquisition and field of view. Overcoming these challenges will push forward spectroscopic imaging as a viable tool for disease diagnostics and medical research.Graphical abstractAbsorbance images of a biopsy obtained using an FTIR imaging microscope with and without an added lens, and also using a QCL microscope with high-NA objective.

Characterization of a low-cost, commercially available, vanadium oxide microbolometer array for spectroscopic imaging

Many long-wave infrared spectroscopic imaging applications are limited by the portability and cost of detector arrays. We present a characterization of a newly available, low-cost, uncooled vanadium oxide microbolometer array, the Seek Compact, in accordance with common infrared detector specifications: noise-equivalent differential temperature (NEDT), optical responsivity spectra, and Allan variance. The Compact’s imaging array consists of 156×206 pixels with a 12-μm pixel pitch, 93% of the pixels yield useful temperature readings. Characterization results show optical response between λ=7.4 and 12 μm with an NEDT of 148 mK (at ≈7 fps). Comparing these results to a research-grade camera, the Seek Compact exhibits a 4× and 48× reduction in weight (2.0/0.5 lbs) and cost ($12,000/$250) but takes 93× longer to achieve the same NEDT (1.55 s/16.6 ms for 45 mK). Additionally, a proof-of-concept spectral imaging experiment of SiN thin films is conducted. Leveraging this price reduction and spectroscopic imaging capability, the Seek Compact has potential in enabling field-deployable and distributed active midinfrared spectroscopic imaging, where cost and portability are the dominate inhibitors and high frame rates are not required.

Rapid visualization of macromolecular orientation by discrete frequency mid-infrared spectroscopic imaging

Infrared (IR) spectroscopic imaging has been used to measure the composition and orientation of polymeric systems for decades. IR microscopy can provide detailed views of microscopic regions, allowing the observation of both morphology and molecular properties of a sample, but involves a trade-off between the spatial extent and details of molecular content. Here we describe an approximately two orders of magnitude faster approach to measure the spherulitic structure and molecular orientation in large semi-crystalline polymer samples compared to extant Fourier transform infrared (FT-IR) spectroscopic imaging. This discrete frequency approach utilizes individual narrowband emission lines of a quantum cascade laser (QCL) source to spectrally image large areas rapidly. The inherent polarization of the laser beam is employed to measure orientation, enabling calculation of Hermans in-plane orientation function along with molecular chain angles distribution.

Polycyclic aromatic hydrocarbons and molecular hydrogen in oxygen-rich planetary nebulae: the case of NGC 6720.

Evolved stars are primary sources for the formation of polycyclic aromatic hydrocarbons (PAHs) and dust grains. Their circumstellar chemistry is usually designated as either oxygen-rich or carbon-rich, although dual-dust chemistry objects, whose infrared spectra reveal both silicate- and carbon-dust features, are also known. The exact origin and nature of this dual-dust chemistry is not yet understood. Spitzer-IRS mid-infrared spectroscopic imaging of the nearby, oxygen-rich planetary nebula NGC 6720 reveals the presence of the 11.3 μm aromatic (PAH) emission band. It is attributed to emission from neutral PAHs, since no band is observed in the 7-8 μm range. The spatial distribution of PAHs is found to closely follow that of the warm clumpy molecular hydrogen emission. Emission from both neutral PAHs and warm H2 is likely to arise from photo-dissociation regions associated with dense knots that are located within the main ring. The presence of PAHs together with the previously derived high abundance of free carbon (relative to CO) suggest that the local conditions in an oxygen-rich environment can also become conducive to in-situ formation of large carbonaceous molecules, such as PAHs, via a bottom-up chemical pathway. In this scenario, the same stellar source can enrich the interstellar medium with both oxygen-rich dust and large carbonaceous molecules.

Raman and mid-infrared spectroscopic imaging: applications and advancements

Using Raman and Mid-Infrared (MIR) spectroscopic imaging techniques one can examine the spatial distribution of various molecular constituents in a heterogeneous sample at a microscopic scale. Raman and MIR spectroscopy techniques provide bond-specific vibrational frequencies to characterize molecular species without external labelling. In addition, these techniques are rapid, non-invasive and provide multiplex advantage. In this review we discuss the instrumentation, applications and latest advancements of Raman and MIR spectroscopic imaging in various fields, ranging from materials science to medicine and illustrate with specific examples from our recent research.

Mid-infrared spectroscopic imaging enabled by an array of Ge-filled waveguides in a microstructured optical fiber probe

We demonstrate mid-infrared spectroscopic imaging using a unique optical fiber probe consisting of an array of Ge waveguide cores embedded in a silica fiber matrix. Biological tissue slices are characterized to illustrate its potential endoscopic uses. The fiber probe based transmission measurements show excellent agreement with the result obtained from standard Fourier Transform Infrared spectroscopy transmission measurements in the wavelength range of 3289.8 nm to 3383.3 nm, where fat and muscle tissues could be spectroscopically distinguished.