Non-destructive NIR spectral imaging assessment of bone water: Comparison to MRI measurements

The Multifactorial Relationship Between Bone Tissue Water and Stiffness at the Proximal Femur

Bone mechanical function is determined by multiple factors, some of which are still being elucidated. Here, we present a multivariate analysis of the role of bone tissue composition in the proximal femur stiffness of cadaver bones (n = 12, age 44-93). Stiffness was assessed by testing under loading conditions simulating a sideways fall onto the hip. Compositional properties of cortical and trabecular tissues were quantified in femoral neck cross sections by Fourier transform infrared (FTIR) spectroscopy and near infrared (NIR) spectroscopy. In addition, cross-sectional areas and cortical thickness and tissue mineral density (TMD) were measured at the femoral neck. Pearson correlation analysis showed a significant (p < 0.05) negative relationship between bone stiffness and cortical and trabecular water content, both total (r = -0.63) and tightly bound to matrix and mineral (r = -55). Additionally, significant (p < 0.05) positive correlations were found between stiffness and bone area, both total (r = 0.67) and trabecular (r = 0.58). However, linear regression using each of these properties to predict bone stiffness resulted in weak models (R2 = 0.36-0.48). Interestingly, we found markedly stronger models (cross-validated R2 = 0.80-0.92) by using partial least squares (PLS) regression to predict stiffness based on combinations of bone properties. The models with highest R2 values were found when including bone water parameters as explanatory variables, both total and tightly bound, in cortical and trabecular. This study provides new insights by revealing a multifactorial relationship in which higher bone water content across different tissue compartments contributes to lower bone stiffness, highlighting bone water as a potential biomarker of bone quality and proximal femur mechanical function.

Stimulus-Responsive Drug Delivery Nanoplatforms for Osteoarthritis Therapy

Osteoarthritis (OA) is one of the most prevalent age-related degenerative diseases. With an increasingly aging global population, greater numbers of OA patients are providing clear economic and societal burdens. Surgical and pharmacological treatments are the most common and conventional therapeutic strategies for OA, but often fall considerably short of desired or optimal outcomes. With the development of stimulus-responsive nanoplatforms has come the potential for improved therapeutic strategies for OA. Enhanced control, longer retention time, higher loading rates, and increased sensitivity are among the potential benefits. This review summarizes the advanced application of stimulus-responsive drug delivery nanoplatforms for OA, categorized by either those that depend on endogenous stimulus (reactive oxygen species, pH, enzyme, and temperature), or those that depend on exogenous stimulus (near-infrared ray, ultrasound, magnetic fields). The opportunities, restrictions, and limitations related to these various drug delivery systems, or their combinations, are discussed in areas such as multi-functionality, image guidance, and multi-stimulus response. The remaining constraints and potential solutions that are represented by the clinical application of stimulus-responsive drug delivery nanoplatforms are finally summarized.

Automated, calibration-free quantification of cortical bone porosity and geometry in postmenopausal osteoporosis from ultrashort echo time MRI and deep learning

BACKGROUND

Assessment of cortical bone porosity and geometry by imaging in vivo can provide useful information about bone quality that is independent of bone mineral density (BMD). Ultrashort echo time (UTE) MRI techniques of measuring cortical bone porosity and geometry have been extensively validated in preclinical studies and have recently been shown to detect impaired bone quality in vivo in patients with osteoporosis. However, these techniques rely on laborious image segmentation, which is clinically impractical. Additionally, UTE MRI porosity techniques typically require long scan times or external calibration samples and elaborate physics processing, which limit their translatability. To this end, the UTE MRI-derived Suppression Ratio has been proposed as a simple-to-calculate, reference-free biomarker of porosity which can be acquired in clinically feasible acquisition times.

PURPOSE

To explore whether a deep learning method can automate cortical bone segmentation and the corresponding analysis of cortical bone imaging biomarkers, and to investigate the Suppression Ratio as a fast, simple, and reference-free biomarker of cortical bone porosity.

METHODS

In this retrospective study, a deep learning 2D U-Net was trained to segment the tibial cortex from 48 individual image sets comprised of 46 slices each, corresponding to 2208 training slices. Network performance was validated through an external test dataset comprised of 28 scans from 3 groups: (1) 10 healthy, young participants, (2) 9 postmenopausal, non-osteoporotic women, and (3) 9 postmenopausal, osteoporotic women. The accuracy of automated porosity and geometry quantifications were assessed with the coefficient of determination and the intraclass correlation coefficient (ICC). Furthermore, automated MRI biomarkers were compared between groups and to dual energy X-ray absorptiometry (DXA)- and peripheral quantitative CT (pQCT)-derived BMD. Additionally, the Suppression Ratio was compared to UTE porosity techniques based on calibration samples.

RESULTS

The deep learning model provided accurate labeling (Dice score 0.93, intersection-over-union 0.88) and similar results to manual segmentation in quantifying cortical porosity (R2 ≥ 0.97, ICC ≥ 0.98) and geometry (R2 ≥ 0.82, ICC ≥ 0.75) parameters in vivo. Furthermore, the Suppression Ratio was validated compared to established porosity protocols (R2 ≥ 0.78). Automated parameters detected age- and osteoporosis-related impairments in cortical bone porosity (P ≤ .002) and geometry (P values ranging from <0.001 to 0.08). Finally, automated porosity markers showed strong, inverse Pearson's correlations with BMD measured by pQCT (|R| ≥ 0.88) and DXA (|R| ≥ 0.76) in postmenopausal women, confirming that lower mineral density corresponds to greater porosity.

CONCLUSION

This study demonstrated feasibility of a simple, automated, and ionizing-radiation-free protocol for quantifying cortical bone porosity and geometry in vivo from UTE MRI and deep learning.

Transmission-Based Vertebrae Strength Probe Development: Far Field Probe Property Extraction and Integrated Machine Vision Distance Validation Experiments

We are developing a transmission-based probe for point-of-care assessment of vertebrae strength needed for fabricating the instrumentation used in supporting the spinal column during spinal fusion surgery. The device is based on a transmission probe whereby thin coaxial probes are inserted into the small canals through the pedicles and into the vertebrae, and a broad band signal is transmitted from one probe to the other across the bone tissue. Simultaneously, a machine vision scheme has been developed to measure the separation distance between the probe tips while they are inserted into the vertebrae. The latter technique includes a small camera mounted to the handle of one probe and associated fiducials printed on the other. Machine vision techniques make it possible to track the location of the fiducial-based probe tip and compare it to the fixed coordinate location of the camera-based probe tip. The combination of the two methods allows for straightforward calculation of tissue characteristics by exploiting the antenna far field approximation. Validation tests of the two concepts are presented as a precursor to clinical prototype development.

Near-infrared spectroscopy for structural bone assessment

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

Bone hydration: How we can evaluate it, what can it tell us, and is it an effective therapeutic target?

Water constitutes roughly a quarter of the cortical bone by volume yet can greatly influence mechanical properties and tissue quality. There is a growing appreciation for how water can dynamically change due to age, disease, and treatment. A key emerging area related to bone mechanical and tissue properties lies in differentiating the role of water in its four different compartments, including free/pore water, water loosely bound at the collagen/mineral interfaces, water tightly bound within collagen triple helices, and structural water within the mineral. This review summarizes our current knowledge of bone water across the four functional compartments and discusses how alterations in each compartment relate to mechanical changes. It provides an overview on the advent of- and improvements to- imaging and spectroscopic techniques able to probe nano-and molecular scales of bone water. These technical advances have led to an emerging understanding of how bone water changes in various conditions, of which aging, chronic kidney disease, diabetes, osteoporosis, and osteogenesis imperfecta are reviewed. Finally, it summarizes work focused on therapeutically targeting water to improve mechanical properties.

Infrared Spectroscopy-Determined Bone Compositional Changes Associated with Anti-Resorptive Treatment of the oim/oim Mouse Model of Osteogenesis Imperfecta

Applications of vibrational spectroscopy to assess bone disease and therapeutic interventions are continually advancing, with tissue mineral and protein composition frequently investigated. Here, we used two spectroscopic approaches for determining bone composition in a mouse model (oim) of the brittle bone disease osteogenesis imperfecta (OI) with and without antiresorptive agent treatment (alendronate, or ALN, and RANK-Fc). Near-infrared (NIR) spectral analysis using a fiber optic probe and attenuated total reflection Fourier transform infrared spectroscopy (ATR FTIR) mode were applied to investigate bone composition, including water, mineral, and protein content. Spectral parameters revealed differences among the control wildtype (WT) and OIM groups. NIR spectral analysis of protein and water showed that OIM mouse humerii had ∼50% lower protein and ∼50% higher overall water content compared to WT bone. Moreover, some OIM-treated groups showed a reduction in bone water compared to OIM controls, approximating values observed in WT bone. Differences in bone quality based on increased mineral content and reduced carbonate content were also found between some groups of treated OIM and WT bone, but crystallinity did not differ among all groups. The spectroscopically determined parameters were evaluated for correlations with gold-standard mechanical testing values to gain insight into how composition influenced bone strength. As expected, bone mechanical strength parameters were consistently up to threefold greater in WT mice compared to OIM groups, except for stiffness in the ALN-treated OIM groups. Furthermore, bone stiffness, maximum load, and post-yield displacement showed the strongest correlations with NIR-determined protein content (positive correlations) and bound-water content (negative correlations). These results demonstrate that in this study, NIR spectral parameters were more sensitive to bone composition differences than ATR parameters, highlighting the potential of this nondestructive approach for screening of bone diseases and therapeutic efficacy in pre-clinical models.

The Identification of Spherical Engineered Microplastics and Microalgae by Micro-hyperspectral Imaging

Based on the micro-hyperspectral imaging technique, spherical engineered microplastic (polyethylene, 10-45 μm) and microalgae (Isochrysis galbana) (4-7 μm) were identified. In transmittance mode of MHSI, micro image cubes from 400 to 1000 nm were obtained from slides containing MP and MA in thin seawater. Classifiers like Support Vector Machine (SVM(Radial Basis Function (RBF))), Least Squares Support Vector Machine (LSSVM(RBF)), k-Nearest Neighbors, etc. were adopted and compared to classify MP and MA. In order to expand the imaging range of micro imaging, image stitching technology was adopted. In allusion to the stitched image cube, SVM(RBF) is suggested for the identification of MA and MP, with recall and precision > 0.86. The above results demonstrate that the MHSI is a promising technique, which can detect MPs with particle size Limit of Detection of 10-45 μm, and it is potential to further expand this LOD.

Applications of Vibrational Spectroscopy for Analysis of Connective Tissues

Advances in vibrational spectroscopy have propelled new insights into the molecular composition and structure of biological tissues. In this review, we discuss common modalities and techniques of vibrational spectroscopy, and present key examples to illustrate how they have been applied to enrich the assessment of connective tissues. In particular, we focus on applications of Fourier transform infrared (FTIR), near infrared (NIR) and Raman spectroscopy to assess cartilage and bone properties. We present strengths and limitations of each approach and discuss how the combination of spectrometers with microscopes (hyperspectral imaging) and fiber optic probes have greatly advanced their biomedical applications. We show how these modalities may be used to evaluate virtually any type of sample (ex vivo, in situ or in vivo) and how "spectral fingerprints" can be interpreted to quantify outcomes related to tissue composition and quality. We highlight the unparalleled advantage of vibrational spectroscopy as a label-free and often nondestructive approach to assess properties of the extracellular matrix (ECM) associated with normal, developing, aging, pathological and treated tissues. We believe this review will assist readers not only in better understanding applications of FTIR, NIR and Raman spectroscopy, but also in implementing these approaches for their own research projects.

Characterization of connective tissues using near-infrared spectroscopy and imaging

Near-infrared (NIR) spectroscopy is a powerful analytical method for rapid, non-destructive and label-free assessment of biological materials. Compared to mid-infrared spectroscopy, NIR spectroscopy excels in penetration depth, allowing intact biological tissue assessment, albeit at the cost of reduced molecular specificity. Furthermore, it is relatively safe compared to Raman spectroscopy, with no risk of laser-induced photothermal damage. A typical NIR spectroscopy workflow for biological tissue characterization involves sample preparation, spectral acquisition, pre-processing and analysis. The resulting spectrum embeds intrinsic information on the tissue's biomolecular, structural and functional properties. Here we demonstrate the analytical power of NIR spectroscopy for exploratory and diagnostic applications by providing instructions for acquiring NIR spectra, maps and images in biological tissues. By adapting and extending this protocol from the demonstrated application in connective tissues to other biological tissues, we expect that a typical NIR spectroscopic study can be performed by a non-specialist user to characterize biological tissues in basic research or clinical settings. We also describe how to use this protocol for exploratory study on connective tissues, including differentiating among ligament types, non-destructively monitoring changes in matrix formation during engineered cartilage development, mapping articular cartilage proteoglycan content across bovine patella and spectral imaging across the depth-wise zones of articular cartilage and subchondral bone. Depending on acquisition mode and experiment objectives, a typical exploratory study can be completed within 6 h, including sample preparation and data analysis.

3D UTE bicomponent imaging of cortical bone using a soft-hard composite pulse for excitation

PURPOSE

To evaluate 3D UTE bicomponent imaging of cortical bone ex vivo and in vivo using a newly designed soft-hard composite pulse for excitation.

METHODS

Chemical shift artifacts, presenting as fat-water oscillation or combination-induced signal oscillation, significantly reduce the accuracy of quantitative UTE bicomponent analysis of cortical bone. To achieve fat suppression for more reliable bicomponent analysis, a newly developed soft-hard excitation pulse was used with UTE imaging and compared with a single rectangular pulse excitation without and with a conventional fat saturation (FatSat) module. These 3 sequences were applied to 8 bovine bone samples without marrow fat, 3 bovine bone samples with marrow fat, and tibial midshafts of 5 healthy human volunteers. Bicomponent analyses were performed in both ex vivo and in vivo studies.

RESULTS

The soft-hard pulse provided comparable fat suppression, but much reduced bone signal attenuation compared with the FatSat module. Better bicomponent T 2 ∗ fitting was also achieved with the soft-hard excitation pulse because it greatly reduced chemical shift artifacts and outperformed the single rectangular pulse without or with FatSat. Although the FatSat module reduced fat signals and related fat-water oscillation, the water signals were significantly attenuated with more than 40% reduction due to direction saturation. For the inner layer of tibial midshaft in healthy volunteers, fitting errors increased from 3.78% for the soft-hard pulse to 11.43% and 5.16%, respectively, for the single rectangular pulse without and with the FatSat module.

CONCLUSION

The 3D UTE sequence with a new soft-hard excitation pulse allows more reliable bicomponent imaging of cortical bone.

Near infrared spectroscopic assessment of loosely and tightly bound cortical bone water

Water is an important component of bone and plays a key role in its mechanical and structural integrity. Water molecules in bone are present in different locations, including loosely or tightly bound to the matrix and/or mineral (biological apatite) phases. Identification of water location and interactions with matrix components impact bone function but have been challenging to assess. Here, we used near infrared (NIR) spectroscopy to identify loosely and tightly bound water present in cortical bone. In hydrated samples, NIR spectra have two primary water absorption bands at frequencies of ∼5200 and 7000 cm^-1. Using lyophilization and hydrogen-deuterium exchange assays, we showed that these absorption bands are primarily associated with loosely bound bone water. Using further demineralization assays, thermal denaturation, and comparison to standards, we found that these absorption bands have underlying components associated with water molecules tightly bound to bone. In dehydrated samples, the peak at ∼5200 cm^-1 was assigned to a combination of water tightly bound to collagen and to mineral, whereas the peak at 7000 cm^-1 was exclusively associated with tightly bound mineral water. We also found significant positive correlations between the NIR mineral absorption bands and the mineral content as determined by an established mid infrared spectroscopic parameter, phosphate/amide I. Moreover, the NIR water data showed correlation trends with tissue mineral density (TMD) in cortical bone tissues. These observations reveal the ability of NIR spectroscopy to non-destructively identify loosely and tightly bound water in bone, which could have further applications in biomineralization and biomedical studies.

Clinical application of near infrared fiber optic spectroscopy for noninvasive bone assessment

Approaches for noninvasive bone quality assessment are of great clinical need, particularly in individuals that require close monitoring of disease progression. X-ray measurements are standard approaches to assess bone quality; however, they have several disadvantages. Here, a nonionizing approach for noninvasive assessment of the second metacarpal bone based on near infrared (NIR) spectroscopy was investigated. Transcutaneous bone signal detection was experimentally confirmed with cadaveric hand data, and Monte Carlo modeling further indicated that 50% of the measured signals arise from bone. Spectral data were collected via a NIR fiber optic from the bone of individuals with osteogenesis imperfecta, a disease marked by frequent bone fractures and fragility. Multiple significant correlations were found between spectral parameters related to water, protein and fat, and standard bone quality parameters obtained by X-ray measurements. The results from this preliminary study highlight the potential application of NIR spectroscopy for the noninvasive assessment of bone quality.

Approaches for In Situ Monitoring of Matrix Development in Hydrogel-Based Engineered Cartilage

Near infrared (NIR) spectroscopy using a fiber optic probe shows great promise for the nondestructive in situ monitoring of tissue engineered construct development; however, the NIR evaluation of matrix components in samples with high water content is challenging, as water absorbances overwhelm the spectra. In this study, we established approaches by which NIR spectroscopy can be used to select optimal individual engineered hydrogel constructs based on matrix content and mechanical properties. NIR spectroscopy of dry standard compounds allowed identification of several absorbances related to collagen and/or proteoglycan (PG), of which only two could be identified in spectra obtained from hydrated constructs, at ∼5940 and 5800 cm-1. In dry sample mixtures, the ratio of these peaks correlated positively to collagen and negatively to PG. In NIR spectra from engineered cartilage hydrogels, these peaks reflected higher collagen and PG content and dynamic modulus values, permitting the differentiation of constructs with poor and good matrix development. Similarly, the increasing baseline offset in raw NIR spectra also reflected matrix development in hydrated constructs. However, weekly monitoring of NIR spectra and the peaks at ∼5940 and 5800 cm-1 was not adequate to differentiate individual constructs based on matrix composition. Interestingly, changes in the baseline offset of raw spectra could be used to evaluate the growth trajectory of individual constructs. These results demonstrate an optimal approach for the use of fiber optic NIR spectroscopy for in situ monitoring of the development of engineered cartilage, which will aid in identifying individual constructs for implantation. Impact statement A current demand in tissue engineering is the establishment of nondestructive approaches to evaluate construct development during growth in vitro. In this article, we demonstrate original nondestructive approaches by which fiber optic NIR spectroscopy can be used to assess matrix (PG and collagen) formation and mechanical properties in hydrogel-based constructs. Our data provide a cohesive molecular-based approach for in situ longitudinal evaluation of construct development during growth in vitro. The establishment of these approaches is a valuable step toward the real-time identification and selection of constructs with optimal properties, which may lead to successful tissue integration upon in vivo implantation.

Environmentally-Controlled Near Infrared Spectroscopic Imaging of Bone Water

We have designed an environmentally-controlled chamber for near infrared spectroscopic imaging (NIRSI) to monitor changes in cortical bone water content, an emerging biomarker related to bone quality assessment. The chamber is required to ensure repeatable spectroscopic measurements of tissues without the influence of atmospheric moisture. A calibration curve to predict gravimetric water content from human cadaveric cortical bone was created using NIRSI data obtained at six different lyophilization time points. Partial least squares (PLS) models successfully predicted bone water content that ranged from 0–10% (R = 0.96, p < 0.05, root mean square error of prediction (RMSEP) = 7.39%), as well as in the physiologic range of 4–10% of wet tissue weight (R = 0.87, p < 0.05, RMSEP = 14.5%). Similar results were obtained with univariate and bivariate regression models for prediction of water in the 0–10% range. Further, we identified two new NIR bone absorbances, at 6560 cm⁻¹ and 6688 cm⁻¹, associated with water and collagen respectively. Such data will be useful in pre-clinical studies that investigate changes in bone quality with disease, aging and with therapeutic use.

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.

MRI-derived bone porosity index correlates to bone composition and mechanical stiffness

The MRI-derived porosity index (PI) is a non-invasively obtained biomarker based on an ultrashort echo time sequence that images both bound and pore water protons in bone, corresponding to water bound to organic collagenous matrix and freely moving water, respectively. This measure is known to strongly correlate with the actual volumetric cortical bone porosity. However, it is unknown whether PI may also be able to directly quantify bone organic composition and/or mechanical properties. We investigated this in human cadaveric tibiae by comparing PI values to near infrared spectral imaging (NIRSI) compositional data and mechanical compression data. Data were obtained from a cohort of eighteen tibiae from male and female donors with a mean ± SD age of 70 ± 21 years. Biomechanical stiffness in compression and NIRSI-derived collagen and bound water content all had significant inverse correlations with PI (r = −0.79, −0.73, and −0.95 and p = 0.002, 0.007, and <0.001, respectively). The MRI-derived bone PI alone was a moderate predictor of bone stiffness (R² = 0.63, p = 0.002), and multivariate analyses showed that neither cortical bone cross-sectional area nor NIRSI values improved bone stiffness prediction compared to PI alone. However, NIRSI-obtained collagen and water data together were a moderate predictor of bone stiffness (R² = 0.52, p = 0.04). Our data validates the MRI-derived porosity index as a strong predictor of organic composition of bone and a moderate predictor of bone stiffness, and also provides preliminary evidence that NIRSI measures may be useful in future pre-clinical studies on bone pathology.

Spatial correlation of native and engineered cartilage components at micron resolution

Tissue engineering (TE) approaches are being widely investigated for repair of focal defects in articular cartilage. However, the amount and/or type of extracellular matrix (ECM) produced in engineered constructs does not always correlate with the resultant mechanical properties. This could be related to the specifics of ECM distribution throughout the construct. Here, we present data on the amount and distribution of the primary components of native and engineered cartilage (i.e., collagen, proteoglycan (PG), and water) using Fourier transform infrared imaging spectroscopy (FT-IRIS). These data permit visualization of matrix and water at 25 μm resolution throughout the tissues, and subsequent colocalization of these components using image processing methods. Native and engineered cartilage were cryosectioned at 80 μm for evaluation by FT-IRIS in the mid-infrared (MIR) and near-infrared (NIR) regions. PG distribution correlated strongly with water in native and engineered cartilage, supporting the binding of water to PG in both tissues. In addition, NIR-derived matrix peaks correlated significantly with MIR-derived collagen peaks, confirming the interpretation that these absorbances arise primarily from collagen and not PG. The combined use of MIR and NIR permits assessment of ECM and water spatial distribution at the micron level, which may aid in improved development of TE techniques.