Smart-Dust-Nanorice for Enhancement of Endogenous Raman Signal, Contrast in Photoacoustic Imaging, and T2-Shortening in Magnetic Resonance Imaging.

Raman microspectroscopy provides chemo-selective image contrast, sub-micrometer resolution, and multiplexing capabilities. However, it suffers from weak signals resulting in image-acquisition times of up to several hours. Surface-enhanced Raman scattering (SERS) can dramatically enhance signals of molecules in close vicinity of metallic surfaces and overcome this limitation. Multimodal, SERS-active nanoparticles are usually labeled with Raman marker molecules, limiting SERS to the coating material. In order to realize multimodal imaging while acquiring the rich endogenous vibronic information of the specimen, a core-shell particle based on "Nanorice", where a spindle-shaped iron oxide core is encapsulated by a closed gold shell, is developed. An ultrathin layer of silica prevents agglomeration and unwanted chemical interaction with the specimen. This approach provides Raman signal enhancement due to plasmon resonance effects of the shell while the optical absorption in the near-infrared spectral region provides contrast in photoacoustic tomography. Finally, T2-relaxation of a magnetic resonance imaging (MRI) experiment is altered by taking advantage of the iron oxide core. The feasibility for Raman imaging is evaluated by nearfield simulations and experimental studies on the primate cell line COS1. MRI and photoacoustics are demonstrated in agarose phantoms illustrating the promising translational nature of this strategy for clinical applications in radiology.

Comparison of Deconvolution Filters for Photoacoustic Tomography.

In this work, we compare the merits of three temporal data deconvolution methods for use in the filtered backprojection algorithm for photoacoustic tomography (PAT). We evaluate the standard Fourier division technique, the Wiener deconvolution filter, and a Tikhonov L-2 norm regularized matrix inversion method. Our experiments were carried out on subjects of various appearances, namely a pencil lead, two man-made phantoms, an in vivo subcutaneous mouse tumor model, and a perfused and excised mouse brain. All subjects were scanned using an imaging system with a rotatable hemispherical bowl, into which 128 ultrasound transducer elements were embedded in a spiral pattern. We characterized the frequency response of each deconvolution method, compared the final image quality achieved by each deconvolution technique, and evaluated each method's robustness to noise. The frequency response was quantified by measuring the accuracy with which each filter recovered the ideal flat frequency spectrum of an experimentally measured impulse response. Image quality under the various scenarios was quantified by computing noise versus resolution curves for a point source phantom, as well as the full width at half maximum (FWHM) and contrast-to-noise ratio (CNR) of selected image features such as dots and linear structures in additional imaging subjects. It was found that the Tikhonov filter yielded the most accurate balance of lower and higher frequency content (as measured by comparing the spectra of deconvolved impulse response signals to the ideal flat frequency spectrum), achieved a competitive image resolution and contrast-to-noise ratio, and yielded the greatest robustness to noise. While the Wiener filter achieved a similar image resolution, it tended to underrepresent the lower frequency content of the deconvolved signals, and hence of the reconstructed images after backprojection. In addition, its robustness to noise was poorer than that of the Tikhonov filter. The performance of the Fourier filter was found to be the poorest of all three methods, based on the reconstructed images' lowest resolution (blurriest appearance), generally lowest contrast-to-noise ratio, and lowest robustness to noise. Overall, the Tikhonov filter was deemed to produce the most desirable image reconstructions.

Cellulose Nanoparticles are a Biodegradable Photoacoustic Contrast Agent for Use in Living Mice.

Molecular imaging with photoacoustic ultrasound is an emerging field that combines the spatial and temporal resolution of ultrasound with the contrast of optical imaging. However, there are few imaging agents that offer both high signal intensity and biodegradation into small molecules. Here we describe a cellulose-based nanoparticle with peak photoacoustic signal at 700 nm and an in vitro limit of detection of 6 pM (0.02 mg/mL). Doses down to 0.35 nM (1.2 mg/mL) were used to image mouse models of ovarian cancer. Most importantly, the nanoparticles were shown to biodegrade in the presence of cellulase both through a glucose assay and electron microscopy.

Development and application of stable phantoms for the evaluation of photoacoustic imaging instruments.

Photoacoustic imaging combines the high contrast of optical imaging with the spatial resolution and penetration depth of ultrasound. This technique holds tremendous potential for imaging in small animals and importantly, is clinically translatable. At present, there is no accepted standard physical phantom that can be used to provide routine quality control and performance evaluation of photoacoustic imaging instruments. With the growing popularity of the technique and the advent of several commercial small animal imaging systems, it is important to develop a strategy for assessment of such instruments. Here, we developed a protocol for fabrication of physical phantoms for photoacoustic imaging from polyvinyl chloride plastisol (PVCP). Using this material, we designed and constructed a range of phantoms by tuning the optical properties of the background matrix and embedding spherical absorbing targets of the same material at different depths. We created specific designs to enable: routine quality control; the testing of robustness of photoacoustic signals as a function of background; and the evaluation of the maximum imaging depth available. Furthermore, we demonstrated that we could, for the first time, evaluate two small animal photoacoustic imaging systems with distinctly different light delivery, ultrasound imaging geometries and center frequencies, using stable physical phantoms and directly compare the results from both systems.

High-sensitivity, real-time, ratiometric imaging of surface-enhanced Raman scattering nanoparticles with a clinically translatable Raman endoscope device.

Topical application and quantification of targeted, surface-enhanced Raman scattering (SERS) nanoparticles offer a new technique that has the potential for early detection of epithelial cancers of hollow organs. Although less toxic than intravenous delivery, the additional washing required to remove unbound nanoparticles cannot necessarily eliminate nonspecific pooling. Therefore, we developed a real-time, ratiometric imaging technique to determine the relative concentrations of at least two spectrally unique nanoparticle types, where one serves as a nontargeted control. This approach improves the specific detection of bound, targeted nanoparticles by adjusting for working distance and for any nonspecific accumulation following washing. We engineered hardware and software to acquire SERS signals and ratios in real time and display them via a graphical user interface. We report quantitative, ratiometric imaging with nanoparticles at pM and sub-pM concentrations and at varying working distances, up to 50 mm. Additionally, we discuss optimization of a Raman endoscope by evaluating the effects of lens material and fiber coating on background noise, and theoretically modeling and simulating collection efficiency at various working distances. This work will enable the development of a clinically translatable, noncontact Raman endoscope capable of rapidly scanning large, topographically complex tissue surfaces for small and otherwise hard to detect lesions.

A small animal Raman instrument for rapid, wide-area, spectroscopic imaging.

Raman spectroscopy, amplified by surface enhanced Raman scattering (SERS) nanoparticles, is a molecular imaging modality with ultra-high sensitivity and the unique ability to multiplex readouts from different molecular targets using a single wavelength of excitation. This approach holds exciting prospects for a range of applications in medicine, including identification and characterization of malignancy during endoscopy and intraoperative image guidance of surgical resection. The development of Raman molecular imaging with SERS nanoparticles is presently limited by long acquisition times, poor spatial resolution, small field of view, and difficulty in animal handling with existing Raman spectroscopy instruments. Our goal is to overcome these limitations by designing a bespoke instrument for Raman molecular imaging in small animals. Here, we present a unique and dedicated small-animal Raman imaging instrument that enables rapid, high-spatial resolution, spectroscopic imaging over a wide field of view (> 6 cm(2)), with simplified animal handling. Imaging of SERS nanoparticles in small animals demonstrated that this small animal Raman imaging system can detect multiplexed SERS signals in both superficial and deep tissue locations at least an order of magnitude faster than existing systems without compromising sensitivity.

A Raman-based endoscopic strategy for multiplexed molecular imaging.

Endoscopic imaging is an invaluable diagnostic tool allowing minimally invasive access to tissues deep within the body. It has played a key role in screening colon cancer and is credited with preventing deaths through the detection and removal of precancerous polyps. However, conventional white-light endoscopy offers physicians structural information without the biochemical information that would be advantageous for early detection and is essential for molecular typing. To address this unmet need, we have developed a unique accessory, noncontact, fiber optic-based Raman spectroscopy device that has the potential to provide real-time, multiplexed functional information during routine endoscopy. This device is ideally suited for detection of functionalized surface-enhanced Raman scattering (SERS) nanoparticles as molecular imaging contrast agents. This device was designed for insertion through a clinical endoscope and has the potential to detect and quantify the presence of a multiplexed panel of tumor-targeting SERS nanoparticles. Characterization of the Raman instrument was performed with SERS particles on excised human tissue samples, and it has shown unsurpassed sensitivity and multiplexing capabilities, detecting 326-fM concentrations of SERS nanoparticles and unmixing 10 variations of colocalized SERS nanoparticles. Another unique feature of our noncontact Raman endoscope is that it has been designed for efficient use over a wide range of working distances from 1 to 10 mm. This is necessary to accommodate for imperfect centering during endoscopy and the nonuniform surface topology of human tissue. Using this endoscope as a key part of a multiplexed detection approach could allow endoscopists to distinguish between normal and precancerous tissues rapidly and to identify flat lesions that are otherwise missed.

Improving image quality by accounting for changes in water temperature during a photoacoustic tomography scan.

The emerging field of photoacoustic tomography is rapidly evolving with many new system designs and reconstruction algorithms being published. Many systems use water as a coupling medium between the scanned object and the ultrasound transducers. Prior to a scan, the water is heated to body temperature to enable small animal imaging. During the scan, the water heating system of some systems is switched off to minimize the risk of bubble formation, which leads to a gradual decrease in water temperature and hence the speed of sound. In this work, we use a commercially available scanner that follows this procedure, and show that a failure to model intra-scan temperature decreases as small as 1.5°C leads to image artifacts that may be difficult to distinguish from true structures, particularly in complex scenes. We then improve image quality by continuously monitoring the water temperature during the scan and applying variable speed of sound corrections in the image reconstruction algorithm. While upgrading to an air bubble-free heating pump and keeping it running during the scan could also solve the changing temperature problem, we show that a software correction for the temperature changes provides a cost-effective alternative to a hardware upgrade. The efficacy of the software corrections was shown to be consistent across objects of widely varying appearances, namely physical phantoms, ex vivo tissue, and in vivo mouse imaging. To the best of our knowledge, this is the first study to demonstrate the efficacy of modeling temporal variations in the speed of sound during photoacoustic scans, as opposed to spatial variations as focused on by previous studies. Since air bubbles pose a common problem in ultrasonic and photoacoustic imaging systems, our results will be useful to future small animal imaging studies that use scanners with similarly limited heating units.

Gold nanorods for ovarian cancer detection with photoacoustic imaging and resection guidance via Raman imaging in living mice.

Improved imaging approaches are needed for ovarian cancer screening, diagnosis, staging, and resection guidance. Here, we propose a combined photoacoustic (PA)/Raman approach using gold nanorods (GNRs) as a passively targeted molecular imaging agent. GNRs with three different aspect ratios were studied. Those with an aspect ratio of 3.5 were selected for their highest ex vivo and in vivo PA signal and used to image subcutaneous xenografts of the 2008, HEY, and SKOV3 ovarian cancer cell lines in living mice. Maximum PA signal was observed within 3 h for all three lines tested and increased signal persisted for at least two days postadministration. There was a linear relationship (R(2) = 0.95) between the PA signal and the concentration of injected molecular imaging agent with a calculated limit of detection of 0.40 nM GNRs in the 2008 cell line. The same molecular imaging agent could be used for clear visualization of the margin between tumor and normal tissue and tumor debulking via surface-enhanced Raman spectroscopy (SERS) imaging. Finally, we validated the imaging findings with biodistribution data and elemental analysis. To the best of our knowledge, this is the first report of in vivo imaging of ovarian cancer tumors with a photoacoustic and Raman imaging agent.

A hybrid least squares and principal component analysis algorithm for Raman spectroscopy.

Raman spectroscopy is a powerful technique for detecting and quantifying analytes in chemical mixtures. A critical part of Raman spectroscopy is the use of a computer algorithm to analyze the measured Raman spectra. The most commonly used algorithm is the classical least squares method, which is popular due to its speed and ease of implementation. However, it is sensitive to inaccuracies or variations in the reference spectra of the analytes (compounds of interest) and the background. Many algorithms, primarily multivariate calibration methods, have been proposed that increase robustness to such variations. In this study, we propose a novel method that improves robustness even further by explicitly modeling variations in both the background and analyte signals. More specifically, it extends the classical least squares model by allowing the declared reference spectra to vary in accordance with the principal components obtained from training sets of spectra measured in prior characterization experiments. The amount of variation allowed is constrained by the eigenvalues of this principal component analysis. We compare the novel algorithm to the least squares method with a low-order polynomial residual model, as well as a state-of-the-art hybrid linear analysis method. The latter is a multivariate calibration method designed specifically to improve robustness to background variability in cases where training spectra of the background, as well as the mean spectrum of the analyte, are available. We demonstrate the novel algorithm's superior performance by comparing quantitative error metrics generated by each method. The experiments consider both simulated data and experimental data acquired from in vitro solutions of Raman-enhanced gold-silica nanoparticles.

Regularising limited view tomography using anatomical reference images and information theoretic similarity metrics.

This paper is concerned with limited view tomography. Inspired by the application of digital breast tomosynthesis (DBT), which is but one of an increasing number of applications of limited view tomography, we concentrate primarily on cases where the angular range is restricted to a narrow wedge of approximately ±30°, and the number of views is restricted to 10-30. The main challenge posed by these conditions is undersampling, also known as the null space problem. As a consequence of the Fourier Slice Theorem, a limited angular range leaves large swathes of the object's Fourier space unsampled, leaving a large space of possible solutions, reconstructed volumes, for a given set of inputs. We explore the feasibility of using same- or different-modality images as anatomical priors to constrain the null space, hence the solution. To allow for different-modality priors, we choose information theoretic measures to quantify the similarity between reconstructions and their priors. We demonstrate the limitations of two popular choices, namely mutual information and joint entropy, and propose robust alternatives that overcome their limitations. One of these alternatives is essentially a joint mixture model of the image and its prior. Promising mitigation of the data insufficiency problem is demonstrated using 2D synthetic as well as clinical phantoms. This work initially assumes a priori registered priors, and is then extended to allow for the registration to be performed simultaneously with the reconstruction.

Task-based performance analysis of FBP, SART and ML for digital breast tomosynthesis using signal CNR and Channelised Hotelling Observers.

We assess the performance of filtered backprojection (FBP), the simultaneous algebraic reconstruction technique (SART) and the maximum likelihood (ML) algorithm for digital breast tomosynthesis (DBT) under variations in key imaging parameters, including the number of iterations, number of projections, angular range, initial guess, and radiation dose. This is the first study to compare these algorithms for the application of DBT. We present a methodology for the evaluation of DBT reconstructions, and use it to conduct preliminary experiments investigating trade-offs between the selected imaging parameters. This investigation includes trade-offs not previously considered in the DBT literature, such as the use of a stationary detector versus a C-arm imaging geometry. A real breast CT volume serves as a ground truth digital phantom from which to simulate X-ray projections under the various acquisition parameters. The reconstructed image quality is measured using task-based metrics, namely signal CNR and the AUC of a Channelised Hotelling Observer with Laguerre-Gauss basis functions. The task at hand is the detection of a simulated mass inserted into the breast CT volume. We find that the image quality in limited view tomography is highly dependent on the particular acquisition and reconstruction parameters used. In particular, we draw the following conclusions. First, we find that optimising the FBP filter design and SART relaxation parameter yields significant improvements in reconstruction quality from the same projection data. Second, we show that the convergence rate of the maximum likelihood algorithm, optimised with paraboloidal surrogates and conjugate gradient ascent (ML-PSCG), can be greatly accelerated using view-by-view updates. Third, we find that the optimal initial guess is algorithm dependent. In particular, we obtained best results with a zero initial guess for SART, and an FBP initial guess for ML-PSCG. Fourth, when the exposure per view is constant, increasing the total number of views within a given angular range improves the reconstruction quality, albeit with diminishing returns. When the total dose of all views combined is constant, there is a trade-off between increased sampling using a larger number of views and increased levels of quantum noise in each view. Fifth, we do not observe significant differences when testing various access ordering schemes, presumably due to the limited angular range of DBT. Sixth, we find that adjusting the z-resolution of the reconstruction can improve image quality, but that this resolution is best adjusted by using post-reconstruction binning, rather than by declaring lower-resolution voxels. Seventh, we find that the C-arm configuration yields higher image quality than a stationary detector geometry, the difference being most outspoken for the FBP algorithm. Lastly, we find that not all prototype systems found in the literature are currently being run under the best possible system or algorithm configurations. In other words, the present study demonstrates the critical importance (and reward) of using optimisation methodologies such as the one presented here to maximise the DBT reconstruction quality from a single scan of the patient.

A hybrid least squares and principal component analysis algorithm for Raman spectroscopy.

The least squares fitting algorithm is the most commonly used algorithm in Raman spectroscopy. In this paper, however, we show that it is sensitive to variations in the background signal when the signal of interest is weak. To address this problem, we propose a novel algorithm to analyze measured spectra in Raman spectroscopy. The method is a hybrid least squares and principal component analysis algorithm. It explicitly accounts for any variations expected in the reference spectra used in the signal decomposition. We compare the novel algorithm to the least squares method with a low-order polynomial residual model, and demonstrate the novel algorithm's superior performance by comparing quantitative error metrics. Our experiments use both simulated data and data acquired from an in vitro solution of Raman-enhanced gold nanoparticles.

Avoiding local optima of the joint entropy prior in limited view tomography using a multiresolution scheme.

The incorporation of anatomical reference images into limited view transmission tomography has been attempted previously by using the joint entropy prior. However, this prior has been found to be sensitive to local optima. Here, we propose to increase robustness to local optima by using a multiresolution optimisation scheme. To our knowledge, this is the first work to apply multiresolution optimisation to the joint entropy prior in limited view transmission tomography. The results show a substantial mitigation of the sensitivity to local optima, as well as a robustness to missing as well as extra regions in the anatomical reference image. In addition, we demonstrate the method's robustness to misalignment between the reconstruction and the anatomical reference image.

Model of accommodation: contributions of lens geometry and mechanical properties to the development of presbyopia.

PURPOSE: To determine the relative importance of lens geometry and mechanical properties for the mechanics of accommodation and the role of these elements in the causes and potential correction of presbyopia. SETTING: Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey, USA. DESIGN: Experimental study. METHODS: Finite element methods and ray-tracing algorithms were used to model the deformation and optical power of the human crystalline lens during accommodation. The mechanical model treats the lens as an axisymmetric object, and the optical model incorporates a gradient refractive index. Using these models, the accommodation of a broad range of lenses with different geometries and mechanical properties were investigated. RESULTS: The most significant result was that reshaping the 45-year-old lens to the geometry of the 29-year-old lens, while retaining the mechanical properties, restored the former's accommodation amplitude to 72% to 94% of that of the 29-year-old lens, depending on ciliary body displacement. That is, reshaping can add 1.8 to 3.7 diopters of accommodation. A sensitivity analysis showed that this result was robust over a wide range of mechanical and geometrical properties. CONCLUSION: The study results suggest that a significant amount of the loss of accommodation is due to changes in lens geometry.

Simultaneous reconstruction and registration algorithm for limited view transmission tomography using a multiple cluster approximation to the joint histogram with an anatomical prior.

We develop a novel simultaneous reconstruction and registration algorithm for limited view transmission tomography. We derive a cost function using Bayesian probability theory, and propose a similarity metric based on the explicit modeling of the joint histogram as a sum of bivariate clusters. The resulting algorithm shows a robust mitigation of the data insufficiency problem in limited view tomography. To our knowledge, our work represents the first attempt to incorporate non-registered, multimodal anatomical priors into limited view transmission tomography by using joint histogram based similarity measures.

Robust joint entropy regularization of limited view transmission tomography using Gaussian approximations to the joint histogram.

Information theoretic measures to incorporate anatomical priors have been explored in the field of emission tomography, but not in transmission tomography. In this work, we apply the joint entropy prior to the case of limited angle transmission tomography. Due to the data insufficiency problem, the joint entropy prior is found to be very sensitive to local optima. Two methods for robust joint entropy minimization are proposed. The first approximates the joint probability density function by a single 2D Gaussian, and is found to be appropriate for reconstructions where the ground truth joint histogram is dominated by two clusters, or multiple clusters that are roughly aligned. The second method is an extension to the case of multiple Gaussians. The intended application for the single Gaussian approximation is digital breast tomosynthesis, where reconstructed volumes are approximately bimodal, consisting mainly of fatty and fibroglandular tissues.

Modelling of experimentally created partial-thickness human skin burns and subsequent therapeutic cooling: a new measure for cooling effectiveness.

Rapid post-injury cooling of a skin burn has been shown to have both symptomatic and therapeutic benefits. However, the latter cannot be explained by temperature reduction alone, and must thus be secondary to an altered biological response. In this study, we construct a computational model to calculate the heat transfer and damage accumulation in human skin during and after a burn. This enables us to assess the effectiveness of various cooling protocols (involving both free and forced convection to air and water respectively) in terms of their reduction in Arrhenius tissue damage. In this process, we propose an extension of the Arrhenius damage model in the form of a new measure xi, which estimates the relevance of post-burn accrued damage. It was found that the reduction in Arrhenius damage integrals near the skin surface was too small to be physiologically relevant. Hence our results confirm that while the reduction in tissue temperatures is indeed quicker, the therapeutic benefit of cooling cannot be explained by thermal arguments (i.e. based on Arrhenius damage models) alone. We plan to validate this hypothesis by conducting future microarray analyses of differential gene expression in cooled and non-cooled burn lesions. Our computational model will support such experiments by calculating the necessary conditions to produce a burn of specified severity for a given experimental setup.

A systematic performance analysis of the simultaneous algebraic reconstruction technique (SART) for limited angle tomography.

The design of limited angle tomography systems requires the optimization of various imaging parameters in order to achieve useful as well as reliable results. Algebraic reconstruction techniques, specifically the SART algorithm, have given excellent results in CT and are being actively considered for limited angle commercial applications such as tomosynthesis. In this study, we simulate a range of limited angle scenarios by systematically varying a number of key imaging parameters, and examine the performance of the SART algorithm under these variations. The phantoms used are basic ellipsoids in 2D, yielding analytical projections, and an MR-derived breast phantom in 3D.