Efficient estimation of subdiffusive optical parameters in real time from spatially resolved reflectance by artificial neural networks.

Subdiffusive reflectance captured at short source-detector separations provides increased sensitivity to the scattering phase function and hence allows superficial probing of the tissue ultrastructure. Consequently, estimation of subdiffusive optical parameters has been the subject of many recent studies focusing on lookup-table-based (LUT) inverse models. Since an adequate description of the subdiffusive reflectance requires additional scattering phase function related optical parameters, the LUT inverse models, which grow exponentially with the number of estimated parameters, become excessively large and computationally inefficient. Herein, we propose, to the best of our knowledge, the first artificial-neural-network-based inverse Monte Carlo model that overcomes the limitations of the LUT inverse models and thus allows efficient real-time estimation of optical parameters from subdiffusive spatially resolved reflectance. The proposed inverse model retains the accuracy, is about four orders of magnitude faster than the LUT inverse models, grows only linearly with the number of estimated optical parameters, and can be easily extended to estimate additional optical parameters.

In-Line Film Coating Thickness Estimation of Minitablets in a Fluid-Bed Coating Equipment.

Film coating thickness of minitablets was estimated in-line during coating in a fluid-bed equipment by means of visual imaging. An existing, commercially available image acquisition system was used for image acquisition, while dedicated image analysis and data analysis methods were developed for this purpose. The methods were first tested against simulated minitablet's images and after that examined on a laboratory-scale fluid-bed Wurster coating process. An observation window cleaning mechanism was developed for this purpose. Six batches of minitablets were coated in total, using two different dispersions, where for the second dispersion coating endpoint was determined based on the in-line measurement. Coating thickness estimates were calculated from the increasing size distributions of the minitablet's major and minor lengths, assessed from the acquired images. Information on both the minitablet's average band and average cap coating thicknesses was obtained. The in-line coating thickness estimates were compared to the coating thickness weight gain calculations and the optical microscope measurements as a reference method. Average band coating thickness estimate was found the most accurate in comparison to microscope measurements, with root mean square error of 1.30 µm. The window cleaning mechanism was crucial for the accuracy of the in-line measurements as was evident from the corresponding decrease of the root mean square error (9.52 µm, band coating thickness). The presented visual imaging approach exhibits accuracy of at least 2 µm and is not susceptible to coating formulation or color variations. It presents a promising alternative to other existing techniques for the in-line coating thickness estimation.

Adopting higher-order similarity relations for improved estimation of optical properties from subdiffusive reflectance.

Estimation of optical properties from subdiffusive reflectance acquired at short source-detector separations is challenging due to the sensitivity to the underlying scattering phase function. In recent studies, a second-order similarity parameter γ has been increasingly used alongside the absorption and reduced scattering coefficients to account for some of the phase function variability. By using Monte Carlo simulations, we show that the influence of the scattering phase function on the subdiffusive reflectance for the biologically relevant variations can be captured sufficiently well by considering γ and a third-order similarity parameter δ. Utilizing this knowledge, we construct an inverse model that estimates the absorption and reduced scattering coefficients, γ and δ, from spatially resolved reflectance. Nearly an order of magnitude smaller errors of the estimated optical properties are obtained in comparison to the inverse model that only composes γ.

Stratified mixture modeling for segmentation of white-matter lesions in brain MR images.

Accurate characterization of white-matter lesions from magnetic resonance (MR) images has increasing importance for diagnosis and management of treatment of certain neurological diseases, and can be performed in an objective and effective way by automated lesion segmentation. This usually involves modeling the whole-brain MR intensity distribution, however, capturing various sources of MR intensity variability and lesion heterogeneity results in highly complex whole-brain MR intensity models, thus their robust estimation on a large set of MR images presents a huge challenge. We propose a novel approach employing stratified mixture modeling, where the main premise is that the otherwise complex whole-brain model can be reduced to a tractable parametric form in small brain subregions. We show on MR images of multiple sclerosis (MS) patients with different lesion loads that robust estimators enable accurate mixture modeling of MR intensity in small brain subregions even in the presence of lesions. Recombination of the mixture models across strata provided an accurate whole-brain MR intensity model. Increasing the number of subregions and, thereby, the model complexity, consistently improved the accuracy of whole-brain MR intensity modeling and segmentation of normal structures. The proposed approach was incorporated into three unsupervised lesion segmentation methods and, compared to original and three other state-of-the-art methods, the proposed modeling approach significantly improved lesion segmentation according to increased Dice similarity indices and lower number of false positives on real MR images of 30 patients with MS.

Deconvolution-based restoration of SWIR pushbroom imaging spectrometer images.

Properties of the short wave infrared (SWIR) imaging spectrograph and the front lens along with the misalignment of optical elements contribute to positionally variant displacements and blur that can significantly degrade the overall quality of the acquired images. In this work, we devise a complete routine for simultaneous displacement correction and resolution enhancement of SWIR spectral images along the two spatial and the spectral direction. The proposed restoration routine requires images of widely available and inexpensive calibration targets from which the response function of the imaging spectrometer is extracted. Extensive validation reveals that the displacement error observed in the restored images is reduced to the manufacturing accuracy of the calibration targets. Furthermore, the restored images exhibit up to a two-fold improvement in the spectral and spatial resolution.

Push-broom hyperspectral image calibration and enhancement by 2D deconvolution with a variant response function estimate.

In this paper, we propose a novel method for spectral and spatial calibration and resolution enhancement of hyperspectral images by a two-step procedure. The spectral and spatial variability of the hyperspectral imaging system response function is characterized by a global parametric model, which is derived from a pair of calibration images corresponding to an exactly defined calibration target and a set of gas-discharge lamps. A 2D Richardson-Lucy deconvolution-based algorithm is used to remove the distortions and enhance the resolution of subsequently acquired hyperspectral images. The results of the characterization and deconvolution process obtained by the proposed method are thoroughly evaluated by an independent set of exactly defined calibration and spectral targets, and compared to the existing state-of-the-art characterization method. The proposed method significantly improves the spectral and spatial coregistration and provides more than five-fold resolution enhancement in the spatial and two-fold resolution enhancement in the spectral domain.

A method for characterizing illumination systems for hyperspectral imaging.

Near-infrared hyperspectral imaging is becoming a popular tool in various fields. In all imaging systems, proper illumination is crucial for attaining optimal image quality that is needed for the best performance of image analysis algorithms. In hyperspectral imaging, the acquired spectral signature has to be representative in all parts of the imaged object. Therefore, the whole object must be equally well illuminated-without shadows or specular reflections. As there are no restrictions imposed on the material and geometry of the object, the desired illumination of the object can only be achieved with completely diffuse illumination. In order to minimize shadows and specular reflections, the light illuminating the object must be spatially, angularly and spectrally uniform. The quality of illumination systems for hyperspectral imaging can therefore be assessed using spatial-intensity, spatial-spectral, angular-intensity and angular-spectral non-uniformity measures that are presented in this paper. Emphasis is given to the angular-intensity and angular-spectral non-uniformity measures, which are the most important contributions of this paper. The measures were defined on images of two reference targets-a flat, white diffuse reflectance target and a sphere grid target-acquired with an acousto-optic tunable filter (AOTF) based hyperspectral imaging system. The proposed measures were tested on a ring light and on a diffuse dome illumination system.

Characterization of a spectrograph based hyperspectral imaging system.

A significant part of the uniformity degradation in the acquired hyperspectral images can be attributed to the coregistration distortions and spectrally and spatially dependent resolution arising from the misalignments and the operation principle of the spectrograph based hyperspectral imaging system. The aim of this study was the development and validation of a practical method for characterization of the geometric coregistration distortions and position dependent resolution. The proposed method is based on modeling the imaging system response to several affordable reference objects. The results of the characterization can be used for calibration of the acquired images or as a tool for assessment of the expected errors in various hyperspectral imaging systems.

Radiometric calibration and noise estimation of acousto-optic tunable filter hyperspectral imaging systems.

The accuracy of the radiometric response of acousto-optic tunable filter (AOTF) hyperspectral imaging systems is crucial for obtaining reliable measurements. It is therefore important to know the radiometric response and noise characteristics of the hyperspectral imaging system used. A radiometric model of an AOTF hyperspectral imaging system composed of an imaging sensor radiometric model (CCD, CMOS, and sCMOS) and an AOTF light transmission model is proposed. Using the radiometric model, a method for obtaining the fixed pattern noise (FPN) of the imaging system by displacing and imaging an illuminated reference target is developed. Methods for estimating the temporal noise of the imaging system, using the photon transfer method, and for correcting FPN are also presented. Noise estimation and image restoration methods were tested on an AOTF hyperspectral imaging system. The results indicate that the developed methods can accurately calculate temporal and FPN, and can effectively correct the acquired images. After correction, the signal-to-noise ratio of the acquired images was shown to increase by 26%.

Validation for 2D/3D registration. II: The comparison of intensity- and gradient-based merit functions using a new gold standard data set.

PURPOSE: A new gold standard data set for validation of 2D/3D registration based on a porcine cadaver head with attached fiducial markers was presented in the first part of this article. The advantage of this new phantom is the large amount of soft tissue, which simulates realistic conditions for registration. This article tests the performance of intensity- and gradient-based algorithms for 2D/3D registration using the new phantom data set. METHODS: Intensity-based methods with four merit functions, namely, cross correlation, rank correlation, correlation ratio, and mutual information (MI), and two gradient-based algorithms, the backprojection gradient-based (BGB) registration method and the reconstruction gradient-based (RGB) registration method, were compared. Four volumes consisting of CBCT with two fields of view, 64 slice multidetector CT, and magnetic resonance-T1 weighted images were registered to a pair of kV x-ray images and a pair of MV images. A standardized evaluation methodology was employed. Targets were evenly spread over the volumes and 250 starting positions of the 3D volumes with initial displacements of up to 25 mm from the gold standard position were calculated. After the registration, the displacement from the gold standard was retrieved and the root mean square (RMS), mean, and standard deviation mean target registration errors (mTREs) over 250 registrations were derived. Additionally, the following merit properties were computed: Accuracy, capture range, number of minima, risk of nonconvergence, and distinctiveness of optimum for better comparison of the robustness of each merit. RESULTS: Among the merit functions used for the intensity-based method, MI reached the best accuracy with an RMS mTRE down to 1.30 mm. Furthermore, it was the only merit function that could accurately register the CT to the kV x rays with the presence of tissue deformation. As for the gradient-based methods, BGB and RGB methods achieved subvoxel accuracy (RMS mTRE down to 0.56 and 0.70 mm, respectively). Overall, gradient-based similarity measures were found to be substantially more accurate than intensity-based methods and could cope with soft tissue deformation and enabled also accurate registrations of the MR-T1 volume to the kV x-ray image. CONCLUSIONS: In this article, the authors demonstrate the usefulness of a new phantom image data set for the evaluation of 2D/3D registration methods, which featured soft tissue deformation. The author's evaluation shows that gradient-based methods are more accurate than intensity-based methods, especially when soft tissue deformation is present. However, the current nonoptimized implementations make them prohibitively slow for practical applications. On the other hand, the speed of the intensity-based method renders these more suitable for clinical use, while the accuracy is still competitive.

Standardized evaluation methodology for 3D/2D registration based on the Visible Human data set.

PURPOSE: A new image database with a reference-based standardized evaluation methodology for objective evaluation and comparison of three-dimensional/two-dimensional (3D/2D) registration methods has been introduced. METHODS: Computed tomography (CT) images of a male and female from the Visible Human Project were used and 16 subvolumes, each containing one of vertebrae T3-T12 and L1-L5 and the pelvis, were defined from the CTs. Six pairs of 2D fluoroscopic x-ray images from different views, showing the thoracic, lumbar, and pelvic regions, were rendered from the CT data using a ray-casting algorithm with an energy conversion function. Furthermore, a single 13-gauge needle was analytically simulated and projected onto the 2D images. By the novel standardized evaluation methodology, a 3D/2D registration method is evaluated by four evaluation criteria: Accuracy, reliability, robustness, and algorithm complexity. RESULTS: To demonstrate the usefulness of the proposed data set and the standardized evaluation methodology, a part of the data set was used in an evaluation study of two gradient-based 3D/2D registration methods. It was shown that the use of a failure criterion to calculate the registration accuracy and reliability is not required, since all the information about a registration method can be determined from the estimated distribution of registration errors. CONCLUSIONS: The proposed simulated image data set with quite realistic synthetic 2D images, depicting soft tissues and outliers, is especially suitable for preliminary testing of 3D/2D registration algorithms. Since the aim of this article is to provide objective comparison and unbiased evaluation of 3D/2D registration methods, the standardized evaluation methodology is available upon request from the authors.

Geometric calibration of a hyperspectral imaging system.

Every imaging system requires a geometric calibration to yield accurate optical measurements. Geometric calibration typically involves imaging of a known calibration object and finding the parameters of a camera model and a model of optical aberrations. Optical aberrations can vary significantly across the wide spectral ranges of hyperspectral imaging systems, which can lead to inaccurate geometric calibrations if conventional methods were used. We propose a method based on a B-spline transformation field to align the spectral images of the calibration object to the model image of the calibration object. The degree of spatial alignment between the ideal and the spectral images is measured by normalized cross correlation. Geometric calibration was performed on a hyperspectral imaging system based on an acousto-optic tunable filter designed for the near-infrared spectral range (1.0-1.7microm). The proposed method can accurately characterize wavelength dependent optical aberrations and produce transformations for efficient subpixel geometric calibration.

Determination of axial vertebral rotation in MR images: comparison of four manual and a computerized method.

Axial vertebral rotation (AVR) of 14 normal and 14 scoliotic vertebrae from magnetic resonance (MR) images was determined by three observers using four manual methods and a computerized method, which were based on the evaluation of vertebral symmetry in two dimensions (2D) and in three dimensions (3D). The method of Aaro and Dahlborn proved to be the manual method with the highest intra-observer (1.7 degrees SD) and inter-observer (1.2 degrees SD) reliabilities, and was also most in agreement with the computerized method (1.3 degrees SD, 1.0 degrees MAD). The computerized method yielded higher intra-observer (1.3 degrees SD) and inter-observer (1.4 degrees SD) reliabilities than the manual methods, indicating it to be an efficient alternative for repeatable and reliable AVR measurements.

Determination of refractive index, size, and solid content of monodisperse polystyrene microsphere suspensions for the characterization of optical phantoms.

Monodisperse polystyrene microspheres are often utilized in optical phantoms since optical properties such as the scattering coefficient and the scattering phase function can be calculated using the Mie theory. However, the calculated values depend on the inherent physical parameters of the microspheres which include the size, refractive index, and solid content. These parameters are often provided only approximately or can be affected by long shelf times. We propose a simple method to obtain the values of these parameters by measuring the collimated transmission of polystyrene microsphere suspensions from which the wavelength-dependent scattering coefficient can be calculated using the Beer-Lambert law. Since a wavelength-dependent scattering coefficient of a single suspension is insufficient to uniquely derive the size, refractive index and solid content by the Mie theory, the crucial and novel step involves suspending the polystyrene microspheres in aqueous sucrose solutions with different sucrose concentrations that modulates the refractive index of the medium and yields several wavelength-dependent scattering coefficients. With the proposed method, we are able to obtain the refractive index within 0.2% in the wavelength range from 500 to 800 nm, the microsphere size to approximately 15 nm and solid content within 2% of their respective reference values.

Fast and accurate Monte Carlo simulations of subdiffusive spatially resolved reflectance for a realistic optical fiber probe tip model aided by a deep neural network.

In this work, we introduce a framework for efficient and accurate Monte Carlo (MC) simulations of spatially resolved reflectance (SRR) acquired by optical fiber probes that account for all the details of the probe tip including reflectivity of the stainless steel and the properties of the epoxy fill and optical fibers. While using full details of the probe tip is essential for accurate MC simulations of SRR, the break-down of the radial symmetry in the detection scheme leads to about two orders of magnitude longer simulation times. The introduced framework mitigates this performance degradation, by an efficient reflectance regression model that maps SRR obtained by fast MC simulations based on a simplified probe tip model to SRR simulated using the full details of the probe tip. We show that a small number of SRR samples is sufficient to determine the parameters of the regression model. Finally, we use the regression model to simulate SRR for a stainless steel optical probe with six linearly placed fibers and experimentally validate the framework through the use of inverse models for estimation of absorption and reduced scattering coefficients and subdiffusive scattering phase function quantifiers.

Automated Cutting Plane Positioning for Intracranial Aneurysm Quantification.

OBJECTIVE: Aneurysm rupture risk can be assessed by its morphologic and hemodynamics features extracted based on angiographic images. Feature extraction entails aneurysm isolation, typically by manually positioning a cutting plane (MCP). To eliminate intra- and inter-rater variabilities, we propose automatic cutting plane (ACP) positioning based on the analysis of vascular surface mesh. METHODS: Innovative Hough-like and multi-hypothesis-based detection of aneurysm center, parent vessel inlets, and centerlines were proposed. These were used for initialization and iterative ACP positioning by geometry-inspired cost function optimization. For validation and baseline comparison, we tested MCP and manual neck curve-based isolation. Isolated aneurysm morphology was characterized by size, dome height, aspect ratio, and nonsphericity index. RESULTS: Methods were applied to 55 intracranial saccular aneurysms from two sites, involving 3-D digital subtraction angiography, computed tomography angiography, and magnetic resonance angiography modalities. Isolation based on ACP resulted in smaller average inter-curve distances (AICDs), compared to those obtained by MCP. One case had AICD higher than 1.0 mm, while 90% of cases had AICD 0.5 mm. Intra- and inter-rater AICD variability of manual neck curves was higher compared to MCP, validating its robustness for clinical purposes. CONCLUSION: The ACP method achieved high accuracy and reliability of aneurysm isolation, also confirmed by expert visual analysis. So extracted morphologic features were in good agreement with MCP-based ones, therefore, ACP has great potential for aneurysm morphology and hemodynamics quantification in clinical applications. SIGNIFICANCE: The novel method is angiographic modality agnostic; it delivers repeatable isolation important in follow-up aneurysm assessment; its performance is comparable to MCP; and re-evaluation is fast and simple.

A review of methods for quantitative evaluation of spinal curvature.

The aim of this paper is to provide a complete overview of the existing methods for quantitative evaluation of spinal curvature from medical images, and to summarize the relevant publications, which may not only assist in the introduction of other researchers to the field, but also be a valuable resource for studying the existing methods or developing new methods and evaluation strategies. Key evaluation issues and future considerations, supported by the results of the overview, are also discussed.

A review of methods for quantitative evaluation of axial vertebral rotation.

Quantitative evaluation of axial vertebral rotation is essential for the determination of reference values in normal and pathological conditions and for understanding the mechanisms of the progression of spinal deformities. However, routine quantitative evaluation of axial vertebral rotation is difficult and error-prone due to the limitations of the observer, characteristics of the observed vertebral anatomy and specific imaging properties. The scope of this paper is to review the existing methods for quantitative evaluation of axial vertebral rotation from medical images along with all relevant publications, which may provide a valuable resource for studying the existing methods or developing new methods and evaluation strategies. The reviewed methods are divided into the methods for evaluation of axial vertebral rotation in 2D images and the methods for evaluation of axial vertebral rotation in 3D images. Key evaluation issues and future considerations, supported by the results of the overview, are also discussed.

Quantitative analysis of spinal curvature in 3D: application to CT images of normal spine.

The purpose of this study is to present a framework for quantitative analysis of spinal curvature in 3D. In order to study the properties of such complex 3D structures, we propose two descriptors that capture the characteristics of spinal curvature in 3D. The descriptors are the geometric curvature (GC) and curvature angle (CA), which are independent of the orientation and size of spine anatomy. We demonstrate the two descriptors that characterize the spinal curvature in 3D on 30 computed tomography (CT) images of normal spine and on a scoliotic spine. The descriptors are determined from 3D vertebral body lines, which are obtained by two different methods. The first method is based on the least-squares technique that approximates the manually identified vertebra centroids, while the second method searches for vertebra centroids in an automated optimization scheme, based on computer-assisted image analysis. Polynomial functions of the fourth and fifth degree were used for the description of normal and scoliotic spinal curvature in 3D, respectively. The mean distance to vertebra centroids was 1.1 mm (+/-0.6 mm) for the first and 2.1 mm (+/-1.4 mm) for the second method. The distributions of GC and CA values were obtained along the 30 images of normal spine at each vertebral level and show that maximal thoracic kyphosis (TK), thoracolumbar junction (TJ) and maximal lumbar lordosis (LL) on average occur at T3/T4, T12/L1 and L4/L5, respectively. The main advantage of GC and CA is that the measurements are independent of the orientation and size of the spine, thus allowing objective intra- and inter-subject comparisons. The positions of maximal TK, TJ and maximal LL can be easily identified by observing the GC and CA distributions at different vertebral levels. The obtained courses of the GC and CA for the scoliotic spine were compared to the distributions of GC and CA for the normal spines. The significant difference in values indicates that the descriptors of GC and CA may be used to detect and quantify scoliotic spinal curvatures. The proposed framework may therefore improve the understanding of spine anatomy and aid in the clinical quantitative evaluation of spinal deformities.

3D-2D registration in endovascular image-guided surgery: evaluation of state-of-the-art methods on cerebral angiograms.

PURPOSE: Image guidance for minimally invasive surgery is based on spatial co-registration and fusion of 3D pre-interventional images and treatment plans with the 2D live intra-interventional images. The spatial co-registration or 3D-2D registration is the key enabling technology; however, the performance of state-of-the-art automated methods is rather unclear as they have not been assessed under the same test conditions. Herein we perform a quantitative and comparative evaluation of ten state-of-the-art methods for 3D-2D registration on a public dataset of clinical angiograms. METHODS: Image database consisted of 3D and 2D angiograms of 25 patients undergoing treatment for cerebral aneurysms or arteriovenous malformations. On each of the datasets, highly accurate "gold-standard" registrations of 3D and 2D images were established based on patient-attached fiducial markers. The database was used to rigorously evaluate ten state-of-the-art 3D-2D registration methods, namely two intensity-, two gradient-, three feature-based and three hybrid methods, both for registration of 3D pre-interventional image to monoplane or biplane 2D images. RESULTS: Intensity-based methods were most accurate in all tests (0.3 mm). One of the hybrid methods was most robust with 98.75% of successful registrations (SR) and capture range of 18 mm for registrations of 3D to biplane 2D angiograms. In general, registration accuracy was similar whether registration of 3D image was performed onto mono- or biplanar 2D images; however, the SR was substantially lower in case of 3D to monoplane 2D registration. Two feature-based and two hybrid methods had clinically feasible execution times in the order of a second. CONCLUSIONS: Performance of methods seems to fall below expectations in terms of robustness in case of registration of 3D to monoplane 2D images, while translation into clinical image guidance systems seems readily feasible for methods that perform registration of the 3D pre-interventional image onto biplanar intra-interventional 2D images.