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.

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.

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.

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.

Variability Analysis of Manual and Computer-Assisted Preoperative Thoracic Pedicle Screw Placement Planning.

STUDY DESIGN: A comparison among preoperative pedicle screw placement plans, obtained from computed tomography (CT) images manually by two spine surgeons and automatically by a computer-assisted method. OBJECTIVE: To analyze and compare the manual and computer-assisted approach to pedicle screw placement planning in terms of the inter- and intraobserver variability. SUMMARY OF BACKGROUND DATA: Several methods for computer-assisted pedicle screw placement planning have been proposed; however, a systematic variability analysis against manual planning has not been performed yet. METHODS: For 256 pedicle screws, preoperative placement plans were determined manually by two experienced spine surgeons, each independently performing two sets of measurements by using a dedicated software for surgery planning. For the same 256 pedicle screws, preoperative placement plans were also obtained automatically by a computer-assisted method that was based on modeling of the vertebral structures in 3D, which were used to determine the pedicle screw size and insertion trajectory by maximizing its fastening strength through the underlying bone mineral density. RESULTS: A total of 1024 manually (2 observers × 2 sets × 256 screws) and 256 automatically (1 computer-assisted method × 256 screws) determined preoperative pedicle screw placement plans were obtained and compared in terms of the inter- and intraobserver variability. A large difference was observed for the pedicle screw sagittal inclination that was, in terms of the mean absolute difference and the corresponding standard deviation, equal to 18.3°â€Š±â€Š7.6° and 12.3°â€Š±â€Š6.5°, respectively for the intraobserver variability of the second observer and for the interobserver variability between the first observer and the computer-assisted method. CONCLUSION: The interobserver variability among the observers and the computer-assisted method is within the intraobserver variability of each observer, which indicates on the potential use of the computer-assisted approach as a useful tool for spine surgery that can be adapted according to the preferences of the surgeon. LEVEL OF EVIDENCE: 3.

Segmentation of parotid glands from registered CT and MR images.

PURPOSE: To develop an automatic multimodal method for segmentation of parotid glands (PGs) from pre-registered computed tomography (CT) and magnetic resonance (MR) images and compare its results to the results of an existing state-of-the-art algorithm that segments PGs from CT images only. METHODS: Magnetic resonance images of head and neck were registered to the accompanying CT images using two different state-of-the-art registration procedures. The reference domains of registered image pairs were divided on the complementary PG regions and backgrounds according to the manual delineation of PGs on CT images, provided by a physician. Patches of intensity values from both image modalities, centered around randomly sampled voxels from the reference domain, served as positive or negative samples in the training of the convolutional neural network (CNN) classifier. The trained CNN accepted a previously unseen (registered) image pair and classified its voxels according to the resemblance of its patches to the patches used for training. The final segmentation was refined using a graph-cut algorithm, followed by the dilate-erode operations. RESULTS: Using the same image dataset, segmentation of PGs was performed using the proposed multimodal algorithm and an existing monomodal algorithm, which segments PGs from CT images only. The mean value of the achieved Dice overlapping coefficient for the proposed algorithm was 78.8%, while the corresponding mean value for the monomodal algorithm was 76.5%. CONCLUSIONS: Automatic PG segmentation on the planning CT image can be augmented with the MR image modality, leading to an improved RT planning of head and neck cancer.

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 agglomeration degree estimation in fluidized bed pellet coating processes using visual imaging.

Agglomeration of pellets in fluidized bed coating processes is an undesirable phenomenon that affects the yield and quality of the product. In scope of PAT guidance, we present a system that utilizes visual imaging for in-line monitoring of the agglomeration degree. Seven pilot-scale Wurster coating processes were executed under various process conditions, providing a wide spectrum of process outcomes. Images of pellets were acquired during the coating processes in a contactless manner through an observation window of the coating apparatus. Efficient image analysis methods were developed for automatic recognition of discrete pellets and agglomerates in the acquired images. In-line obtained agglomeration degree trends revealed the agglomeration dynamics in distinct phases of the coating processes. We compared the in-line estimated agglomeration degree in the end point of each process to the results obtained by the off-line sieve analysis reference method. A strong positive correlation was obtained (coefficient of determination R2=0.99), confirming the feasibility of the approach. The in-line estimated agglomeration degree enables early detection of agglomeration and provides means for timely interventions to retain it in an acceptable range.

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.

Locally adaptive magnetic resonance intensity models for unsupervised segmentation of multiple sclerosis lesions.

Multiple sclerosis (MS) is a neurological disease characterized by focal lesions and morphological changes in the brain captured on magnetic resonance (MR) images. However, extraction of the corresponding imaging markers requires accurate segmentation of normal-appearing brain structures (NABS) and the lesions in MR images. On MR images of healthy brains, the NABS can be accurately captured by MR intensity mixture models, which, in combination with regularization techniques, such as in Markov random field (MRF) models, are known to give reliable NABS segmentation. However, on MR images that also contain abnormalities such as MS lesions, obtaining an accurate and reliable estimate of NABS intensity models is a challenge. We propose a method for automated segmentation of normal-appearing and abnormal structures in brain MR images that is based on a locally adaptive NABS model, a robust model parameters estimation method, and an MRF-based segmentation framework. Experiments on multisequence brain MR images of 30 MS patients show that, compared to whole-brain MR intensity model and compared to four popular unsupervised lesion segmentation methods, the proposed method increases the accuracy of MS lesion segmentation.

A Novel Public MR Image Dataset of Multiple Sclerosis Patients With Lesion Segmentations Based on Multi-rater Consensus.

Quantified volume and count of white-matter lesions based on magnetic resonance (MR) images are important biomarkers in several neurodegenerative diseases. For a routine extraction of these biomarkers an accurate and reliable automated lesion segmentation is required. To objectively and reliably determine a standard automated method, however, creation of standard validation datasets is of extremely high importance. Ideally, these datasets should be publicly available in conjunction with standardized evaluation methodology to enable objective validation of novel and existing methods. For validation purposes, we present a novel MR dataset of 30 multiple sclerosis patients and a novel protocol for creating reference white-matter lesion segmentations based on multi-rater consensus. On these datasets three expert raters individually segmented white-matter lesions, using in-house developed semi-automated lesion contouring tools. Later, the raters revised the segmentations in several joint sessions to reach a consensus on segmentation of lesions. To evaluate the variability, and as quality assurance, the protocol was executed twice on the same MR images, with a six months break. The obtained intra-consensus variability was substantially lower compared to the intra- and inter-rater variabilities, showing improved reliability of lesion segmentation by the proposed protocol. Hence, the obtained reference segmentations may represent a more precise target to evaluate, compare against and also train, the automatic segmentations. To encourage further use and research we will publicly disseminate on our website http://lit.fe.uni-lj.si/tools the tools used to create lesion segmentations, the original and preprocessed MR image datasets and the consensus lesion segmentations.

Virtually increased acceptance angle for efficient estimation of spatially resolved reflectance in the subdiffusive regime: a Monte Carlo study.

Light propagation in biological tissues is frequently modeled by the Monte Carlo (MC) method, which requires processing of many photon packets to obtain adequate quality of the observed backscattered signal. The computation times further increase for detection schemes with small acceptance angles and hence small fraction of the collected backscattered photon packets. In this paper, we investigate the use of a virtually increased acceptance angle for efficient MC simulation of spatially resolved reflectance and estimation of optical properties by an inverse model. We devise a robust criterion for approximation of the maximum virtual acceptance angle and evaluate the proposed methodology for a wide range of tissue-like optical properties and various source configurations.

Lookup table-based sampling of the phase function for Monte Carlo simulations of light propagation in turbid media.

Analytical expressions for sampling the scattering angle from a phase function in Monte Carlo simulations of light propagation are available only for a limited number of phase functions. Consequently, numerical sampling methods based on tabulated values are often required instead. By using Monte Carlo simulated reflectance, we compare two existing and propose an improved numerical sampling method and show that both the number of the tabulated values and the numerical sampling method significantly influence the accuracy of the simulated reflectance. The provided results and guidelines should serve as a good starting point for conducting computationally efficient Monte Carlo simulations with numerical phase function sampling.

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.

Segmentation of Pathological Structures by Landmark-Assisted Deformable Models.

Computerized segmentation of pathological structures in medical images is challenging, as, in addition to unclear image boundaries, image artifacts, and traces of surgical activities, the shape of pathological structures may be very different from the shape of normal structures. Even if a sufficient number of pathological training samples are collected, statistical shape modeling cannot always capture shape features of pathological samples as they may be suppressed by shape features of a considerably larger number of healthy samples. At the same time, landmarking can be efficient in analyzing pathological structures but often lacks robustness. In this paper, we combine the advantages of landmark detection and deformable models into a novel supervised multi-energy segmentation framework that can efficiently segment structures with pathological shape. The framework adopts the theory of Laplacian shape editing, that was introduced in the field of computer graphics, so that the limitations of statistical shape modeling are avoided. The performance of the proposed framework was validated by segmenting fractured lumbar vertebrae from 3-D computed tomography images, atrophic corpora callosa from 2-D magnetic resonance (MR) cross-sections and cancerous prostates from 3D MR images, resulting respectively in a Dice coefficient of 84.7 ± 5.0%, 85.3 ± 4.8% and 78.3 ± 5.1%, and boundary distance of 1.14 ± 0.49mm, 1.42 ± 0.45mm and 2.27 ± 0.52mm. The obtained results were shown to be superior in comparison to existing deformable model-based segmentation algorithms.

A framework for automatic creation of gold-standard rigid 3D-2D registration datasets.

PURPOSE: Advanced image-guided medical procedures incorporate 2D intra-interventional information into pre-interventional 3D image and plan of the procedure through 3D/2D image registration (32R). To enter clinical use, and even for publication purposes, novel and existing 32R methods have to be rigorously validated. The performance of a 32R method can be estimated by comparing it to an accurate reference or gold standard method (usually based on fiducial markers) on the same set of images (gold standard dataset). Objective validation and comparison of methods are possible only if evaluation methodology is standardized, and the gold standard  dataset is made publicly available. Currently, very few such datasets exist and only one contains images of multiple patients acquired during a procedure. To encourage the creation of gold standard 32R datasets, we propose an automatic framework. METHODS: The framework is based on rigid registration of fiducial markers. The main novelty is spatial grouping of fiducial markers on the carrier device, which enables automatic marker localization and identification across the 3D and 2D images. RESULTS: The proposed framework was demonstrated on clinical angiograms of 20 patients. Rigid 32R computed by the framework was more accurate than that obtained manually, with the respective target registration error below 0.027 mm compared to 0.040 mm. CONCLUSION: The framework is applicable for gold standard setup on any rigid anatomy, provided that the acquired images contain spatially grouped fiducial markers. The gold standard datasets and software will be made publicly available.

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.

Estimation of optical properties by spatially resolved reflectance spectroscopy in the subdiffusive regime.

We propose and objectively evaluate an inverse Monte Carlo model for estimation of absorption and reduced scattering coefficients and similarity parameter ? from spatially resolved reflectance (SRR) profiles in the subdiffusive regime. The similarity parameter ? carries additional information on the phase function that governs the angular properties of scattering in turbid media. The SRR profiles at five source-detector separations were acquired with an optical fiber probe. The inverse Monte Carlo model was based on a cost function that enabled robust estimation of optical properties from a few SRR measurements without a priori knowledge about spectral dependencies of the optical properties. Validation of the inverse Monte Carlo model was performed on synthetic datasets and measured SRR profiles of turbid phantoms comprising molecular dye and polystyrene microspheres. We observed that the additional similarity parameter ? substantially reduced the reflectance variability arising from the phase function properties and significantly improved the accuracy of the inverse Monte Carlo model. However, the observed improvement of the extended inverse Monte Carlo model was limited to reduced scattering coefficients exceeding ?15??cm?1, where the relative root-mean-square errors of the estimated optical properties were well within 10%.

Validation of White-Matter Lesion Change Detection Methods on a Novel Publicly Available MRI Image Database.

Changes of white-matter lesions (WMLs) are good predictors of the progression of neurodegenerative diseases like multiple sclerosis (MS). Based on longitudinal magnetic resonance (MR) imaging the changes can be monitored, while the need for their accurate and reliable quantification led to the development of several automated MR image analysis methods. However, an objective comparison of the methods is difficult, because publicly unavailable validation datasets with ground truth and different sets of performance metrics were used. In this study, we acquired longitudinal MR datasets of 20 MS patients, in which brain regions were extracted, spatially aligned and intensity normalized. Two expert raters then delineated and jointly revised the WML changes on subtracted baseline and follow-up MR images to obtain ground truth WML segmentations. The main contribution of this paper is an objective, quantitative and systematic evaluation of two unsupervised and one supervised intensity based change detection method on the publicly available datasets with ground truth segmentations, using common pre- and post-processing steps and common evaluation metrics. Besides, different combinations of the two main steps of the studied change detection methods, i.e. dissimilarity map construction and its segmentation, were tested to identify the best performing combination.

Enhancement of Vascular Structures in 3D and 2D Angiographic Images.

A number of imaging techniques are being used for diagnosis and treatment of vascular pathologies like stenoses, aneurysms, embolisms, malformations and remodelings, which may affect a wide range of anatomical sites. For computer-aided detection and highlighting of potential sites of pathology or to improve visualization and segmentation, angiographic images are often enhanced by Hessian based filters. These filters aim to indicate elongated and/or rounded structures by an enhancement function based on Hessian eigenvalues. However, established enhancement functions generally produce a response, which exhibits deficiencies such as poor and non-uniform response for vessels of different sizes and varying contrast, at bifurcations and aneurysms. This may compromise subsequent analysis of the enhanced images. This paper has three important contributions: i) reviews several established enhancement functions and elaborates their deficiencies, ii) proposes a novel enhancement function, which overcomes the deficiencies of the established functions, and iii) quantitatively evaluates and compares the novel and the established enhancement functions on clinical image datasets of the lung, cerebral and fundus vasculatures.