3D medical volume reconstruction using web services

Second-order accurate and unconditionally stable algorithm with unique solvability for a phase-field model of 3D volume reconstruction

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 208632, “Reconstruction and Synthesis of Source-Rock Images at the Pore Scale,” by Timothy Anderson, SPE, Stanford University. The paper has not been peer reviewed. Nanoimaging techniques for characterizing pore-scale structure of shales trade off between high-resolution/high-contrast sample-destructive imaging modalities and low-contrast/low-resolution sample-preserving modalities. Acquisition of nanoscale images also is often time-consuming and expensive. In the complete paper, the author introduces methods for overcoming these challenges in image-based characterization of the fabric of shale source rocks using deep-learning models. Introduction A promising application of data-driven scale-translation techniques is nanoscale imaging. This application is important for studying shales because of the importance of nanoporosity in shale gas storage. Nanoimaging techniques, however, present specific challenges and can result in small image data sets that do not allow for accurate quantification of petrophysical or morphological properties. Consequently, data translation and generation both offer many opportunities to assimilate multiple nano- and microscale modalities and to overcome limitations of nanoimaging systems. The author proposes an image-based characterization work flow (Fig. 1) for data-driven scale translation that uses deep-learning image synthesis and translation models to assimilate multimodal, multiscale, and data-scarce source-rock images for predicting petrophysical and morphological properties. A central challenge in source-rock characterization addressed in this work is the reconstruction of 3D volumes when only 2D training images are available. Image-translation models are presented for reconstruction of 3D image volumes from 2D training data and a porous media image-synthesis algorithm that generalizes to 3D grayscale and multimodal volume generation from 2D training data. The complete paper describes the translation and synthesis models, applies these models to source-rock image data sets, and discusses extensions and future directions for the introduced work flow.

D volume reconstruction is used to identify the skin cancer at earlier stage. Through the person may be preventing from death. The subsurface information such as blood layer thickness and blood volume components can be identifying by multispectral transillumination images of the skin lesions. Lesions are simulated by using volume reconstruction, thus the result shows excellent volume with accuracy rate. Preliminary validation is also done by the small set of clinical lesions. The lesion severity can be categorized by an expert dermatologist using two features average blood layer thickness and ratio of blood volume to total lesion volume. The lesion can be classified into three types moderate, mild and severe class with high accuracy. However, these methods do not perform efficiently over a large set of images. An inverse volume reconstruction method is presented which uses a genetic algorithm optimization procedure with a novel population initialization routine and nudge operator based on the multispectral images to reconstruct the melanin and blood layer volume components. In our system, it is expected to have the ability to differentiate classes of lesions' severity based on multispectral trans-illumination and it can be perform over a large set of images. Therefore it has features like fast screening, tracking and detection of early skin cancers such as melanoma.

We address the problem of automated image alignment for 3D volume reconstruction from stacks of fluorescent confocal laser scanning microscope (CLSM) imagery acquired at multiple confocal depths, from a sequence of consecutive slides. We focus on automated image alignment based on centroid and area shape features by solving feature correspondence problem, also known as Procrustes problem, in highly deformable and ill-conditioned feature space. In result, we compare image alignment accuracy of a fully automated method with registration accuracy achieved by human subjects using a manual alignment method. Our work demonstrates significant benefits of automation for 3D volume reconstruction in terms of accuracy, consistency, and performance time. We also outline the limitations of fully automated and manual 3D volume reconstruction system.

Liver segmentation plays a crucial role mainly in tumor segmentation, 3D volume reconstruction and treatment planning of liver disease. In the present work, a comparative evaluation of two segmentation methods named as active contour and level set is presented. Comparative evaluation of both methods has been done individually by applying on Computed Tomography (CT) abdomen images. Comparative study is done by following steps (i) Preprocessing using morphological functions (ii) Segmentation of liver using active contour/level set algorithms (iii) 3-Dimensional volume reconstruction. Results of applied liver segmentation algorithms are compared with manually performed delineations by radiologist. Volume overlapping of above methods is assessed using Dice similarity coefficient (DSC) which gives mean DSC of 93.52%±.62 and 89.48%±.76 in case of active contour and level set method respectively. Finally it can be demonstrated that active contour is superior to level set in terms of accuracy, processing time and 3D volume reconstruction.

Three-dimensional reconstruction methods in Single Particle Analysis from transmission electron microscopy data

This paper aims to provide a sound estimation of the true value and proportion of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) of the brain DTI images for a proper 3D volume reconstruction. During the pre-processing stage, two nonlinear filters are operated, i.e. bilateral and anisotropic diffusion. The segmentation of each brain tissue is performed using the k-means clustering algorithm. To minimize filters bias and for obtaining the best reproducible results, a statistical analysis has been performed. Thus, the skewness and kurtosis statistics features were computed for each segmented brain tissue and filter. The fuzzy k-means method allows for clustering analysis and the Bland-Altman analysis investigates the agreement between two filtering techniques of the same statistics feature and brain tissue. Then the 3D reconstruction method is presented using ImageJ and the image stacks for raw and processed data. We conclude that anisotropic diffusion filter offers the best results and 3D reconstruction of brain tissues is feasible.

Shape recovery and volume calculation from biplane angiography in the stereotactic radiosurgical treatment of arteriovenous malformations

Radiologic evaluation of images from computed tomography (CT) or magnetic resonance imaging for diagnostic purposes is based on the analysis of single slices, occasionally supplementing this information with 3D reconstructions as well as surface or volume rendered images. However, due to the complexity of anatomical or pathological structures in biomedical imaging, innovative visualization techniques are required to display morphological characteristics three dimensionally. Virtual reality is a modern tool of representing visual data, The observer has the impression of being “inside” a virtual surrounding, which is referred to as immersive imaging. Such techniques are currently being used in technical applications, e.g. in the automobile industry. Our aim is to introduce a workflow realized within one simple program which processes common image stacks from CT, produces 3D volume and surface reconstruction and rendering, and finally includes the data into a virtual reality device equipped with a motion head tracking cave automatic virtual environment system. Such techniques have the potential to augment the possibilities in non-invasive medical imaging, e.g. for surgical planning or educational purposes to add another dimension for advanced understanding of complex anatomical and pathological structures. To this end, the reconstructions are based on advanced mathematical techniques and the corresponding grids which we can export are intended to form the basis for simulations of mathematical models of the pathogenesis of different diseases.

State-of-the-art focused ion beam (FIB) instruments have an ion column for sample modification and an electron column for scanning electron microscopy (SEM). 3D reconstruction of a sample volume can be achieved by serial sectioning using the FIB in combination with high-resolution SEM imaging of each cross-section. Usually, the resolution in the direction in which the sections are milled (z-direction) is much lower than in the plane of the cross-section (xy-direction) itself. Increased sampling in the z-direction can only be achieved by decreasing the distance between single sections. For a constant volume this is equivalent to increasing the number of sections, i.e. time and effort. To perform efficient 3D reconstructions the effect of the reduced sampling in the z-direction to the overall accuracy of the 3D reconstruction has to be known. We tested this approach with FIB conical test structures that were slice-and-view processed and subsequently reconstructed. Using a reference data set with a slice thickness (z-resolution) of 22 nm, data with z-resolutions ranging from 44 nm to 440 nm were created and reconstructed with commercial software. The calculated volumes for the simulated z-resolutions and their deviations from the reference volume are shown. Deviations of up to 35% occur and reach about 10% once the z-resolution was one fifth of the upper diameter of the conical structures.

A need exists to use High Intensity Focused Ultrasound (HIFU) for vessel cauterization in current and future Critical Systems for Trauma and Transport (CSTAT) systems to improve battlefield medicine to save lives. For this purpose, a rugged CSTAT-compatible hemostatic HIFU system was designed that consists of a robotic manipulator, a detachable end-effector containing the HIFU applicator, and a HIFU therapy planning and control strategy. The HIFU applicator is a 23-annuli phased array operating at 2.2 MHz, capable of cauterizing vessels from the skin surface to a depth of 70 mm at in-situ intensities exceeding 1500 W/cm 2. It is coupled to a sector confocal imaging array (8-5 MHz), which uses Doppler technology to detect bleeding, and is capable of acquiring 3D volume images for treatment planning. The applicator is scanned in the XY plane across the skin of the patient by the end-effector under ultrasound guidance to cauterize larger volumes. An off-the-shelf robotic manipulator places (and later retrieves) the end-effector in the appropriate location on the patient. The system is robust against vibration, as the manipulator is only used to place/retrieve the end-effector on the patient. All subsequent treatment activities are performed by the end-effector, which is tightly registered to the treatment volume using suction. Diagnosis, treatment, and treatment monitoring are performed by the end-effector under local or remote control with ultrasound imaging, Doppler, and video feedback during the transport of the injured soldier. Key aspects of the design, including the suction mechanism, Doppler laceration detection, and 3D volume reconstruction were investigated using phantoms, while ultrasound and mechanical simulation tools were used to define the system specifications, the HIFU applicator, the treatment parameters, and the hemostasis control strategy

This paper addresses the problem of intensity correction of fluorescent confocal laser scanning microscope images. Confocal laser scanning microscope images are frequently used in medicine for obtaining 3D information about specimen structures by imaging a set of 2D cross sections and performing 3D volume reconstruction afterwards. However, 2D images acquired from fluorescent confocal laser scanning microscope images demonstrate significant intensity heterogeneity, for example, due to photo-bleaching and fluorescent attenuation in depth. We developed an intensity heterogeneity correction technique that (a) adjusts the intensity heterogeneity of 2D images, (b) preserves fine structural details and (c) enhances image contrast, by performing spatially adaptive mean-weight filtering. Our solution is obtained by formulating an optimization problem, followed by filter design and automated selection of filtering parameters. The proposed filtering method is experimentally compared with several existing techniques by using four quality metrics: contrast, intensity heterogeneity (entropy) in a low frequency domain, intensity distortion in a high frequency domain and saturation. Based on our experiments and the four quality metrics, the developed mean-weight filtering outperforms other intensity correction methods by at least a factor of 1.5 when applied to fluorescent confocal laser scanning microscope images.

The distribution of looping patterns of laminin in uveal melanomas and other tumours has been associated with adverse outcome. Moreover, these patterns are generated by highly invasive tumour cells through the process of vasculogenic mimicry and are not therefore blood vessels. Nevertheless, these extravascular matrix patterns conduct plasma. The three-dimensional (3D) configuration of these laminin-rich patterns compared with blood vessels has been the subject of speculation and intensive investigation. We have developed a method for the 3D reconstruction of volume for these extravascular matrix proteins from serial paraffin sections cut at 4 microm thicknesses and stained with a fluorescently labelled antibody to laminin (Maniotis et al., 2002). Each section was examined via confocal laser-scanning focal microscopy (CLSM) and 13 images were recorded in the Z-dimension for each slide. The input CLSM imagery is composed of a set of 3D sub-volumes (stacks of 2D images) acquired at multiple confocal depths, from a sequence of consecutive slides. Steps for automated reconstruction included (1) unsupervised methods for selecting an image frame from a sub-volume based on entropy and contrast criteria, (2) a fully automated registration technique for image alignment and (3) an improved histogram equalization method that compensates for spatially varying image intensities in CLSM imagery due to photo-bleaching. We compared image alignment accuracy of a fully automated method with registration accuracy achieved by human subjects using a manual method. Automated 3D volume reconstruction was found to provide significant improvement in accuracy, consistency of results and performance time for CLSM images acquired from serial paraffin sections.

Estimating the oxygen saturation of blood surrounding skin lesions is critical for the early detection of skin cancer and malignancy such as melanoma. Through multispectral transillumination of the skin, a set of images can be obtained which helps visualize areas of subsurface absorption due to chromophores such as melanin, hemoglobin, and deoxyhemoglobin. To relate pixel intensity in these images to spatially located absorption coefficient values, we develop a correction factor to Beer's law, estimated through a Monte Carlo simulation of light propagation in skin, which takes into account the specific geometry of our transillumination imaging apparatus. We then use this relation on the multispectral imaging set for chromophore separation and oxygen saturation estimation. The separation method is validated through Monte Carlo simulation, as well as on a skin phantom. Results show that subsurface oxygen saturation can be reasonably estimated with good implications for the reconstruction of 3D skin lesion volumes using transillumination towards early detection of malignancy.

Intramyocardial course, an inborn coronary anomaly, is defined as a segment of a major epicardial coronary artery that runs intramurally through the myocardium; in particular, we distinguish myocardial bridging, in which the vessel returns to an epicardial position after the muscle bridge, and intramyocardial course, which is described as a vessel running and ending in the myocardium. To evaluate the prevalence of myocardial bridging and intramyocardial course of coronary arteries as defined by multidetector computed tomography (MDCT) angiography. The study population consisted of 242 consecutive patients (211 men, 31 women; mean age 59+/-6 years) with atypical chest pain admitted to our hospital between December 2004 and September 2006. All MDCT examinations were performed using a 16-detector-row scanner (Aquilion 16 CFX; Toshiba Medical System, Tokyo, Japan). Patients with heart rate above 65 bpm received 50 mg atenolol orally for 3 days prior to the MDCT scan, or they increased their usual therapy with beta-blockers, in order to obtain a prescan heart rate <60 bpm. Curved multiplanar and 3D volume reconstructions were performed to explore coronary anatomy. In 235 patients, the CT scan was successful and images were appropriate for evaluation. The prevalence of myocardial bridging and intramyocardial course of coronary arteries was 18.7% (47 cases) in our patient population. In 30 segments (63.8%), the vessels ran and ended in the myocardium. In the remaining 17 segments (36.2%), the vessels returned to an epicardial position after the muscle bridge. We found no difference in the prevalence of this inborn coronary anomaly when comparing different clinical characteristics of the study population (sex, age, body-mass index [BMI], etc.). The mean length of the subepicardial artery was 7 mm (range 5-12 mm), and the mean depth in the diastolic phase was 1.9 mm (range 1.2-2.3 mm). There was no significant difference of diameter in these segments between the different R-R phases examined. Our study is in agreement with major angiographic literature reporting a prevalence of myocardial bridging and intramyocardial course between 0.5% and 33%. MDCT technology represents a useful, noninvasive imaging method to assess and evaluate the location, depth, and length of this anatomical variation.