Deformable image registration of liver with consideration of lung sliding motion.

PURPOSE: A feature based deformable registration model with sliding transformation was developed in the upper abdominal region for liver cancer. METHODS: A two-step thin-plate spline (bi-TPS) algorithm was implemented to deformably register the liver organ. The first TPS registration was performed to exclusively quantify the sliding displacement component. A manual segmentation of the thoracic and abdominal cavity was performed as a priori knowledge. Tissue feature points were automatically identified inside the segmented contour on the images. The scale invariant feature transform method was utilized to match feature points that served as landmarks for the subsequent TPS registration to derive the sliding displacement vector field. To a good approximation, only motion along superior/inferior (SI) direction of voxels on each slice was averaged to obtain the sliding displacement for each slice. A second TPS transformation, as the last step, was carried out to obtain the local deformation field. Manual identification of bifurcation on liver, together with the manual segmentation of liver organ, was employed as a "ground truth" for assessing the algorithm's performance. RESULTS: The proposed two-step TPS was assessed with six liver patients. The average error of liver bifurcation between manual identification and calculation for these patients was less than 1.8 mm. The residual errors between manual contour and propagated contour of liver organ using the algorithm fell in the range between 2.1 and 2.8 mm. An index of Dice similarity coefficient (DSC) between manual contour and calculated contour for liver tumor was 93.6% compared with 71.2% from the conventional TPS calculation. CONCLUSIONS: A high accuracy (∼2 mm) of the two-step feature based TPS registration algorithm was achievable for registering the liver organ. The discontinuous motion in the upper abdominal region was properly taken into consideration. Clinical implementation of the algorithm will find broad application in radiation therapy of liver cancer.

Comprehensive Modeling and Visualization of Cardiac Anatomy and Physiology from CT Imaging and Computer Simulations.

In clinical cardiology, both anatomy and physiology are needed to diagnose cardiac pathologies. CT imaging and computer simulations provide valuable and complementary data for this purpose. However, it remains challenging to gain useful information from the large amount of high-dimensional diverse data. The current tools are not adequately integrated to visualize anatomic and physiologic data from a complete yet focused perspective. We introduce a new computer-aided diagnosis framework, which allows for comprehensive modeling and visualization of cardiac anatomy and physiology from CT imaging data and computer simulations, with a primary focus on ischemic heart disease. The following visual information is presented: (1) Anatomy from CT imaging: geometric modeling and visualization of cardiac anatomy, including four heart chambers, left and right ventricular outflow tracts, and coronary arteries; (2) Function from CT imaging: motion modeling, strain calculation, and visualization of four heart chambers; (3) Physiology from CT imaging: quantification and visualization of myocardial perfusion and contextual integration with coronary artery anatomy; (4) Physiology from computer simulation: computation and visualization of hemodynamics (e.g., coronary blood velocity, pressure, shear stress, and fluid forces on the vessel wall). Substantially, feedback from cardiologists have confirmed the practical utility of integrating these features for the purpose of computer-aided diagnosis of ischemic heart disease.

Effect of Varying Hemodynamic and Vascular Conditions on Fractional Flow Reserve: An In Vitro Study.

BACKGROUND: The aim of this study was to investigate the impact of varying hemodynamic conditions on fractional flow reserve (ratio of pressure distal [Pd] and proximal [Pa] to stenosis under hyperemia) in an in vitro setting. Failure to achieve maximal hyperemia and the choice of hyperemic agents may have differential effects on coronary hemodynamics and, consequently, on the determination of fractional flow reserve. METHODS AND RESULTS: An in vitro flow system was developed to experimentally model the physiological coronary circulation as flow-dependent stenosis resistance in series with variable downstream resistance. Five idealized models with 30% to 70% diameter stenosis severity were fabricated using VeroClear rigid material in an Objet260 Connex printer. Mean aortic pressure was maintained at 7 levels (60-140 mm Hg) from hypotension to hypertension using a needle valve that mimicked adjustable microcirculatory resistance. A range of physiological flow rates was applied by a steady flow pump and titrated by a flow sensor. The pressure drop and the pressure ratio (Pd/Pa) were assessed for the 7 levels of aortic pressure and differing flow rates. The in vitro experimental data were coupled with pressure-flow relationships from clinical data for populations with and without myocardial infarction, respectively, to evaluate fractional flow reserve. The curve for pressure ratio and flow rate demonstrated a quadratic relationship with a decreasing slope. The absolute decrease in fractional flow reserve in the group without myocardial infarction (with myocardial infarction) was on the order of 0.03 (0.02), 0.05 (0.02), 0.07 (0.05), 0.17 (0.13) and 0.20 (0.24), respectively, for 30%, 40%, 50%, 60%, and 70% diameter stenosis, for an increase in aortic pressure from 60 to 140 mm Hg. CONCLUSIONS: The fractional flow reserve value, an index of physiological stenosis significance, was observed to decrease with increasing aortic pressure for a given stenosis in this idealized in vitro experiment for vascular groups with and without myocardial infarction.

Watertight modeling and segmentation of bifurcated Coronary arteries for blood flow simulation using CT imaging.

Image-based simulation of blood flow using computational fluid dynamics has been shown to play an important role in the diagnosis of ischemic coronary artery disease. Accurate extraction of complex coronary artery structures in a watertight geometry is a prerequisite, but manual segmentation is both tedious and subjective. Several semi- and fully automated coronary artery extraction approaches have been developed but have faced several challenges. Conventional voxel-based methods allow for watertight segmentation but are slow and difficult to incorporate expert knowledge. Machine learning based methods are relatively fast and capture rich information embedded in manual annotations. Although sufficient for visualization and analysis of coronary anatomy, these methods cannot be used directly for blood flow simulation if the coronary vasculature is represented as a loose combination of tubular structures and the bifurcation geometry is improperly modeled. In this paper, we propose a novel method to extract branching coronary arteries from CT imaging with a focus on explicit bifurcation modeling and application of machine learning. A bifurcation lumen is firstly modeled by generating the convex hull to join tubular vessel branches. Guided by the pre-determined centerline, machine learning based segmentation is performed to adapt the bifurcation lumen model to target vessel boundaries and smoothed by subdivision surfaces. Our experiments show the constructed coronary artery geometry from CT imaging is accurate by comparing results against the manually annotated ground-truths, and can be directly applied to coronary blood flow simulation.

Using edge-preserving algorithm with non-local mean for significantly improved image-domain material decomposition in dual-energy CT.

Increased noise is a general concern for dual-energy material decomposition. Here, we develop an image-domain material decomposition algorithm for dual-energy CT (DECT) by incorporating an edge-preserving filter into the Local HighlY constrained backPRojection reconstruction (HYPR-LR) framework. With effective use of the non-local mean, the proposed algorithm, which is referred to as HYPR-NLM, reduces the noise in dual-energy decomposition while preserving the accuracy of quantitative measurement and spatial resolution of the material-specific dual-energy images. We demonstrate the noise reduction and resolution preservation of the algorithm with an iodine concentrate numerical phantom by comparing the HYPR-NLM algorithm to the direct matrix inversion, HYPR-LR and iterative image-domain material decomposition (Iter-DECT). We also show the superior performance of the HYPR-NLM over the existing methods by using two sets of cardiac perfusing imaging data. The DECT material decomposition comparison study shows that all four algorithms yield acceptable quantitative measurements of iodine concentrate. Direct matrix inversion yields the highest noise level, followed by HYPR-LR and Iter-DECT. HYPR-NLM in an iterative formulation significantly reduces image noise and the image noise is comparable to or even lower than that generated using Iter-DECT. For the HYPR-NLM method, there are marginal edge effects in the difference image, suggesting the high-frequency details are well preserved. In addition, when the search window size increases from to , there are no significant changes or marginal edge effects in the HYPR-NLM difference images. The reference drawn from the comparison study includes: (1) HYPR-NLM significantly reduces the DECT material decomposition noise while preserving quantitative measurements and high-frequency edge information, and (2) HYPR-NLM is robust with respect to parameter selection.

Particle-fixed Monte Carlo model for optical coherence tomography.

Optical Coherence Tomography (OCT) is a new technique mainly used in biomedical imaging. We present here a Particle-Fixed Monte Carlo (PFMC) simulation for OCT signal. In the PFMC model, the scattering particles of the sample are assumed to be temporarily fixed and randomly distributed in the simulation of the backscattered light. An efficient partitioning scheme is proposed to speed up this simulation process. The new model explains the exponential decay signal at the interfaces of different media layers observed in OCT experimental measurements.

Influence of virtual intervention and blood rheology on mass transfer through thoracic aortic aneurysm.

Computational fluid dynamics tools have been used to investigate blood flow through the human thoracic aortic models with aneurysm before and after virtual stent graft operation. The impact of blood rheology and aortic geometry on the wall shear stress (WSS), luminal surface low-density lipoproteins (LDL) concentration, and oxygen flux along the arterial wall is investigated. The stent graft at the aneurysm has significant effects on WSS and mass transport in blood flow. Due to the low flow rate, Newtonian blood assumption generally under-estimates the WSS. The non-Newtonian blood rheology play an important role in the LDL transport as well as oxygen transport. It is found that WSS alone is insufficient to correctly predict the location with high risk of atherogenesis. The results suggest that WSS, luminal surface LDL concentration, and the oxygen flux on the wall have to be considered together to evaluate the performance of virtual operation.

Current progress in 3D printing for cardiovascular tissue engineering.

3D printing is a technology that allows the fabrication of structures with arbitrary geometries and heterogeneous material properties. The application of this technology to biological structures that match the complexity of native tissue is of great interest to researchers. This mini-review highlights the current progress of 3D printing for fabricating artificial tissues of the cardiovascular system, specifically the myocardium, heart valves, and coronary arteries. In addition, how 3D printed sensors and actuators can play a role in tissue engineering is discussed. To date, all the work with building 3D cardiac tissues have been proof-of-principle demonstrations, and in most cases, yielded products less effective than other traditional tissue engineering strategies. However, this technology is in its infancy and therefore there is much promise that through collaboration between biologists, engineers and material scientists, 3D bioprinting can make a significant impact on the field of cardiovascular tissue engineering.

Registration of coronary arteries in computed tomography angiography images using Hidden Markov Model.

Computed tomography angiography (CTA) allows for not only diagnosis of coronary artery disease (CAD) with high spatial resolution but also monitoring the remodeling of vessel walls in the progression of CAD. Alignment of coronary arteries in CTA images acquired at different times (with a 3-7 years interval) is required to visualize and analyze the geometric and structural changes quantitatively. Previous work in image registration primarily focused on large anatomical structures and leads to suboptimal results when applying to registration of coronary arteries. In this paper, we develop a novel method to directly align the straightened coronary arteries in the cylindrical coordinate system guided by the extracted centerlines. By using a Hidden Markov Model (HMM), image intensity information from CTA and geometric information of extracted coronary arteries are combined to align coronary arteries. After registration, the pathological features in two straightened coronary arteries can be directly visualized side by side by synchronizing the corresponding cross-sectional slices and circumferential rotation angles. By evaluating with manually labeled landmarks, the average distance error is 1.6 mm.

Analysis of ventricular function by CT.

The assessment of ventricular function, cardiac chamber dimensions, and ventricular mass is fundamental for clinical diagnosis, risk assessment, therapeutic decisions, and prognosis in patients with cardiac disease. Although cardiac CT is a noninvasive imaging technique often used for the assessment of coronary artery disease, it can also be used to obtain important data about left and right ventricular function and morphology. In this review, we will discuss the clinical indications for the use of cardiac CT for ventricular analysis, review the evidence on the assessment of ventricular function compared with existing imaging modalities such cardiac magnetic resonance imaging and echocardiography, provide a typical cardiac CT protocol for image acquisition and postprocessing for ventricular analysis, and provide step-by-step instructions to acquire multiplanar cardiac views for ventricular assessment from the standard axial, coronal, and sagittal planes. Furthermore, both qualitative and quantitative assessments of ventricular function as well as sample reporting are detailed.