A new method for registration-based medical image interpolation

Medical Image Registration Algorithm Based on Bounded Generalized Gaussian Mixture Model

In this paper, a method for medical image registration based on the bounded generalized Gaussian mixture model is proposed. The bounded generalized Gaussian mixture model is used to approach the joint intensity of source medical images. The mixture model is formulated based on a maximum likelihood framework, and is solved by an expectation-maximization algorithm. The registration performance of the proposed approach on different medical images is verified through extensive computer simulations. Empirical findings confirm that the proposed approach is significantly better than other conventional ones.

Realizing the Effective Detection of Tumor in Magnetic Resonance Imaging using Cluster-Sparse Assisted Super-Resolution

Recently, significant research has been done in Super-Resolution (SR) methods for augmenting the spatial resolution of the Magnetic Resonance (MR) images, which aids the physician in improved disease diagnoses. Single SR methods have drawbacks; they fail to capture self-similarity in non-local patches and are not robust to noise. To exploit the non-local self-similarity and intrinsic sparsity in MR images, this paper proposes the use of Cluster-Sparse Assisted Super-Resolution. This SR method effectively captures similarity in non-locally positioned patches by training on clusters of patches using a self-adaptive dictionary. This method of training also leads to better edge and texture detection. Experiments show that using Cluster-Sparse Assisted Super-Resolution for brain MR images results in enhanced detection of lesions leading to better diagnosis.

Super-Resolution of Cardiac MR Cine Imaging using Conditional GANs and Unsupervised Transfer Learning

High-resolution (HR), isotropic cardiac Magnetic Resonance (MR) cine imaging is challenging since it requires long acquisition and patient breath-hold times. Instead, 2D balanced steady-state free precession (SSFP) sequence is widely used in clinical routine. However, it produces highly-anisotropic image stacks, with large through-plane spacing that can hinder subsequent image analysis. To resolve this, we propose a novel, robust adversarial learning super-resolution (SR) algorithm based on conditional generative adversarial nets (GANs), that incorporates a state-of-the-art optical flow component to generate an auxiliary image to guide image synthesis. The approach is designed for real-world clinical scenarios and requires neither multiple low-resolution (LR) scans with multiple views, nor the corresponding HR scans, and is trained in an end-to-end unsupervised transfer learning fashion. The designed framework effectively incorporates visual properties and relevant structures of input images and can synthesise 3D isotropic, anatomically plausible cardiac MR images, consistent with the acquired slices. Experimental results show that the proposed SR method outperforms several state-of-the-art methods both qualitatively and quantitatively. We show that subsequent image analyses including ventricle segmentation, cardiac quantification, and non-rigid registration can benefit from the super-resolved, isotropic cardiac MR images, to produce more accurate quantitative results, without increasing the acquisition time. The average Dice similarity coefficient (DSC) for the left ventricular (LV) cavity and myocardium are 0.95 and 0.81, respectively, between real and synthesised slice segmentation. For non-rigid registration and motion tracking through the cardiac cycle, the proposed method improves the average DSC from 0.75 to 0.86, compared to the original resolution images.

Recovering from missing data in population imaging - Cardiac MR image imputation via conditional generative adversarial nets

Accurate ventricular volume measurements are the primary indicators of normal/abnor- mal cardiac function and are dependent on the Cardiac Magnetic Resonance (CMR) volumes being complete. However, missing or unusable slices owing to the presence of image artefacts such as respiratory or motion ghosting, aliasing, ringing and signal loss in CMR sequences, significantly hinder accuracy of anatomical and functional cardiac quantification, and recovering from those is insufficiently addressed in population imaging. In this work, we propose a new robust approach, coined Image Imputation Generative Adversarial Network (I2-GAN), to learn key features of cardiac short axis (SAX) slices near missing information, and use them as conditional variables to infer missing slices in the query volumes. In I2-GAN, the slices are first mapped to latent vectors with position features through a regression net. The latent vector corresponding to the desired position is then projected onto the slice manifold, conditioned on intensity features through a generator net. The generator comprises residual blocks with normalisation layers that are modulated with auxiliary slice information, enabling propagation of fine details through the network. In addition, a multi-scale discriminator was implemented, along with a discriminator-based feature matching loss, to further enhance performance and encourage the synthesis of visually realistic slices. Experimental results show that our method achieves significant improvements over the state-of-the-art, in missing slice imputation for CMR, with an average SSIM of 0.872. Linear regression analysis yields good agreement between reference and imputed CMR images for all cardiac measurements, with correlation coefficients of 0.991 for left ventricular volume, 0.977 for left ventricular mass and 0.961 for right ventricular volume.

Driving Maximal Frequency Content and Natural Slopes Sharpening for Image Amplification with High Scale Factor

Background:

In this paper, a method for adaptive Pure Interpolation (PI) in the frequency domain, with gradient auto-regularization, is proposed.

Methods:

The input image is transformed into the frequency domain and convolved with the Fourier Transform (FT) of a 2D sampling array (interpolation kernel) of initial size L × M. The Inverse Fourier Transform (IFT) is applied to the output coefficients and the edges are detected and counted. To get a denser kernel, the sampling array is interpolated in the frequency domain and convolved again with the transform coefficients of the original image of low resolution and transformed back into the spatial domain. The process is repeated until a maximum number of edges is reached in the output image, indicating that a locally optimal magnification factor has been attained. Finally, a maximum ascend–descend gradient auto-regularization method is designed and the edges are sharpened.

Results:

For the gradient management, a new strategy is proposed, referred to as the Natural bi- Directional Gradient Field (NBGF). It uses a natural following of a pair of directional and orthogonal gradient fields.

Conclusion:

The proposed procedure is comparable to novel algorithms reported in the state of the art with good results for high scales of amplification.

A Fast Segmentation and Efficient Slice Reconstruction Technique for Head CT Images

The three-dimensional (3D) reconstruction of medical images usually requires hundreds of two-dimensional (2D) scan images. Segmentation, an obligatory part in reconstruction, needs to be performed for all the slices consuming enormous storage space and time. To reduce storage space and time, this paper proposes a three-stage procedure, namely, slice selection, segmentation and interpolation. The methodology will have the potential to 3D reconstruct the human head from minimum selected slices. The first stage of slice selection is based on structural similarity measurement, discarding the most similar slices with none or minimal impact on details. The second stage of segmentation of the selected slices is performed using our proposed phase-field segmentation method. Validation of our segmentation results is done via comparison with other deformable models, and results show that the proposed method provides fast and accurate segmentation. The third stage of interpolation is based on modified curvature registration-based interpolation, and it is applied to re-create the discarded slices. This method is compared to both standard linear interpolation and registration-based interpolation in 100 tomographic data sets. Results show that the modified curvature registration-based interpolation reconstructs missing slices with 96% accuracy and shows an improvement in sensitivity (95.802%) on par with specificity (95.901%).

Slice interpolation of medical images using enhanced fuzzy radial basis function neural networks

Volume data composed of complete slice images play an indispensable role in medical diagnoses. However, system or human factors often lead to the loss of slice images. In recent years, various interpolation algorithms have been proposed to solve these problems. Although these algorithms are effective, the interpolated images have some shortcomings, such as less accurate recovery and missing details. In this study, we propose a new method based on an enhanced fuzzy radial basis function neural network to improve the performance of the interpolation method. The neural network includes an input layer (six input neurons), three hidden layers of neurons, and the output layer (one output neuron), and we propose a patch matching method to select the input variables of the neural network. Accordingly, we use two normal pending images to be interpolated as the input. Final output data is obtained by applying the trained neural network. In examining four groups of medical images, the proposed method outperforms five other methods, achieving the highest similarity image metric (ESSIM) values of 0.96, 0.95, 0.94, and 0.92 and the lowest mean squared difference (MSD) values of 35.5, 41.2, 50.9, and 47.1. In addition, for a whole MRI brain volume data experiment, the average MSD and ESSIM values of the proposed method and other methods are (41.62, 0.95) and (57.13, 0.90), respectively. The results indicate that the proposed method is superior to the other methods.

Temporal Scalability of Dynamic Volume Data Using Mesh Compensated Wavelet Lifting

Due to their high resolution, dynamic medical 2D+t and 3D+t volumes from computed tomography (CT) and magnetic resonance tomography (MR) reach a size which makes them very unhandy for teleradiologic applications. A lossless scalable representation offers the advantage of a down-scaled version which can be used for orientation or previewing, while the remaining information for reconstructing the full resolution is transmitted on demand. The wavelet transform offers the desired scalability. A very high quality of the lowpass sub-band is crucial in order to use it as a down-scaled representation. We propose an approach based on compensated wavelet lifting for obtaining a scalable representation of dynamic CT and MR volumes with very high quality. The mesh compensation is feasible to model the displacement in dynamic volumes which is mainly given by expansion and contraction of tissue over time. To achieve this, we propose an optimized estimation of the mesh compensation parameters to optimally fit for dynamic volumes. Within the lifting structure, the inversion of the motion compensation is crucial in the update step. We propose to take this inversion directly into account during the estimation step and can improve the quality of the lowpass sub-band by 0.63 and 0.43 dB on average for our tested dynamic CT and MR volumes at the cost of an increase of the rate by 2.4% and 1.2% on average.

Can time-averaged flow boundary conditions be used to meet the clinical timeline for Fontan surgical planning?

Cardiovascular simulations have great potential as a clinical tool for planning and evaluating patient-specific treatment strategies for those suffering from congenital heart diseases, specifically Fontan patients. However, several bottlenecks have delayed wider deployment of the simulations for clinical use; the main obstacle is simulation cost. Currently, time-averaged clinical flow measurements are utilized as numerical boundary conditions (BCs) in order to reduce the computational power and time needed to offer surgical planning within a clinical time frame. Nevertheless, pulsatile blood flow is observed in vivo, and its significant impact on numerical simulations has been demonstrated. Therefore, it is imperative to carry out a comprehensive study analyzing the sensitivity of using time-averaged BCs. In this study, sensitivity is evaluated based on the discrepancies between hemodynamic metrics calculated using time-averaged and pulsatile BCs; smaller discrepancies indicate less sensitivity. The current study incorporates a comparison between 3D patient-specific CFD simulations using both the time-averaged and pulsatile BCs for 101 Fontan patients. The sensitivity analysis involves two clinically important hemodynamic metrics: hepatic flow distribution (HFD) and indexed power loss (iPL). Paired demographic group comparisons revealed that HFD sensitivity is significantly different between single and bilateral superior vena cava cohorts but no other demographic discrepancies were observed for HFD or iPL. Multivariate regression analyses show that the best predictors for sensitivity involve flow pulsatilities, time-averaged flow rates, and geometric characteristics of the Fontan connection. These predictors provide patient-specific guidelines to determine the effectiveness of analyzing patient-specific surgical options with time-averaged BCs within a clinical time frame.

In Vitro Investigation of a New Thin Film Nitinol-Based Neurovascular Flow Diverter

Fusiform and wide-neck cerebral aneurysms (CAs) can be challenging to treat with conventional endovascular or surgical approaches. Recently, flow diverters have been developed to treat these cases by diverting flow away from the aneurysm rather than occluding it. The pipeline embolization device (PED), which embodies a single-layer braided design, is best known among available flow diverters. While the device has demonstrated success in recent trials, late aneurysmal rupture after PED treatment has been a concern. More recently, a new generation of dual-layer devices has emerged that includes a novel hyperelastic thin film nitinol (HE-TFN)-covered design. In this study, we compare fluid dynamic performance between the PED and HE-TFN devices using particle image velocimetry (PIV). The PED has a pore density of 12.5–20 pores/mm2 and a porosity of 65–70%. The two HE-TFN flow diverters have pore densities of 14.75 pores/mm2 and 40 pores/mm2, and porosities of 82% and 77%, respectively. Conventional wisdom suggests that the lower porosity PED would decrease intra-aneurysmal flow to the greatest degree. However, under physiologically realistic pulsatile flow conditions, average drops in root-mean-square (RMS) velocity (VRMS) within the aneurysm of an idealized physical flow model were 42.8–73.7% for the PED and 68.9–82.7% for the HE-TFN device with the highest pore density. Interestingly, examination of collateral vessel flows in the same model also showed that the HE-TFN design allowed for greater collateral perfusion than the PED. Similar trends were observed under steady flow conditions in the idealized model. In a more clinically realistic scenario wherein an anatomical aneurysm model was investigated, the PED affected intra-aneurysmal VRMS reductions of 64.3% and 56.3% under steady and pulsatile flow conditions, respectively. In comparison, the high pore density HE-TFN device reduced intra-aneurysmal VRMS by 88% and 71.3% under steady and pulsatile flow conditions, respectively. We attribute the superior performance of the HE-TFN device to higher pore density, which may play a more important role in modifying aneurysmal fluid dynamics than the conventional flow diverter design parameter of greatest general interest, absolute porosity. Finally, the PED led to more elevated intra-aneurysmal pressures after deployment, which provides insight into a potential mechanism for late rupture following treatment with the device.

Surgical planning of the total cavopulmonary connection: robustness analysis

In surgical planning of the Fontan connection for single ventricle physiologies, there can be differences between the proposed and implemented options. Here, we developed a surgical planning framework that help determine the best performing option and ensures that the results will be comparable if there are slight geometrical variations. Eight patients with different underlying anatomies were evaluated in this study; surgical variations were created for each connection by changing either angle, offset or baffle diameter. Computational fluid dynamics were performed and the energy efficiency (indexed power loss-iPL) and hepatic flow distribution (HFD) computed. Differences with the original connection were evaluated: iPL was not considerably affected by the changes in geometry. For HFD, the single superior vena cava (SVC) connections presented less variability compared to the other anatomies. The Y-graft connection was the most robust overall, while the extra-cardiac connections showed dependency to offset. Bilateral SVC and interrupted inferior vena cava with azygous continuation showed high variability in HFD. We have developed a framework to assess the robustness of a surgical option for the TCPC; this will be useful to assess the most complex cases where pre-surgery planning could be most beneficial to ensure an efficient and robust hemodynamic performance.

MRI upsampling using feature-based nonlocal means approach

In magnetic resonance imaging (MRI), spatial resolution is limited by several factors such as acquisition time, short physiological phenomena, and organ motion. The acquired image usually has higher resolution in two dimensions (the acquisition plane) in comparison with the third dimension, resulting in highly anisotropic voxel size. Interpolation of these low resolution (LR) images using standard techniques, such as linear or spline interpolation, results in distorted edges in the planes perpendicular to the acquisition plane. This poses limitation on conducting quantitative analyses of LR images, particularly on their voxel-wise analysis and registration. We have proposed a new non-local means feature-based technique that uses structural information of a high resolution (HR) image with a different contrast and interpolates the LR image. In this approach, the similarity between voxels is estimated using a feature vector that characterizes the laminar pattern of the brain structures, resulting in a more accurate similarity measure in comparison with conventional patch-based approach. This technique can be applied to LR images with both anisotropic and isotropic voxel sizes. Experimental results conducted on brain MRI scans of patients with brain tumors, multiple sclerosis, epilepsy, as well as schizophrenic patients and normal controls show that the proposed method is more accurate, requires fewer computations, and thus is significantly faster than a previous state-of-the-art patch-based technique. We also show how the proposed method may be used to upsample regions of interest drawn on LR images.

Data-adapted moving least squares method for 3-D image interpolation

In this paper, we present a nonlinear three-dimensional interpolation scheme for gray-level medical images. The scheme is based on the moving least squares method but introduces a fundamental modification. For a given evaluation point, the proposed method finds the local best approximation by reproducing polynomials of a certain degree. In particular, in order to obtain a better match to the local structures of the given image, we employ locally data-adapted least squares methods that can improve the classical one. Some numerical experiments are presented to demonstrate the performance of the proposed method. Five types of data sets are used: MR brain, MR foot, MR abdomen, CT head, and CT foot. From each of the five types, we choose five volumes. The scheme is compared with some well-known linear methods and other recently developed nonlinear methods. For quantitative comparison, we follow the paradigm proposed by Grevera and Udupa (1998). (Each slice is first assumed to be unknown then interpolated by each method. The performance of each interpolation method is assessed statistically.) The PSNR results for the estimated volumes are also provided. We observe that the new method generates better results in both quantitative and visual quality comparisons.

Fast acquisition and reconstruction of optical coherence tomography images via sparse representation

In this paper, we present a novel technique, based on compressive sensing principles, for reconstruction and enhancement of multi-dimensional image data. Our method is a major improvement and generalization of the multi-scale sparsity based tomographic denoising (MSBTD) algorithm we recently introduced for reducing speckle noise. Our new technique exhibits several advantages over MSBTD, including its capability to simultaneously reduce noise and interpolate missing data. Unlike MSBTD, our new method does not require an a priori high-quality image from the target imaging subject and thus offers the potential to shorten clinical imaging sessions. This novel image restoration method, which we termed sparsity based simultaneous denoising and interpolation (SBSDI), utilizes sparse representation dictionaries constructed from previously collected datasets. We tested the SBSDI algorithm on retinal spectral domain optical coherence tomography images captured in the clinic. Experiments showed that the SBSDI algorithm qualitatively and quantitatively outperforms other state-of-the-art methods.

Spatiotemporal quantification of local drug delivery using MRI

Controlled release formulations for local, in vivo drug delivery are of growing interest to device manufacturers, research scientists, and clinicians; however, most research characterizing controlled release formulations occurs in vitro because the spatial and temporal distribution of drug delivery is difficult to measure in vivo. In this work, in vivo magnetic resonance imaging (MRI) of local drug delivery was performed to visualize and quantify the time resolved distribution of MRI contrast agents. Three-dimensional T1 maps (generated from T1-weighted images with varied TR) were processed using noise-reducing filtering. A segmented region of contrast, from a thresholded image, was converted to concentration maps using the equation 1/T1=1/T1,0+R1C, where T1,0 and T1 are the precontrast and postcontrast T1 map values, respectively. In this technique, a uniform estimated value for T 1,0 was used. Error estimations were performed for each step. The practical usefulness of this method was assessed using comparisons between devices located in different locations both with and without contrast. The method using a uniform T1,0, requiring no registration of pre- and postcontrast image volumes, was compared to a method using either affine or deformation registrations.

Hierarchical patch-based sparse representation--a new approach for resolution enhancement of 4D-CT lung data

4D-CT plays an important role in lung cancer treatment because of its capability in providing a comprehensive characterization of respiratory motion for high-precision radiation therapy. However, due to the inherent high-dose exposure associated with CT, dense sampling along superior-inferior direction is often not practical, thus resulting in an inter-slice thickness that is much greater than in-plane voxel resolutions. As a consequence, artifacts such as lung vessel discontinuity and partial volume effects are often observed in 4D-CT images, which may mislead dose administration in radiation therapy. In this paper, we present a novel patch-based technique for resolution enhancement of 4D-CT images along the superior-inferior direction. Our working premise is that anatomical information that is missing in one particular phase can be recovered from other phases. Based on this assumption, we employ a hierarchical patch-based sparse representation mechanism to enhance the superior-inferior resolution of 4D-CT by reconstructing additional intermediate CT slices. Specifically, for each spatial location on an intermediate CT slice that we intend to reconstruct, we first agglomerate a dictionary of patches from images of all other phases in the 4D-CT. We then employ a sparse combination of patches from this dictionary, with guidance from neighboring (upper and lower) slices, to reconstruct a series of patches, which we progressively refine in a hierarchical fashion to reconstruct the final intermediate slices with significantly enhanced anatomical details. Our method was extensively evaluated using a public dataset. In all experiments, our method outperforms the conventional linear and cubic-spline interpolation methods in preserving image details and also in suppressing misleading artifacts, indicating that our proposed method can potentially be applied to better image-guided radiation therapy of lung cancer in the future.

Effect of flow pulsatility on modeling the hemodynamics in the total cavopulmonary connection

Total cavopulmonary connection is the result of a series of palliative surgical repairs performed on patients with single ventricle heart defects. The resulting anatomy has complex and unsteady hemodynamics characterized by flow mixing and flow separation. Although varying degrees of flow pulsatility have been observed in vivo, non-pulsatile (time-averaged) boundary conditions have traditionally been assumed in hemodynamic modeling, and only recently have pulsatile conditions been incorporated without completely characterizing their effect or importance. In this study, 3D numerical simulations with both pulsatile and non-pulsatile boundary conditions were performed for 24 patients with different anatomies and flow boundary conditions from Georgia Tech database. Flow structures, energy dissipation rates and pressure drops were compared under rest and simulated exercise conditions. It was found that flow pulsatility is the primary factor in determining the appropriate choice of boundary conditions, whereas the anatomic configuration and cardiac output had secondary effects. Results show that the hemodynamics can be strongly influenced by the presence of pulsatile flow. However, there was a minimum pulsatility threshold, identified by defining a weighted pulsatility index (wPI), above which the influence was significant. It was shown that when wPI<30%, the relative error in hemodynamic predictions using time-averaged boundary conditions was less than 10% compared to pulsatile simulations. In addition, when wPI<50, the relative error was less than 20%. A correlation was introduced to relate wPI to the relative error in predicting the flow metrics with non-pulsatile flow conditions.

A Markov random field approach for topology-preserving registration: application to object-based tomographic image interpolation

This paper proposes a topology-preserving multiresolution elastic registration method based on a discrete Markov random field of deformations and a block-matching procedure. The method is applied to the object-based interpolation of tomographic slices. For that purpose, the fidelity of a given deformation to the data is established by a block-matching strategy based on intensity- and gradient-related features, the smoothness of the transformation is favored by an appropriate prior on the field, and the deformation is guaranteed to maintain the topology by imposing some hard constraints on the local configurations of the field. The resulting deformation is defined as the maximum a posteriori configuration. Additionally, the relative influence of the fidelity and smoothness terms is weighted by the unsupervised estimation of the field parameters. In order to obtain an unbiased interpolation result, the registration is performed both in the forward and backward directions, and the resulting transformations are combined by using the local information content of the deformation. The method is applied to magnetic resonance and computed tomography acquisitions of the brain and the torso. Quantitative comparisons offer an overall improvement in performance with respect to related works in the literature. Additionally, the application of the interpolation method to cardiac magnetic resonance images has shown that the removal of any of the main components of the algorithm results in a decrease in performance which has proven to be statistically significant.

Constrained reverse diffusion for thick slice interpolation of 3D volumetric MRI images

Due to physical limitations inherent in magnetic resonance imaging scanners, three dimensional volumetric scans are often acquired with anisotropic voxel resolution. We investigate several interpolation approaches to reduce the anisotropy and present a novel approach - constrained reverse diffusion for thick slice interpolation. This technique was compared to common methods: linear and cubic B-Spline interpolation and a technique based on non-rigid registration of neighboring slices. The methods were evaluated on artificial MR phantoms and real MR scans of human brain. The constrained reverse diffusion approach delivered promising results and provides an alternative for thick slice interpolation, especially for higher anisotropy factors.

Context-based interpolation of 3-D medical images

A context-based 3D interpolation technique is proposed to enhance the out-of plane resolution of 3D medical images. The proposed technique represents a new approach of aiding 3D interpolation and improving its performance by efficient use of domain knowledge about the anatomy, objects and imaging modalities. In the new approach a family of adaptive 3D interpolation filters are designed and conditioned on different spatial contexts (classes of feature vectors). Training is used to incorporate the domain knowledge into the design of these interpolators. Experimental results show significant improvement of the new approach over some existing 3D interpolation techniques.

Fusion of multi-planar images for improved three-dimensional object reconstruction

Due to the scan time limitation, our MRI studies of the human tongue can acquire only a limited number of contiguous two-dimensional (2D) slices to form a volumetric data set in a given series. An interpolated three-dimensional (3D) reconstruction using images acquired in a single plane presents artifacts. To address this issue, we developed a wavelet-based bidirectional linear fusion method that uses slices acquired from sagittal and coronal planes to estimate the unknown values of the inter-slice voxels. We use an interpolation method to estimate the voxel value based on neighboring fiducial voxels in the bounding slices. This interpolation is followed by a wavelet fusion to recover image details by integrating prominent coefficients from the interpolated images. Our method was evaluated using 2D MR images and 3D phantoms. Experiments demonstrated that our method reduces interpolation artifacts and greatly improves the 3D reconstruction accuracy. The advantage of our method casts new light on MR imaging and image processing and permits us to achieve high resolution and short acquisition time simultaneously.

Mechanobiology and the microcirculation: cellular, nuclear and fluid mechanics

Endothelial cells are stimulated by shear stress throughout the vasculature and respond with changes in gene expression and by morphological reorganization. Mechanical sensors of the cell are varied and include cell surface sensors that activate intracellular chemical signaling pathways. Here, possible mechanical sensors of the cell including reorganization of the cytoskeleton and the nucleus are discussed in relation to shear flow. A mutation in the nuclear structural protein lamin A, related to Hutchinson-Gilford progeria syndrome, is reviewed specifically as the mutation results in altered nuclear structure and stiffer nuclei; animal models also suggest significantly altered vascular structure. Nuclear and cellular deformation of endothelial cells in response to shear stress provides partial understanding of possible mechanical regulation in the microcirculation. Increasing sophistication of fluid flow simulations inside the vessel is also an emerging area relevant to the microcirculation as visualization in situ is difficult. This integrated approach to study--including medicine, molecular and cell biology, biophysics and engineering--provides a unique understanding of multi-scale interactions in the microcirculation.

A non-local approach for image super-resolution using intermodality priors

Image enhancement is of great importance in medical imaging where image resolution remains a crucial point in many image analysis algorithms. In this paper, we investigate brain hallucination (Rousseau, 2008), or generating a high-resolution brain image from an input low-resolution image, with the help of another high-resolution brain image. We propose an approach for image super-resolution by using anatomical intermodality priors from a reference image. Contrary to interpolation techniques, in order to be able to recover fine details in images, the reconstruction process is based on a physical model of image acquisition. Another contribution to this inverse problem is a new regularization approach that uses an example-based framework integrating non-local similarity constraints to handle in a better way repetitive structures and texture. The effectiveness of our approach is demonstrated by experiments on realistic Brainweb Magnetic Resonance images and on clinical images from ADNI, generating automatically high-quality brain images from low-resolution input.

Modified control grid interpolation for the volumetric reconstruction of fluid flows

Complex applications in fluid dynamics research often require more highly resolved velocity data than direct measurements or simulations provide. The advent of stereo PIV and PCMR techniques has advanced the state-of-the-art in flow velocity measurement, but 3D spatial resolution remains limited. Here a new technique is proposed for velocity data interpolation to address this problem. The new method performs with higher quality than competing solutions from the literature in terms of accurately interpolating velocities, maintaining fluid structure and domain boundaries, and preserving coherent structures.

Hemodynamic performance of stage-2 univentricular reconstruction: Glenn vs. hemi-Fontan templates

Flow structures, hemodynamics and the hydrodynamic surgical pathway resistances of the final stage functional single ventricle reconstruction, namely the total cavopulmonary connection (TCPC) anatomy, have been investigated extensively. However, the second stage surgical anatomy (i.e., bi-directional Glenn or hemi-Fontan template) has received little attention. We thus initiated a multi-faceted study, involving magnetic resonance imaging (MRI), phase contrast MRI, computational and experimental fluid dynamics methodologies, focused on the second stage of the procedure. Twenty three-dimensional computer and rapid prototype models of 2nd stage TCPC anatomies were created, including idealized parametric geometries (n = 6), patient-specific anatomies (n = 7), and their virtual surgery variant (n = 7). Results in patient-specific and idealized models showed that the Glenn connection template is hemodynamically more efficient with (83% p = 0.08 in patient-specific models and 66% in idealized models) lower power losses compared to hemi-Fontan template, respectively, due to its direct end-to-side anastomosis. Among the several secondary surgical geometrical features, stenosis at the SVC anastomosis or in pulmonary branches was found to be the most critical parameter in increasing the power loss. The pouch size and flare shape were found to be less significant. Compared to the third stage surgery the hydrodynamic resistance of the 2nd stage is considerably lower (both in idealized models and in anatomical models at MRI resting conditions) for both hemi- and Glenn templates. These results can impact the surgical design and planning of the staged TCPC reconstruction.

Optical flow based volumetric spatio-temporal interpolation

Acquisition of three-dimensional medical images with excellent spatial resolution is achieved almost always at the cost-dictating expense of increased acquisition time. This can be mitigated using shape-respecting inter-slice interpolation of sparsely acquired datasets. Existing shape and registration based interpolation techniques are suboptimal. This paper proposes a new optical flow based approach to interpolate between sparse data. The Horn and Schunck technique is used to estimate the pixel-wise flow vectors across the slices; intermediate data planes can be reconstructed from these flow vectors. The proposed technique is accurate and computationally fast. Coupled with anatomy aware sparse acquisitions, the proposed technique shows promise of judiciously managing the patient-specific acquisition.