Coronary segmentation based motion corrected cardiac CT reconstruction

In this paper, a dense motion field estimation technique based on the Bayesian framework is proposed to estimate the true dense motion fields of video sequences. Previous stochastic techniques of dense motion field estimation adopts piecewise smooth motion model and use MAP estimation to find the motion field with joint minimization of motion compensation errors and maximization of motion smoothness. However, such random process does not guarantee to converge to the true motion field. In this paper, the motion of landmark points in the video sequence is introduced into the MAP estimation process to regularize the estimated motion field. Experimental results show that the proposed algorithm produces estimated motion fields which preserve piecewise smooth nature and are visually close to the true motion of the video sequences.

In this paper, we present a new algorithm of video coding for very low bit-rate applications. The algorithm is based on: (1) dense motion field, which can achieve better motion compensation than sparse (say, block-wise) motion field; (2) DCT applied to the dense motion field to drastically save overhead information; (3) region-based segmentation with morphological techniques which can segment video frames into different regions according to their content significance; (4) discrete wavelet transform (DWT) applied to residual data with adaptive bit allocation. Consequently, this algorithm avoids annoying block artifacts, thus making reconstructed video frames much more visually pleasant, while maintains similar bit-rate to H.263.

In two-view correspondence learning, prevalent multi-layer perceptron (MLP)-based methods struggle with context capturing. To remedy this issue, recent advances innovatively stack convolutional neural network (CNN)-based Resblocks sequentially, showing an inherent proficiency in local context extraction. Yet, such non-issue-specific designs inherit the drawback of CNN’s difficulty in aggregating global context, leading to performance bottlenecks. To address this problem, this prospective study further explores the potential of the CNN-based framework and proposes MC-Net, a top-performing network that integrates both local and global context elegantly and seamlessly. Specifically, considering that sparse motion vectors and a dense motion field can be converted into each other through interpolation and sampling, we first transform unordered matches into image-structured data by estimating the dense motion field implicitly. Then, we design a hierarchical rectifying module to rectify the error of each ordered motion vector with CNN at multiple levels, enabling MC-Net to perceive global context from coarse-level features and local context from fine-level features simultaneously, which facilitates to tackle the discontinuities of the motion field in case of large scene disparity. Finally, we reconstruct comprehensive context-embedded features from rectified motion fields at all levels. Also, instead of using the residuals between rectified and pre-rectified motion vectors at the same layer to reject outliers as in previous studies, which seriously affects the inlier prediction accuracy, we rethink this operation meticulously and modify it to the difference between motion vectors obtained from each layer’s reconstruction and ones from the first layer before transformation, ensuring purer residuals and enhancing the matching performance without extra computational burden. Extensive experiments show that MC-Net outperforms state-of-the-arts on multiple domains and datasets.

In this paper a new iterative motion-based segmentation algorithm is presented for object-based video coding. At first, dense motion fields between image pairs are obtained by a multiresolution motion estimation algorithm. Based on these motion fields a set of global motion vectors are determined by an iterative vector quantisation procedure. Using the global motion vectors, the images are segmented into homogenous regions. Covered and uncovered areas of the background and objects are properly recognized by examining two consecutive motion fields. Experimental results for artificial as well for real-life video have shown that the proposed algorithm results in good quality segmentation label fields while it requires only moderate computations.

An embedded coding scheme for dense motion (optical flow) fields is proposed. Such a scheme is particularly useful in scalable video compression where one must compensate for inter-frame motion at various visual qualities and resolutions. However, the high cost of coding such fields has often made this option prohibitive. Using our previously developed ‘breakpoint’-adaptive wavelet transform, we show that it is possible to code dense motion fields efficiently while simultaneously endowing the coded motion representation with embedded resolution and quality scalability attributes. Performance comparisons with the traditional non-scalable block-based model are also made and presented with the aid of a modified H.264/AVC JM reference encoder.

Numerical phantoms are a common tool for the evaluation of registration and reconstruction algorithms. For applications concerning motion, dense deformation fields are of particular interest. Phantoms, however, are often described as surfaces and thus motion vectors can only be generated at these surfaces. In order to create dense motion fields, interpolation is required. A frequently used method for this purpose is the Parzen interpolator. However, with a high number of surface motion vectors and a high voxel count, its run time increases dramatically. In this paper, we investigate different methods to accelerate the creation of these motion fields using hierarchical sampling and the random ball cover. In the results, we show that a 64 3 volume can be sampled in less than one second with an error below 0.1 mm. Furthermore, we accelerate the interpolation of a 256 3 dense deformation field to only ˜. 5m inutes using the proposed methods from days with previous methods.

The quantitative estimation of regional cardiac deformation from 3D image sequences has important clinical implications for the assessment of viability in the heart wall. Such estimates have so far been obtained almost exclusively from Magnetic Resonance (MR) images, specifically MR tagging. In this paper we describe a methodology for estimating cardiac deformations from 3D ultrasound images. The images are segmented interactively and then initial correspondence is established using a shape-tracking approach. A dense motion field is then estimated using an anisotropic linear elastic model, which accounts for the fiber directions in the left-ventricle. The dense motion field is in turn used to calculate the deformation of the heart wall in terms of strain in cardiac specific directions. The strains obtained using this approach in open-chest dogs before and after coronary occlusion related to changes in blood flow, show good agreement with previously published results in the literature. This proposed method provides quantitative regional 3D estimates of heart deformation from ultrasound images.KeywordsUltrasound ImageRisk AreaCircumferential StrainStrain Energy FunctionHeart WallThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The quantitative estimation of regional cardiac deformation from three-dimensional (3-D) image sequences has important clinical implications for the assessment of viability in the heart wall. We present here a generic methodology for estimating soft tissue deformation which integrates image-derived information with biomechanical models, and apply it to the problem of cardiac deformation estimation. The method is image modality independent. The images are segmented interactively and then initial correspondence is established using a shape-tracking approach. A dense motion field is then estimated using a transversely isotropic, linear-elastic model, which accounts for the muscle fiber directions in the left ventricle. The dense motion field is in turn used to calculate the deformation of the heart wall in terms of strain in cardiac specific directions. The strains obtained using this approach in open-chest dogs before and after coronary occlusion, exhibit a high correlation with strains produced in the same animals using implanted markers. Further, they show good agreement with previously published results in the literature. This proposed method provides quantitative regional 3-D estimates of heart deformation.

A superresolution imaging method suitable for imaging objects moving in a dynamic scene is described. The main operations are performed over three threads: the computation of a dense interframe 2D motion-field induced by the moving objects at subpixel resolution in the first thread. Concurrently, each video image frame is enlarged by cascade of an ideal low-pass filter and a higher rate sampler, essentially stretching each image onto a larger grid. Then, the main task is to synthesize a higher resolution image, from the stretched image of the first frame, and that of the subsequent frames subjected to suitable motion compensation. A simple averaging process and/or a simplified Kalman filter may be used to minimize the spatio-temporal noise, in the aggregation process. The method takes advantage of widely used MPEG-4 encoding hardware/software tools. A few experimental cases are presented with a basic description of the key operations performed in the overall process.

To evaluate a third-order motion-compensated spin echo (M3-MCSE) sequence at multiple cardiac phases on a clinical 3 T MRI scanner with ultra-high performance (UHP) gradients (200 mT/m), compared with stimulated echo acquisition mode (STEAM) and second-order MCSE (M2-MCSE) for cardiac diffusion tensor imaging (cDTI). Twenty healthy subjects underwent mid-ventricular short-axis cDTI at peak systole and diastasis using STEAM, M2-MCSE, and M3-MCSE. cDTI metrics and image quality were compared. In five additional healthy subjects, diffusion-weighted images were obtained at multiple trigger delays distributed over diastasis to assess motion-induced signal loss. Compared to M2-MCSE, M3-MCSE yielded higher systolic helix angle map scores ( ) but lower diastolic scores ( ), with no significant difference in mean diffusivity, fractional anisotropy, helix angle transmurality or sheetlet angle in systole/diastole. STEAM-derived apparent diffusion coefficients (ADC) were consistent across diastasis, while ADC for MCSE sequences increased at sub-optimal trigger delays. UHP gradients enabled in vivo evaluation of M3-MCSE, showing superior systolic cDTI but reduced diastolic performance versus M2-MCSE due to reduced signal-to-noise ratio and a longer motion-sensitive window. Future work may consider numerically optimized gradient designs to enhance MCSE robustness throughout the cardiac cycle.

Three-dimensional X-ray Coronary Angiography in the Porcine Model: A Feasibility Study

Cardiac motion correction with a deep learning network for perfusion defect assessment in single-photon emission computed tomography myocardial perfusion imaging

Image quality of positron emission tomography (PET) reconstructions is degraded by subject motion occurring during the acquisition. Magnetic resonance (MR)-based motion correction approaches have been studied for PET/MR scanners and have been successful at capturing regular motion patterns, when used in conjunction with surrogate signals (e.g. navigators) to detect motion. However, handling irregular respiratory motion and bulk motion remains challenging. In this work, we propose an MR-based motion correction method relying on subspace-based real-time MR imaging to estimate motion fields used to correct PET reconstructions. We take advantage of the low-rank characteristics of dynamic MR images to reconstruct high-resolution MR images at high frame rates from highly undersampled k-space data. Reconstructed dynamic MR images are used to determine motion phases for PET reconstruction and estimate phase-to-phase nonrigid motion fields able to capture complex motion patterns such as irregular respiratory and bulk motion. MR-derived binning and motion fields are used for PET reconstruction to generate motion-corrected PET images. The proposed method was evaluated on in vivo data with irregular motion patterns. MR reconstructions accurately captured motion, outperforming state-of-the-art dynamic MR reconstruction techniques. Evaluation of PET reconstructions demonstrated the benefits of the proposed method in terms of motion artifacts reduction, improving the contrast-to-noise ratio by up to a factor 3 and achieveing a target-to-background ratio up to 90% superior compared to standard/uncorrected methods. The proposed method can improve the image quality of motion-corrected PET reconstructions in clinical applications.

Facial expression recognition (FER) for emotion inference has become one of the most important research fields in human-computer interaction. Existing study on FER mainly focuses on visible images, whereas varying lighting conditions may influence their performances. Recent studies have demonstrated the advantages of infrared thermal images reflecting the temperature distributions, which are robust to lighting changes. In this paper, a novel infrared image sequence based FER method is proposed using spatiotemporal feature analysis and deep Boltzmann machines (DBM). Firstly, a dense motion field among infrared image sequences is generated using optical flow algorithm. Then, PCA is applied for dimension reduction and a three-layer DBM structure is designed for final expression classification. Finally, the effectiveness of the proposed method is well demonstrated based on several experiments conducted on NVIE database.

The performance of superresolution video enhancement relies heavily on the robustness and accuracy of motion estimation techniques. In this paper, we propose a novel and efficient block matching motion estimation algorithm suitable for estimating general motion existing in low resolution video frames. We exploit the spatial correlations between motion vectors and apply a coarse-to-fine multi-stage scheme to get the dense motion fields. We incorporate our motion estimation technique into the Projection Onto Convex Sets (POCS) superresolution framework. Experimental results show that the resulting high resolution images yield visual sharper enhanced images with significant PSNR improvement.