Machine learning algorithms, especially deep learning architectures, have demonstrated immense potential for biomedical segmentation, often surpassing expert-level performance. For cardiac magnetic resonance (CMR) imaging, semantic segmentation is critical to deriving clinical measures such as myocardial mass and volume. However, challenges still exist. Manual delineation by domain experts is time-consuming and subject to human errors. With semi-automated segmentation techniques, it is challenging to analyze images of the same subject twice, end-diastole and end-systole of the cardiac cycle. To address these challenges, we propose a deep learning-based end-to-end analytical pipeline for automated segmentation of short-axis CMR imaging. The automated pipeline successfully avoids the problem of human subjectivity and achieves expert-level segmentation accuracy. With a large heterogeneous data inclusive of subjects with varying conditions, our model overcomes the data-homogeneity and achieves 99.9% dice similarity score, which outperforms the current state-of-art work.

Gauss-Markov random field (GMRF) image models are commonly used in many Bayesian-based imaging techniques to define priors that lead to computationally tractable solutions for the image restoration problem. Recently, the color reconstruction literature has demonstrated, and effectively employed, the high correlation among the bands of a color image for color reconstruction. In this paper, we formulate a compound GMRF prior based on cross-channel spatial derivatives that reflects the smoothness in the color-difference space in addition to the often used intra-channel smoothness assumption. The proposed model is used to develop an effective method for restoring sparsely sampled color images in the presence of noise. The value of the proposed method is demonstrated on the problem of color reconstruction for single-sensor cameras.

In this paper, we present a new method for reconstructing three-dimensional (3-D) left ventricular myocardial strain from tagged magnetic resonance (MR) image data with a 3-D B-spline deformation model. The B-spline model is based on a cylindrical coordinate system that more closely fits the morphology of the myocardium than previously proposed Cartesian B-spline models and does not require explicit regularization. Our reconstruction method first fits a spatial coordinate B-spline displacement field to the tag line data. This displacement field maps each tag line point in the deformed myocardium back to its reference position (end-diastole). The spatial coordinate displacement field is then converted to material coordinates with another B-spline fit. Finally, strain is computed by analytically differentiating the material coordinate B-spline displacement field with respect to space. We tested our method with strains reconstructed from an analytically defined mathematical left ventricular deformation model and ten human imaging studies. Our results demonstrate that a quadratic cylindrical B-spline with a fixed number of control points can accurately fit a physiologically realistic range of deformations. The average 3-D reconstruction computation time is 20 seconds per time frame on a 450 MHz Sun Ultra80 workstation.

Tracking of cardiac motion using magnetic resonance tagging has attracted increasing attention in recent years. Several methods for tagging the cardiac tissue and tracking the motion of the tags have been developed. However, the choice of tag pattern that minimizes tracking error has received less attention. In this paper, we are concerned with the optimal tagging and acquisition of MR tagged images for cardiac motion analysis. We formulate the measurement of tissue deformation as a multidimensional parametric estimation problem which can be solved using the nonlinear least squares estimator. Along with this, we derive the Cramer-Rao lower bound (CRLB) on the average estimation error variance. We then show that under certain conditions a complex sinusoidal tag shape minimizes the CRLB. We validate our results with computer simulations. Finally, based on the previous findings, we make recommendations concerning the most desirable imaging strategy for images tagged with a complex sinusoidal tag pattern.

Magnetic resonance (MR) tagging has been shown to be a useful technique for noninvasively measuring the deformation of an in vivo heart. An important step in analyzing tagged images is the identification of tag lines in each image of a cine sequence. Most existing tag identification algorithms require user defined myocardial contours. Contour identification, however, is time consuming and requires a considerable amount of user intervention. In this paper, a new method for identifying tag lines, which we call the ML/MAP method, is presented that does not require user defined myocardial contours. The ML/MAP method is composed of three stages. First, a set of candidate tag line centers is estimated across the entire region-of-interest (ROI) with a snake algorithm based on a maximum-likelihood (ML) estimate of the tag center. Next, a maximum a posteriori (MAP) hypothesis test is used to detect the candidate tag centers that are actually part of a tag line. Finally, a pruning algorithm is used to remove any detected tag line centers that do not meet a spatio-temporal continuity criterion. The ML/MAP method is demonstrated on data from ten in vivo human hearts.

we determine the optimal pulse shape for estimating positions of superimposed pulses by deriving the Cramer-Rao lower bound (CRLB) on the average estimation error variance and optimizing it with respect to the pulse shape. Our results show that a significant improvement in estimation error variance can be achieved relative to Gaussian and rectangular pulse shapes.

The authors introduce a new phase-based magnetic resonance (MR) tagging method for a direct and noninvasive measurement of the 2-D myocardial strain tensor. Their high-frequency complex sinusoidal tags can be conveniently generated with the SPAMM tagging pulse sequence, followed by one-sided band-pass imaging. In the authors' method, the instantaneous spatial phase data of the deformed tags carry the essential information with regard to local myocardial activity from which the strain tensor can be computed directly. Segmentation of tags and/or epicardial and endocardial contours is not required. As a result, the authors' proposed method is relatively simple to implement on current clinical MRI scanners, cheap in terms of computational cost, and can be generalized, at least conceptually, for 3-D imaging. The authors present computer simulation results for an idealized 2-D myocardial contraction to demonstrate the key idea as well as the performance of their method.

Tagged magnetic resonance (MR) imaging has been shown to be a useful means for non-invasive measurement of the deformation of the left ventricle (LV) during the cardiac cycle. One can estimate a dense displacement field based on the tag line movements and use it to compute myocardial measures such as strain. Existing methods require that the boundaries of the LV be known before the displacement field is estimated, which results in a time-consuming process since user intervention is needed for the estimation of the boundaries. In this paper, the authors propose a method that jointly estimates the LV boundaries and displacement field without user-intervention. They model the displacement field as a compound Gauss-Markov random field (CGMRF) which is parameterized by two closed and smooth contours. A partial-optimal solution is sought by iteratively updating the displacement field, the contours and the parameters. Experimental results on both simulated vector image and in vivo human data show that the authors' method is capable of automatically tracking the contours and reconstructing displacement field with a decent accuracy.

In this paper we present a new method for estimating the 3-D motion of an incompressible or nearly incompressible body from a set of noisy and possibly sparse measurements. This problem occurs in applications such as the estimation of heart motion and fluid flow. Our method is based on a stochastic motion model where the incompressibility condition is modeled as a correlation between certain components of the displacement or velocity field gradient. This model, which we call the correlation model, allows for local fluctuations in volume, and provides a framework for incorporating a priori knowledge of the directions of expansion and contraction. We derive an optimal estimate equation based on the correlation model, which under certain conditions reduces to that obtained by other authors using a regularisation approach. Computer simulations are presented that demonstrate the correlation model method.

A frequency domain performance analysis of Horn and Schunck's (1981) optical flow (HSOF) algorithm for estimation of deformable motion is presented. Noise sources in the algorithm are modeled using the discrete Fourier transform of the brightness pattern. This noise model along with the estimation error covariance function derived in previous work is used to derive an expression for the expected performance of the optical flow estimate that is valid for an arbitrary discrete brightness pattern. Simulation results are presented that demonstrate the validity of our methods and show that HSOF is more accurate that the optical flow estimate of Anandan for certain low-frequency patterns.

Magnetic resonance (MR) tagging has shown great potential for noninvasive measurement of the motion of a beating heart. In MR tagged images, the heart appears with a spatially encoded pattern that moves with the tissue. The position of the tag pattern in each frame of the image sequence can be used to obtain a measurement of the 3-D displacement field of the myocardium. The measurements are sparse, however, and interpolation is required to reconstruct a dense displacement field from which measures of local contractile performance such as strain can be computed. Here, the authors propose a method for estimating a dense displacement field from sparse displacement measurements. Their approach is based on a multidimensional stochastic model for the smoothness and divergence of the displacement field and the Fisher estimation framework. The main feature of this method is that both the displacement field model and the resulting estimate equation are defined only on the irregular domain of the myocardium. The authors' methods are validated on both simulated and in vivo heart data.

Magnetic resonance (MR) tagging is a technique used to impose a known intensity pattern, called a tag pattern, on an object by spatially modulating the object's proton spin density before the image is acquired. Since the tag pattern moves with the object, MR tagging is particularly useful in the estimation of deformable motion from MR image sequences because some tag patterns can resolve motion ambiguities such as the aperture problem and improve the accuracy of optical flow motion estimates. We present an experimental validation of the tag pattern optimization method of Denney and Prince (see Proc. of the IEEE Int'l Conf. Acoustics, Speech, and Signal Processing, p.V225-V228, Minneapolis, April 1993, and IEEE Transactions on Image Processing, vol.3, no.2, p.178-191, March 1994) in a practical MR imaging environment. This experiment includes a study of the effects of temporal sampling on the optimal frequency that was not done by Denney et. al. Our experimental results show that the tag pattern optimization method of Denney et. al. is valid in a practical MR imaging application.>

Estimation accuracy of Horn and Schunck's (1981) classical optical flow algorithm depends on many factors including the brightness pattern of the measured images. Since some applications can select brightness functions with which to "paint" the object, it is desirable to know what patterns will lead to the best motion estimates. The paper presents a method for determining this pattern a priori using mild assumptions about the velocity field and imaging process. The method is based on formulating Horn and Schunck's algorithm as a linear smoother and rigorously deriving an expression for the corresponding error covariance function. The authors then specify a scalar performance measure and develop an approach to select an optimal brightness function which minimizes this performance measure from within a parametrized class. Conditions for existence of an optimal brightness function are also given. The resulting optimal performance is demonstrated using simulations, and a discussion of these results and potential future research is given.

In the typical optical flow problem, the brightness function is an inherent part of the underlying physical process. In the case of magnetic resonance imaging of the heart, however, it is possible to modulate the brightness function using tagging techniques. Experimental evidence suggests that when a sinusoidal brightness function is used, there is an optimum frequency. The reasons for an optimal frequency in one dimension are investigated by formulating the optical flow problem as an optimal linear smoothing problem. A method based on minimizing the theoretical smoothed error variance is proposed for finding the optimal frequency a priori. Simulations are presented for a Wiener process velocity that show the existence of an optimal frequency and the ability of the proposed method to determine this frequency a priori.>

The authors describe an innovative personal visualization system and its application to several research and engineering problems. The system bridges both hardware and software components to permit a user to graphically describe a visualization problem to the computer; thereby reducing program development time to a few hours. Low-cost visualization is achieved using PC-based software that can either be executed on a PC or drive graphic workstations for high-resolution displays. In either case, supercomputer computation rates are available for the visualization process. On PCs this is done with one or more PiP plug in cards, each of which is capable of 100 million floating point operations per second. On workstations this is done with the QUEN array processor. Applications mentioned include: ocean wave imaging; characterizing superconductors; and solar sail visualization.>