Temporal super-resolution of 2D/3D echocardiography using cubic B-spline interpolation

An Exploratory Study of Laser Scribing Quality through Cross-Section Scribing Profiles

This article presents a novel approach for evaluating laser scribing quality through cross-section profiles generated from a three-dimensional optical profiler. Existing methods for assessing scribing quality only consider the width and depth of a scribe profile. The proposed method uses a cubic spline model for cross-section profiles. Two quality characteristics are proposed to assess scribing accuracy and consistency. Accuracy is measured by the ratio of the actual laser-scribed area to the target area (RA), which reflects the deviation from the desired profile. The mean square error (MSE) is a measure of how close each scribed cross-section under the same scribing conditions is to the fitted cubic spline model. Over 1370 cross-section profiles were generated under 171 scribing conditions. Two response surface polynomial models for RA and MSE were built with 18 scribing conditions with acceptable scribing depth and RA values. Both RA and MSE were considered simultaneously via contour plots. A scatter plot of RA and MSE was then used for Pareto optimization. It was found that the cross-sectional profile of a laser scribe could be accurately represented by a cubic spline model. A multivariate nonlinear regression model for RA and MSE identified pulse energy and repetition rate as the two dominant laser parameters. A Pareto optimization analysis further established a Pareto front, where the best compromised solution could be found.

Increasing frame rate of echocardiography based on a novel 2D spatio-temporal meshless interpolation

BACKGROUND

Increasing temporal resolution through numerical methods aids clinicians to evaluate fast moving structures of the heart with more confidence.

METHODOLOGY

In this study, a spatio-temporal numerical method is proposed to increase the frame rate based on two-dimensional (2D) interpolation. More specifically, we propose a novel intensity variation time surface (IVTS) strategy to incorporate both temporal and spatial information in the reconstruction. In this regard, we exploit radial basis functions (RBFs) for 2D interpolation. The reason for choosing RBFs for this task is manifold. First, RBFs are able to interpolate on large-scale datasets. Moreover, their mathematical implementation is simple. Another important property of this interpolation technique, which is addressed in this study, is its meshless nature. The meshless property enables higher up-sampling (UpS) rates for echocardiography to improve temporal resolution without noticeably degrading image quality. To evaluate the proposed approach, we tested the RBF interpolation on 2D/3D echocardiography datasets. The reconstructed frames were analyzed using different image quality metrics, and the results were compared with two popular techniques from the literature.

RESULTS

The findings demonstrated that, with a down-sampling rate of 3, the proposed technique outperformed the best existing method by 42%, 87%, 8%, and 11%, respectively, in terms of mean square error (MSE), contrast to noise ratio (CNR), peak signal-to-noise ratio (PSNR), and figure of merit (FOM). It should be noted that the proposed method is comparable to the best available method in terms of structural similarity (SSIM) index. Furthermore, when compared to the original images, the results of employing our technique on radio-frequency (RF) level analysis demonstrated that the reconstruction accuracy is satisfactory in terms of image quality criterion.

CONCLUSION

Finally, it is worthwhile noting that the proposed method is better than (or comparable to) the other methods in terms of reconstruction performance and processing time. Therefore, the RBF interpolation can be a promising alternative to the existing methods.

Optical flow estimation of coronary angiography sequences based on semi-supervised learning

Optical flow is widely used in medical image processing, such as image registration, segmentation, 3D reconstruction, and temporal super-resolution. However, high-precision optical flow training datasets for medical images are challenging to produce. The current optical flow estimation models trained on these non-medical datasets, such as KITTI, Sintel, and FlyingChairs are unsuitable for medical images. In this work, we propose a semi-supervised learning mechanism to estimate the optical flow of coronary angiography. Our proposed method only needs the original medical images, segmentation results of regions of interest, and pre-trained models based on other optical flow datasets to train a new optical flow estimation model suitable for medical images. First, we use the coronary segmentation results to perform image enhancement processing on the coronary vascular region to improve the image contrast between the vascular region and the surrounding tissues. Then, we extract the high-precision optical flow of coronary arteries based on the coronary-enhanced images and the pre-trained optical flow estimation model. After estimating the optical flow, we take it and its corresponding original coronary angiography images as the training dataset to train the optical flow estimation network. Furthermore, we generate a large-scale synthetic Flying-artery dataset based on coronary artery segmentation results and original coronary angiography images, which is used to improve and evaluate the accuracy of optical flow estimation for coronary angiography. The experimental results on the coronary angiography datasets demonstrate that our proposed method can significantly improve the optical flow estimation accuracy of coronary angiography sequences compared with other methods.

Artificial intelligence applied to support medical decisions for the automatic analysis of echocardiogram images: A systematic review

The echocardiogram is a test that is widely used in Heart Disease Diagnoses. However, its analysis is largely dependent on the physician's experience. In this regard, artificial intelligence has become an essential technology to assist physicians. This study is a Systematic Literature Review (SLR) of primary state-of-the-art studies that used Artificial Intelligence (AI) techniques to automate echocardiogram analyses. Searches on the leading scientific article indexing platforms using a search string returned approximately 1400 articles. After applying the inclusion and exclusion criteria, 118 articles were selected to compose the detailed SLR. This SLR presents a thorough investigation of AI applied to support medical decisions for the main types of echocardiogram (Transthoracic, Transesophageal, Doppler, Stress, and Fetal). The article's data extraction indicated that the primary research interest of the studies comprised four groups: 1) Improvement of image quality; 2) identification of the cardiac window vision plane; 3) quantification and analysis of cardiac functions, and; 4) detection and classification of cardiac diseases. The articles were categorized and grouped to show the main contributions of the literature to each type of ECHO. The results indicate that the Deep Learning (DL) methods presented the best results for the detection and segmentation of the heart walls, right and left atrium and ventricles, and classification of heart diseases using images/videos obtained by echocardiography. The models that used Convolutional Neural Network (CNN) and its variations showed the best results for all groups. The evidence produced by the results presented in the tabulation of the studies indicates that the DL contributed significantly to advances in echocardiogram automated analysis processes. Although several solutions were presented regarding the automated analysis of ECHO, this area of research still has great potential for further studies to improve the accuracy of results already known in the literature.

Speckle Tracking Accuracy Enhancement by Temporal Super-Resolution of Three-Dimensional Echocardiography Images

Background:

Speckle tracking has always been a challenging issue in echocardiography images due to the lowcontrast and noisy nature of ultrasonic imaging modality. While in ultrasound imaging, framerate is limited by image size and sound speed in tissue, speckle tracking results get worse inthree-dimensional imaging due to its lower frame rate. Therefore, numerous techniques have beenreported to overcome this limitation and enhance tracking accuracy.

Methods:

In this work, we have proposedto increase the frame rate temporally for a sequence of three-dimensional (3D) echocardiographyframes to make tracking more accurate. To increase the number of frames, cubic B-spline is usedto interpolate between intensity variation time curves extracted from every single voxel in theimage during the cardiac cycle. We have shown that the frame rate increase will result in trackingaccuracy improvement.

Results:

To prove the efficiency of the proposed method, numerical evaluation metricsfor tracking are reported to make a comparison between high temporal resolution sequences andlow temporal resolution sequences. Anatomical affine optical flow is selected as the state-of-the-artspeckle tracking method, and a 3D echocardiography dataset is used to evaluate the proposedmethod.

Conclusion:

Results show that it is beneficial for speckle tracking to perform on temporally condensedframes rather than ordinary clinical 3D echocardiography images. Normalized mean enhancementvalues for mean absolute error, Hausdorff distance, and Dice index for all cases and all frames are0.44 ± 0.09, 0.42± 0.09, and 0.36 ± 0.06, respectively.

Spatiotemporal registration and fusion of transthoracic echocardiography and volumetric coronary artery tree

PURPOSE

Cardiac multimodal image fusion can offer an image with various types of information in a single image. Many coronary stenosis, which are anatomically clear, are not functionally significant. The treatment of such kind of stenosis can cause irreversible effects on the patient. Thus, choosing the best treatment planning depend on anatomical and functional information is very beneficial.

METHODS

An algorithm for the fusion of coronary computed tomography angiography (CCTA) as an anatomical and transthoracic echocardiography (TTE) as a functional modality is presented. CCTA and TTE are temporally registered using manifold learning. A pattern search optimization algorithm, using normalized mutual information, is used to find the best match slice to TTE frame from CCTA volume. By employing a free-form deformation, the heart's non-rigid deformations are modeled. The spatiotemporal registered TTE frame is embedded to achieve the fusion result.

RESULTS

The accuracy is evaluated on CCTA and TTE data obtained from 10 patients. In temporal registration, mean absolute error of 1.97 [Formula: see text] 1.23 is resulted from comparing the output frame numbers from the algorithm and from manual assignment by an expert. In spatial registration, the accuracy of the similarity between the best match slice from CCTA volume and TTE frame is resulted in 1.82 [Formula: see text] 0.024 mm, 6.74 [Formula: see text] 0.013 mm, and 0.901 [Formula: see text] 0.0548 due to mean absolute distance, Hausdorff distance, and Dice similarity coefficient, respectively.

CONCLUSION

Without the use of ECG and Optical tracking systems, a semiautomatic framework of spatiotemporal registration and fusion of CCTA volume and TTE frame is presented. The experimental results showed the effectiveness of our proposed method to create complementary information from TTE and CCTA, which may help in the early diagnosis and effective treatment of cardiovascular diseases (CVDs).

Left Atrial Remodeling Mechanisms Associated with Atrial Fibrillation

Heart disease has always been one of the important diseases that endanger health and cause death. Therefore, it is particularly important to understand left atrium reconstruction and atrial fibrillation before heart image processing. The purpose of this paper is to provide an important review of the mechanisms of left atrial remodeling (LAR) associated with atrial fibrillation (AF). LAR refers to the spectrum of pathophysiological changes in (i) atrial structure and physiological function, and (ii) electric, ionic, and molecular milieu of the LA, in response to stresses imposed by conditions such as hypertension, myocardial ischemia, autonomic denervation and congestive heart failure. The main mechanisms of LAR include electrical remodeling, structural remodeling, metabolic remodeling, autonomic remodeling, neurohormones and inflammation, and other influencing factors. LAR is not only the basic mechanism of AF and heart failure, but also the pathophysiological basis of its progression. In clinical practice, AF is the most common persistent arrhythmia, and is believed to be the result of a combination of mechanisms that have triggers and maintenance mechanisms, including spontaneous ectopic pacing and multiple wavelet reentry. While LA electrophysiological, structural, and ultra-structural changes trigger AF, in turn, AF alters the LA electrical and structural properties that promote its maintenance and recurrence. Chronic AF leads to extensive changes in atrial cellular substructures, including loss of myofibrils, accumulation of glycogen, changes in mitochondrial shape and size, fragmentation of sarcoplasmic reticulum, and dispersion of nuclear chromatin. Electrical remodeling and structural remodeling of the atria during AF, involving structural changes and functional impairment of the left atrium, can lead to serious decline in left ventricular function and severe heart failure. Therefore, LAR and AF are inter-activating phenomena, and the resulting complications can cause serious disabling and fatal events. In this paper, we present (i) the mechanisms of LAR, in the form of structural, electrical, metabolic, and neurohormonal changes, and (ii) their interactive roles in initiating and maintaining AF. These in-depth understanding of the atrial remodeling mechanisms can in turn provide useful insights into the treatment of AF and heart failure.