The Assessment of left ventricular Function in MRI using the detection of myocardial borders and optical flow approaches: A Review

The evaluation of left ventricular wall motion in Magnetic Resonance Imaging (MRI) clinical practice is based on a visual assessment of cine-MRI sequences. In fact, clinical interpreters (radiologists) proceed with a global visual evaluation of multiple cine-MRI sequences acquired in the three standard views. In addition, some functional parameters are quantified following a manual or a semi-automatic contouring of the myocardial borders. Although these parameters give information about the functional state of the left ventricle, they are not able to provide the location and the extent of wall motion abnormalities, which are associated with many cardiovascular diseases. In the past years, several approaches were developed to overcome the limitations of the classical evaluation techniques of left ventricular function. The aim of this article is to present an overview of the different methods and to summarize the relevant techniques based on myocardial contour detection and optical flow for regional assessment of left ventricular abnormalities

Automatic Segmentation Measuring Function for Cardiac MR-Left Ventricle (LV) Images

Automatic segmentation approaches are a desirable solution for Endocardium (inner) and Epicardium (outer) contours delineation using cardiac magnetic resonance left ventricle (CMR-LV) short axis images. The Level Set Model (LSM) and Variational LSM (VLSM) is the state-of-the-art in detecting the inner and outer contour for medical images. However, in CMR-LV images segmentation the LSM and VLSM are facing with the issue of re-initialisation because of irregular circle shape. In this paper, we developed an automatic segmentation measuring function based on statistical formulation to solve the re-initialisation issues in huge set of data images. The sign Euclidean distance function successfully classified the negative (inner contour) and positive (outer contour) features. The Fuzzy C mean interaction operator intersects the high membership degree that initialises the centre point. The experiments were conducted using the Sunnybrook and Pusat Juntung Hospital Umum Sarawak (PJHUS) cardiac datasets. This paper aims at developing a distance function to guide the automatic segmentation for LV contours and also to reduce segmentation error

A dual propagation contours technique for semi-automated assessment of systolic and diastolic cardiac function by CMR

<p>Abstract</p> <p>Background</p> <p>Although cardiovascular magnetic resonance (CMR) is frequently performed to measure accurate LV volumes and ejection fractions, LV volume-time curves (VTC) derived ejection and filling rates are not routinely calculated due to lack of robust LV segmentation techniques. VTC derived peak filling rates can be used to accurately assess LV diastolic function, an important clinical parameter. We developed a novel geometry-independent dual-contour propagation technique, making use of LV endocardial contours manually drawn at end systole and end diastole, to compute VTC and measured LV ejection and filling rates in hypertensive patients and normal volunteers.</p> <p>Methods</p> <p>39 normal volunteers and 49 hypertensive patients underwent CMR. LV contours were manually drawn on all time frames in 18 normal volunteers. The dual-contour propagation algorithm was used to propagate contours throughout the cardiac cycle. The results were compared to those obtained with single-contour propagation (using either end-diastolic or end-systolic contours) and commercially available software. We then used the dual-contour propagation technique to measure peak ejection rate (PER) and peak early diastolic and late diastolic filling rates (ePFR and aPFR) in all normal volunteers and hypertensive patients.</p> <p>Results</p> <p>Compared to single-contour propagation methods and the commercial method, VTC by dual-contour propagation showed significantly better agreement with manually-derived VTC. Ejection and filling rates by dual-contour propagation agreed with manual (dual-contour – manual PER: -0.12 ± 0.08; ePFR: -0.07 ± 0.07; aPFR: 0.06 ± 0.03 EDV/s, all P = NS). However, the time for the manual method was ~4 hours per study versus ~7 minutes for dual-contour propagation. LV systolic function measured by LVEF and PER did not differ between normal volunteers and hypertensive patients. However, ePFR was lower in hypertensive patients vs. normal volunteers, while aPFR was higher, indicative of altered diastolic filling rates in hypertensive patients.</p> <p>Conclusion</p> <p>Dual-propagated contours can accurately measure both systolic and diastolic volumetric indices that can be applied in a routine clinical CMR environment. With dual-contour propagation, the user interaction that is routinely performed to measure LVEF is leveraged to obtain additional clinically relevant parameters.</p

Methodology for Jointly Assessing Myocardial Infarct Extent and Regional Contraction in 3-D CMRI

Automated extraction of quantitative parameters from Cardiac Magnetic Resonance Images (CMRI) is crucial for the management of patients with myocardial infarct. This work proposes a post-processing procedure to jointly analyze Cine and Delayed-Enhanced (DE) acquisitions in order to provide an automatic quantification of myocardial contraction and enhancement parameters and a study of their relationship. For that purpose, the following processes are performed: 1) DE/Cine temporal synchronization and 3D scan alignment, 2) 3D DE/Cine rigid registration in a region about the heart, 3) segmentation of the myocardium on Cine MRI and superimposition of the epicardial and endocardial contours on the DE images, 4) quantification of the Myocardial Infarct Extent (MIE), 5) study of the regional contractile function using a new index, the Amplitude to Time Ratio (ATR). The whole procedure was applied to 10 patients with clinically proven myocardial infarction. The comparison between the MIE and the visually assessed regional function scores demonstrated that the MIE is highly related to the severity of the wall motion abnormality. In addition, it was shown that the newly developed regional myocardial contraction parameter (ATR) decreases significantly in delayed enhanced regions. This largely automated approach enables a combined study of regional MIE and left ventricular function

Deep Learning-based Method for Fully Automatic Quantification of Left Ventricle Function from Cine MR Images: A Multivendor, Multicenter Study

Purpose: To develop a deep learning–based method for fully automated quantification of left ventricular (LV) function from short-axis cine MR images and to evaluate its performance in a multivendor and multicenter setting. Materials and Methods: This retrospective study included cine MRI data sets obtained from three major MRI vendors in four medical centers from 2008 to 2016. Three convolutional neural networks (CNNs) with the U-NET architecture were trained on data sets of increasing variability: (a) a single-vendor, single-center, homogeneous cohort of 100 patients (CNN1); (b) a single-vendor, multicenter, heterogeneous cohort of 200 patients (CNN2); and (c) a multivendor, multicenter, heterogeneous cohort of 400 patients (CNN3). All CNNs were tested on an independent multivendor, multicenter data set of 196 patients. CNN performance was evaluated with respect to the manual annotations from three experienced observers in terms of (a) LV detection accuracy, (b) LV segmentation accuracy, and (c) LV functional parameter accuracy. Automatic and manual results were compared with the paired Wilcoxon test, Pearson correlation, and Bland-Altman analysis. Results: CNN3 achieved the highest performance on the independent testing data set. The average perpendicular distance compared with manual analysis was 1.1 mm ± 0.3 for CNN3, compared with 1.5 mm ± 1.0 for CNN1 (P < .05) and 1.3 mm ± 0.6 for CNN2 (P < .05). The LV function parameters derived from CNN3 showed a high correlation (r2 ≥ 0.98) and agreement with those obtained by experts for data sets from different vendors and centers. Conclusion: A deep learning–based method trained on a data set with high variability can achieve fully automated and accurate cine MRI analysis on multivendor, multicenter cine MRI data

Improved quantification of left ventricular volumes and mass based on endocardial and epicardial surface detection from cardiac MR images using level set models

The reproducibility of left ventricular (LV) volume and mass measurements based on subjective slice-by-slice tracing of LV borders is affected by image quality, and volume estimates are biased by geometric modeling. The authors developed a technique for volumetric surface detection (VoSD) and quantification of LV volumes and mass without tracing and geometric approximations. The authors hypothesized that this technique is accurate and more reproducible than the conventional methodology. Methods. Images were obtained in 24 patients in 6 to 10 slices from LV base to apex (GE 1.5 T, FIESTA). Volumetric data were reconstructed, and endocardial and epicardial surfaces were detected using the level set approach. LV volumes were obtained from voxel counts and used to compute ejection fraction (EF) and mass. Conventional measurements (MASS Analysis) were used as a reference to test the accuracy of VoSD technique (linear regression, Bland-Altman). For both techniques, measurements were repeated to compute inter- and intra-observer variability. Results. VoSD values resulted in high correlation with the reference values (EDV: r = 0.98; ESV: r = 0.99; EF: r = 0.91; mass: r = 0.98), with no significant biases (8 ml, 5 ml, 0.2% and 9 g) and narrow limits of agreement (SD: 13 ml, 10 ml, 6% and 9 g). Inter-observer variability of the VoSD technique was lower (range 3 to 5%) than that of the reference technique (5 to 11%; p &lt; 0.05). Intra-observer variability was also lower (1 to 3% vs. 7 to 10%; p &lt; 0.05). Conclusion. VoSD technique allows accurate measurements of LV volumes, EF, and mass, which are more reproducible than the conventional methodology