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We addressed comprehensively the performance of Shortest-Path HARP Refinement (SP-HR), SinMod, and DENSEanalysis using 2D slices of synthetic CSPAMM and DENSE images with realistic contrasts obtained from 3D phantoms. The three motion estimation techniques were interrogated under ideal and no-ideal conditions (with MR induced artifacts, noise, and through-plane motion), considering several resolutions and noise levels. Under noisy conditions, and for isotropic pixel sizes of 1.5 mm and 3.0 mm in CSPAMM and DENSE images respectively, the nRMSE obtained for the circumferential and radial strain components were 10.7 ± 10.8% and 25.5 ± 14.8% using SP-HR, 11.9 ± 2.5% and 29.3 ± 6.5% using SinMod, and 6.4 ± 2.0% and 18.2 ± 4.6% using DENSEanalysis. Overall, the results showed that SP-HR tends to fail for large tissue motions, whereas SinMod and DENSEanalysis gave accurate displacement and strain field estimations, being the last which performed the best. [ABSTRACT FROM AUTHOR]

Abstract Cardiac MRI tagging is a valuable technique for evaluating regional heart function. Currently, there are a number of different techniques for analyzing the tagged images. Specifically, k-space-based analysis techniques showed to be much faster than image-based techniques, where harmonic-phase (HARP) and sine-wave modeling (SinMod) stand as two famous techniques of the former group, which are frequently used in clinical studies. In this study, we compared HARP and SinMod and studied inter-observer variability between the two techniques for evaluating myocardial strain and apical-to-base torsion in numerical phantom, nine healthy controls, and thirty diabetic patients. Based on the ground-truth numerical phantom measurements (strain = −20% and rotation angle = −4.4°), HARP and SinMod resulted in overestimation (in absolute value terms) of strain by 1% and 5% (strain values), and of rotation angle by 0.4° and 2.0°, respectively. For the in-vivo results, global strain and torsion ranges were −10.6% to −35.3% and 1.8°/cm to 12.7°/cm in patients, and −17.8% to −32.7% and 1.8°/cm to 12.3°/cm in volunteers. On average, SinMod overestimated strain measurements by 5.7% and 5.9% (strain values) in the patients and volunteers, respectively, compared to HARP, and overestimated torsion measurements by 2.9°/cm and 2.5°/cm in the patients and volunteers, respectively, compared to HARP. Location-wise, the ranges for basal, mid-ventricular, and apical strain in patients (volunteers) were −8.4% to −31.5% (−11.6% to −33.3%), −6.3% to −37.2% (−17.8% to −33.3%), and −5.2% to −38.4% (−20.0% to −33.2%), respectively. SinMod overestimated strain in the basal, mid-ventricular, and apical slices by 4.7% (5.7%), 5.9% (5.5%), and 8.9% (6.8%), respectively, compared to HARP in the patients (volunteers). Nevertheless, there existed good correlation between the HARP and SinMod measurements. Finally, there were no significant strain or torsion measurement differences between patients and volunteers. There existed good inter-observer agreement, as all measurement differences lied within the Bland-Altman ± 2 standard-deviation (SD) difference limits. In conclusion, despite the consistency of the results by either HARP or SinMod and acceptable agreement of the generated strain and torsion patterns by both techniques, SinMod systematically overestimated the measurements compared to HARP. Under current operating conditions, the measurements from HARP and SinMod cannot be used interchangeably. [ABSTRACT FROM AUTHOR]

Over the last decades, a dense population of the deep-sea/oceanic scyphozoan jellyfishPeriphylla periphyllahas established itself as top predator in the Trondheimsfjord in Norway, which has caused the traditional fisheries in this fjord to suffer. This was, however, not the first fjord this jellyfish invaded, and data suggest that it will not be the last one, either. With warmer temperatures, the jellyfish is moving northward in Norway, but not all fjords are created equal, and it is not thriving or taking up residence just anywhere. The current article explores three fjords in whichPeriphylla periphyllahas become the top predator and outcompeted former ones. The main question of interest is why the jellyfish becomes dominant in one fjord and not another. The next question is which other Norwegian fjords further north exist with similar characteristics where we therefore would expectPeriphylla periphyllato proliferate in the future. The latter is important from a policy and adaptation perspective for the local community of people, since the proliferation of the species unmistakably leads to fewer fish for commercial harvest and potentially less attractive waters for tourism purposes. Results show that three northern fjords, the Skjerstadfjorden the Holandsfjorden, and Stordjupna in the Vestfjord appear to be particularly vulnerable to a futurePeriphylla periphyllainvasion. [ABSTRACT FROM AUTHOR]

A robust and accurate center-frequency (CF) estimation (RACE) algorithm for improving the performance of the local sine-wave modeling (SinMod) method, which is a good motion estimation method for tagged cardiac magnetic resonance (MR) images, is proposed in this study. The RACE algorithm can automatically, effectively and efficiently produce a very appropriate CF estimate for the SinMod method, under the circumstance that the specified tagging parameters are unknown, on account of the following two key techniques: (1) the well-known mean-shift algorithm, which can provide accurate and rapid CF estimation; and (2) an original two-direction-combination strategy, which can further enhance the accuracy and robustness of CF estimation. Some other available CF estimation algorithms are brought out for comparison. Several validation approaches that can work on the real data without ground truths are specially designed. Experimental results on human body in vivo cardiac data demonstrate the significance of accurate CF estimation for SinMod, and validate the effectiveness of RACE in facilitating the motion estimation performance of SinMod. [ABSTRACT FROM AUTHOR]

• Motion information tracking is used for automatic location of the heart. • U-net and curriculum learning are combined to segment the left ventricle. • Curriculum strategies from easy to difficult can capture more detailed TMRI images features. • These strategies showed significant performance on the endocardium segmentation. Automatic left ventricle (LV) segmentation from tagged cardiac magnetic resonance imaging is significant for evaluating heart function and providing follow-up treatments in clinical medicine. However, due to the complicated cardiac structure and extra interference, it is challenging for traditional methods to delineate the LV automatically and get accurate results. Therefore, we proposed the automatic LV segmentation algorithm combined with deep learning and curriculum learning strategy. The key technologies are described as follows: firstly, local sine-wave modeling (SinMod) is practiced to track cardiac motion information, implement automatic heart location and obtain the region of interest. Secondly, U-Net is utilized as the basic model to segment the LV endocardium and epicardium. Additionally, a new curriculum learning training strategy is adopted to improve segmentation accuracy. Finally, comparative results demonstrate the superior performance of our approach to those resulting from traditional methods. [Display omitted] [ABSTRACT FROM AUTHOR]