Segmental Analysis of Cardiac Short-Axis Views Using Lagrangian Radial and Circumferential Strain

Ma, Chi; Wang, Xiao; Varghese, Tomy

doi:10.1177/0161734615613322

Cited by 12 publications

(6 citation statements)

References 61 publications

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“…The polar grids have been previously used to register the myocardial strain and displacement maps using ultrasound imaging [47, 48]. The myocardium contours were first extracted at each cardiac time frame by means of semi-automatic tracking and then a polar grid was used to accumulate the displacement values of the deforming myocardium.…”

Section: Discussionmentioning

confidence: 99%

Automated analysis of cardiovascular magnetic resonance myocardial native T1 mapping images using fully convolutional neural networks

Fahmy

El‐Rewaidy

Nezafat

et al. 2019

Journal of Cardiovascular Magnetic Resonance

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BackgroundCardiovascular magnetic resonance (CMR) myocardial native T1 mapping allows assessment of interstitial diffuse fibrosis. In this technique, the global and regional T1 are measured manually by drawing region of interest in motion-corrected T1 maps. The manual analysis contributes to an already lengthy CMR analysis workflow and impacts measurements reproducibility. In this study, we propose an automated method for combined myocardium segmentation, alignment, and T1 calculation for myocardial T1 mapping.MethodsA deep fully convolutional neural network (FCN) was used for myocardium segmentation in T1 weighted images. The segmented myocardium was then resampled on a polar grid, whose origin is located at the center-of-mass of the segmented myocardium. Myocardium T1 maps were reconstructed from the resampled T1 weighted images using curve fitting. The FCN was trained and tested using manually segmented images for 210 patients (5 slices, 11 inversion times per patient). An additional image dataset for 455 patients (5 slices and 11 inversion times per patient), analyzed by an expert reader using a semi-automatic tool, was used to validate the automatically calculated global and regional T1 values. Bland-Altman analysis, Pearson correlation coefficient, r, and the Dice similarity coefficient (DSC) were used to evaluate the performance of the FCN-based analysis on per-patient and per-slice basis. Inter-observer variability was assessed using intraclass correlation coefficient (ICC) of the T1 values calculated by the FCN-based automatic method and two readers.ResultsThe FCN achieved fast segmentation (< 0.3 s/image) with high DSC (0.85 ± 0.07). The automatically and manually calculated T1 values (1091 ± 59 ms and 1089 ± 59 ms, respectively) were highly correlated in per-patient (r = 0.82; slope = 1.01; p < 0.0001) and per-slice (r = 0.72; slope = 1.01; p < 0.0001) analyses. Bland-Altman analysis showed good agreement between the automated and manual measurements with 95% of measurements within the limits-of-agreement in both per-patient and per-slice analyses. The intraclass correllation of the T1 calculations by the automatic method vs reader 1 and reader 2 was respectively 0.86/0.56 and 0.74/0.49 in the per-patient/per-slice analyses, which were comparable to that between two expert readers (=0.72/0.58 in per-patient/per-slice analyses).ConclusionThe proposed FCN-based image processing platform allows fast and automatic analysis of myocardial native T1 mapping images mitigating the burden and observer-related variability of manual analysis.Electronic supplementary materialThe online version of this article (10.1186/s12968-018-0516-1) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Automated analysis of cardiovascular magnetic resonance myocardial native T1 mapping images using fully convolutional neural networks

Fahmy

El‐Rewaidy

Nezafat

et al. 2019

Journal of Cardiovascular Magnetic Resonance

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“…Non-invasive imaging methods include clinical echocardiography (Eaton et al 1979; Pfeffer et al 1979; Kanno et al 2002), tissue Doppler, and myocardial strain imaging (Konofagou et al 2002; Varghese et al 2003; Bauer et al 2011; Bhan et al 2014; Ma et al 2016). These methods assess cardiac performance by tracking structural changes in heart muscle movement after MI but do not provide specific information regarding perfusion of cardiac muscle which might be more definitive in indicating the extent of ischemia.…”

Section: Introductionmentioning

confidence: 99%

Real-Time in Vivo Photoacoustic Imaging in the Assessment of Myocardial Dynamics in Murine Model of Myocardial Ischemia

Mukaddim

Rodgers

Hacker

et al. 2018

Ultrasound in Medicine & Biology

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Photoacoustic imaging (PAI) is an evolving real-time imaging modality that combines the higher contrast of optical imaging with the higher spatial resolution of ultrasound imaging. We utilized dual-wavelength PAI for the diagnosis and monitoring of myocardial ischemia by assessing variations in blood oxygen saturation estimated in a murine model. The use of high-frequency ultrasound in conjunction with PAI enabled imaging of anatomic and functional changes associated with ischemia. Myocardial ischemia was established in eight mice by ligating the left anterior descending artery (LAD). Longitudinal results reveal that PAI is sensitive to acute myocardial ischemia, with a rapid decline in blood oxygen saturation (p ˂ 0.001) observed after LAD ligation (30 min: 33.05 ± 6.80%, 80 min: 36.59 ± 5.22%, 120 min: 36.70 ± 9.46%, 24 h: 40.55 ± 13.04%) compared with baseline (87.83 ± 5.73%). Variation in blood oxygen saturation was found to be linearly correlated with ejection fraction (%), fractional shortening (%) and stroke volume (µL), with Pearson's correlation coefficient values of 0.66, 0.67 and 0.77, respectively (p ˂ 0.001). Our results indicate that PAI has the potential for real-time diagnosis and monitoring of acute myocardial ischemia.

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“…Relative mechanical elasticity was calculated in the x and y directions. Due to the heart’s complicated geometry and fiber alignment, strain and cardiac elastography results are typically presented in a local cardiac-based coordinate system along the longitudinal, radial, and circumferential directions that form the natural axes of myocardial deformation; 27 however, these are not direct polar transforms of the Cartesian coordinate system, rather local directions along the LV that describe myocardial stiffness in reference to cardiac specific geometry. The resulting stiffness maps are then used to rotate to the circumferential and radial coordinate system in order to present the stiffness estimations in a coordinate system routinely adopted within the cardiac imaging research community to present cardiac motion (and strain) imaging metrics.…”

Section: Methodsmentioning

confidence: 99%

Reproducibility assessment of a biomechanical model-based elasticity imaging method for identifying changes in left ventricular mechanical stiffness

et al. 2022

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. Purpose Cardiotoxicity of antineoplastic therapies is increasingly a risk to cancer patients treated with curative intent with years of life to protect. Studies highlight the importance of identifying early cardiac decline in cancer patients undergoing cardiotoxic therapies. Accurate tools to study this are a critical clinical need. Current and emerging methods for assessing cardiotoxicity are too coarse for identifying preclinical cardiac degradation or too cumbersome for clinical implementation. Approach In the previous work, we developed a noninvasive biomechanical model-based elasticity imaging methodology (BEIM) to assess mechanical stiffness changes of the left ventricle (LV) based on routine cine cardiac magnetic resonance (CMR) images. We examine this methodology to assess methodological reproducibility. We assessed a cohort of 10 participants that underwent test/retest short-axis CMR imaging at baseline and follow-up sessions as part of a previous publicly available study. We compare test images to retest images acquired within the same session to assess within-session reproducibility. We also compare test and retest images acquired at the baseline imaging session to test and retest images acquired at the follow-up imaging session to assess between-session reproducibility. Results We establish the within-session and between-session reproducibility of our method, with global elasticity demonstrating repeatability within a range previously demonstrated in cardiac strain imaging studies. We demonstrate increased repeatability of global elasticity compared to segmental elasticity for both within-session and between-session. Within-subject coefficients of variation for within-session test/retest images globally for all modulus directions and a mechanical fractional mechanical stiffness anisotropy metric ranged from 11% to 28%. Conclusions Results suggest that our methodology can reproducibly generate estimates of relative mechanical elasticity of the LV and provides a threshold for distinguishing true changes in myocardial mechanical stiffness from experimental variation. BEIM has applications in identifying preclinical cardiotoxicity in breast cancer patients undergoing antineoplastic therapies.

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Segmental Analysis of Cardiac Short-Axis Views Using Lagrangian Radial and Circumferential Strain

Cited by 12 publications

References 61 publications

Automated analysis of cardiovascular magnetic resonance myocardial native T1 mapping images using fully convolutional neural networks

Automated analysis of cardiovascular magnetic resonance myocardial native T1 mapping images using fully convolutional neural networks

Real-Time in Vivo Photoacoustic Imaging in the Assessment of Myocardial Dynamics in Murine Model of Myocardial Ischemia

Reproducibility assessment of a biomechanical model-based elasticity imaging method for identifying changes in left ventricular mechanical stiffness

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