Experimental and numerical investigation of a cold-formed steel system used to restore old buildings floor.

This paper presents a novel configuration of built-up cold-formed steel (CFS) flooring system in the shape of a box section. A new technique is applied to produce the components of the flooring system, which are fastened by self-drilling screws. This box section consists of a cast-in-situ concrete slab, trapezoidal steel decking, two sigma section, steel plate and stiffening equal angles.

Radiomics of Late Gadolinium Enhancement Reveals Prognostic Value of Myocardial Scar Heterogeneity in Hypertrophic Cardiomyopathy.

Background: Late gadolinium enhancement (LGE) scar burden by cardiac magnetic resonance is a major risk factor for sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HCM). However, there is currently limited data on the incremental prognostic value of integrating myocardial LGE radiomics (ie, shape and texture features) into SCD risk stratification models.

Objectives: The purpose of this study was to investigate the incremental prognostic value of myocardial LGE radiomics beyond current European Society of Cardiology (ESC) and American College of Cardiology (ACC)/American Heart Association (AHA) models for SCD risk prediction in HCM.

Scanner-Independent MyoMapNet for Accelerated Cardiac MRI T Mapping Across Vendors and Field Strengths.

Background: In cardiac T mapping, a series of T -weighted (T w) images are collected and numerically fitted to a two or three-parameter model of the signal recovery to estimate voxel-wise T values. To reduce the scan time, one can collect fewer T w images, albeit at the cost of precision or/and accuracy. Recently, the feasibility of using a neural network instead of conventional two- or three-parameter fit modeling has been demonstrated.

Radiomics and deep learning for myocardial scar screening in hypertrophic cardiomyopathy.

Background: Myocardial scar burden quantified using late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR), has important prognostic value in hypertrophic cardiomyopathy (HCM). However, nearly 50% of HCM patients have no scar but undergo repeated gadolinium-based CMR over their life span. We sought to develop an artificial intelligence (AI)-based screening model using radiomics and deep learning (DL) features extracted from balanced steady state free precession (bSSFP) cine sequences to identify HCM patients without scar.

An Explainable Machine Learning Approach Reveals Prognostic Significance of Right Ventricular Dysfunction in Nonischemic Cardiomyopathy.

Objectives: The authors implemented an explainable machine learning (ML) model to gain insight into the association between cardiac magnetic resonance markers and adverse outcomes of cardiovascular hospitalization and all-cause death (composite endpoint) in patients with nonischemic dilated cardiomyopathy (NICM).

Background: Risk stratification of patients with NICM remains challenging. An explainable ML model has the potential to provide insight into the contributions of different risk markers in the prediction model.

Multiple two-dimensional active shape model framework for right ventricular segmentation.

Segmentation of the right ventricle (RV) in MRI short axis images is very challenging due to its complex shape and various appearance among the different subjects and cross-sections. Active shape models (ASM) have shown potential for segmenting the complex structures, including the RV, through two formulations: two- and three-dimensional modeling with a reported trade-off between accuracy and complexity of each formulation. In this work, we propose a new framework for modeling the RV surface using multiple 2D contours, where information from multiple cross-sectional images are incorporated into the same model.

Noncontrast Cardiac Magnetic Resonance Imaging Predictors of Heart Failure Hospitalization in Heart Failure With Preserved Ejection Fraction.

Background: Heart failure patients with preserved ejection fraction (HFpEF) are at increased risk of future hospitalization. Contrast agents are often contra-indicated in HFpEF patients due to the high prevalence of concomitant kidney disease. Therefore, the prognostic value of a noncontrast cardiac magnetic resonance imaging (MRI) for HF-hospitalization is important.

Machine Learning for Predicting Heart Failure Progression in Hypertrophic Cardiomyopathy.

Development of advanced heart failure (HF) symptoms is the most common adverse pathway in hypertrophic cardiomyopathy (HCM) patients. Currently, there is a limited ability to identify HCM patients at risk of HF. In this study, we present a machine learning (ML)-based model to identify individual HCM patients who are at high risk of developing advanced HF symptoms.

Improved Quantification of Myocardium Scar in Late Gadolinium Enhancement Images: Deep Learning Based Image Fusion Approach.

Background: Quantification of myocardium scarring in late gadolinium enhanced (LGE) cardiac magnetic resonance imaging can be challenging due to low scar-to-background contrast and low image quality. To resolve ambiguous LGE regions, experienced readers often use conventional cine sequences to accurately identify the myocardium borders.

Purpose: To develop a deep learning model for combining LGE and cine images to improve the robustness and accuracy of LGE scar quantification.

Deep convolution neural networks based artifact suppression in under-sampled radial acquisitions of myocardial T mapping images.

We developed a deep convolutional neural network (CNN) based method to remove streaking artefact from accelerated radial acquisitions of myocardial T -mapping images. A deep CNN based on a modified U-Net architecture was developed and trained to remove the streaking artefacts from under-sampled T mapping images. A total of 2090 T -weighted images for 33 patients (55 ± 15 years, 19 males) and five healthy subjects (30 ± 14 years, 2 males) were used for training and testing the network.

Multi-domain convolutional neural network (MD-CNN) for radial reconstruction of dynamic cardiac MRI.

Purpose: Cardiac MR cine imaging allows accurate and reproducible assessment of cardiac function. However, its long scan time not only limits the spatial and temporal resolutions but is challenging in patients with breath-holding difficulty or non-sinus rhythms. To reduce scan time, we propose a multi-domain convolutional neural network (MD-CNN) for fast reconstruction of highly undersampled radial cine images.

Automated Myocardial T2 and Extracellular Volume Quantification in Cardiac MRI Using Transfer Learning-based Myocardium Segmentation.

Purpose: To assess the performance of an automated myocardial T2 and extracellular volume (ECV) quantification method using transfer learning of a fully convolutional neural network (CNN) pretrained to segment the myocardium on T1 mapping images.

Materials And Methods: A single CNN previously trained and tested using 11 550 manually segmented native T1-weighted images was used to segment the myocardium for automated myocardial T2 and ECV quantification. Reference measurements from 1525 manually processed T2 maps and 1525 ECV maps (from 305 patients) were used to evaluate the performance of the pretrained network.

Detecting liver fibrosis using a machine learning-based approach to the quantification of the heart-induced deformation in tagged MR images.

Liver disease causes millions of deaths per year worldwide, and approximately half of these cases are due to cirrhosis, which is an advanced stage of liver fibrosis that can be accompanied by liver failure and portal hypertension. Early detection of liver fibrosis helps in improving its treatment and prevents its progression to cirrhosis. In this work, we present a novel noninvasive method to detect liver fibrosis from tagged MRI images using a machine learning-based approach.

Three-dimensional Deep Convolutional Neural Networks for Automated Myocardial Scar Quantification in Hypertrophic Cardiomyopathy: A Multicenter Multivendor Study.

Background Cardiac MRI late gadolinium enhancement (LGE) scar volume is an important marker for outcome prediction in patients with hypertrophic cardiomyopathy (HCM); however, its clinical application is hindered by a lack of measurement standardization. Purpose To develop and evaluate a three-dimensional (3D) convolutional neural network (CNN)-based method for automated LGE scar quantification in patients with HCM. Materials and Methods We retrospectively identified LGE MRI data in a multicenter ( = 7) and multivendor ( = 3) HCM study obtained between November 2001 and November 2011.

Cardiovascular magnetic resonance feature tracking strain analysis for discrimination between hypertensive heart disease and hypertrophic cardiomyopathy.

Background: Hypertensive heart disease (HHD) and hypertrophic cardiomyopathy (HCM) are both associated with an increased left ventricular (LV) wall thickness. Whilst LV ejection fraction is frequently normal in both, LV strain assessment could differentiate between the diseases. We sought to establish if cardiovascular magnetic resonance myocardial feature tracking (CMR-FT), an emerging method allowing accurate assessment of myocardial deformation, differentiates between both diseases.

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

Background: Cardiovascular magnetic resonance (CMR) myocardial native T mapping allows assessment of interstitial diffuse fibrosis. In this technique, the global and regional T are measured manually by drawing region of interest in motion-corrected T maps. The manual analysis contributes to an already lengthy CMR analysis workflow and impacts measurements reproducibility.

Non-contrast myocardial infarct scar assessment using a hybrid native T and magnetization transfer imaging sequence at 1.5T.

Purpose: To develop a gadolinium-free cardiac MR technique that simultaneously exploits native T and magnetization transfer (MT) contrast for the imaging of myocardial infarction.

Methods: A novel hybrid T one and magnetization transfer (HYTOM) method was developed based on the modified look-locker inversion recovery (MOLLI) sequence, with a train of MT-prep pulses placed before the balanced SSFP (bSSFP) readout pulses. Numerical simulations, based on Bloch-McConnell equations, were performed to investigate the effects of MT induced by (1) the bSSFP readout pulses, and (2) the MT-prep pulses, on the measured, "apparent," native T values.

Imaging sequence for joint myocardial T mapping and fat/water separation.

Purpose: To develop and evaluate an imaging sequence to simultaneously quantify the epicardial fat volume and myocardial T relaxation time.

Methods: We introduced a novel simultaneous myocardial T mapping and fat/water separation sequence (joint T -fat/water separation). Dixon reconstruction is performed on a dual-echo data set to generate water/fat images.

Gray blood late gadolinium enhancement cardiovascular magnetic resonance for improved detection of myocardial scar.

Background: Low scar-to-blood contrast in late gadolinium enhanced (LGE) MRI limits the visualization of scars adjacent to the blood pool. Nulling the blood signal improves scar detection but results in lack of contrast between myocardium and blood, which makes clinical evaluation of LGE images more difficult.

Methods: GB-LGE contrast is achieved through partial suppression of the blood signal using T magnetization preparation between the inversion pulse and acquisition.

Nonrigid active shape model-based registration framework for motion correction of cardiac T mapping.

Purpose: Accurate reconstruction of myocardial T maps from a series of T -weighted images consists of cardiac motions induced from breathing and diaphragmatic drifts. We propose and evaluate a new framework based on active shape models to correct for motion in myocardial T maps.

Methods: Multiple appearance models were built at different inversion time intervals to model the blood-myocardium contrast and brightness changes during the longitudinal relaxation.

Spectrally-Presaturated Modulation (SPM): An efficient fat suppression technique for STEAM-based cardiac imaging sequences.

Stimulated-echo acquisition mode (STEAM) is a key pulse sequences in MRI in general, and in cardiac imaging in particular. Fat suppression is an important feature in cardiac imaging to improve visualization and eliminate off-resonance and chemical-shift artifacts. Nevertheless, fat suppression comes at the expense of reduced temporal resolution and signal-to-noise ratio (SNR).

Accurate harmonic phase tracking of tagged MRI using locally-uniform myocardium displacement constraint.

Harmonic phase (HARP) tracking is one of the most commonly used techniques for estimating the myocardium regional function from tagged cardiac Magnetic Resonance Imaging sequences. Nevertheless, tag fading and phase distortion can severely limit the tracking accuracy of the technique. In this work, we propose to modify the HARP tracking algorithm to impose a constraint of locally uniform displacement field while tracking the different myocardium points.

Improved estimation of the cardiac global function using combined long and short axis MRI images of the heart.

Background: Estimating the left ventricular (LV) volumes at the different cardiac phases is necessary for evaluating the cardiac global function. In cardiac magnetic resonance imaging, accurate estimation of the LV volumes requires the processing a relatively large number of parallel short-axis cross-sectional images of the LV (typically from 9 to 12). Nevertheless, it is inevitable sometimes to estimate the volume from a small number of cross-sectional images, which can lead to a significant reduction of the volume estimation accuracy.