Automated Detection of Apical Foreshortening in Echocardiography Using Statistical Shape Modelling.

OBJECTIVE: Automated detection of foreshortening, a common challenge in routine 2-D echocardiography, has the potential to improve quality of acquisitions and reduce the variability of left ventricular measurements. Acquiring and labelling the required training data is challenging due to the time-intensive and highly subjective nature of foreshortened apical views. We aimed to develop an automatic pipeline for the detection of foreshortening. To this end, we propose a method to generate synthetic apical-four-chamber (A4C) views with matching ground truth foreshortening labels. METHODS: A statistical shape model of the four chambers of the heart was used to synthesise idealised A4C views with varying degrees of foreshortening. Contours of the left ventricular endocardium were segmented in the images, and a partial least squares (PLS) model was trained to learn the morphological traits of foreshortening. The predictive capability of the learned synthetic features was evaluated on an independent set of manually labelled and automatically curated real echocardiographic A4C images. RESULTS: Acceptable classification accuracy for identification of foreshortened views in the testing set was achieved using logistic regression based on 11 PLS shape modes, with a sensitivity, specificity and area under the receiver operating characteristic curve of 0.84, 0.82 and 0.84, respectively. Both synthetic and real cohorts showed interpretable traits of foreshortening within the first two PLS shape modes, reflected as a shortening in the long-axis length and apical rounding. CONCLUSION: A contour shape model trained only on synthesized A4C views allowed accurate prediction of foreshortening in real echocardiographic images.

The effect of isometric exercise training on arterial stiffness: A randomized crossover controlled study.

Isometric exercise training (IET) is an effective intervention for the management of resting blood pressure (BP). However, the effects of IET on arterial stiffness remain largely unknown. Eighteen unmedicated physically inactive participants were recruited. Participants were randomly allocated in a cross-over design to 4 weeks of home-based wall squat IET and control period, separated by a 3-week washout period. Continuous beat-to-beat hemodynamics, including early and late systolic (sBP 1 and sBP 2, respectively) and diastolic blood pressure (dBP) were recorded for a period of 5 min and waveforms were extracted and analyzed to acquire the augmentation index (AIx) as a measure of arterial stiffness. sBP 1 (-7.7 ± 12.8 mmHg, p = 0.024), sBP 2 (-5.9 ± 9.9 mmHg, p = 0.042) and dBP (-4.4 ± 7.2 mmHg, p = 0.037) all significantly decreased following IET compared to the control period. Importantly, there was a significant reduction in AIx following IET (-6.6 ± 14.5%, p = 0.02) compared to the control period. There were also adjacent significant reductions in total peripheral resistance (-140.7 ± 65.8 dynes·cm-5, p = 0.042) and pulse pressure (-3.8 ± 4.2, p = 0.003) compared to the control period. This study demonstrates an improvement in arterial stiffness following a short-term IET intervention. These findings have important clinical implications regarding cardiovascular risk. Mechanistically, these results suggest that reductions in resting BP following IET are induced via favorable vascular adaptations, although the intricate details of such adaptations are not yet clear.

Automated Echocardiographic Detection of Heart Failure With Preserved Ejection Fraction Using Artificial Intelligence.

Background: Detection of heart failure with preserved ejection fraction (HFpEF) involves integration of multiple imaging and clinical features which are often discordant or indeterminate. Objectives: The authors applied artificial intelligence (AI) to analyze a single apical 4-chamber transthoracic echocardiogram video clip to detect HFpEF. Methods: A 3-dimensional convolutional neural network was developed and trained on apical 4-chamber video clips to classify patients with HFpEF (diagnosis of heart failure, ejection fraction ≥50%, and echocardiographic evidence of increased filling pressure; cases) vs without HFpEF (ejection fraction ≥50%, no diagnosis of heart failure, normal filling pressure; controls). Model outputs were classified as HFpEF, no HFpEF, or nondiagnostic (high uncertainty). Performance was assessed in an independent multisite data set and compared to previously validated clinical scores. Results: Training and validation included 2,971 cases and 3,785 controls (validation holdout, 16.8% patients), and demonstrated excellent discrimination (area under receiver-operating characteristic curve: 0.97 [95% CI: 0.96-0.97] and 0.95 [95% CI: 0.93-0.96] in training and validation, respectively). In independent testing (646 cases, 638 controls), 94 (7.3%) were nondiagnostic; sensitivity (87.8%; 95% CI: 84.5%-90.9%) and specificity (81.9%; 95% CI: 78.2%-85.6%) were maintained in clinically relevant subgroups, with high repeatability and reproducibility. Of 701 and 776 indeterminate outputs from the Heart Failure Association-Pretest Assessment, Echocardiographic and Natriuretic Peptide Score, Functional Testing (HFA-PEFF), and Final Etiology and Heavy, Hypertensive, Atrial Fibrillation, Pulmonary Hypertension, Elder, and Filling Pressure (H2FPEF) scores, the AI HFpEF model correctly reclassified 73.5% and 73.6%, respectively. During follow-up (median: 2.3 [IQR: 0.5-5.6] years), 444 (34.6%) patients died; mortality was higher in patients classified as HFpEF by AI (HR: 1.9 [95% CI: 1.5-2.4]). Conclusions: An AI HFpEF model based on a single, routinely acquired echocardiographic video demonstrated excellent discrimination of patients with vs without HFpEF, more often than clinical scores, and identified patients with higher mortality.

Left ventricular assessment with artificial intelligence increases the diagnostic accuracy of stress echocardiography.

Aims: To evaluate whether left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS), automatically calculated by artificial intelligence (AI), increases the diagnostic performance of stress echocardiography (SE) for coronary artery disease (CAD) detection. Methods and results: SEs from 512 participants who underwent a clinically indicated SE (with or without contrast) for the evaluation of CAD from seven hospitals in the UK and US were studied. Visual wall motion scoring (WMS) was performed to identify inducible ischaemia. In addition, SE images at rest and stress underwent AI contouring for automated calculation of AI-LVEF and AI-GLS (apical two and four chamber images only) with Ultromics EchoGo Core 1.0. Receiver operator characteristic curves and multivariable risk models were used to assess accuracy for identification of participants subsequently found to have CAD on angiography. Participants with significant CAD were more likely to have abnormal WMS, AI-LVEF, and AI-GLS values at rest and stress (all P < 0.001). The areas under the receiver operating characteristics for WMS index, AI-LVEF, and AI-GLS at peak stress were 0.92, 0.86, and 0.82, respectively, with cut-offs of 1.12, 64%, and -17.2%, respectively. Multivariable analysis demonstrated that addition of peak AI-LVEF or peak AI-GLS to WMS significantly improved model discrimination of CAD [C-statistic (bootstrapping 2.5th, 97.5th percentile)] from 0.78 (0.69-0.87) to 0.83 (0.74-0.91) or 0.84 (0.75-0.92), respectively. Conclusion: AI calculation of LVEF and GLS by contouring of contrast-enhanced and unenhanced SEs at rest and stress is feasible and independently improves the identification of obstructive CAD beyond conventional WMSI.

Beyond Simpson's Rule: Accounting for Orientation and Ellipticity Assumptions.

Simpson's biplane rule (SBR) is considered the gold standard method for left ventricle (LV) volume quantification from echocardiography but relies on a summation-of-disks approach that makes assumptions about LV orientation and cross-sectional shape. We aim to identify key limiting factors in SBR and to develop a new robust standard for volume quantification. Three methods for computing LV volume were studied: (i) SBR, (ii) addition of a truncated basal cone (TBC) to SBR and (iii) a novel method of basal-oriented disks (BODs). Three retrospective cohorts representative of the young, adult healthy and heart failure populations were used to study the impact of anatomical variations in volume computations. Results reveal how basal slanting can cause over- and underestimation of volume, with errors by SBR and TBC >10 mL for slanting angles >6°. Only the BOD method correctly accounted for basal slanting, reducing relative volume errors by SBR from -2.23 ± 2.21% to -0.70 ± 1.91% in the adult population and similar qualitative performance in the other two cohorts. In conclusion, the summation of basal oriented disks, a novel interpretation of SBR, is a more accurate and precise method for estimating LV volume.

Automated Echocardiographic Detection of Severe Coronary Artery Disease Using Artificial Intelligence.

OBJECTIVES: The purpose of this study was to establish whether an artificially intelligent (AI) system can be developed to automate stress echocardiography analysis and support clinician interpretation. BACKGROUND: Coronary artery disease is the leading global cause of mortality and morbidity and stress echocardiography remains one of the most commonly used diagnostic imaging tests. METHODS: An automated image processing pipeline was developed to extract novel geometric and kinematic features from stress echocardiograms collected as part of a large, United Kingdom-based prospective, multicenter, multivendor study. An ensemble machine learning classifier was trained, using the extracted features, to identify patients with severe coronary artery disease on invasive coronary angiography. The model was tested in an independent U.S. STUDY: How availability of an AI classification might impact clinical interpretation of stress echocardiograms was evaluated in a randomized crossover reader study. RESULTS: Acceptable classification accuracy for identification of patients with severe coronary artery disease in the training data set was achieved on cross-fold validation based on 31 unique geometric and kinematic features, with a specificity of 92.7% and a sensitivity of 84.4%. This accuracy was maintained in the independent validation data set. The use of the AI classification tool by clinicians increased inter-reader agreement and confidence as well as sensitivity for detection of disease by 10% to achieve an area under the receiver-operating characteristic curve of 0.93. CONCLUSIONS: Automated analysis of stress echocardiograms is possible using AI and provision of automated classifications to clinicians when reading stress echocardiograms could improve accuracy, inter-reader agreement, and reader confidence.

Whole-brain 3D FLAIR at 7T using direct signal control.

PURPOSE: Image quality obtained for brain imaging at 7T can be hampered by inhomogeneities in the static magnetic field, B0 , and the RF electromagnetic field, B1 . In imaging sequences such as fluid-attenuated inversion recovery (FLAIR), which is used to assess neurological disorders, these inhomogeneities cause spatial variations in signal that can reduce clinical efficacy. In this work, we aim to correct for signal inhomogeneities to ensure whole-brain coverage with 3D FLAIR at 7T. METHODS: The direct signal control (DSC) framework was used to optimize channel weightings applied to the 8 transmit channels used in this work on a pulse-by-pulse basis through the echo train in the FLAIR sequences. 3D FLAIR brain images were acquired on 5 different subjects and compared with imaging using a quadrature-like mode of the transmit array. Precomputed "universal" DSC solutions calculated from a separate set of 5 subjects were also explored. RESULTS: DSC consistently enabled improved imaging across all subjects, with no dropouts in signal seen over the entire brain volume, which contrasted with imaging in quadrature mode. Further, the universal DSC solutions also consistently improved imaging despite not being optimized specifically for the subject being imaged. CONCLUSION: 3D FLAIR brain imaging at 7T is substantially improved using DSC and is able to recover regions of low signal without increasing imaging time or interecho spacing.

Extended RF shimming: Sequence-level parallel transmission optimization applied to steady-state free precession MRI of the heart.

Cardiac magnetic resonance imaging (MRI) at high field presents challenges because of the high specific absorption rate and significant transmit field (B1+ ) inhomogeneities. Parallel transmission MRI offers the ability to correct for both issues at the level of individual radiofrequency (RF) pulses, but must operate within strict hardware and safety constraints. The constraints are themselves affected by sequence parameters, such as the RF pulse duration and TR, meaning that an overall optimal operating point exists for a given sequence. This work seeks to obtain optimal performance by performing a 'sequence-level' optimization in which pulse sequence parameters are included as part of an RF shimming calculation. The method is applied to balanced steady-state free precession cardiac MRI with the objective of minimizing TR, hence reducing the imaging duration. Results are demonstrated using an eight-channel parallel transmit system operating at 3 T, with an in vivo study carried out on seven male subjects of varying body mass index (BMI). Compared with single-channel operation, a mean-squared-error shimming approach leads to reduced imaging durations of 32 ± 3% with simultaneous improvement in flip angle homogeneity of 32 ± 8% within the myocardium.

RF system calibration for global Q matrix determination.

The use of multiple transmission channels (known as Parallel Transmission, or PTx) provides increased control of the MRI signal formation process. This extra flexibility comes at a cost of uncertainty of the power deposited in the patient under examination: the electric fields produced by each transmitter can interfere in such a way to lead to excessively high heating. Although it is not possible to determine local heating, the global Q matrix (which allows the whole-body Specific Absorption Rate (SAR) to be known for any PTx pulse) can be measured in-situ by monitoring the power incident upon and reflected by each transmit element during transmission. Recent observations have shown that measured global Q matrices can be corrupted by losses between the coil array and location of power measurement. In this work we demonstrate that these losses can be accounted for, allowing accurate global Q matrix measurement independent of the location of the power measurement devices.

Parallel transmission for ultrahigh-field imaging.

The development of MRI systems operating at or above 7 T has provided researchers with a new window into the human body, yielding improved imaging speed, resolution and signal-to-noise ratio. In order to fully realise the potential of ultrahigh-field MRI, a range of technical hurdles must be overcome. The non-uniformity of the transmit field is one of such issues, as it leads to non-uniform images with spatially varying contrast. Parallel transmission (i.e. the use of multiple independent transmission channels) provides previously unavailable degrees of freedom that allow full spatial and temporal control of the radiofrequency (RF) fields. This review discusses the many ways in which these degrees of freedom can be used, ranging from making more uniform transmit fields to the design of subject-tailored RF pulses for both uniform excitation and spatial selection, and also the control of the specific absorption rate. © 2015 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.

Specific absorption rate in neonates undergoing magnetic resonance procedures at 1.5 T and 3 T.

MRI is finding increased clinical use in neonatal populations; the extent to which electromagnetic models used for quantification of specific absorption rate (SAR) by commercial MRI scanners accurately reflect this alternative scenario is unclear. This study investigates how SAR predictions relating to adults can be related to neonates under differing conditions when imaged using 1.5 T and 3 T MRI scanners. Electromagnetic simulations were produced in neonatal subjects of different sizes and positions within a generic MRI body transmit device operating at both 64 MHz and 128 MHz, corresponding to 1.5 T and 3 T MRI scanners, respectively. An adult model was also simulated, as was a spherical salt-water phantom, which was also used in a calorimetry experiment. The SAR in neonatal subjects was found to be less than that experienced in an adult in all scenarios; however, the overestimation factor was variable. For example a 3 T body scan resulting in local 10 g SAR of 10.1 W kg(-1) in an adult would deposit 2.6 W kg(-1) in a neonate: an approximately fourfold difference. The SAR experienced by neonatal subjects undergoing MRI is lower than that in adults in equivalent situations. If the safety of such procedures is assessed using adult-appropriate models then the result is a conservative estimate.

PRIMO: Precise radiofrequency inference from multiple observations.

PURPOSE: This paper presents Precise Radiofrequency Inference from Multiple Observations (PRIMO), a comprehensive reconstruction framework for calibrating MRI systems with parallel transmit and parallel receive radiofrequency capabilities. THEORY AND METHODS: To date, the vast majority of radiofrequency (RF) calibration methods have considered transmit and receive calibration separately, without acknowledging that transmit field calibration sequences measure sufficient data for receive calibration. PRIMO provides a method of extracting both transmit and receive fields from transmit calibration data without presuming knowledge of either. The method is tested for accuracy through simulation, comparison to a gold standard dataset, and is demonstrated on in-vivo data acquired at 3T. RESULTS: PRIMO is shown to produce RF fields faithful to the gold standard with errors of less than 3% in realistic noise conditions. The in-vivo reconstructions demonstrate the method's ability to produce high quality transmit and receive maps, with an 8 transmit/8 receive channel system being fully calibrated in three dimensions in approximately 2 minutes. CONCLUSION: PRIMO provides a unified framework for estimating all transmit and receive fields in a single calibration step. This is becoming increasingly relevant in an era of MRI systems with highly parallel RF architectures.

Comparison between simulated decoupling regimes for specific absorption rate prediction in parallel transmit MRI.

PURPOSE: The use of electromagnetic (EM) modeling is critical for specific absorption rate (SAR) characterization in parallel transmission MRI. Radiofrequency arrays that include decoupling networks can be difficult to characterize accurately in simulation. A practical method of simplifying modeling is to exclude the decoupling networks and model each transmit element in isolation. Results from this type of model can be related to a real device by applying "active decoupling" to the real device to suppress residual coupling when in use. Here, we compare this approach with a full model that includes decoupling networks. METHODS: EM simulations for a variety of adult male voxel models placed within an eight-channel transverse electromagnetic (TEM) array tuned for 3 Tesla operation were run with and without decoupling networks included. The resulting EM fields and SAR estimates were compared using basic normalization, and simulated active decoupling. RESULTS: Modeling the transmit elements independently leads to variations which have significantly different SAR estimates of ∼20% on average compared with the full model if not normalized appropriately. After "active decoupling," SAR was still generally seen to be overestimated by ∼7% with independent channel modeling; despite having similar B1(+) field distributions. CONCLUSION: Modeling transmission elements independently may lead to substantially incorrect SAR estimates if the corresponding MRI system is not run in an analogous manner.

Direct signal control of the steady-state response of 3D-FSE sequences.

PURPOSE: Parallel transmission (PTx) offers spatial control of radiofrequency (RF) fields that can be used to mitigate nonuniformity effects in high-field MRI. In practice, the ability to achieve uniform RF fields by static shimming is limited by the typically small number of channels. Thus, tailored RF pulses that mix gradient with RF encoding have been proposed. A complementary approach termed "Direct Signal Control" (DSC) is to dynamically update RF shims throughout a sequence, exploiting interactions between each pulse and the spin system to achieve uniform signal properties from potentially nonuniform fields. This work applied DSC to T2-weighted driven-equilibrium three-dimensional fast spin echo (3D-FSE) brain imaging at 3T. THEORY AND METHODS: The DSC concept requires an accurate signal model, provided by extending the spatially resolved extended phase graph framework to include the steady-state response of driven-equilibrium sequences. An 8-channel PTx body coil was used for experiments. RESULTS: Phantom experiments showed the model to be accurate to within 0.3% (root mean square error). In vivo imaging showed over two-fold improvement in signal homogeneity compared with quadrature excitation. Although the nonlinear optimization cannot guarantee a global optimum, significantly improved local solutions were found. CONCLUSION: DSC has been demonstrated for 3D-FSE brain imaging at 3T. The concept is generally applicable to higher field strengths and other anatomies.