Deep learning approximation of attenuation maps for myocardial perfusion SPECT with an IQ SPECT collimator

$\bf Background$ The use of CT images for attenuation correction of myocardial perfusion imaging with single photon emission computer tomography (SPECT) increases diagnostic confidence. However, acquiring a CT image registered to a SPECT image is often not possible because most scanners are SPECT-only. It is possible to approximate attenuation maps using deep learning, but this has not yet been shown to work for a SPECT scanner with an IQ SPECT collimator. This study investigates whether it is possible to approximate attenuation maps from non-attenuation-corrected (nAC) reconstructions acquired with a SPECT scanner equipped with an IQ SPECT collimator. $\bf Methods$ Attenuation maps and reconstructions were acquired retrospectively for 150 studies. A U-Net was trained to predict attenuation maps from nAC reconstructions using the conditional generative adversarial network framework. Predicted attenuation maps are compared to real attenuation maps using the normalized mean absolute error (NMAE). Attenuation-corrected reconstructions were computed, and the resulting polar maps were compared by pixel and by average perfusion per segment using the absolute percent error (APE). The training and evaluation code is available at https://gitlab.ub.uni-bielefeld.de/thuxohl/mu-map. $\bf Results$ Predicted attenuation maps are similar to real attenuation maps, achieving an NMAE of 0.020$\pm$0.007. The same is true for polar maps generated from reconstructions with predicted attenuation maps compared to CT-based attenuation maps. Their pixel-wise absolute distance is 3.095$\pm$3.199, and the segment-wise APE is 1.155$\pm$0.769. $\bf Conclusions$ It is feasible to approximate attenuation maps from nAC reconstructions acquired by a scanner with an IQ SPECT collimator using deep learning

Pre-operative risk factors for driveline infection in left ventricular-assist device patients

$\bf Aims$ Implantation of left ventricular-assist devices (LVAD) to treat end-stage heart failure is of increasing relevance due to donor shortage. Infections of the driveline are common adverse events. LVAD infections can lead to high urgency listings for transplantation. However, transplantation in patients with infection leads to worse post-transplantation outcomes. This study aims to evaluate specific risk factors for driveline infections at the time of implantation. $\textbf {Methods and results}$ Four hundred forty-one patients receiving either Heartmate II or Heartware system from August 2009 to October 2013 were assessed. An expert committee sorted patients into four different groups concerning the likeliness of infection. Twenty-eight (6%) of discussed infection cases were judged as secured, 33 (7%) as likely, 18 (4%) as possible, and 20 (4%) as unlikely. The remaining 342 (78%) subjects showed either no signs of infection at all times (329 [75%]) or developed signs of infection in a second observation period within 1 year after ending of the first observation period (13 [3%]). For a better discriminatory power, cases of secured and likely infections were tested against the group with no infection at all times in a Cox proportional hazard model. Among all variables tested by univariate analysis (significance level $\it P$ < 0.15), only age ($\it P$ = 0.07), LVAD-type ($\it P$ = 0.12), need for another thoracic operation ($\it P$ = 0.02), and serum creatinine value ($\it P$ = 0.02) reached statistical significance. These were subsequently subjected to multivariate analysis to calculate the cumulative risk of developing a drive infection. The multivariate analysis showed that of all the potential risk factors tested, only the necessity of re-thoracotomy or secondary thoracic closure had a significant, protective effect (hazard ratio [95% CI] = 0.45 [0.21–0.95]; $\it P$ = 0.04). $\bf Conclusion$ This single-centre cohort study shows that driveline infections are common adverse events. The duration of support represents the major risk factor for LVAD driveline infections

Machine-learning-based diagnostics of cardiac sarcoidosis using multi-chamber wall motion analyses

Background: Hindered by its unspecific clinical and phenotypical presentation, cardiac sarcoidosis (CS) remains a challenging diagnosis. Objective: Utilizing cardiac magnetic resonance imaging (CMR), we acquired multi-chamber volumetrics and strain feature tracking for a support vector machine learning (SVM)-based diagnostic approach to CS. Method: Forty-five CMR-negative (CMR(−), 56.5(53.0;63.0)years), eighteen CMR-positive (CMR(+), 64.0(57.8;67.0)years) sarcoidosis patients and forty-four controls (CTRL, 56.5(53.0;63.0)years)) underwent CMR examination. Cardiac parameters were processed using the classifiers of logistic regression, KNN(K-nearest-neighbor), DT (decision tree), RF (random forest), SVM, GBoost, XGBoost, Voting and feature selection. Results: In a three-cluster analysis of CTRL versus vs. CMR(+) vs. CMR(−), RF and Voting classifier yielded the highest prediction rates (81.82%). The two-cluster analysis of CTRL vs. all sarcoidosis (All Sarc.) yielded high prediction rates with the classifiers logistic regression, RF and SVM (96.97%), and low prediction rates for the analysis of CMR(+) vs. CMR(−), which were augmented using feature selection with logistic regression (89.47%). Conclusion: Multi-chamber cardiac function and strain-based supervised machine learning provides a non-contrast approach to accurately differentiate between healthy individuals and sarcoidosis patients. Feature selection overcomes the algorithmically challenging discrimination between CMR(+) and CMR(−) patients, yielding high accuracy predictions. The study findings imply higher prevalence of cardiac involvement than previously anticipated, which may impact clinical disease management

Multi-parametric analyses to investigate dependencies of normal left atrial strain by cardiovascular magnetic resonance feature tracking

Left-atrial (LA) strain is the result of complex hemodynamics, which may be better characterized using a multiparametric approach. Cardiovascular magnetic resonance (CMR) feature tracking was used to perform a comprehensive LA strain assessment of 183 enrolled healthy volunteers (11–70 years, 97 females, median 32.9 $\pm$ 28.3 years). Novel strain dependencies were assessed using multi-parametric regression (MPR) analyses. LA volumetric data, left ventricular strain, transmitral and pulmonary venous blood flow parameters were utilized to create clusters for MPR of all subjects and a heart rate controlled subgroup (pulse: 60–75/min, N = 106). The LA reservoir(r) and conduit(c) strains of the total cohort were significantly elevated (p $\leq$ 0.001) in women (r: 49.7 $\pm$ 12.9%, c: 32.0 $\pm$ 11.0%) compared to men (r: 42.9 $\pm$ 11.4%, c: 26.1 IQ 10.5%). In contrast, there were no gender-specific differences (p > 0.05) for subgroup LA reservoir, conduit and booster(b) strains (all, r: 47.3 $\pm$ 12.7%; c: 29.0 IQ 15.5%; b: 17.6 $\pm$ 5.4%) and strain rates (all, 2.1 IQ 1.0 $s^{−1}$; − 2.9 IQ 1.5 $s^{−1}$; − 2.3 IQ 1.0 $s^{−1}$). MPR found large effect sizes (|$R^{2}$|$\geq$ 0.26) for correlations between strain and various cardiac functional parameters. Largest effect size was found for the association between LA conduit strain and LA indexed booster volume, LA total ejection fraction, left ventricular global radial strain and E-wave (|$R^{2}$|= 0.437). In addition to providing normal values for sex-dependent LA strain and strain rate, no gender differences were found with modified heart rate. MPR analyses of LA strain/strain rate and various cardiac functional parameters revealed that heart rate control improved goodness-of-fit for the overall model

A machine learning challenge

Background: This study challenges state-of-the-art cardiac amyloidosis (CA) diagnostics by feeding multi-chamber strain and cardiac function into supervised machine (SVM) learning algorithms. Methods: Forty-three CA (32 males; 79 years (IQR 71; 85)), 20 patients with hypertrophic cardiomyopathy (HCM, 10 males; 63.9 years ($\pm$7.4)) and 44 healthy controls (CTRL, 23 males; 56.3 years (IQR 52.5; 62.9)) received cardiovascular magnetic resonance imaging. Left atrial, right atrial and right ventricular strain parameters and cardiac function generated a 41-feature matrix for decision tree (DT), k-nearest neighbor (KNN), SVM linear and SVM radial basis function (RBF) kernel algorithm processing. A 10-feature principal component analysis (PCA) was conducted using SVM linear and RBF. Results: Forty-one features resulted in diagnostic accuracies of 87.9% (AUC = 0.960) for SVM linear, 90.9% (0.996; Precision = 94%; Sensitivity = 100%; F1-Score = 97%) using RBF kernel, 84.9% (0.970) for KNN, and 78.8% (0.787) for DT. The 10-feature PCA achieved 78.9% (0.962) via linear SVM and 81.8% (0.996) via RBF SVM. Explained variance presented bi-atrial longitudinal strain and left and right atrial ejection fraction as valuable CA predictors. Conclusion: SVM RBF kernel achieved competitive diagnostic accuracies under supervised conditions. Machine learning of multi-chamber cardiac strain and function may offer novel perspectives for non-contrast clinical decision-support systems in CA diagnostics

Assessment of pulmonary artery stiffness by multiparametric cardiac magnetic resonance-surrogate for right heart catheterization

$\bf Background:$ Cardiac magnetic resonance (CMR) imaging allows for multiparametric assessment of healthy pulmonary artery (PA) hemodynamics. Gender- and aging-associated PA stiffness and pressure alterations have remained clinically unestablished, however may demonstrate epidemiological differences in disease development. The aim of this study is to evaluate the role of CMR as a surrogate for catheter examinations by providing a comprehensive CMR assessment of sex- and age-related reference values for PA stiffness, flow, and pressure. $\textbf {Methods and Results:}$ PA hemodynamics were studied between gender and age groups (>/50 years) exhibited reduced PA elasticity (41.7% [31.0; 52.9] vs. 66.4% [47.7; 83.0]; $\it P$ < 0.001), reduced PA compliance (15.4 $mm^{2}$/mmHg [12.3; 20.7] vs. 21.3 $\pm$ 6.8 $mm^{2}$/mmHg; $\it P$ < 0.001), higher pulse wave velocity (2.59 m/s [1.57; 3.59] vs. 1.76 m/s [1.24; 2.34]; $\it P$ < 0.001) and a reduced FWHM (218 $\pm$ 29 ms vs. 231 $\pm$ 21 ms; $\it P$ < 0.001) than younger subjects. $\bf Conclusions:$ Velocity-time profiles are dependent on age and gender. PA stiffness indices deteriorate with age. CMR has potential to serve as a surrogate for right heart catheterization

Left-ventricular reference myocardial strain assessed by cardiovascular magnetic resonance feature tracking and fSENC

$\bf Aims:$ Cardiac strain parameters are increasingly measured to overcome shortcomings of ejection fraction. For broad clinical use, this study provides reference values for the two strain assessment methods feature tracking (FT) and fast strain-encoded (fSENC) cardiovascular magnetic resonance (CMR) imaging, including the child/adolescent group and systematically evaluates the influence of temporal resolution and muscle mass on strain. $\textbf {Methods and Results:}$ Global longitudinal (GLS), circumferential (GCS), and radial (GRS) strain values in 181 participants (54% women, 11–70 years) without cardiac illness were assessed with FT (CVI42® software). GLS and GCS were also analyzed using fSENC (MyoStrain® software) in a subgroup of 84 participants (60% women). Fourteen patients suffering hypertrophic cardiomyopathy (HCM) were examined with both techniques. CMR examinations were done on a 3.0T MR-system. FT-GLS, FT-GCS, and FT-GRS were −16.9 $\pm$ 1.8%, −19.2 $\pm$ 2.1% and 34.2 $\pm$ 6.1%. fSENC-GLS was higher at −20.3 $\pm$ 1.8% ($\it p$ < 0.001). fSENC-GCS was comparable at−19.7 $\pm$ 1.8% ($\it p$ = 0.06). All values were lower in men ($\it p$ < 0.001). Cardiac muscle mass correlated ($\it p$ < 0.001) with FT-GLS ($\it r$ = 0.433), FT-GCS ($\it r$ = 0.483) as well as FT-GRS ($\it r$ = −0.464) and acts as partial mediator for sex differences. FT-GCS, FT-GRS and fSENC-GLS correlated weakly with age. FT strain values were significantly lower at lower cine temporal resolutions, represented by heart rates ($\it r$ = −0.301, −0.379, 0.385) and 28 or 45 cardiac phases per cardiac cycle (0.3–1.9% differences). All values were lower in HCM patients than in matched controls ($\it p$ < 0.01). Cut-off values were −15.0% (FT-GLS), −19.3% (FT-GCS), 32.7% (FT-GRS), −17.2% (fSENC-GLS), and −17.7% (fSENC-GCS). $\bf Conclusion:$ The analysis of reference values highlights the influence of gender, temporal resolution, cardiac muscle mass and age on myocardial strain values

GMP-compliant radiosynthesis of [18^{18}F]GP1, a novel PET tracer for the detection of thrombi

Thrombus formation and thromboembolic events play important roles in various cardiovascular pathologies. The key receptor involved in platelet aggregation is the fibrinogen receptor glycoprotein IIb/IIIa. [$^{18}$F]GP1, a derivative of the GPIIb/IIIa antagonist elarofiban, is a specific $^{18}$F-labeled small-molecule radiotracer that binds with high affinity to GPIIb/IIIa receptors of activated platelets. An improved, robust and fully automated radiosynthesis of [$^{18}$F]GP1 has been developed. [$^{18}$F]GP1 has been synthesized with decay corrected radiochemical yields of 38 $\pm$ 6%, with a radiochemical concentration up to 1900 MBq/mL, molar activities of 952–9428 GBq/$\mu$mol and a radio-chemical purity >98%. After determination of the optimal reaction conditions, in particular for HPLC separation, adaption of the reaction conditions to PET center requirements, validation of the manufacturing process and the quality control methods, the synthesis of [$^{18}$F]GP1 was successfully implemented to GMP standards and was available for clinical application. We describe the GMP-compliant synthesis of the novel radiotracer [$^{18}$F]GP1. Moreover, we provide some proof-of-concept examples for clinical application in the cardiovascular field. PET/CT with the novel small-molecular radiotracer [18F]GP1 may serve as a novel highly sensitive tool for visualizing active platelet aggregation at the molecular level

Cardiovascular magnetic resonance imaging-based right atrial strain analysis of cardiac amyloidosis

$\bf Background:$ Cardiac amyloidosis (CA) manifests in a hypertrophic phenotype with a poor prognosis, making differentiation from hypertrophic cardiomyopathy (HCM) challenging and delaying early treatment. The extent to which magnetic resonance imaging (MRI) quantifies the right atrial strain (RAS) and strain rate (RASR), providing valuable diagnostic information, is not yet clinically established. $\bf Aims:$ This study assesses diagnostic differences in the longitudinal RAS and RASR between CA and HCM patients, control subjects (CTRL) and CA subtypes in addition to the impact of atrial fibrillation (AF) on the right atrial function in CA patients. The RAS and RASR of tricuspid regurgitation (TR) patients are used to assess the potential for diagnostic overlap. $\bf Methods:$ RAS and RASR quantification was conducted via MRI feature-tracking for biopsy-confirmed CA patients with subtypes identified. Strain parameters were compared for CTRL, HCM and TR patients. Post hoc testing identified intergroup differences. $\bf Results:$ In total, 41 CA patients were compared to 47 CTRL, 20 HCM and 31 TR patients. Reservoir (R), conduit and booster RAS and RASRs allow for significant differentiation ($\it p$ 0.8). CA patients with AF, in contrast to sinus rhythm, demonstrated a significantly impaired reservoir RAS and RASR and booster RASR. The discriminative power of RAS for CA vs. TR was insufficient (R: 10.6% $\pm$ 14.3% vs. 7.0% $\pm$ 6.0%, $\it p$ = 0.069). Differentiation between 21 transthyretin and 20 light-chain amyloidosis subtypes was not achievable (R: 0.7% $\pm$ 1.0% vs. 0.7% $\pm$ 1.0%, $\it p$ = 0.827). $\bf Conclusion:$ The MRI-derived RAS and RASR are impaired in CA patients and may support noninvasive differentiation between CA, HCM and CTRL