Fetterly, Kenneth

Abstract Aims Coronary angiography might contain clinically relevant information, beyond its traditional role in delineating coronary artery disease. We sought to explore the role of deep learning (DL) in detecting right ventricular dysfunction from routinely acquired cine images during coronary angiography. Methods and results 3D-convolutional neural networks (CNN) were trained to identify right ventricle (RV) dysfunction from diagnostic angiography of the right coronary artery (RCA). The model’s input was two cine angiograms from LAO and RAO projections. The model’s output was normal vs. abnormal RV function. Ground truth on RV function was obtained from the nearest transthoracic echocardiogram to the coronary angiogram. Two analyses were performed to identify (i) any RV dysfunction (≥mild) and (ii) significant RV dysfunction (≥mild–moderate). A total of 10 336 coronary angiograms from 9849 patients (mean age 66, 36% women) were included. The cohort was split into training (70%), validation (15%), and testing (15%) sets. In the testing sets, the models attained an area under the ROC curve (AUC), sensitivity, and specificity of 0.82 (95% CI: 0.80–0.84), 0.75, and 0.74, for detecting any RV dysfunction, and 0.83 (95% CI: 0.80–0.86), 0.82, and 0.70 for detecting significant (≥mild–moderate) RV dysfunction. Combining an ECG-driven AI model with the angio-driven model improved the AUC values to 0.83 and 0.87 for detecting any or advanced RV dysfunction, respectively. Conclusion A novel deep learning algorithm yielded acceptable classification accuracy in detecting RV dysfunction from routine cine angiography of the RCA. The model’s performance improved further by adding ECG to its inputs.

Abstract Assessment of x-ray angiography system performance is typically performed using stationary test objects with simple geometries such as a disk on a uniform background. However, these methods do not represent realistic imaging conditions in interventional cardiology as anatomy and devices are inherently non-stationary due to cardiac motion. In this work, a novel implementation of the channelized Hotelling observer (CHO) was used to assess the influence of motion blur on object detectability. A standard CHO model assumes imaging system stationarity whereby the detectability index of a test object is independent of location. However, real angiography systems are inherently non-stationary. While vendor correction gain factors and offset maps are used to compensate for visual non-uniformities, these corrections do not restore stationarity to the images. Methods to accommodate non-stationarity and allow assessment of the influence of motion blur on test object detectability will be presented. The effect of motion blur was quantified with the relative detectability index ( ), where the for an object when moving with constant linear velocity was compared to a low velocity ‘pseudo-stationary’ condition to account for system non-stationarity. The pseudo-stationary condition was used to isolate the influences of spatial non-stationarity and motion blur. Three different test object shapes (disks, spheres and capsules) with linear velocity in the range 0–30 cm · s −1 were tested. For 1 mm diameter objects and linear velocity 30 cm · s −1 , was degraded by 37%, 33% and 42% for the disk, sphere and capsule respectively, relative to the pseudo-stationary condition. Considering all test objects with diameter greater than 2 mm and linear velocity 30 cm · s −1 , was degraded by less than 10% due to motion. In summary, this work describes a new approach to assess performance of x-ray angiography systems using the CHO model and moving test objects.

ObjectivesThe purpose of this work is to describe methods to measure the 3D angular orientation of cardiovascular structures based on a planar image of a valvuloplasty balloon. These methods facilitate X‐ray beam alignment with respect to the anatomy of interest.BackgroundX‐ray beam projections which are perpendicular to the long axis of cardiovascular structures are required to support interventional procedures, including transcatheter aortic valve implant (TAVI).MethodsDuring the TAVI procedure, the 3D angular orientation of the LVOT of 10 patients was measured from a single planar image of an aortic valvuloplasty balloon and the continuous range of X‐ray projection angles which are aligned with the aortic valve plane were calculated (research method). Misalignment of the X‐ray beam and TAVI valve frame was measured from images of the deployed valve. The accuracy of the research method was compared to clinical standard method to determine appropriate X‐ray projection angles, which utilized CT and aortography.ResultsUsing the clinical standard method, the median misalignment of the X‐ray beam and TAVI valve frame was 8.6° (range 2.6° to 21°). Misalignment was reduced to 2.5° (range 0° to 10°) using the research method.ConclusionsThe 3D angular orientation of cardiovascular structures can be measured accurately from a single X‐ray projection image of a known cardiovascular device contained within the anatomy of interest. For TAVI procedures, improved X‐ray beam alignment may help facilitate procedural success. © 2016 Wiley Periodicals, Inc.

Background— Pulmonary hypertension and right ventricular (RV) dysfunction are common in patients with advanced heart failure with preserved ejection fraction (HFpEF), yet their underlying mechanisms remain poorly understood. We sought to examine RV–pulmonary artery (PA) functional reserve responses and RV–PA coupling at rest and during β-adrenergic stimulation in subjects with earlier stage HFpEF. Methods and Results— In a prospective trial, subjects with HFpEF (n=39) and controls (n=18) underwent comprehensive invasive and noninvasive hemodynamic assessment using high fidelity micromanometer catheters, echocardiography, and expired gas analysis at rest and during dobutamine infusion. HFpEF subjects displayed similar RV structure but significantly impaired RV systolic (lower RV d P /d t max /IP and s′) and diastolic function (higher RV τ) coupled with more severe pulmonary vascular disease, manifest by higher PA pressures, higher PA resistance, and lower PA compliance compared with controls. Dobutamine infusion caused greater pulmonary vasodilation in HFpEF compared with controls, with enhanced reductions in PA resistance, greater increase in PA compliance, and a more negative slope in the PA pressure–flow relationship when compared with controls (all P <0.001). RV–PA coupling analysis revealed that dobutamine improved RV ejection in HFpEF subjects through afterload reduction alone, rather than through enhanced contractility, indicating RV systolic reserve dysfunction. Conclusions— Pulmonary hypertension in early stage HFpEF is related to partially reversible pulmonary vasoconstriction coupled with RV systolic and diastolic dysfunction, even in the absence of RV structural remodeling. Pulmonary vascular tone is more favorably responsive to β-adrenergic stimulation in HFpEF than controls, suggesting a potential role for β-agonists in the treatment of patients with HFpEF and pulmonary hypertension. Clinical Trial Registration— URL: http://www.clinicaltrials.gov . Unique identifier: NCT01418248.