Michaela Hell

Non-invasive prediction of hemodynamically significant coronary artery stenoses by contrast density difference in coronary CT angiography

OBJECTIVES Coronary computed tomography angiography (CTA) allows the detection of obstructive coronary artery disease. However, its ability to predict the hemodynamic significance of stenoses is limited. We assessed differences in plaque characteristics and contrast density difference between hemodynamically significant and non-significant stenoses, as defined by invasive fractional flow reserve (FFR). METHODS Lesion characteristics of 59 consecutive patients (72 lesions) in whom invasive FFR was performed in at least one coronary artery with moderate to high-grade stenoses in coronary CTA were evaluated by two experienced readers. Coronary CTA data sets were acquired on a second-generation dual-source CT scanner using retrospectively ECG-gated spiral acquisition or prospectively ECG-triggered axial acquisition mode. Plaque volume and composition (non-calcified, calcified), remodeling index as well as contrast density difference (defined as the percentage decline in luminal CT attenuation/cross-sectional area over the lesion) were assessed using a semi-automatic software tool (Autoplaq). Additionally, the transluminal attenuation gradient (defined as the linear regression coefficient between intraluminal CT attenuation and length from the ostium) was determined. Differences in lesion characteristics between hemodynamically significant (invasively measured FFR ≤0.80) and non-significant lesions (FFR >0.80) were determined. RESULTS Mean patient age was 64±11 years with 44 males (75%). 21 out of 72 coronary artery lesions (29%) were hemodynamically significant according to invasive FFR. Mean invasive FFR was 0.66±0.12 vs. 0.91±0.05 for hemodynamically significant versus non-significant lesions. Hemodynamically significant lesions showed a significantly greater percentage of non-calcified plaque compared to non-hemodynamically relevant lesions (51.3±15.3% vs. 43.6±16.5%, p=0.021). Contrast density difference was significantly increased in hemodynamically relevant lesions (26.0±20.2% vs. 16.6±10.9% for non-significant lesions; p=0.013). At a threshold of ≥24%, the contrast density difference predicted hemodynamically significant lesions with a specificity of 75%, sensitivity of 33%, PPV of 35% and NPV of 73%. The transluminal attenuation gradient showed no significant difference between hemodynamically significant and non-significant lesions (-1.4±1.4HU/mm vs. -1.1±1.3HU/mm, p=n.s.). CONCLUSIONS Quantitative contrast density difference across coronary lesions in coronary CTA data sets may be applied as a non-invasive tool to identify hemodynamically significant stenoses.

3D printing for sizing left atrial appendage closure device: head-to-head comparison with computed tomography and transoesophageal echocardiography

AIMS Device sizing for LAA closure using transoesophageal echocardiography (TEE) can be challenging due to complex LAA anatomy. We investigated whether the use of 3D-printed left atrial appendage (LAA) models based on preprocedural computed tomography (CT) permits accurate device sizing. METHODS AND RESULTS Twenty-two (22) patients (73±8 years, 55% male) with atrial fibrillation requiring anticoagulation at high bleeding risk underwent LAA closure (WATCHMAN device). Preprocedurally, LAA was sized by TEE and third-generation dual-source CT. Based on CT, 3D printing models of LAA anatomy were created for simulation of device implantation. Device compression was assessed in a CT scan of the 3D model with the implanted device. Implantation was successful in all patients. Mean LAA ostium diameter based on TEE was 22±4 mm and based on CT 25±3 mm (p=0.014). Predicted device size based on simulated implantation in the 3D model was equal to the device finally implanted in 21/22 patients (95%). TEE would have undersized the device in 10/22 patients (45%). Device compression determined in the 3D-CT model corresponded closely with compression upon implantation (16±3% vs. 18±5%, r=0.622, p=0.003). CONCLUSIONS Patient-specific CT-based 3D printing models may assist device selection and prediction of device compression in the context of interventional LAA closure.

Relationship of epicardial fat volume from noncontrast CT with impaired myocardial flow reserve by positron emission tomography

BACKGROUND Impaired myocardial flow reserve (MFR) is a marker of coronary vascular dysfunction with prognostic significance. OBJECTIVES We aimed to investigate the relationship between epicardial fat volume (EFV) measured from noncontrast CT and impaired MFR derived from rest-stress Rb-82 positron emission tomography (PET). METHODS We retrospectively studied 85 consecutive patients without known coronary artery disease who underwent rest-stress Rb-82 myocardial PET/CT and were subsequently referred for invasive coronary angiography. EFV was computed from noncontrast CT by validated software and indexed to body surface area (EFVi, cm3/m2). Global stress and rest MFR were automatically derived from PET. Patient age, sex, cardiovascular risk factors, coronary calcium score (CCS), and EFVi were combined by boosted ensemble machine learning algorithm into a novel composite risk score, using 10-fold cross-validation, to predict impaired global MFR (MFR ≤2.0) by PET. RESULTS Patients with impaired MFR (44 of 85; 52%) were older (71 vs. 65 years; P = .03) and had higher frequency of CCS (≥400; P = .02) with significantly higher EFVi (63.1 ± 20.4 vs. 51.3 ± 14.1 cm3/m2; P = .003). On multivariate logistic regression (with age, sex, number of risk factors, CCS, and EFVi), EFVi was the only independent predictor of impaired MFR (odds ratio, 7.39; P = .02). The machine learning composite risk score significantly improved risk reclassification of impaired MFR compared to CCS or EFVi alone (integrated discrimination improvement = 0.19; P = .007 and IDI = 0.22; P = .002, respectively). CONCLUSIONS Increased EFVi and composite risk score combining EFVi and CCS significantly improve identification of impaired global MFR by PET.

Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study

ObjectivesWe aimed to investigate if lesion-specific ischaemia by invasive fractional flow reserve (FFR) can be predicted by an integrated machine learning (ML) ischaemia risk score from quantitative plaque measures from coronary computed tomography angiography (CTA).MethodsIn a multicentre trial of 254 patients, CTA and invasive coronary angiography were performed, with FFR in 484 vessels. CTA data sets were analysed by semi-automated software to quantify stenosis and non-calcified (NCP), low-density NCP (LD-NCP, < 30 HU), calcified and total plaque volumes, contrast density difference (CDD, maximum difference in luminal attenuation per unit area) and plaque length. ML integration included automated feature selection and model building from quantitative CTA with a boosted ensemble algorithm, and tenfold stratified cross-validation.ResultsEighty patients had ischaemia by FFR (FFR ≤ 0.80) in 100 vessels. Information gain for predicting ischaemia was highest for CDD (0.172), followed by LD-NCP (0.125), NCP (0.097), and total plaque volumes (0.092). ML exhibited higher area-under-the-curve (0.84) than individual CTA measures, including stenosis (0.76), LD-NCP volume (0.77), total plaque volume (0.74) and pre-test likelihood of coronary artery disease (CAD) (0.63); p < 0.006.ConclusionsIntegrated ML ischaemia risk score improved the prediction of lesion-specific ischaemia by invasive FFR, over stenosis, plaque measures and pre-test likelihood of CAD.Key Points• Integrated ischaemia risk score improved prediction of ischaemia over quantitative plaque measures• Integrated ischaemia risk score showed higher prediction of ischaemia than standard approach• Contrast density difference had the highest information gain to identify lesion-specific ischaemia

Symmetry, outliers, and geodesics in coronary artery centerline reconstruction from rotational angiography

Purpose: The performance of many state‐of‐the‐art coronary artery centerline reconstruction algorithms in rotational angiography heavily depends on accurate two‐dimensional centerline information that, in practice, is not available due to segmentation errors. To alleviate the need for correct segmentations, we propose generic extensions to symbolic centerline reconstruction algorithms that target symmetrization, outlier rejection, and topology recovery on asymmetrically reconstructed point clouds. Methods: Epipolar geometry‐ and graph cut‐based reconstruction algorithms are used to reconstruct three‐dimensional point clouds from centerlines in reference views. These clouds are input to the proposed methods that consist of (a) merging of asymmetric reconstructions, (b) removal of inconsistent three‐dimensional points using the reprojection error, and (c) projection domain‐informed geodesic computation. We validate our extensions in a numerical phantom study and on two clinical datasets. Results: In the phantom study, the overlap measure between the reconstructed point clouds and the three‐dimensional ground truth increased from 68.4 ± 9.6% to 85.9 ± 3.3% when the proposed extensions were applied. In addition, the averaged mean and maximum reprojection error decreased from 4.32 ± 3.03 mm to 0.189 ± 0.182 mm and from 8.39 ± 6.08 mm to 0.392 ± 0.434 mm. For the clinical data, the mean and maximum reprojection error improved from 1.73 ± 0.97 mm to 0.882 ± 0.428 mm and from 3.83 ± 1.87 mm to 1.48 ± 0.61 mm, respectively. Conclusions: The application of the proposed extensions yielded superior reconstruction quality in all cases and effectively removed erroneously reconstructed points. Future work will investigate possibilities to integrate parts of the proposed extensions directly into reconstruction.

Quantitative global plaque characteristics from coronary computed tomography angiography for the prediction of future cardiac mortality during long-term follow-up

Aims Adverse plaque characteristics determined by coronary computed tomography angiography (CTA) have been associated with future cardiac events. Our aim was to investigate whether quantitative global per-patient plaque characteristics from coronary CTA can predict subsequent cardiac death during long-term follow-up. Methods and results Out of 2748 patients without prior history of coronary artery disease undergoing CTA with dual-source CT, 32 patients suffered cardiac death (mean follow-up of 5 ± 2 years). These patients were matched to 32 controls by age, gender, risk factors, and symptoms (total 64 patients, 59% male, age 69 ± 10 years). Coronary CTA data sets were analysed by semi-automated software to quantify plaque characteristics over the entire coronary tree, including total plaque volume, volumes of non-calcified plaque (NCP), low-density non-calcified plaque (LD-NCP, attenuation <30 Hounsfield units), calcified plaque (CP), and corresponding burden (plaque volume × 100%/vessel volume), as well as stenosis and contrast density difference (CDD, maximum percent difference in luminal attenuation/cross-sectional area compared to proximal cross-section). In patients who died from cardiac cause, NCP, LD-NCP, CP and total plaque volumes, quantitative stenosis, and CDD were significantly increased compared to controls (P < 0.025 for all). NCP > 146 mm³ [hazards ratio (HR) 2.24; 1.09-4.58; P = 0.027], LD-NCP > 10.6 mm³ (HR 2.26; 1.11-4.63; P = 0.025), total plaque volume > 179 mm³ (HR 2.30; 1.12-4.71; P = 0.022), and CDD > 35% in any vessel (HR 2.85;1.4-5.9; P = 0.005) were associated with increased risk of future cardiac death, when adjusted for segment involvement score. Conclusion Among quantitative global plaque characteristics, total, non-calcified, and low-density plaque volumes as well as CDD predict cardiac death in long-term follow-up.

Respiratory motion compensation in rotational angiography: Graphical model-based optimization of auto-focus measures

Non-recurrent intra-scan motion, such as respiration, corrupts rotational coronary angiography acquisitions and inhibits uncompensated 3D reconstruction. Therefore, state-of-the-art algorithms that rely on 3D/2D registration of initial reconstructions to the projection data are unfavorable as prior models of sufficient quality cannot be obtained. To overcome this limitation, we propose a compensation method that optimizes a task-based autofocus measure using graphical model-based optimization.

Prediction of fluoroscopic angulations for transcatheter aortic valve implantation by CT angiography: influence on procedural parameters.

Aims Repeated angiograms to achieve an exactly orthogonal visualization of the aortic valve plane can substantially contribute to the total contrast amount required for transcatheter aortic valve implantation (TAVI). We investigated whether pre-procedural identification of an optimal fluoroscopic projection by cardiac computed tomography (CT) can significantly reduce the amount of a procedure-related contrast agent compared with angiographic determination of suitable angulations. Methods and results Eighty consecutive patients (81 ± 5 years, 55% male) with symptomatic severe aortic valve stenosis and normal renal function who underwent cardiac CT prior to TAVI were prospectively randomized. In 40 patients, a CT-predicted suitable angulation was used for the first aortic angiogram (CT cohort); in the other 40 patients, the first aortogram was acquired at LAO 10°/cranial 10 (angiography cohort). Additional aortograms were performed if no satisfactory view of the aortic valve plane was obtained. The number of aortograms needed to achieve a satisfactory fluoroscopic projection (1.2 ± 0.6 vs. 3.2 ± 1.7; P < 0.001) and the total amount of contrast agent per TAVI procedure were significantly lower in the CT cohort (95 ± 21 vs. 125 ± 36 mL; P < 0.001). Incidence of acute kidney injury was not significantly different. There was no significant difference regarding radiation dose, time of procedure, degree of post-procedural aortic regurgitation, complications and 30-day mortality between the cohorts. Conclusion Pre-procedural identification of a suitable fluoroscopic projection by cardiac CT significantly reduces a procedural contrast agent volume required for TAVI.

Software innovations in computed tomography for structural heart disease interventions

Computed tomography (CT) provides high, isotropic spatial resolution and has become firmly established in pre-procedural imaging for structural heart disease interventions. It allows determination of the exact dimensions of the target structure, provides information regarding the access route and permits identification of fluoroscopic projection angles to provide optimal visualisation for device placement. Several software solutions are available and have been systematically evaluated in the context of transcatheter aortic valve implantation (TAVI). The use of software products to perform automated measurements can be useful, especially when the experience and expertise regarding evaluation of CT in the context of structural heart disease are limited. In scientific studies, software has been demonstrated to provide accurate support for annulus sizing and prosthesis selection, to aid in reliably identifying patients in whom a transfemoral access may be problematic, and to suggest suitable angulations for fluoroscopic imaging to achieve an orthogonal view onto the aortic valve during implantation.

CT SIZING OF LEFT ATRIAL APPENDAGE PRIOR TO PERCUTANEOUS CLOSURE USING WATCHMAN DEVICE: FEASIBILITY AND INITIAL EXPERIENCE

We assessed the feasibility of CT sizing of the LAA prior to closure using the Watchman device. 16 patients referred for interventional LAA closure were examined using Dual Source CT prior to the interventional procedure. Multiplanar reconstructions were aligned with the plane of the LAA ostium and