Amit Ramesh

Pericardial Fat Burden on ECG-Gated Noncontrast CT in Asymptomatic Patients Who Subsequently Experience Adverse Cardiovascular Events

OBJECTIVES We aimed to evaluate whether pericardial fat has value in predicting the risk of future adverse cardiovascular outcomes. BACKGROUND Pericardial fat volume (PFV) and thoracic fat volume (TFV) can be routinely measured from noncontrast computed tomography (NCT) performed for calculating coronary calcium score (CCS) and may predict major adverse cardiac event (MACE) risk. METHODS From a registry of 2,751 asymptomatic patients without known cardiac artery disease and 4-year follow-up for MACE (cardiac death, myocardial infarction, stroke, late revascularization) after NCT, we compared 58 patients with MACE with 174 same-sex, event-free control subjects matched by a propensity score to account for age, risk factors, and CCS. The TFV was automatically calculated, and PFV was calculated with manual assistance in defining the pericardial contour, within which fat voxels were automatically identified. Independent relationships of PFV and TFV to MACE were evaluated using conditional multivariable logistic regression. RESULTS Patients experiencing MACE had higher mean PFV (101.8 +/- 49.2 cm(3) vs. 84.9 +/- 37.7 cm(3), p = 0.007) and TFV (204.7 +/- 90.3 cm(3) vs. 177 +/- 80.3 cm(3), p = 0.029) and higher frequencies of PFV >125 cm(3) (33% vs. 14%, p = 0.002) and TFV >250 cm(3) (31% vs. 17%, p = 0.025). After adjustment for Framingham risk score (FRS), CCS, and body mass index, PFV and TFV were significantly associated with MACE (odds ratio [OR]: 1.74, 95% confidence interval [CI]: 1.03 to 2.95 for each doubling of PFV; OR: 1.78, 95% CI: 1.01 to 3.14 for TFV). The area under the curve from receiver-operator characteristic analyses showed a trend of improved MACE prediction when PFV was added to FRS and CCS (0.73 vs. 0.68, p = 0.058). Addition of PFV, but not TFV, to FRS and CCS improved estimated specificity (0.72 vs. 0.66, p = 0.008) and overall accuracy (0.70 vs. 0.65, p = 0.009) in predicting MACE. CONCLUSIONS Asymptomatic patients who experience MACE exhibit greater PFV on pre-MACE NCT when they are compared with event-free control subjects with similar cardiovascular risk profiles. Our preliminary findings suggest that PFV may help improve prediction of MACE.

Computer-aided Non-contrast CT-based Quantification of Pericardial and Thoracic Fat and Their Associations with Coronary Calcium and Metabolic Syndrome

INTRODUCTION Pericardial fat is emerging as an important parameter for cardiovascular risk stratification. We extended previously developed quantitation of thoracic fat volume (TFV) from non-contrast coronary calcium (CC) CT scans to also quantify pericardial fat volume (PFV) and investigated the associations of PFV and TFV with CC and the Metabolic Syndrome (METS). METHODS TFV is quantified automatically from user-defined range of CT slices covering the heart. Pericardial fat contours are generated by spline interpolation between 5-7 control points, placed manually on the pericardium within this cardiac range. Contiguous fat voxels within the pericardium are identified as pericardial fat. PFV and TFV were measured from non-contrast CT for 201 patients. In 105 patients, abdominal visceral fat area (VFA) was measured from an additional single-slice CT. In 26 patients, images were quantified by two readers to establish inter-observer variability. TFV and PFV were examined in relation to Body Mass Index (BMI), waist circumference and VFA, standard coronary risk factors (RF), CC (Agatston score >0) and METS. RESULTS PFV and TFV showed excellent correlation with VFA (R=0.79, R=0.89, p<0.0001), and moderate correlation with BMI (R=0.49, R=0.48, p<0.0001). In 26 scans, the inter-observer variability was greater for PFV (8.0+/-5.3%) than for TFV (4.4+/-3.9%, p=0.001). PFV and TFV, but not RF, were associated with CC [PFV: p=0.04, Odds Ratio 3.1; TFV: p<0.001, OR 7.9]. PFV and TFV were also associated with METS [PFV: p<0.001, OR 6.1; TFV p<0.001, OR 5.7], unlike CC [OR=1.0 p=NS] or RF. PFV correlated with low-HDL and high-glucose; TFV correlated with low-HDL, low-adiponectin, and high glucose and triglyceride levels. CONCLUSIONS PFV and TFV can be obtained easily and reproducibly from routine CC scoring scans, and may be important for risk stratification and monitoring.

Quantitative Analysis of Myocardial Perfusion SPECT Anatomically Guided by Coregistered 64-Slice Coronary CT Angiography

Sequential testing by coronary CT angiography (CTA) and myocardial perfusion SPECT (MPS) obtained on stand-alone scanners may be needed to diagnose coronary artery disease in equivocal cases. We have developed an automated technique for MPS–CTA registration and demonstrate its utility for improved MPS quantification by guiding the coregistered physiologic (MPS) with anatomic CTA information. Methods: Automated registration of MPS left ventricular (LV) surfaces with CTA coronary trees was accomplished by iterative minimization of voxel differences between presegmented CTA volumes and motion-frozen MPS data. Studies of 35 sequential patients (26 men; mean age, 67 ± 12 y) with 64-slice coronary CTA, MPS, and available results of the invasive coronary angiography performed within 3 mo were retrospectively analyzed. Three-dimensional coronary vessels and CTA slices were extracted and fused with quantitative MPS results mapped on LV surfaces and MPS coronary regions. Automatically coregistered CTA images and extracted trees were used to correct the MPS contours and to adjust the standard vascular region definitions for MPS quantification. Results: Automated coregistration of MPS and coronary CTA had the success rate of 96% as assessed visually; the average errors were 4.3 ± 3.3 mm in translation and 1.5 ± 2.6 degrees in rotation on stress and 4.2 ± 3.1 mm in translation and 1.7 ± 3.2 degrees in rotation on rest. MPS vascular region definition was adjusted in 17 studies, and LV contours were adjusted in 11 studies using coregistered CTA images as a guide. CTA-guided myocardial perfusion analysis, compared with standard MPS analysis, resulted in improved area under the receiver-operating-characteristic (ROC) curves for the detection of right coronary artery (RCA) and left circumflex artery (LCX) lesions (0.84 ± 0.08 vs. 0.70 ± 0.11 for LCX, P = 0.03, and 0.92 ± 0.05 vs. 0.75 ± 0.09 for RCA, P = 0.02). Conclusion: Software image coregistration of stand-alone coronary CTA and MPS obtained on separate scanners can be performed rapidly and automatically, allowing CTA-guided contour and vascular territory adjustment on MPS for improved quantitative MPS analysis.

Automated 3-dimensional quantification of noncalcified and calcified coronary plaque from coronary CT angiography

INTRODUCTION We aimed to develop an automated algorithm (APQ) for accurate volumetric quantification of non-calcified (NCP) and calcified plaque (CP) from coronary CT angiography (CCTA). METHODS APQ determines scan-specific attenuation thresholds for lumen, NCP, CP and epicardial fat, and applies knowledge-based segmentation and modeling of coronary arteries, to define NCP and CP components in 3D. We tested APQ in 29 plaques for 24 consecutive scans, acquired with dual-source CT scanner. APQ results were compared to volumes obtained by manual slice-by-slice NCP/CP definition and by interactive adjustment of plaque thresholds (ITA) by 2 independent experts. RESULTS APQ analysis time was <2 sec per lesion. There was strong correlation between the 2 readers for manual quantification (r = 0.99, p < 0.0001 for NCP; r = 0.85, p < 0.0001 for CP). The mean HU determined by APQ was 419 +/- 78 for luminal contrast at mid-lesion, 227 +/- 40 for NCP upper threshold, and 511 +/- 80 for the CP lower threshold. APQ showed a significantly lower absolute difference (26.7 mm(3) vs. 42.1 mm(3), p = 0.01), lower bias than ITA (32.6 mm(3) vs 64.4 mm(3), p = 0.01) for NCP. There was strong correlation between APQ and readers (R = 0.94, p < 0.0001 for NCP volumes; R = 0.88, p < 0.0001, for CP volumes; R = 0.90, p < 0.0001 for NCP and CP composition). CONCLUSIONS We developed a fast automated algorithm for quantification of NCP and CP from CCTA, which is in close agreement with expert manual quantification.

Automated Quality Control for Segmentation of Myocardial Perfusion SPECT

Left ventricular (LV) segmentation, including accurate assignment of LV contours, is essential for the quantitative assessment of myocardial perfusion SPECT (MPS). Two major types of segmentation failures are observed in clinical practices: incorrect LV shape determination and incorrect valve-plane (VP) positioning. We have developed a technique to automatically detect these failures for both nongated and gated studies. Methods: A standard Cedars-Sinai perfusion SPECT (quantitative perfusion SPECT [QPS]) algorithm was applied to derive LV contours in 318 consecutive 99mTc-sestamibi rest/stress MPS studies consisting of stress/rest scans with or without attenuation correction and gated stress/rest images (1,903 scans total). Two numeric parameters, shape quality control (SQC) and valve-plane quality control, were derived to categorize the respective contour segmentation failures. The results were compared with the visual classification of automatic contour adequacy by 3 experienced observers. Results: The overall success of automatic LV segmentation in the 1,903 scans ranged from 66% on nongated images (incorrect shape, 8%; incorrect VP, 26%) to 87% on gated images (incorrect shape, 3%; incorrect VP, 10%). The overall interobserver agreement for visual classification of automatic LV segmentation was 61% for nongated scans and 80% for gated images; the agreement between gray-scale and color-scale display for these scans was 86% and 91%, respectively. To improve the reliability of visual evaluation as a reference, the cases with intra- and interobserver discrepancies were excluded, and the remaining 1,277 datasets were considered (101 with incorrect LV shape and 102 with incorrect VP position). For the SQC, the receiver-operating-characteristic area under the curve (ROC-AUC) was 1.0 ± 0.00 for the overall dataset, with an optimal sensitivity of 100% and a specificity of 98%. The ROC-AUC was 1.0 in all specific datasets. The algorithm was also able to detect the VP position errors: VP overshooting with ROC-AUC, 0.91 ± 0.01; sensitivity, 100%; and specificity, 70%; and VP undershooting with ROC-AUC, 0.96 ± 0.01; sensitivity, 100%; and specificity, 70%. Conclusion: A new automated method for quality control of LV MPS contours has been developed and shows high accuracy for the detection of failures in LV segmentation with a variety of acquisition protocols. This technique may lead to an improvement in the objective, automated quantitative analysis of MPS.

Automatic 3D registration of dynamic stress and rest 82Rb and flurpiridaz F 18 myocardial perfusion PET data for patient motion detection and correction

PURPOSE The authors aimed to develop an image-based registration scheme to detect and correct patient motion in stress and rest cardiac positron emission tomography (PET)/CT images. The patient motion correction was of primary interest and the effects of patient motion with the use of flurpiridaz F 18 and (82)Rb were demonstrated. METHODS The authors evaluated stress/rest PET myocardial perfusion imaging datasets in 30 patients (60 datasets in total, 21 male and 9 female) using a new perfusion agent (flurpiridaz F 18) (n = 16) and (82)Rb (n = 14), acquired on a Siemens Biograph-64 scanner in list mode. Stress and rest images were reconstructed into 4 ((82)Rb) or 10 (flurpiridaz F 18) dynamic frames (60 s each) using standard reconstruction (2D attenuation weighted ordered subsets expectation maximization). Patient motion correction was achieved by an image-based registration scheme optimizing a cost function using modified normalized cross-correlation that combined global and local features. For comparison, visual scoring of motion was performed on the scale of 0 to 2 (no motion, moderate motion, and large motion) by two experienced observers. RESULTS The proposed registration technique had a 93% success rate in removing left ventricular motion, as visually assessed. The maximum detected motion extent for stress and rest were 5.2 mm and 4.9 mm for flurpiridaz F 18 perfusion and 3.0 mm and 4.3 mm for (82)Rb perfusion studies, respectively. Motion extent (maximum frame-to-frame displacement) obtained for stress and rest were (2.2 ± 1.1, 1.4 ± 0.7, 1.9 ± 1.3) mm and (2.0 ± 1.1, 1.2 ±0 .9, 1.9 ± 0.9) mm for flurpiridaz F 18 perfusion studies and (1.9 ± 0.7, 0.7 ± 0.6, 1.3 ± 0.6) mm and (2.0 ± 0.9, 0.6 ± 0.4, 1.2 ± 1.2) mm for (82)Rb perfusion studies, respectively. A visually detectable patient motion threshold was established to be ≥2.2 mm, corresponding to visual user scores of 1 and 2. After motion correction, the average increases in contrast-to-noise ratio (CNR) from all frames for larger than the motion threshold were 16.2% in stress flurpiridaz F 18 and 12.2% in rest flurpiridaz F 18 studies. The average increases in CNR were 4.6% in stress (82)Rb studies and 4.3% in rest (82)Rb studies. CONCLUSIONS Fully automatic motion correction of dynamic PET frames can be performed accurately, potentially allowing improved image quantification of cardiac PET data.

Patient motion correction for multiplanar, multi‐breath‐hold cardiac cine MR imaging

To correct for spatial misregistration of multi‐breath‐hold short‐axis (SA), two‐chamber (2CH), and four‐chamber (4CH) cine cardiac MR (CMR) images caused by respiratory and patient motion.

Geometric feature-based multimodal image registration of contrast-enhanced cardiac CT with gated myocardial perfusion SPECT.

PURPOSE Cardiac computed tomography (CT) and single photon emission computed tomography (SPECT) provide clinically complementary information in the diagnosis of coronary artery disease (CAD). Fused anatomical and physiological data acquired sequentially on separate scanners can be coregistered to accurately diagnose CAD in specific coronary vessels. METHODS A fully automated registration method is presented utilizing geometric features from a reliable segmentation of gated myocardial perfusion SPECT (MPS) volumes, where regions of myocardium and blood pools are extracted and used as an anatomical mask to de-emphasize the inhomogeneities of intensity distribution caused by perfusion defects and physiological variations. A multiresolution approach is employed to represent coarse-to-fine details of both volumes. The extracted voxels from each level are aligned using a similarity measure with a piecewise constant image model and minimized using a gradient descent method. The authors then perform limited nonlinear registration of gated MPS to adjust for phase differences by automatic cardiac phase matching between CT and MPS. For phase matching, they incorporate nonlinear registration using thin-plate-spline-based warping. Rigid registration has been compared with manual alignment (n=45) on 20 stress/rest MPS and coronary CTA data sets acquired from two different sites and five stress CT perfusion data sets. Phase matching was also compared to expert visual assessment. RESULTS As compared with manual alignment obtained from two expert observers, the mean and standard deviation of absolute registration errors of the proposed method for MPS were4.3±3.5, 3.6±2.6, and 3.6±2.1mm for translation and 2.1±3.2°, 0.3±0.8°, and 0.7±1.2° for rotation at site A and 3.8±2.7, 4.0±2.9, and 2.2±1.8mm for translation and 1.1±2.0°, 1.6±3.1°, and 1.9±3.8° for rotation at site B. The results for CT perfusion were 3.0±2.9, 3.5±2.4, and 2.8±1.0mm for translation and 3.0±2.4°, 0.6±0.9°, and 1.2±1.3° for rotation. The registration error shows that the proposed method achieves registration accuracy of less than 1 voxel (6.4×6.4×6.4mm) misalignment. The proposed method was robust for different initializations in the range from -80 to 70, -80 to 70, and -50to50mm in the x-, y-, and z-axes, respectively. Validation results of finding best matching phase showed that best matching phases were not different by more than two phases, as visually determined. CONCLUSIONS The authors have developed a fast and fully automated method for registration of contrast cardiac CT with gated MPS which includes nonlinear cardiac phase matching and is capable of registering these modalities with accuracy<10mm in 87% of the cases.

Quantification of 3D regional myocardial wall thickening from gated magnetic resonance images

To develop 3D quantitative measures of regional myocardial wall motion and thickening using cardiac magnetic resonance imaging (MRI) and to validate them by comparison to standard visual scoring assessment.

Automated algorithm for atlas-based segmentation of the heart and pericardium from non-contrast CT.

Automated segmentation of the 3D heart region from non-contrast CT is a pre-requisite for automated quantification of coronary calcium and pericardial fat. We aimed to develop and validate an automated, efficient atlas-based algorithm for segmentation of the heart and pericardium from non-contrast CT. A co-registered non-contrast CT atlas is first created from multiple manually segmented non-contrast CT data. Noncontrast CT data included in the atlas are co-registered to each other using iterative affine registration, followed by a deformable transformation using the iterative demons algorithm; the final transformation is also applied to the segmented masks. New CT datasets are segmented by first co-registering to an atlas image, and by voxel classification using a weighted decision function applied to all co-registered/pre-segmented atlas images. This automated segmentation method was applied to 12 CT datasets, with a co-registered atlas created from 8 datasets. Algorithm performance was compared to expert manual quantification. Cardiac region volume quantified by the algorithm (609.0 ± 39.8 cc) and the expert (624.4 ± 38.4 cc) were not significantly different (p=0.1, mean percent difference 3.8 ± 3.0%) and showed excellent correlation (r=0.98, p<0.0001). The algorithm achieved a mean voxel overlap of 0.89 (range 0.86-0.91). The total time was <45 sec on a standard windows computer (100 iterations). Fast robust automated atlas-based segmentation of the heart and pericardium from non-contrast CT is feasible.