Aortic valve imaging using 18F-sodium fluoride: impact of triple motion correction

BACKGROUND: Current (18)F-NaF assessments of aortic valve microcalcification using (18)F-NaF PET/CT are based on evaluations of end-diastolic or cardiac motion-corrected (ECG-MC) images, which are affected by both patient and respiratory motion. We aimed to test the impact of employing a triple motion correction technique (3 × MC), including cardiorespiratory and gross patient motion, on quantitative and qualitative measurements. MATERIALS AND METHODS: Fourteen patients with aortic stenosis underwent two repeat 30-min PET aortic valve scans within (29 ± 24) days. We considered three different image reconstruction protocols; an end-diastolic reconstruction protocol (standard) utilizing 25% of the acquired data, an ECG-gated (four ECG gates) reconstruction (ECG-MC), and a triple motion-corrected (3 × MC) dataset which corrects for both cardiorespiratory and patient motion. All datasets were compared to aortic valve calcification scores (AVCS), using the Agatston method, obtained from CT scans using correlation plots. We report SUV(max) values measured in the aortic valve and maximum target-to-background ratios (TBR(max)) values after correcting for blood pool activity. RESULTS: Compared to standard and ECG-MC reconstructions, increases in both SUV(max) and TBR(max) were observed following 3 × MC (SUV(max): Standard = 2.8 ± 0.7, ECG-MC = 2.6 ± 0.6, and 3 × MC = 3.3 ± 0.9; TBR(max): Standard = 2.7 ± 0.7, ECG-MC = 2.5 ± 0.6, and 3 × MC = 3.3 ± 1.2, all p values ≤ 0.05). 3 × MC had improved correlations (R(2) value) to the AVCS when compared to the standard methods (SUV(max): Standard = 0.10, ECG-MC = 0.10, and 3 × MC = 0.20; TBR(max): Standard = 0.20, ECG-MC = 0.28, and 3 × MC = 0.46). CONCLUSION: 3 × MC improves the correlation between the AVCS and SUV(max) and TBR(max) and should be considered in PET studies of aortic valves using (18)F-NaF. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40658-022-00433-7

Feasibility of Coronary 18F-Sodium Fluoride Positron-Emission Tomography Assessment With the Utilization of Previously Acquired Computed Tomography Angiography

BACKGROUND: We assessed the feasibility of utilizing previously acquired computed tomography angiography (CTA) with subsequent positron-emission tomography (PET)-only scan for the quantitative evaluation of 18F-NaF PET coronary uptake. METHODS AND RESULTS: Forty-five patients (age 67.1±6.9 years; 76% males) underwent CTA (CTA1) and combined 18F-NaF PET/CTA (CTA2) imaging within 14 [10, 21] days. We fused CTA1 from visit 1 with 18F-NaF PET (PET) from visit 2 and compared visual pattern of activity, maximal standard uptake (SUVmax) values, and target to background ratio (TBR) measurements on (PET/CTA1) fused versus hybrid (PET/CTA2). On PET/CTA2, 226 coronary plaques were identified. Fifty-eight coronary segments from 28 (62%) patients had high 18F-NaF uptake (TBR >1.25), whereas 168 segments had lesions with 18F-NaF TBR ≤1.25. Uptake in all lesions was categorized identically on coregistered PET/CTA1. There was no significant difference in 18F-NaF uptake values between PET/CTA1 and PET/CTA2 (SUVmax, 1.16±0.40 versus 1.15±0.39; P=0.53; TBR, 1.10±0.45 versus 1.09±0.46; P=0.55). The intraclass correlation coefficient for SUVmax and TBR was 0.987 (95% CI, 0.983-0.991) and 0.986 (95% CI, 0.981-0.992). There was no fixed or proportional bias between PET/CTA1 and PET/CTA2 for SUVmax and TBR. Cardiac motion correction of PET scans improved reproducibility with tighter 95% limits of agreement (±0.14 for SUVmax and ±0.15 for TBR versus ±0.20 and ±0.20 on diastolic imaging; P<0.001). CONCLUSIONS: Coronary CTA/PET protocol with CTA first followed by PET-only allows for reliable and reproducible quantification of 18F-NaF coronary uptake. This approach may facilitate selection of high-risk patients for PET-only imaging based on results from prior CTA, providing a practical workflow for clinical application.ope

Triple-gated motion and blood pool clearance corrections improve reproducibility of coronary 18F-NaF PET

PurposeTo improve the test-retest reproducibility of coronary plaque 18F-sodium fluoride (18F-NaF) positron emission tomography (PET) uptake measurements.MethodsWe recruited 20 patients with coronary artery disease who underwent repeated hybrid PET/CT angiography (CTA) imaging within 3&nbsp;weeks. All patients had 30-min PET acquisition and CTA during a single imaging session. Five PET image-sets with progressive motion correction were reconstructed: (i) a static dataset (no-MC), (ii) end-diastolic PET (standard), (iii) cardiac motion corrected (MC), (iv) combined cardiac and gross patient motion corrected (2 × MC) and, (v) cardiorespiratory and gross patient motion corrected (3 × MC). In addition to motion correction, all datasets were corrected for variations in the background activities which are introduced by variations in the injection-to-scan delays (background blood pool clearance correction, BC). Test-retest reproducibility of PET target-to-background ratio (TBR) was assessed by Bland-Altman analysis and coefficient of reproducibility.ResultsA total of 47 unique coronary lesions were identified on CTA. Motion correction in combination with BC improved the PET TBR test-retest reproducibility for all lesions (coefficient of reproducibility: standard = 0.437, no-MC = 0.345 (27% improvement), standard&nbsp;+&nbsp;BC = 0.365 (20% improvement), no-MC + BC = 0.341 (27% improvement), MC + BC = 0.288 (52% improvement), 2 × MC + BC = 0.278 (57% improvement) and 3 × C + BC = 0.254 (72% improvement), all p &lt; 0.001). Importantly, in a sub-analysis of 18F-NaF-avid lesions with gross patient motion &gt;&nbsp;10&nbsp;mm following corrections, reproducibility was improved by 133% (coefficient of reproducibility: standard = 0.745, 3 × MC = 0.320).ConclusionJoint corrections for cardiac, respiratory, and gross patient motion in combination with background blood pool corrections markedly improve test-retest reproducibility of coronary 18F-NaF PET

Machine-learning with 18F-sodium fluoride PET and quantitative plaque analysis on CT angiography for the future risk of myocardial infarction

Coronary (18)F-sodium fluoride ((18)F-NaF) PET and CT angiography–based quantitative plaque analysis have shown promise in refining risk stratification in patients with coronary artery disease. We combined both of these novel imaging approaches to develop an optimal machine-learning model for the future risk of myocardial infarction in patients with stable coronary disease. Methods: Patients with known coronary artery disease underwent coronary (18)F-NaF PET and CT angiography on a hybrid PET/CT scanner. Machine-learning by extreme gradient boosting was trained using clinical data, CT quantitative plaque analysis, measures and (18)F-NaF PET, and it was tested using repeated 10-fold hold-out testing. Results: Among 293 study participants (65 ± 9 y; 84% male), 22 subjects experienced a myocardial infarction over the 53 (40–59) months of follow-up. On univariable receiver-operator-curve analysis, only (18)F-NaF coronary uptake emerged as a predictor of myocardial infarction (c-statistic 0.76, 95% CI 0.68–0.83). When incorporated into machine-learning models, clinical characteristics showed limited predictive performance (c-statistic 0.64, 95% CI 0.53–0.76) and were outperformed by a quantitative plaque analysis-based machine-learning model (c-statistic 0.72, 95% CI 0.60–0.84). After inclusion of all available data (clinical, quantitative plaque and (18)F-NaF PET), we achieved a substantial improvement (P = 0.008 versus (18)F-NaF PET alone) in the model performance (c-statistic 0.85, 95% CI 0.79–0.91). Conclusion: Both (18)F-NaF uptake and quantitative plaque analysis measures are additive and strong predictors of outcome in patients with established coronary artery disease. Optimal risk stratification can be achieved by combining clinical data with these approaches in a machine-learning model

Contrast-enhanced computed tomography assessment of aortic stenosis

Abstract Objectives Non-contrast CT aortic valve calcium scoring ignores the contribution of valvular fibrosis in aortic stenosis. We assessed aortic valve calcific and non-calcific disease using contrast-enhanced CT. Methods This was a post hoc analysis of 164 patients (median age 71 (IQR 66–77) years, 78% male) with aortic stenosis (41 mild, 89 moderate, 34 severe; 7% bicuspid) who underwent echocardiography and contrast-enhanced CT as part of imaging studies. Calcific and non-calcific (fibrosis) valve tissue volumes were quantified and indexed to annulus area, using Hounsfield unit thresholds calibrated against blood pool radiodensity. The fibrocalcific ratio assessed the relative contributions of valve fibrosis and calcification. The fibrocalcific volume (sum of indexed non-calcific and calcific volumes) was compared with aortic valve peak velocity and, in a subgroup, histology and valve weight. Results Contrast-enhanced CT calcium volumes correlated with CT calcium score (r=0.80, p<0.001) and peak aortic jet velocity (r=0.55, p<0.001). The fibrocalcific ratio decreased with increasing aortic stenosis severity (mild: 1.29 (0.98–2.38), moderate: 0.87 (1.48–1.72), severe: 0.47 (0.33–0.78), p<0.001) while the fibrocalcific volume increased (mild: 109 (75–150), moderate: 191 (117–253), severe: 274 (213–344) mm3/cm2). Fibrocalcific volume correlated with ex vivo valve weight (r=0.72, p<0.001). Compared with the Agatston score, fibrocalcific volume demonstrated a better correlation with peak aortic jet velocity (r=0.59 and r=0.67, respectively), particularly in females (r=0.38 and r=0.72, respectively). Conclusions Contrast-enhanced CT assessment of aortic valve calcific and non-calcific volumes correlates with aortic stenosis severity and may be preferable to non-contrast CT when fibrosis is a significant contributor to valve obstruction

Serum Lipoprotein(a) and Bioprosthetic Aortic Valve Degeneration

AIMS: Bioprosthetic aortic valve degeneration demonstrates pathological similarities to aortic stenosis. Lipoprotein(a) [Lp(a)] is a well-recognized risk factor for incident aortic stenosis and disease progression. The aim of this study is to investigate whether serum Lp(a) concentrations are associated with bioprosthetic aortic valve degeneration. METHODS AND RESULTS: In a post hoc analysis of a prospective multimodality imaging study (NCT02304276), serum Lp(a) concentrations, echocardiography, contrast-enhanced computed tomography (CT) angiography, and 18F-sodium fluoride (18F-NaF) positron emission tomography (PET) were assessed in patients with bioprosthetic aortic valves. Patients were also followed up for 2 years with serial echocardiography. Serum Lp(a) concentrations [median 19.9 (8.4-76.4) mg/dL] were available in 97 participants (mean age 75 ± 7 years, 54% men). There were no baseline differences across the tertiles of serum Lp(a) concentrations for disease severity assessed by echocardiography [median peak aortic valve velocity: highest tertile 2.5 (2.3-2.9) m/s vs. lower tertiles 2.7 (2.4-3.0) m/s, P = 0.204], or valve degeneration on CT angiography (highest tertile n = 8 vs. lower tertiles n = 12, P = 0.552) and 18F-NaF PET (median tissue-to-background ratio: highest tertile 1.13 (1.05-1.41) vs. lower tertiles 1.17 (1.06-1.53), P = 0.889]. After 2 years of follow-up, there were no differences in annualized change in bioprosthetic hemodynamic progression [change in peak aortic valve velocity: highest tertile [0.0 (-0.1-0.2) m/s/year vs. lower tertiles 0.1 (0.0-0.2) m/s/year, P = 0.528] or the development of structural valve degeneration. CONCLUSION: Serum lipoprotein(a) concentrations do not appear to be a major determinant or mediator of bioprosthetic aortic valve degeneration

Three-hour delayed imaging improves assessment of coronary 18F-sodium fluoride PET.

Coronary 18F-sodium fluoride (18F-NaF) PET identifies ruptured plaques in patients with recent myocardial infarction and localizes to atherosclerotic lesions with active calcification. Most studies to date have performed the PET acquisition 1 h after injection. Although qualitative and semiquantitative analysis is feasible with 1-h images, residual blood-pool activity often makes it difficult to discriminate plaques with 18F-NaF uptake from noise. We aimed to assess whether delayed PET performed 3 h after injection improves image quality and uptake measurements. Methods: Twenty patients (67 ± 7 y old, 55% male) with stable coronary artery disease underwent coronary CT angiography (CTA) and PET/CT both 1 h and 3 h after the injection of 266.2 ± 13.3 MBq of 18F-NaF. We compared the visual pattern of coronary uptake, maximal background (blood pool) activity, noise, SUVmax, corrected SUVmax (cSUVmax), and target-to-background (TBR) ratio in lesions defined by CTA on 1-h versus 3-h 18F-NaF PET. Results: On 1-h PET, 26 CTA lesions with 18F-NaF PET uptake were identified in 12 (60%) patients. On 3-h PET, we detected 18F-NaF PET uptake in 7 lesions that were not identified on 1-h PET. The median cSUVmax and TBRs of these lesions were 0.48 (interquartile range [IQR], 0.44-0.51) and 1.45 (IQR, 1.39-1.52), respectively, compared with -0.01 (IQR, -0.03-0.001) and 0.95 (IQR, 0.90-0.98), respectively, on 1-h PET (both P < 0.001). Across the entire cohort, 3-h PET SUVmax was similar to 1-h PET measurements (1.63 [IQR, 1.37-1.98] vs. 1.55 [IQR, 1.43-1.89], P = 0.30), and the background activity was lower (0.71 [IQR, 0.65-0.81] vs. 1.24 [IQR, 1.05-1.31], P < 0.001). On 3-h PET, TBR, cSUVmax, and noise were significantly higher (respectively: 2.30 [IQR, 1.70-2.68] vs. 1.28 [IQR, 0.98-1.56], P < 0.001; 0.38 [IQR, 0.27-0.70] vs. 0.90 [IQR, 0.64-1.17], P < 0.001; and 0.10 [IQR, 0.09-0.12] vs. 0.07 [IQR, 0.06-0.09], P = 0.02). Median cSUVmax and TBR increased by 92% (range, 33%-225%) and 80% (range, 20%-177%), respectively. Conclusion: Blood-pool activity decreases on delayed imaging, facilitating the assessment of 18F-NaF uptake in coronary plaques. Median TBR increases by 80%, leading to the detection of more plaques with significant uptake than are detected using the standard 1-h protocol. A greater than 1-h delay may improve the detection of 18F-NaF uptake in coronary artery plaques.restrictio

Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging

PURPOSE Patients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion. We use cluster analysis to risk-stratify patients with known CAD undergoing single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). METHODS From 37,298 patients in the REFINE SPECT registry, we identified 9221 patients with known coronary artery disease. Unsupervised machine learning was performed using clinical (23), acquisition (17), and image analysis (24) parameters from 4774 patients (internal cohort) and validated with 4447 patients (external cohort). Risk stratification for all-cause mortality was compared to stress total perfusion deficit (< 5%, 5-10%, ≥10%). RESULTS Three clusters were identified, with patients in Cluster 3 having a higher body mass index, more diabetes mellitus and hypertension, and less likely to be male, have dyslipidemia, or undergo exercise stress imaging (p < 0.001 for all). In the external cohort, during median follow-up of 2.6 [0.14, 3.3] years, all-cause mortality occurred in 312 patients (7%). Cluster analysis provided better risk stratification for all-cause mortality (Cluster 3: hazard ratio (HR) 5.9, 95% confidence interval (CI) 4.0, 8.6, p < 0.001; Cluster 2: HR 3.3, 95% CI 2.5, 4.5, p < 0.001; Cluster 1, reference) compared to stress total perfusion deficit (≥10%: HR 1.9, 95% CI 1.5, 2.5 p < 0.001; < 5%: reference). CONCLUSIONS Our unsupervised cluster analysis in patients with known CAD undergoing SPECT MPI identified three distinct phenotypic clusters and predicted all-cause mortality better than ischemia alone