Inter-Scan and Inter-Rater Agreement for CAC Scoring in ECG-Gated CAC Scans and Ungated Low-Dose CT Attenuation Correction Scans for Positron Emission Tomography
K. Pieszko*,1 A. Shanbhag,1 S. D. Van Kriekinge,1 M. Lemley,1 M. C. Hyun,1 Y. Otaki,1 D. Dey,2 D. S. Berman,1 P. J. Slomka1; 1Cedars-Sinai Medical Center, Los Angeles, CA, 2Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA
Introduction: We aimed to evaluate inter-scan and inter-rater agreement of Coronary Calcium (CAC) scores obtained from ECG-gated CAC scans and low-dose, ungated computer tomography attenuation correction (CTAC) scans obtained routinely during myocardial perfusion PET imaging.
Methods: From the patients who underwent Rb-82 cardiac positron emission tomography (PET/CT and gated CAC scans without prior revascularization, we have studied 200 cases selected randomly after stratification for the calcium scores categories. Both dedicated gated CAC and ungated CTAC scans were scored for coronary calcium with the quantitative clinical software by two experienced readers. The score agreement was assessed using 5 CAC categories (no CAC: 0, very low: 1-10, low: 11-100; moderate 101-400; and high > 400). The absolute inter-reader differences in scores (bias) between CAC scans and ungated CTAC maps were compared with the Wilcoxon signed-rank test.
Results: Median age of included patients was 70 (inter-quartile range 61-77), 51% were male. The inter-reader concordance index and Cohen’s Kappa were 0.9 and 0.87, respectively, for CAC scans and 0.86 and 0.8 for CTAC scans respectively. Class agreement is shown in figure. Inter-reader bias was larger for CTAC than for gated CAC scans: 16.74 (95% confidence interval [CI] − 1.13, 34.61) vs − 5.06 (95% CI − 14.93,4.82), P < 0.0001. Inter-reader levels of agreement (LOA) were wider for CTAC compared to CAC scans: (− 233.81 to 267.29) vs (− 143.47 to 133.36). The inter-scan concordance index and Cohen’s Kappa for reader 1 and 2 were 0.7; 0.62 and 0.74; 0.67, respectively.
Conclusion: The overall concordance in 5 classes of CAC scores was good for both gated CAC and CTAC scans. Overall, inter-scan (CTAC vs gated CAC) agreement was comparable to inter-reader agreement in terms of bias and LOA. However, inter-reader agreement was worse on the ungated CTAC maps with significantly larger bias.
Repeatability of 18F-Sodium Fluoride Coronary Quantification Exported from Non-contrast PET/CT Scans
E. Tzolos*,1 J. Kwiecinski,2 T. Pawade,1 T. R. G. Cartlidge,1 M. Doris,1 W. Jenkins,1 D. S. Berman,2 D. Newby,1 P. J. Slomka,2 M. R. Dweck1; 1University of Edinburgh, Edinburgh, United Kingdom, 2Cedars-Sinai Medical Center, Los Angeles, CA
Introduction: Coronary 18F-sodium fluoride (18F-NaF) on uptake positron emission tomography/computed tomography (PET/CT), determined by coronary microcalcification activity (CMA), displays excellent observer reproducibility and interscan repeatability. We wanted to test whether we could quantify CMA using non-contrast PET/CT with similar precision.
Methods: Patients underwent 18F-NaF PET/CT scanning on 2 occasions in close succession. Subjects were administered 125 MBq 18F-NaF and underwent PET/CT (Biograph mCT; Siemens) 60 minutes later. We used 3 methods to evaluate coronary 18F-NaF activity: the maximum standard unit value (SUVmax); the maximum target-to-background (TBR) approach; and the CMA which represents the integrated coronary activity in SUV units exceeding blood-pool activity in the right atrium (mean blood-pool SUV plus 2 standard deviations. We calculated intraobserver, interobserver, and interscan reproducibility using Bland-Altman analysis repeatability coefficients and coefficients of variation.
Results: Fifteen patients (73 ± 7 years, 67% men) had 2 scans, 3.9 ± 3.3 weeks apart. 40 (89%) coronary arteries were analysed in total; vessels with no visible uptake were not assessed. Median [interquartile interval] SUVmax for LAD, RCA, and LCx arteries was 1.39 [1.08-1.73], 1.21 [0.95-1.49], and 1.50 [1.06-1.87], respectively; TBRmax was 1.32 [1.10-1.58], 1.15 [0.99-1.38], and 1.36 [1.18-1.54]; and CMA was 0.07 [0.00-0.79], 0.00 [0.00-1.21], and 1.15 [0.00-1.93]. SUVmax, TBRmax and CMA analysis across all vessels showed wide limits of agreement; Table 1 with interscan coefficients of reproducibility showing of 0.45, 0.40, and 2.72, respectively, and coefficients of variation of 34%, 35%, and 135%: Similar results were observed for interobserver and intraobserver reproducibility.
Conclusion: The precision of coronary 18F-NaF PET quantification (SUV, TBRmax and CMA) is sub-optimal when using non-contrast, ECG-gated CT scans.
Multidisciplinary Cardiovascular Integrated Report – a Novel Method to Communicate Cardiovascular Imaging Results
C. T. Mesquita*,1 J. Serafim,2 A. C. Oliveira-Junior,2 C. E. Rochitte,2 A. Rabyschoffsky,2 A. C. Rocha,2 P. R. D. Silva,2 D. Machado,3 F. Salomao,2 A. Chambi,2 N. Correa,2 W. Ker,2 I. Palazzo,4 M. Montera,2 A. C. Siciliano2; 1Universidade Federal Fluminense, Niteroi, Brazil, 2Hospital Procardiaco, Rio de Janeiro, Brazil, 3Hospital Vitoria e Samaritano Barra, Rio de Janeiro, Brazil, 4Nuclear Medicine, Hospital Procardiaco, rio de janeiro, Brazil
Introduction: Integrating multiple specialties in a single meaningful report requires coordinated multispecialty collaboration. To meet this need, we developed a new strategy: a multidisciplinary cardiovascular integrated report (MCIR). In this report, we provide the first analysis of this experience in a tertiary cardiology hospital.
Methods: Our Multidisciplinary Cardiovascular Imaging Reporting Team (MCIRT) includes specialists in cardiovascular medicine and surgery, echocardiography, nuclear medicine, and radiology. MCIRT is organized as a team discussion that meets weekly in-person or online (as social distancing is needed) and generates a single integrated report of cardiovascular imaging studies (MCIR) as demanded by requesting physicians or by the imaging team. The online tool used was TEAMS by Microsoft. We prospectively obtained clinical, diagnostic aspects, and decision-making data during the first 10 months of experience.
Results: In 10 months, there were 56 clinical cases that were reported as MCIR. Coronary artery disease (CAD) was the most common etiology demanding integrated reports (23 cases – 41%), most frequently including coronary CT angiography and myocardial perfusion scintigraphy. The second commonest disease was cardiac infectious endocarditis (IE) in 8 cases (14%). The other diagnosis reported was cardiac amyloidosis (CA - 5), dilated cardiomyopathies (5), myocarditis (4), valvar diseases (3), hypertrophic cardiomyopathy (2), pulmonary hypertension (2), coronary fistula (1), COVID-19 complication (1), cardiac tumor (1), and pacemaker complication (1). The online discussion was limited because of internet instability in less than 5% of cases. The impact in decision making and clinician satisfaction was significant with some physicians bringing cases from other institutions for discussion.
Conclusions: We report a novel method to communicate cardiovascular imaging results as a single integrated report. This report was produced by a multidisciplinary team that engages multiple clinical/surgical and imaging specialists contributing to delivering efficient, organized, and evidence and value-based care. MCIR was technically successful in almost all cases, and it was mostly used in diseases that demand difficult decision making like CAD, IE, and CA.
Incremental Prognostic Role of SPECT to Computed Tomography Angiography Anatomic Assessment in Patients with Suspected Coronary Artery Disease
A. Javaid*,1 A. Ahmed,2 Y. Han,2 J. Saad,2 M. H. Al-Mallah2; 1Internal Medicine, University of Nevada Las Vegas, Las Vegas, NV, 2Houston Methodist DeBakey Heart and Vascular Center, Houston, TX
Introduction: Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) has an established role in both the accurate detection of ischemia and identification of patients at high risk of future cardiovascular events. We aimed to assess the incremental prognostic role of SPECT to a coronary computed tomography angiography (CCTA) anatomic assessment in patients with suspected coronary artery disease (CAD).
Methods: Consecutive patients with suspected CAD who underwent CCTA and SPECT MPI within 180 days of each other were reviewed. Anatomically obstructive CAD by CCTA was defined as ≥ 50% in the left main artery and ≥ 70% stenosis severity in proximal, mid, and distal branches of the left anterior descending, left circumflex, and right coronary artery without including side branches. Ischemia and scar on SPECT were defined as summed difference score and rest score > 0, respectively. Patients were followed from the date of first imaging to incident major adverse cardiovascular events (MACE – composite of all-cause death, myocardial infarction (MI), and unplanned revascularization – percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG) occurring more than 90 days after index imaging).
Results: Our study population consisted of 956 patients (mean (SD) age 61.1 ± 14.2 years, 54.1% men, 89% hypertension, 81% diabetes, and 84% dyslipidemia). After a median follow-up of 31 months (IQR 12-65 months), 102 patients (10.7%, 29.2 events per 1000 person-year) experienced the primary outcome. In multivariable Cox regression models adjusted for several CAD risk factors, the addition of SPECT variables to CCTA obstructive stenosis significantly improved model C-statistic (P = 0.037) and net reclassification (P < 0.001).
Conclusion: In this high-risk cohort of patients with suspected CAD, SPECT added incremental prognostic value to CCTA anatomic assessment.
Feasibility of Early Post-Injection Myocardial Perfusion Imaging using Tetrofosmin and Attenuation-Corrected CZT SPECT
J. A. Case*,1 B. W. Sperry,2 K. K. Patel,3 A. McGhie,4 E. Moloney,2 S. A. Courter,1 E. V. Burgett,5 T. M. Bateman4; 1Cardiovascular Imaging Technologies, Kansas City, MO, 2Saint Luke's Health System, Kansas City, MO, 3Cardiology, Saint Lukes' Mid America Heart Institute, Kansas City, MO, 4St. Luke's Mid America Heart Institute, Kansas City, MO, 5St. Luke's Hospital, Kansas City, MO
Introduction: Recent societal guidelines recommend stress-first and stress-only if normal myocardial perfusion imaging (MPI) as a means of improving laboratory efficiency, reducing radiation and minimizing patient and staff interaction times to lower the risk of COVID-19 transmission. This protocol could be further enhanced if the post-injection delay could also be reduced. This study tested a rapid and early post-injection stress-only Tc-99m tetrofosmin (TETRO) protocol using attenuation-corrected CZT SPECT to establish comparability of images acquired early (Early) versus the standard (Late) 45-60 minute delay post-injection.
Methods: A total of 95 patients (61 male, BMI = 29 ± 5 km⋅m2) referred for MPI were examined as part of a quality improvement project. Patients were imaged upright and then supine on a Spectrum Dynamics, D-SPECT CZT system following injection of 16.7 ± 0.7 mCi of TETRO at peak stress. All had both early and late post-injection images acquired for 106 myocardial counts (approx. 2-5 minutes). Supine images were reconstructed using CT-based attenuation correction. Early and late images were interpreted in random sequence by a blinded panel of three expert readers for image quality (excellent, good, fair, poor), reader confidence (Low, Moderate, High), overall diagnosis (Normal, Abnormal) and need for follow-up rest imaging.
Results: The average time delay between tracer injection and start of imaging was 17 ± 4 minutes (Early) and 67 ± 15 minutes (Late). Image quality was good or excellent in 86% Early vs 94% Late (P = 0.001), with 63% of Early and Late images judged equal in quality. Reader confidence was high for Early (78%) and Late (76%) images (P = ns). Diagnosis was identical for 86% of Early vs Late images, and perceived need for a rest image was the same (12% for both).
Conclusions: Acquiring images much earlier after injection of Tc-99m tetrofosmin than is standard in practice appears feasible using CZT instrumentation and attenuation correction. Despite small differences in image quality, the rates for needing follow-up rest imaging were identical and there was a very high degree of agreement in overall diagnosis.
Techniques to Reduce Extracardiac Activity in Myocardial Perfusion Imaging After Pharmacological Stress in Obese Patients
M. Štalc*, M. Dolenc Novak, B. Gužič Salobir; University Medical Centre Ljubljana, Ljubljana, Slovenia
Introduction: Extracardiac activity can produce artefacts which can degrade the quality of myocardial perfusion imaging (MPI), especially after vasodilator pharmacological stress. Various techniques have been used to reduce intestinal activity in MPI with inconsistent results. There are limited data for their application in obese patients. The purpose of this study was to investigate the effect of delayed imaging time and carbonated water on extracardiac activity in obese patients.
Methods: Consecutive patients referred for MPI with pharmacological stress using 99mTc tetrofosmin (Myoview, GE Healthcare) were assigned to three different groups: A – imaging 60 min after radiopharmaceutical injection, B – delayed imaging (75-90 min post-injection), C – delayed imaging and drinking of 200 ml of carbonated water. Patients with BMI ≥ 30 kg/m2 were considered obese. Patients were imaged in the sitting position using a Cardius® X-ACT camera (Digirad, California, USA). The extracardiac activity adjacent to the inferior myocardial wall was determined visually by two experienced readers who accepted MPI for interpretation or decided for repeated acquisition. The proportion of accepted scans was collected prospectively.
Results: We studied 490 patients (60% women, age 69.5 ± 10.5 years) and 213 obese (BMI 34.3 ± 3.5). Figure 1 shows the proportion of accepted MPI scans. Obese patients in group A had significantly lower acceptance rate than non-obese patients while there were no differences between them in groups B and C (Figure 1). In obese patients, delayed scanning time was shown to increase the acceptance rate by 86% versus a regular scanning time (HR 1.86, 95% CI 1.3-2.6; P = 0.0006). The addition of carbonated water to delayed scanning time further improved the success rate by 20% (HR 1.20, 95% CI 1.0-1.4; P = 0.04).
Conclusions: A combination of delayed image acquisition and drinking of carbonated water led to a significant and clinically important decrease of interfering extracardiac activity in obese patients referred to MPI with pharmacological stress.
The Feasibility of Formal Coronary Artery Calcium Score CT to Accurately Replace SPECT /CT Attenuation Correction Scans When Used for Processing Perfusion Images
M. Elsadany,1 C. Godoy Rivas,2 P. Jayapal,2 B. Stringer,3 D. Pelletier,2 S. Lee,2 S. McMahon,2 V. Nadig,2 W. Duvall*2; 1Cardiology department, Hartford Hospital, Hartford, CT, 2Hartford Hospital, Hartford, CT, 3University of Connecticut School of Medicine, Farmington, CT
Background: Modern SPECT MPI includes attenuation correction (AC) using a low-dose CT scan to improve image reconstruction. This AC CT can grossly identify coronary artery calcification (CAC), but a formal CAC score more accurately provides this information adding valuable diagnostic and prognostic information. However, performing both CT scans during a MPI study may unnecessarily increase radiation exposure and duplicate data needed for AC. If a formal CAC score could replace the standard AC CT scan, one would save time and radiation exposure while gaining important clinical information.
Methods: Patients presenting for a SPECT MPI study who consented to have a formal CAC score performed were included. Patients under 45, with previous PCI or CABG, were excluded. AC images were processed by the same nuclear technologist with the routine 5 mm low-dose attenuation scan and with a 5 mm CT reformatted from the CAC score acquisition. These perfusion images were reviewed by two board certified nuclear cardiologists for image quality (1-4, with 4 = excellent), sub-diaphragmatic tracer uptake (1-4, with 4 = severe), summed stress (SSS), summed rest (SRS), and summed difference scores (SDS). Any datasets with large discrepancies were reprocessed and re-reviewed to exclude processing errors.
Results: A total of 20 patients (mean age 62.6 ± 8.3, and 65% male) were included. The average coronary artery calcium score was 636 ± 1251 with four patients having a calcium score of 0. The average image quality of the AC CT scan images was 3.2 ± 0.5 compared to 2.9 ± 0.5 (P = 0.02) and the average GI tracer uptake was 2.3 ± 0.9 vs 2.4 ± 1.0 (P = 0.19). The average difference in the SSS between groups was 0.7 ± 4.3, in the SRS was -0.6 ± 4.8, and in the SDS was 1.4 ± 3.6 (Figure). Three patients had a summed score difference greater than 5 when using the CAC score for AC versus the standard AC CT.
Conclusion: In this small patient cohort, use of the CAC score CT for attenuation correction was feasible, but further study to confirm the consistency of perfusion findings and interpretation is necessary.
Diagnostic Accuracy of Single-Photon Emission-Computed Tomography with Deep-Learning-Based Attenuation Correction
A. Shanbhag*,1 K. Pieszko,2 R. Miller,3 E. J. Miller,4 M. Lemley,2 D. Dey,5 D. S. Berman,2 P. J. Slomka2; 1Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, 2Cedars-Sinai Medical Center, Los Angeles, CA, 3University of Calgary, Calgary, AB, Canada, 4Yale University, New Haven, CT, 5Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA
Introduction: Cardiac single-photon emission-computed tomography (SPECT) studies commonly use computed tomography (CT)-based attenuation correction (AC) to improve diagnostic accuracy. However, this is unavailable for SPECT-only scanners and increases radiation exposure to the patient. We developed a method to simulate CTAC images from non-corrected (NC) myocardial perfusion imaging (MPI).
Methods: SPECT-MPI was performed using Tc-99m sestamibi or Tc-99m tetrofosmin on scanners with solid-state multi-pinhole detectors. We developed Conditional Generative Adversarial Neural Network (cGAN) which generates simulated attenuation-corrected images (PseudoAC). The model was trained using 798 (train 700: validation 98) pairs of non-corrected and CT-AC MPI studies performed at a single site. We tested the model using studies from an external dataset (N = 178). We assessed the agreement of measures obtained automatically with using quantitative clinical software: stress total perfusion deficit (S-TPD) and stress volume (S-VOL) as well as perfusion change for AC vs PseudoAC and NC scans. Wilcoxon rank-sum test was used to compare median values of S-TPD and perfusion change.
Results: The median (IQR) of S-TPD was 4.54 (1.39, 11.29) for AC and 5 5.50 (2.02, 11.69) for Pseudo-AC scans (P = 0.4). The mean difference in S-TPD was 0.32 (95% Confidence Interval [CI] − 0.06,0.71) between AC and Pseudo-AC and −1.15 (95% CI −1.66,-0.64) between AC and NC. The median S-VOL was 76.45 (54.35, 103.04) for AC and 76.43 (53.31, 105.44) Pseudo-AC (P > 0.9). The median change (IQR) was 11.33% (7.31, 17.75) and 2.25 (1.07, 4.10) for AC vs NC and AC vs Pseudo-AC, respectively (P < 0.001).
Conclusion: Automatic clinical measurements of stress TPD myocardial volume do not differ significantly between Pseudo-AC and AC MPI scans. Perfusion change values as measured by the clinical software are significantly smaller between AC vs Pseudo-AC than AC vs NC scans.
Myocardial Blood Flow Estimation with Automated Motion Correction in 82Rb PET Myocardial Perfusion Imaging
Y. Otaki*, C. Wei, S. D. Van Kriekinge, T. Parekh, M. H. Lemley, P. B. Kavanagh, D. S. Berman, P. J. Slomka; Cedars-Sinai Medical Center, Los Angeles, CA
Introduction Patient motion correction (MC) is critical for the accurate quantification of myocardial blood flow (MBF) and flow reserve (MFR) from dynamic PET myocardial perfusion imaging (MPI). However, frame-by-frame manual correction is time consuming and is not reproducible. Therefore, we aimed to develop and validate an automated algorithm to perform motion correction in dynamic PET MPI.
Methods The algorithm uses simplex iterative optimization of a count-based cost function customized to different dynamic phases for performing frame-by-frame MC. Two experienced operators performed MC in 224 consecutive patients undergoing dynamic rest/stress 82Rb PET MPI across 16 frames for stress and rest images in three directions (inferior–superior, septal–lateral, apex–base). The third operator reconciled the MC results by a consensus with each operator. 224 patients were split into a tuning group (N = 112) and a validation group (N = 112). Automated and manual MC were compared in the early (first 2 minutes) and late phases for the validation group. Additionally, operators performed MC on a population undergoing 82Rb PET and invasive angiogram within 18 days (N = 112) which is separate from the tuning and validation groups. MFR was obtained by fitting the corresponding time–activity curves for each polar map region using QPET software (Cedars-Sinai). The per-patient diagnostic performance for the detection of obstructive coronary artery disease (CAD) by minimal 17-segment MFR was compared for automated MC in the angiographic group. Obstructive CAD was defined as ≥50% stenosis in the left main trunk or ≥ 70% stenosis in any of the main coronary arteries.
Results The automated algorithm generates the corrections in < 12 seconds per case (stress and rest). The mean/max manual shifts in any direction were 0.8/16 mm at stress and 0.5/14 mm at rest in early phase, and 0.3/8 mm at stress and 0.2/13 mm at rest in late phase. Manual shifts ≥ 5 mm at stress and rest, respectively, were made in 10% and 7% in septal–lateral, 51% and 17% in anterior–inferior, and 27% and 15% in apex–base directions. The frequency of motion differences ≥ 5mm between manual and automated MC in septal–lateral and anterior–inferior directions were < 5% across all frames at stress and rest. In base–apex direction, motion differences ≥ 5mm were observed still in 14% in frame 3 at stress and < 5% in remaining frames at stress and rest. There was no significant difference in area under the curve for obstructive CAD detection by MFR between operator MC and the automatic MC (0.77 [0.68-0.86] vs 0.79[0.71-0.88], P = 0.38).
Conclusion Patient MC on dynamic rest/stress 82Rb PET MPI can be performed automatically and rapidly with good agreement with experienced operators. Automatic and manual MC demonstrate similar diagnostic performance for the detection of CAD.
Prognostic Concordance of Computed Tomography Angiography Anatomic Assessment and Myocardial Perfusion Imaging in Patients with Suspected Coronary Artery Disease
A. Ahmed*,1 Y. Han,1 J. Saad,1 M. Al Rifai,2 F. Nabi,1 M. H. Al-Mallah1; 1Houston Methodist DeBakey Heart and Vascular Center, Houston, TX, 2Baylor College of Medicine, Houston, TX
Introduction: In patients with suspected coronary artery disease (CAD), evaluation using coronary computed tomography angiography (CCTA) and Single-Photon Emission-Computed Tomography (SPECT) Myocardial Perfusion Imaging (MPI) provide complimentary information on the anatomical extent and functional significance of disease. We aimed to assess the prognostic significance of concordant vs discordant test findings in patients who were investigated with both tests.
Methods: Consecutive patients with suspected CAD who underwent CCTA and SPECT MPI within 180 days of each other were reviewed. Anatomically obstructive CAD by CCTA was defined as ≥ 50% in the left main artery and ≥ 70% stenosis severity in proximal, mid, and distal branches of the left anterior descending, left circumflex, and right coronary artery without including side branches. Ischemia on SPECT was defined as summed difference score > 0. Patients were followed from the date of first imaging to incident major adverse cardiovascular events (MACE – composite of all-cause death, myocardial infarction (MI), and unplanned revascularization – Percutaneous Coronary Intervention (PCI) or Coronary Artery Bypass Graft (CABG) occurring more than 90 days after index imaging.)
Results: Our study population consisted of 956 patients (mean (SD) age 61.1 ±14.2 years, 54.1% men, 89% hypertension, 81% diabetes, and 84% dyslipidemia). Obstructive stenosis on CCTA and any ischemic defect on SPECT were present in 14% of patients. After a median follow-up of 31 months (IQR 12-65 months), 102 patients (10.7%, 29.2 events per 1000 person-year) experienced the primary outcome. The highest event rates were in patients with both stenosis on CCTA and ischemia on SPECT (Figure 1). Patients with abnormal test on either CCTA or SPECT had higher event rates compared to those with normal tests.
Conclusion: In this analysis, we have shown that in our high-risk cohort of patients with suspected coronary artery disease, stenosis on CCTA and ischemia on SPECT can be used to identify patients at higher risk of incident cardiovascular outcomes.
Change in Positron Emission Tomography Perfusion Imaging Quality with a Novel Data-Driven Motion Correction Algorithm
Y. Han*,1 A. Ahmed,1 C. Hayden,2 A. K. Jung,3 F. Nabi,1 A. Khan,1 J. Saad,1 B. Spottiswoode,2 M. AI-mallah1; 1Houston Methodist DeBakey Heart and Vascular Center, Houston, TX, 2Siemens Medical Solutions USA, Knoxville, TN, 3Siemens Medical Solutions USA, Houston, TX
Introduction: Respiratory and bulk motion frequently reduce the interpretability of cardiac PET images. This study utilized a prototype data-driven motion correction (DDMC) algorithm to generate corrected images, and evaluated image quality and change of perfusion defect size and severity by comparing DDMC images with non-corrected images (NMC).
Methods: Rest and stress images with NMC and DDMC from 40 consecutive patients with motion were rated by 2 blinded investigators on a 4-point visual ordinal scale (VOS) (0: no motion; 1: mild motion; 2: moderate motion; 3: severe motion/uninterpretable). DDMC tracks heart motion at a high spatiotemporal resolution from list mode PET data. Resulting motion vectors can be used to quantify the severity of motion, which is represented here as the fraction of time the heart is within 6mm craniocaudal of the average position (Dwell Z).
Results: A total of 40 patients had mild, moderate, and severe motion, respectively. Fig.1 shows example NMC (1A, VOS = 3) and DDMC (1B, VOS = 0) images from the same patient. All corrected images showed an improvement in quality and were interpretable after processing (Fig 1C). This was confirmed by a significant correlation between data-driven measurements of motion quantification and physician interpretation (Fig 1D-F).
Conclusions: The novel DDMC algorithm improved quality of cardiac PET images with motion. Correlation between data-driven motion quantification and physician interpretation was significant.
Prior SARS-CoV-2 Infection is Associated Coronary Vasomotor Dysfunction as Assessed by Coronary Flow Reserve from Cardiac Positron Emission Tomography
B. Weber*,1 S. Parks,1 A. Kim,1 C. Bay,1 J. Brown,1 S. Divakaran,2 J. Hainer,1 C. Bibbo,1 V. R. Taqueti,1 S. Dorbala,1 R. Blankstein,3 A. Woolley,1 M. DiCarli1; 1Brigham and Women's Hospital, Boston, MA, 2Brigham & Women’s Hospital, Boston, MA, 3Brigham & Women's Hospital, Boston, MA
Background: Cardiovascular complications from COVID19 contribute to its high morbidity and mortality. The vasculature is affected in COVID-19, both directly by the SARS-CoV-2 virus and indirectly due to systemic inflammation. The effect of COVID-19 infection on the coronary vasculature is not known. Coronary flow reserve (CFR), an integrated measure of focal, diffuse, and small-vessel coronary artery disease, identifies patients at risk for cardiac death. We hypothesized that COVID-19 infection is associated with coronary vasomotor dysfunction.
Methods: We identified subjects with prior PCR-confirmed SARS-CoV-2 infection who underwent clinically indicated myocardial stress perfusion PET imaging. We obtained a matched control group without SARS-CoV-2 infection (PCR negative). Baseline and stress hemodynamics were obtained, and the CFR was calculated as the ratio of myocardial blood flow (ml⋅min⋅g) at peak stress over rest.
Results: We studied 15 COVID-19 patients and 43 matched controls (median 3 per case) (Table). The median time from SARS-CoV-2 PCR to cardiac PET was 4 (IQR 1.2-5.6) months. 9/15 (60%) of patients were previously hospitalized for COVID-19 infection. Baseline cardiac risk factors were common, and 8 (53%) patients in the COVID-19 group had abnormal perfusion (defined as summed stress score >3). CFR was abnormal (< 2) in 46% (7/15) of the COVID-19 patients compared to 16.2% (7/43) of matched controls (P = 0.041). The mean CFR was 16.4% lower in COVID-19 patients compared with control patients (2.05 ± 0.5 vs 2.45 ± 0.53, P = 0.029).
Conclusion: CFR was impaired in patients with prior COVID-19 infection compared with matched controls, suggesting a relationship between SARS-CoV-2 infection and coronary vascular health. These data provide potential insight into long-term vascular health and highlight the need to assess long-term consequences of COVID-19 in future prospective studies.
Visual Patterns of Breast Attenuation Artifacts in Women and Men with an Upright and Supine Cadmium-Zinc-Telluride Camera
F. Waqar*,1 M. Athar,1 A. Dwivedi,2 S. Ahmad,1 S. Sanghvi,3 E. Scott,4 N. Khan,1 M. Gerson1; 1University of Cincinnati College of Medicine, Cincinnati, OH, 2Texas Tech University Health Sciences Center El Paso, El Paso, TX, 3University of Illinois College of Medicine at Chicago, Chicago, IL, 4University of Cincinnati Medical Center, Cincinnati, OH
Introduction: Breast attenuation is a common source of artifacts in single-photon emission-computerized tomography-based myocardial perfusion imaging. Breast attenuation artifacts occurring with upright Cadmium-Zinc-Telluride (CZT) cardiac imaging systems have not been well characterized.
Methods: 216 consecutive patients with Single-Photon Emission-Computerized Tomography myocardial perfusion imaging and no angiographically significant obstructive coronary artery disease were identified. All upright and supine SPECT images as well as coronary angiograms were reviewed and analyzed in blinded fashion. Patients were sub-grouped as obese or non-obese. Comparisons of visual defects between anterior and inferior myocardial territories were evaluated for rest and stress conditions and separately for each gender. All stress and rest images were acquired in upright as well as supine position.
Results: In women imaged upright, more visual false-positive defects were noted in the inferior wall compared to the anterior wall (26 vs 10 at rest, P = 0.006, and 33 vs 13 at stress, P < 0.001). Visual inferior wall defects were more common in the upright than supine position at stress (33 vs 23, P = 0.018) and rest (26 vs 14, P = 0.011), and most apparent in non-obese women (13 vs 8, at stress, P = 0.059 and 11 vs 5, at rest, P = 0.014).
Conclusions: With upright CZT myocardial perfusion imaging, women often have visible inferior wall attenuation artifact defects, likely from pendant breast tissue. These inferior wall attenuation artifacts may be seen in non-obese female patients.
99mTc-Doxycycline Encapsulated in a Radiopaque Hydrogel for Dual-Modality Theranostic Imaging for Modulation of Post-Infarction Remodeling
S. Lee*,1 S. Uman,2 S. Thorn,3 B. Marquez-Nostra,1 F. G. Spinale,4 J. A. Burdick,2 A. J. Sinusas3; 1Yale School of Medicine, New Haven, CT, 2University of Pennsylvania, Philadelphia, PA, 3Yale University School of Medicine, New Haven, CT, 4University of South Carolina, Columbia, SC
Introduction: Myocardial infarction (MI) is complicated by post-MI remodeling, which is mediated in part by matrix metalloproteinase (MMP) activation. Intramyocardial injections of hydrogels post-MI can mitigate remodeling, particularly when incorporating MMP inhibitors. Doxycycline (DOX) is a weak MMP inhibitor with limited clinical benefit when administered i.v. post-MI. We radiolabel DOX with 99mTc (Tc-DOX) and encapsulate tracer into a two-component hydrogel (HG) made with: cyclodextrin-modified hyaluronic acid (CD-HA) which forms an inclusion complex with DOX, and adamantane-modified hyaluronic acid (Ad-HA) to form a supramolecular hydrogel with encapsulated CT contrast agent (iohexol), creating a dual modality theranostic hydrogel for tracking local DOX delivery.
Methods: A one-pot reaction was developed to radiolabel 10 mg doxycycline hyclate with [99mTcO4], ascorbic acid, and SnCl2. A range of conditions were tested to optimize specific activity (SA) and radiochemical purity (RP) of Tc-DOX. Quality-control testing was performed via radio-TLC and radio-HPLC. Ad-HA was dissolved in iohexol solution, while CD-HA was dissolved in equal volume of Tc-DOX and combined into HG in Eppendorf tubes. Saline was added to mixture to define Tc-DOX effusion from hydrogel over 24 hours. Supernatant was assayed for radioactivity and exchanged with saline at 1, 2, and 4 hours after gel formation, and HG serially imaged via SPECT/CT.
Results: The optimal SA of Tc-DOX was 100 MBq⋅µmol, with RP > 95%. Tc-DOX is stable at room temperature 6 hours after labeling, while precursor was stable for labeling for 2 mon at − 80 °C. Once in HG, Tc-DOX is slowly released with ~ 50% HG retention over 24 hours.
Conclusion: DOX can be labeled with [99mTcO4]- with high radiochemical purity and encapsulated in a HG that is imageable via hybrid SPECT/CT. Our novel Tc-DOX theranostic HG demonstrated favorable release kinetics for tracking local DOX release following intramyocardial delivery post-MI.
ASNC2021 Abstract Author Index
Abazid, R. M.; 207-05
Adamson, P. D.; 207-01
Agoston, I.; 112-14
Ahmad, A. A.; 125-05
Ahmad, F. S.; 015-03
Ahmad, S.; 212-13
Ahmed, A.; 112-09, 112-11, 207-04, 212-04, 212-10, 212-11
Akincioglu, C.; 207-05
Al Badarin, F.; 015-12
Al-Darzi, W.; 112-13
Al-Mallah, M. H.; 112-09, 112-11, 207-04, 212-04, 212-10, 212-11
Alnabelsi, T.; 112-09, 112-11, 207-04
Al-Rashdan, L.; 015-09, 015-10, 125-04
Al Rifai, M.; 112-11, 212-10
AlShaheen, M.; 125-02
Alsomali, H.; 112-05
Altaha, Z.; 015-12
Ananthasubramaniam, K.; 112-13
Argenziano, M.; 112-07
Arora, S.; 015-08
Athar, M.; 212-13
Ayeni, A.; 112-12
Baker, A. H.; 207-01
Barutcu, S.; 015-03
Baskaran, L.; 112-03
Bateman, T. M.; 112-02, 125-01, 125-03, 212-05
Bay, C.; 212-12
Beanlands, R. S.; 015-02
Becerra, A. F.; 112-08
Berman, D. S.; 125-01, 125-03, 207-01, 207-02, 207-03, 212-01, 212-02, 212-08, 212-09
Bhattaru, A.; 015-07
Bianchi, G.; 125-06
Bibbo, C.; 212-12
Birnie, D.; 015-02
Blankstein, R.; 212-12
Bravo, P. E.; 015-01, 015-07
Brown, J.; 212-12
Buch, C.; 112-14
Burdick, J. A.; 212-14
Burgett, E. V.; 212-05
Burton, Y.; 015-09, 015-10, 125-04
Cacko, M.; 015-11
Cadet, S.; 015-05, 125-01, 207-01
Carey, C.; 125-02
Cartlidge, T. R. G.; 212-02
Case, J. A.; 212-05
Chambi, A.; 212-03
Chamsi-Pasha, M. A.; 112-11
Chan, D.; 015-05
Chandrashekar, P.; 015-09, 015-10, 125-04
Chang, S.; 112-11
Chareonthaitawee, P.; 125-01
Chen, N.; 207-03
Choi, B. Y.; 112-14
Choo, R.; 015-05
Chua, T.; 112-03
Correa, N.; 015-13, 212-03
Costa, F.; 015-13
Cotrado, A.; 015-13
Courter, S. A.; 212-05
Cuddy, S.; 125-06
Datar, Y.; 125-06
Davis, C.; 112-01
deKemp, R. A.; 015-02
Dey, D.; 125-01, 125-03, 207-01, 212-01, 212-08
Di Carli, M.; 125-01, 125-03, 125-06, 212-12
Dietz, J.; 015-06
Divakaran, S.; 212-12
Dolenc Novak, M.; 212-06
Dorbala, S.; 125-01, 125-03, 125-06, 212-12
Doris, M.; 212-02
Dutta, S.; 015-03
Duvall, W.; 015-04, 015-08, 112-08, 212-07
Dweck, M. R.; 207-01, 212-02
Dwivedi, A.; 212-13
Einstein, A. J.; 125-01, 125-03
El Nihum, L. I.; 207-04
Elsadany, M.; 015-04, 015-08, 212-07
ElZouhbi, A.; 015-12
Falk, R.; 125-06
Fernandez, R.; 112-14
Fichardt, H. J.; 112-14
Fine, N. M.; 015-05
Fish, M. B.; 125-01, 125-03
Fletcher, A.; 207-01
Friedman, J. D.; 207-02
Gawor, M.; 015-11
Gerson, M.; 212-13
Ghim, M.; 125-05
Gill, S.; 015-09, 015-10, 125-04
Godoy Rivas, C.; 015-04, 015-08, 212-07
Goldberg, L. R.; 015-01
Gona, K.; 125-05
Gould, K. L.; 112-07
Gransar, H.; 207-02, 207-03
Grodecki, K.; 207-01
Grzybowski, J.; 015-11
Gužič Salobir, B.; 212-06
Gutierrez, L.; 112-04
Hainer, J.; 212-12
Han, D.; 207-02
Han, Y.; 112-09, 112-11, 207-04, 212-04, 212-10, 212-11
Hayden, C.; 212-11
Hayes, S. W.; 125-01, 207-02
Hobocan, M.; 015-04
Holly, T. A.; 015-03
Hynal, K.; 015-06
Hyun, M. C.; 212-01
Ibrahim, J.; 015-06
Irvine, P.; 125-02
Jaiswal, A.; 015-08
Jamal, S.; 015-12
Javaid, A.; 212-04
Jayapal, P.; 212-07
Jenkins, W.; 212-02
Jerosch-Herold, M.; 125-06
Joshi, N.; 207-01
Jozwik-Plebanek, K.; 015-11
Jung, A. K.; 212-11
Jung, J. J.; 125-05
Kansal, P.; 015-03
Karambelkar, P.; 015-07
Katsaggelos, A. K.; 015-03
Kaufmann, P. A.; 125-01, 125-03
Kavanagh, P.; 125-01
Kavanagh, P. B.; 207-03, 212-09
Keng, B. M.; 112-03
Keng, F.; 112-03
Keppler, J.; 112-01
Ker, W.; 015-13, 212-03
Khan, A.; 212-11
Khan, N.; 212-13
Khatami, A.; 207-05
Kijewski, M. F.; 125-06
Kim, A.; 212-12
Knapp, C.; 112-01
Koh, A. S.; 112-03
Kuronuma, K.; 125-01
Kwiecinski, J.; 207-01, 207-03, 212-02
Kwong, R.; 125-06
Landau, H.; 125-06
Lang, F. M.; 112-07
Lee, H.; 015-01, 015-07
Lee, S.; 212-07
Lee, S.; 212-14
Lehenbauer, K.; 112-02
Leipsic, J.; 207-01
Lemley, M. H.; 212-01, 212-08, 212-09
Liang, J. X.; 125-01, 125-03
Liao, R.; 125-06
Lindner, J.; 125-04
MacAskill, M. G.; 207-01
Machado, D.; 212-03
Mah, D.; 015-05
Mahmarian, J. J.; 112-11
Makki, T.; 112-13
Malhotra, S.; 207-05
Manla, Y.; 015-12
Marchlinski, F. E.; 015-01
Marquez-Nostra, B.; 212-14
Marvin, B.; 125-02
Masri, A.; 015-09, 015-10, 125-04
McCarthy, P. M.; 015-03
McCorry, C.; 207-05
McGhie, A.; 112-02, 212-05
McMahon, S.; 212-07
Meah, M. N.; 207-01
Merhige, M. E.; 112-01
Mesquita, C. T.; 015-13, 212-03
Miller, E. J.; 125-01, 125-03, 212-08
Miller, R.; 015-05, 125-01, 207-02, 207-03, 212-08
Mittra, E.; 125-04
Moloney, E.; 212-05
Momodu, J.; 112-12
Montera, M.; 212-03
Nabi, F.; 112-09, 112-11, 207-04, 212-10, 212-11
Nadig, V.; 212-07
Nasr, H.; 112-05
Nery, P. B.; 015-02
Newby, D.; 207-01, 212-02
Nieves, R. A.; 015-06
Ojha, D.; 125-05
Oliveira-Junior, A. C.; 212-03
Otaki, Y.; 125-01, 125-03, 207-02, 207-03, 212-01, 212-09
Palazzo, I.; 015-13, 212-03
Parekh, T.; 125-01, 207-03, 212-09
Parks, S.; 212-12
Patel, F.; 112-02
Patel, K. K.; 112-02, 212-05
Patel, V.; 015-07
Patterson, K. C.; 015-01
Pawade, T.; 212-02
Pelletier, D.; 212-07
Peñafort, F. A.; 112-04
Perucki, W.; 112-08
Peyster, E. G.; 015-01
Pieszko, K.; 125-03, 207-02, 212-01, 212-08
Purbhoo, K.; 112-12
Rabyschoffsky, A.; 212-03
Ramos-Manalaysay, A. L.; 112-06
Rao, S. R.; 125-06
Rentrop, K. P.; 112-07
Rideout, N.; 207-05
Rocha, A. C.; 212-03
Rochitte, C. E.; 212-03
Rodriguez, J.; 015-07
Rojulpote, C.; 015-07
Romsa, J.; 207-05
Rossman, M. D.; 015-01
Rosu, D. I.; 112-14
Rozanski, A.; 207-02
Ruberg, F. L.; 125-06
Ruddy, T. D.; 125-01, 125-02, 125-03
Saad, J.; 112-09, 207-04, 212-04, 212-10, 212-11
Saco, R.; 112-13
Sadeghi, M. M.; 125-05
Salarian, M.; 125-05
Salomao, F.; 212-03
Sanchorawala, V.; 125-06
Sanghvi, S.; 212-13
Satriano, A.; 015-05
Scott, E.; 212-13
Sellers, S.; 207-01
Serafim, J.; 212-03
Shah, S. J.; 015-03
Shaik, A.; 112-08
Shaikh, R.; 112-08
Shanbhag, A.; 212-01, 212-08
Sharir, T.; 125-01, 125-03
Siciliano, A. C.; 212-03
Silva, P. R. D.; 212-03
Singh, A.; 112-02, 125-01, 125-03, 207-03
Sinusas, A. J.; 125-01, 125-03, 212-14
Slomka, P. J.; 015-05, 125-01, 125-03, 207-01, 207-02, 207-03, 212-01, 212-02, 212-08, 212-09
Small, G. R.; 125-02
Soman, P.; 015-06
Sperry, B. W.; 212-05
Spinale, F. G.; 212-14
Spottiswoode, B.; 212-11
Stringer, B.; 212-07
Sutherland, D.; 207-05
Swiha, M.; 207-05
Szava-Kovats, A.; 015-05
Štalc, M.; 212-06
Tamarappoo, B. K.; 125-01, 207-02
Tan, R.; 112-03
Taqueti, V. R.; 212-12
Tavares, A. A.; 207-01
Tavoosi, A.; 125-02
Taylor, A.; 125-06
Teng, X.; 112-03
Teresinska, A.; 015-11
Thomas, J. D.; 015-03
Thomson, L.; 207-02
Thorn, S.; 212-14
Toczek, J.; 125-05
Tzolos, E.; 207-01, 212-02
Uman, S.; 212-14
van Beek, E. J.; 207-01
Vangu, W.; 112-12
Van Harn, M.; 112-13
Van Kriekinge, S. D.; 207-03, 212-01, 212-09
Waqar, F.; 212-13
Warner, S.; 015-09, 015-10, 125-04
Warrington, J.; 207-05
Weber, B.; 212-12
Wehbe, R. M.; 015-03
Wei, C.-C.;207-03, 212-09
Wei, L.; 125-05
Weissler-Snir, A.; 015-08
Wells, R.; 125-02
White, G. C.; 015-05
White, J. A.; 015-05
Wiefels, C.; 015-02
Williams, M.; 207-01
Wilson, K.; 112-01
Wnuk, J.; 015-11
Woolley, A.; 212-12
Yee, A.; 125-06
Zhang, J.; 125-05
Zoghbi, W.; 112-11
ASNC2021 Keyword Index
Acute Coronary Syndrome: 212-10
Atherosclerosis: 125-05, 207-01, 212-02
Attenuation: 212-01, 212-08
Calcium Scoring: 112-13, 207-02, 212-01, 212-07
Cardiac MRI: 015-05
Cardiomyopathy: 015-03, 015-07, 015-09, 015-10, 015-11, 015-12, 015-13, 125-04, 207-05
Clinical Trials: 112-07
Computer Processing: 015-03, 015-04, 212-08
Coronary Flow Reserve: 015-06, 112-03, 212-09, 212-12
CT Angiography: 112-11, 212-04
Diabetes: 112-05, 112-13
Diastolic Function: 112-06
Fluorodeoxyglucose: 015-02, 015-07
Hybrid Imaging: 207-05
Image Processing: 207-03, 212-11
Molecular Probes: 125-06
Myocardial Perfusion: 112-01, 112-07, 112-12, 112-14, 125-01, 125-02, 212-04, 212-05
Other: 015-04, 015-08, 015-11, 015-13, 112-11, 125-03
Outcomes: 015-08, 015-12, 212-10
PET: 015-01, 015-02, 112-01, 112-02, 112-09, 125-05, 207-01, 207-02, 207-03, 207-04, 212-02, 212-09, 212-11, 212-12
Quantification: 015-10, 125-04
Radiopharmaceutical: 015-05, 212-14
Receptor Imaging: 015-01
Right Ventricle: 125-06
Risk Assessment: 112-04, 112-08, 212-03
Solid State Detectors: 015-06
SPECT Techniques: 015-09, 112-03, 112-04, 112-05, 112-06, 125-01, 125-02, 125-03, 212-06, 212-07, 212-13, 212-14
Vasodilators: 112-08, 112-09, 112-12, 207-04, 212-06