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9. Temporal Evolution of Percutaneous Recanalisation for Coronary Chronic Total Occlusions: An Italian Multicenter Experience

Author

Ciardetti, Niccolò, primary, Chechi, Tania, additional, Mattesini, Alessio, additional, Bonechi, Francesco, additional, Nardi, Giulia, additional, Di Muro, Francesca Maria, additional, Furlani, Priscilla, additional, Crociani, Maria Federica, additional, Cenni, Noemi, additional, Kucukseymen, Selcuk, additional, Meucci, Francesco, additional, Stolcova, Miroslava, additional, Ristalli, Francesca, additional, and Di Mario, Carlo, additional

Published
2023
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10. The Importance of Intravascular Ultrasound and Intravascular Lithotripsy for Complex Coronary Chronic Total Occlusion Recanalisation: An Italian Multicenter Experience

Author

Ciardetti, Niccolò, primary, Chechi, Tania, additional, Mattesini, Alessio, additional, Bonechi, Francesco, additional, Nardi, Giulia, additional, Di Muro, Francesca Maria, additional, Furlani, Priscilla, additional, Crociani, Maria Federica, additional, Cenni, Noemi, additional, Kucukseymen, Selcuk, additional, Meucci, Francesco, additional, Stolcova, Miroslava, additional, Ristalli, Francesca, additional, and Di Mario, Carlo, additional

Published
2023
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11. Association of Successful Percutaneous Revascularization of Chronic Total Occlusions With Quality of Life

Author

Kucukseymen, Selcuk, primary, Iannaccone, Mario, additional, Grantham, James A., additional, Sapontis, James, additional, Juricic, Stefan, additional, Ciardetti, Niccolò, additional, Mattesini, Alessio, additional, Stojkovic, Sinisa, additional, Strauss, Bradley H., additional, Wijeysundera, Harindra C., additional, Werner, Gerald S., additional, D’Ascenzo, Fabrizio, additional, and Di Mario, Carlo, additional

Published
2023
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13. CRT-100.38 Temporal Evolution of Percutaneous Recanalisation for Coronary Chronic Total Occlusions: An Italian Multicenter Experience

Author

Ciardetti, Niccolò, primary, Chechi, Tania, additional, Mattesini, Alessio, additional, Bonechi, Francesco, additional, Nardi, Giulia, additional, Di Muro, Francesca Maria, additional, Furlani, Priscilla, additional, Crociani, Maria Federica, additional, Cenni, Noemi, additional, Kucukseymen, Selcuk, additional, Meucci, Francesco, additional, Stolcova, Miroslava, additional, Ristalli, Francesca, additional, and Di Mario, Carlo, additional

Published
2023
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14. CRT-100.34 The Importance of Intravascular Ultrasound and Intravascular Lithotripsy for Complex Coronary Chronic Total Occlusion Recanalisation: An Italian Multicenter Experience

Author

Ciardetti, Niccolò, primary, Chechi, Tania, additional, Mattesini, Alessio, additional, Bonechi, Francesco, additional, Nardi, Giulia, additional, Di Muro, Francesca Maria, additional, Furlani, Priscilla, additional, Crociani, Maria Federica, additional, Cenni, Noemi, additional, Kucukseymen, Selcuk, additional, Meucci, Francesco, additional, Stolcova, Miroslava, additional, Ristalli, Francesca, additional, and Di Mario, Carlo, additional

Published
2023
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16. 279 LAYERED PLAQUES: NOT ALL ARE HEALED

Author

Ciardetti, Niccolò, primary, Mattesini, Alessio, additional, Di Muro, Francesca Maria, additional, Nardi, Giulia, additional, Kucukseymen, Selcuk, additional, Francesca, Ristalli, additional, Stolcova, Miroslava, additional, Meucci, Francesco, additional, and Di Mario, Carlo, additional

Published
2022
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23. Quantification of changes in myocardial T1* values with exercise cardiac MRI using a free‐breathing non‐electrocardiograph radial imaging

Author

Guo, Rui, primary, Qi, Haikun, additional, Amyar, Amine, additional, Cai, Xiaoying, additional, Kucukseymen, Selcuk, additional, Haji‐Valizadeh, Hassan, additional, Rodriguez, Jennifer, additional, Paskavitz, Amanda, additional, Pierce, Patrick, additional, Goddu, Beth, additional, Thompson, Richard B., additional, and Nezafat, Reza, additional

Published
2022
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27. Comparison of DeepStrain and Feature Tracking for Cardiac MRI Strain Analysis.

Author

Morales, Manuel A., Cirillo, Julia, Nakata, Kei, Kucukseymen, Selcuk, Ngo, Long H., Izquierdo‐Garcia, David, Catana, Ciprian, and Nezafat, Reza

Subjects
CARDIAC magnetic resonance imaging, PEARSON correlation (Statistics)
Abstract

Background: Myocardial feature tracking (FT) provides a comprehensive analysis of myocardial deformation from cine balanced steady‐state free‐precession images (bSSFP). However, FT remains time‐consuming, precluding its clinical adoption. Purpose: To compare left‐ventricular global radial strain (GRS) and global circumferential strain (GCS) values measured using automated DeepStrain analysis of short‐axis cine images to those calculated using manual commercially available FT analysis. Study Type: Retrospective, single‐center. Population: A total of 30 healthy subjects and 120 patients with cardiac disease for DeepStrain development. For evaluation, 47 healthy subjects (36 male, 53 ± 5 years) and 533 patients who had undergone a clinical cardiac MRI (373 male, 59 ± 14 years). Field Strength/Sequence: bSSFP sequence at 1.5 T (Phillips) and 3 T (Siemens). Assessment: Automated DeepStrain measurements of GRS and GCS were compared to commercially available FT (Circle, cvi42) measures obtained by readers with 1 year and 3 years of experience. Comparisons were performed overall and stratified by scanner manufacturer. Statistical Tests: Paired t‐test, linear regression slope, Pearson correlation coefficient (r). Results: Overall, FT and DeepStrain measurements of GCS were not significantly different (P = 0.207), but measures of GRS were significantly different. Measurements of GRS from Philips (slope = 1.06 [1.03 1.08], r = 0.85) and Siemens (slope = 1.04 [0.99 1.09], r = 0.83) data showed a very strong correlation and agreement between techniques. Measurements of GCS from Philips (slope = 0.98 [0.98 1.01], r = 0.91) and Siemens (slope = 1.0 [0.96 1.03], r = 0.88) data similarly showed a very strong correlation. The average analysis time per subject was 4.1 ± 1.2 minutes for FT and 34.7 ± 3.3 seconds for DeepStrain, representing a 7‐fold reduction in analysis time. Data Conclusion: This study demonstrated high correlation of myocardial GCS and GRS measurements between freely available fully automated DeepStrain and commercially available manual FT software, with substantial time‐saving in the analysis. Evidence Level: 3 Technical Efficacy: Stage 3 [ABSTRACT FROM AUTHOR]

Published
2023
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31. Multi‐domain convolutional neural network (MD‐CNN) for radial reconstruction of dynamic cardiac MRI

Author

El‐Rewaidy, Hossam, Fahmy, Ahmed S, Pashakhanloo, Farhad, Cai, Xiaoying, Kucukseymen, Selcuk, Csecs, Ibolya, Neisius, Ulf, Haji‐Valizadeh, Hassan, Menze, Bjoern, Nezafat, Reza, University of Zurich, and Nezafat, Reza

Subjects
Radiology Nuclear Medicine and imaging, 2741 Radiology, Nuclear Medicine and Imaging, 610 Medicine & health, 11493 Department of Quantitative Biomedicine
Published
2021

32. Quantification of changes in myocardial T1* values with exercise cardiac MRI using a free‐breathing non‐electrocardiograph radial imaging.

Author

Guo, Rui, Qi, Haikun, Amyar, Amine, Cai, Xiaoying, Kucukseymen, Selcuk, Haji‐Valizadeh, Hassan, Rodriguez, Jennifer, Paskavitz, Amanda, Pierce, Patrick, Goddu, Beth, Thompson, Richard B., and Nezafat, Reza

Subjects
CARDIAC magnetic resonance imaging, CONSTRAINT algorithms, HEART beat, IN vivo studies
Abstract

Purpose: To develop and evaluate a free breathing non‐electrocardiograph (ECG) myocardial T1* mapping sequence using radial imaging to quantify the changes in myocardial T1* between rest and exercise (T1*reactivity) in exercise cardiac MRI (Ex‐CMR). Methods: A free‐running T1* sequence was developed using a saturation pulse followed by three Look‐Locker inversion‐recovery experiments. Each Look‐Locker continuously acquired data as radial trajectory using a low flip‐angle spoiled gradient‐echo readout. Self‐navigation was performed with a temporal resolution of ∼100 ms for retrospectively extracting respiratory motion. The mid‐diastole phase for every cardiac cycle was retrospectively detected on the recorded electrocardiogram signal using an empirical model. Multiple measurements were performed to obtain mean value to reduce effects from the free‐breathing acquisition. Finally, data acquired at both mid‐diastole and end‐expiration are picked and reconstructed by a low‐rank plus sparsity constraint algorithm. The performance of this sequence was evaluated by simulations, phantoms, and in vivo studies at rest and after physiological exercise. Results: Numerical simulation demonstrated that changes in T1* are related to the changes in T1; however, other factors such as breathing motion could influence T1* measurements. Phantom T1* values measured using free‐running T1* mapping sequence had good correlation with spin‐echo T1 values and was insensitive to heart rate. In the Ex‐CMR study, the measured T1* reactivity was 10% immediately after exercise and declined over time. Conclusion: The free‐running T1* mapping sequence allows free‐breathing non‐ECG quantification of changes in myocardial T1* with physiological exercise. Although, absolute myocardial T1* value is sensitive to various confounders such as B1 and B0 inhomogeneity, quantification of its change may be useful in revealing myocardial tissue properties with exercise. [ABSTRACT FROM AUTHOR]

Published
2022
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33. Multi‐domain convolutional neural network (MD‐CNN) for radial reconstruction of dynamic cardiac MRI

Author

El‐Rewaidy, Hossam; https://orcid.org/0000-0002-5266-8702, Fahmy, Ahmed S, Pashakhanloo, Farhad, Cai, Xiaoying, Kucukseymen, Selcuk, Csecs, Ibolya, Neisius, Ulf, Haji‐Valizadeh, Hassan, Menze, Bjoern, Nezafat, Reza, El‐Rewaidy, Hossam; https://orcid.org/0000-0002-5266-8702, Fahmy, Ahmed S, Pashakhanloo, Farhad, Cai, Xiaoying, Kucukseymen, Selcuk, Csecs, Ibolya, Neisius, Ulf, Haji‐Valizadeh, Hassan, Menze, Bjoern, and Nezafat, Reza

Abstract

Purpose Cardiac MR cine imaging allows accurate and reproducible assessment of cardiac function. However, its long scan time not only limits the spatial and temporal resolutions but is challenging in patients with breath‐holding difficulty or non‐sinus rhythms. To reduce scan time, we propose a multi‐domain convolutional neural network (MD‐CNN) for fast reconstruction of highly undersampled radial cine images. Methods MD‐CNN is a complex‐valued network that processes MR data in k‐space and image domains via k‐space interpolation and image‐domain subnetworks for residual artifact suppression. MD‐CNN exploits spatio‐temporal correlations across timeframes and multi‐coil redundancies to enable high acceleration. Radial cine data were prospectively collected in 108 subjects (50 ± 17 y, 72 males) using retrospective‐gated acquisition with 80%:20% split for training/testing. Images were reconstructed by MD‐CNN and k‐t Radial Sparse‐Sense(kt‐RASPS) using an undersampled dataset (14 of 196 acquired views; relative acceleration rate = 14). MD‐CNN images were evaluated quantitatively using mean‐squared‐error (MSE) and structural similarity index (SSIM) relative to reference images, and qualitatively by three independent readers for left ventricular (LV) border sharpness and temporal fidelity using 5‐point Likert‐scale (1‐non‐diagnostic, 2‐poor, 3‐fair, 4‐good, and 5‐excellent). Results MD‐CNN showed improved MSE and SSIM compared to kt‐RASPS (0.11 ± 0.10 vs. 0.61 ± 0.51, and 0.87 ± 0.07 vs. 0.72 ± 0.07, respectively; P < .01). Qualitatively, MD‐CCN significantly outperformed kt‐RASPS in LV border sharpness (3.87 ± 0.66 vs. 2.71 ± 0.58 at end‐diastole, and 3.57 ± 0.6 vs. 2.56 ± 0.6 at end‐systole, respectively; P < .01) and temporal fidelity (3.27 ± 0.65 vs. 2.59 ± 0.59; P < .01). Conclusion MD‐CNN reduces the scan time of cine imaging by a factor of 23.3 and provides superior image quality compared to kt‐RASPS.

Published
2021

37. Noncontrast Cardiac Magnetic Resonance Imaging Predictors of Heart Failure Hospitalization in Heart Failure With Preserved Ejection Fraction.

Author

Kucukseymen, Selcuk, Arafati, Arghavan, Al‐Otaibi, Talal, El‐Rewaidy, Hossam, Fahmy, Ahmed S., Ngo, Long H., and Nezafat, Reza

Subjects
CARDIAC magnetic resonance imaging, CARDIAC imaging, HEART failure, VENTRICULAR ejection fraction, HEART failure patients
Abstract

Background: Heart failure patients with preserved ejection fraction (HFpEF) are at increased risk of future hospitalization. Contrast agents are often contra‐indicated in HFpEF patients due to the high prevalence of concomitant kidney disease. Therefore, the prognostic value of a noncontrast cardiac magnetic resonance imaging (MRI) for HF‐hospitalization is important. Purpose: To develop and test an explainable machine learning (ML) model to investigate incremental value of noncontrast cardiac MRI for predicting HF‐hospitalization. Study Type: Retrospective. Population: A total of 203 HFpEF patients (mean, 64 ± 12 years, 48% women) referred for cardiac MRI were randomly split into training validation (143 patients, ~70%) and test sets (60 patients, ~30%). Field strength: A 1.5 T, balanced steady‐state free precession (bSSFP) sequence. Assessment: Two ML models were built based on the tree boosting technique and the eXtreme Gradient Boosting model (XGBoost): 1) basic clinical ML model using clinical and echocardiographic data and 2) cardiac MRI‐based ML model that included noncontrast cardiac MRI markers in addition to the basic model. The primary end point was defined as HF‐hospitalization. Statistical Tests: ML tool was used for advanced statistics, and the Elastic Net method for feature selection. Area under the receiver operating characteristic (ROC) curve (AUC) was compared between models using DeLong's test. To gain insight into the ML model, the SHapley Additive exPlanations (SHAP) method was leveraged. A P‐value <0.05 was considered statistically significant. Results: During follow‐up (mean, 50 ± 39 months), 85 patients (42%) reached the end point. The cardiac MRI‐based ML model using the XGBoost algorithm provided a significantly superior prediction of HF‐hospitalization (AUC: 0.81) compared to the basic model (AUC: 0.64). The SHAP analysis revealed left atrium (LA) and right atrium (RV) strains as top imaging markers contributing to its performance with cutoff values of 17.5% and −15%, respectively. Data Conclusions: Using an ML model, RV and LA strains measured in noncontrast cardiac MRI provide incremental value in predicting future hospitalization in HFpEF. Evidence Level: 3 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Published
2022
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40. Discordance in Scar Detection Between Electroanatomical Mapping and Cardiac MRI in an Infarct Swine Model

Author

Kucukseymen, Selcuk, primary, Yavin, Hagai, additional, Barkagan, Michael, additional, Jang, Jihye, additional, Shapira-Daniels, Ayelet, additional, Rodriguez, Jennifer, additional, Shim, David, additional, Pashakhanloo, Farhad, additional, Pierce, Patrick, additional, Botzer, Lior, additional, Manning, Warren J., additional, Anter, Elad, additional, and Nezafat, Reza, additional

Published
2020
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48. Free‐breathing simultaneous myocardial T1 and T2 mapping with whole left ventricle coverage.

Author

Guo, Rui, Cai, Xiaoying, Kucukseymen, Selcuk, Rodriguez, Jennifer, Paskavitz, Amanda, Pierce, Patrick, Goddu, Beth, Thompson, Richard B., and Nezafat, Reza

Subjects
RESPIRATION, IMAGE registration, HEART beat
Abstract

Purpose: To develop a free‐breathing sequence, that is, Multislice Joint T1‐T2, for simultaneous measurement of myocardial T1 and T2 for multiple slices to achieve whole left‐ventricular coverage. Methods: Multislice Joint T1‐T2 adopts slice‐interleaved acquisition to collect 10 single‐shot electrocardiogram‐triggered images for each slice prepared by saturation and T2 preparation to simultaneously estimate myocardial T1 and T2 and achieve whole left‐ventricular coverage. Prospective slice‐tracking using a respiratory navigator and retrospective image registration are used to reduce through‐plane and in‐plane motion, respectively. Multislice Joint T1‐T2 was validated through numerical simulations and phantom and in vivo experiments, and compared with saturation‐recovery single‐shot acquisition and T2‐prepared balanced Steady‐State Free Precession (T2‐prep SSFP) sequences. Results: Phantom T1 and T2 from Multislice Joint T1‐T2 had good accuracy and precision, and were insensitive to heart rate. Multislice Joint T1‐T2 yielded T1 and T2 maps of nine left‐ventricular slices in 1.4 minutes. The mean left‐ventricular T1 difference between saturation‐recovery single‐shot acquisition and Multislice Joint T1‐T2 across healthy subjects and patients was 191 ms (1564 ± 60 ms versus 1373 ± 50 ms; P <.05) and 111 ms (1535 ± 49 ms vs 1423 ± 49 ms; P <.05), respectively. The mean difference in left‐ventricular T2 between T2‐prep SSFP and Multislice Joint T1‐T2 across healthy subjects and patients was −6.3 ms (42.4 ± 1.4 ms vs 48.7 ± 2.5; P <.05) and −5.7 ms (41.6 ± 2.5 ms vs 47.3 ± 2.7; P <.05), respectively. Conclusion: Multislice Joint T1‐T2 enables quantification of whole left‐ventricular T1 and T2 during free breathing within a clinically feasible scan time of less than 2 minutes. [ABSTRACT FROM AUTHOR]

Published
2021
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49. Heart Rate Recovery as a Novel Test for Predicting Cardiac Involvement in Beta-Thalassemia Major

Author

Kucukseymen, Selcuk, Oner Yuksel, Isa, Cagirci, Goksel, Koklu, Erkan, Karakus, Volkan, Cay, Serkan, Kus, Gorkem, Kurtoglu, Erdal, and Arslan, Sakir

Subjects
Original Article
Abstract

Abnormal heart rate recovery (HRR) is predictive of cardiac mortality. Autonomic abnormalities in beta-thalassemia major (TM) patients have been reported in previous studies. However, the importance of low HRR in exercise stress test in TM patients has not yet been ascertained. Therefore, this study will be the first of its kind in the literature.Exercise stress test was performed on 56 TM patients who were being treated at the Thalassemia Center of our hospital, along with 46 non-TM iron deficiency anemia (IDA) patients as a control group. Values for HHR were recorded at 1, 2, 3, 4 and 5 min, and HRR was calculated by the difference of heart rate at peak exercise and at a specific time interval following the onset of recovery.All HRR values were found to be lower in TM patients compared to those in the IDA group. Exercise capacity [metabolic equivalents (METs)] was also found to be low in these patients (p0.001) as well. Total exercise time was significantly lower in the TM group compared to the IDA group (8.40 ± 1.7 min vs. 11.17 ± 1.51 min, p0.001). Exercise capacity (METs) was also lower in the TM group compared to the IDA group. Mean T2* value was 28.3 ± 13.7 ms in TM patients on magnetic resonance imaging (MRI). In addition, there are 18 TM patients with T2* value was20 ms.This study found that TM was independently associated with low HRR. Such a condition is an indicator of autonomic dysfunction in TM patients, since abnormal HRR is related to impaired autonomic response. In addition, impaired HRR may be a marker of early cardiac involvement in patients, whose T2* value is high on MRI. Modifying HRR with a cardiac rehabilitation program in TM patients with impaired HRR is a field open for further investigation.

Published
2017

50. Management of Left Ventricular Free Wall Rupture Associated with Acute Myocardial Infarction

Author

Koklu, Erkan, Arslan, Sakir, Yuksel, Isa Oner, Bayar, Nermin, Yilmaz, Gulsum Meral, and Kucukseymen, Selcuk

Subjects
cardiovascular system, Case Report, cardiovascular diseases
Abstract

Left ventricular free wall rupture is one of the mechanical complications of acute myocardial infarction and it may result in cardiac tamponade as well as limiting itself by forming a pseudoaneurysm. In this report, a case of left ventricular free wall rupture and pseudoaneurysm that developed during the course of posterior myocardial infarction has been presented. Left ventricular free wall rupture and pseudoaneurysm were identified by three-dimensional transthoracic echocardiography and surgically repaired at a late stage.

Published
2017

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