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Ganna DegtiarovaDepartment of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Zurich, Switzerland

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Fran MikulicicDepartment of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Zurich, Switzerland

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Jan VontobelDepartment of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Zurich, Switzerland

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Chrysoula GarefaDepartment of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Zurich, Switzerland

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Lukas S. KellerDepartment of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Zurich, Switzerland

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Reto BoehmDepartment of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Zurich, Switzerland

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Domenico CianconeDepartment of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Zurich, Switzerland

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Catherine GebhardDepartment of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Zurich, Switzerland

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Aju P. PazhenkottilDepartment of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Zurich, Switzerland

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Philipp A. KaufmannDepartment of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Zurich, Switzerland

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Ronny R. BuechelDepartment of Nuclear Medicine, Cardiac Imaging, University and University Hospital Zurich, Zurich, Switzerland

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Abstract

Objective

To evaluate the impact of a motion-correction (MC) algorithm, applicable post-hoc and not dependent on extended padding, on the image quality and interpretability of coronary computed tomography angiography (CCTA).

Methods

Ninety consecutive patients undergoing CCTA on a latest-generation 256-slice CT device were prospectively included. CCTA was performed with prospective electrocardiogram-triggering and the shortest possible acquisition window (without padding) at 75% of the R-R-interval. All datasets were reconstructed without and with MC of the coronaries. The latter exploits the minimal padding inherent in cardiac CT scans with this device due to data acquisition also during the short time interval needed for the tube to reach target currents and voltage (“free” multiphase). Two blinded readers independently assessed image quality on a 4-point Likert scale for all segments.

Results

A total of 1,030 coronary segments were evaluated. Application of MC both with automatic and manual coronary centerline tracking resulted in a significant improvement in image quality as compared to the standard reconstruction without MC (mean Likert score 3.67 [3.50;3.81] vs 3.58 [3.40;3.73], P = 0.005, and 3.7 [3.55;3.82] vs 3.58 [3.40;3.73], P < 0.001, respectively). Furthermore, MC significantly reduced the proportion of non-evaluable segments and patients with at least one non-evaluable coronary segment from 2% to as low as 0.3%, and from 14% to as low as 3%. Reduction of motion artifacts was predominantly observed in the right coronary artery.

Conclusions

A post-hoc device-specific MC algorithm improves image quality and interpretability of prospectively electrocardiogram-triggered CCTA and reduces the proportion of non-evaluable scans without any additional radiation dose exposure.

Abstract

Objective

To evaluate the impact of a motion-correction (MC) algorithm, applicable post-hoc and not dependent on extended padding, on the image quality and interpretability of coronary computed tomography angiography (CCTA).

Methods

Ninety consecutive patients undergoing CCTA on a latest-generation 256-slice CT device were prospectively included. CCTA was performed with prospective electrocardiogram-triggering and the shortest possible acquisition window (without padding) at 75% of the R-R-interval. All datasets were reconstructed without and with MC of the coronaries. The latter exploits the minimal padding inherent in cardiac CT scans with this device due to data acquisition also during the short time interval needed for the tube to reach target currents and voltage (“free” multiphase). Two blinded readers independently assessed image quality on a 4-point Likert scale for all segments.

Results

A total of 1,030 coronary segments were evaluated. Application of MC both with automatic and manual coronary centerline tracking resulted in a significant improvement in image quality as compared to the standard reconstruction without MC (mean Likert score 3.67 [3.50;3.81] vs 3.58 [3.40;3.73], P = 0.005, and 3.7 [3.55;3.82] vs 3.58 [3.40;3.73], P < 0.001, respectively). Furthermore, MC significantly reduced the proportion of non-evaluable segments and patients with at least one non-evaluable coronary segment from 2% to as low as 0.3%, and from 14% to as low as 3%. Reduction of motion artifacts was predominantly observed in the right coronary artery.

Conclusions

A post-hoc device-specific MC algorithm improves image quality and interpretability of prospectively electrocardiogram-triggered CCTA and reduces the proportion of non-evaluable scans without any additional radiation dose exposure.

Introduction

Coronary computed tomography angiography (CCTA) is a well-established diagnostic modality for the evaluation of suspected coronary artery disease (CAD) in low-to intermediate-risk patients [1]. Due to the high negative predictive value, it reliably serves as a gatekeeper for invasive coronary angiography [2]. However, its diagnostic accuracy is highly dependent on the image quality, which, in turn, is affected by patient, cardiac or respiratory motion. Despite the recent technological advancements of CT scanners, motion artifacts can still impair image quality, rendering scans or parts thereof non-evaluable and necessitating repeated CT acquisitions. Low heart rate (HR) and low heart rate variability are the prerequisites for diagnostic image quality, especially if prospective electrocardiogram (ECG) triggering is applied [3]. Therefore, routine administration of beta-blockers prior to CCTA is a crucial part of adequate patient preparation in most cases [4, 5]. However, despite the use of beta-blockers, not all patients reach the target HR, potentially resulting in motion artifacts particularly affecting the right coronary artery [6]. Motion-correction algorithms have been developed for patients in whom the target HR or low variability cannot be achieved with rate-control medication. The algorithms rely on integrating information on coronary motion from different cardiac phases within a single cardiac cycle, resulting in a new motion-corrected dataset [7, 8]. However, such algorithms require an extension of the temporal acquisition window and, consequently, imply an increase in a radiation dose. Furthermore, the specific acquisition protocols must be planned a priori and cannot be applied to any existing CT dataset. By contrast, a vendor- and device-specific algorithm used in the current study allows for motion correction without the need for padding and is applicable post-hoc. It allows for correcting for motion-induced image quality degradation, but for “free”, that is, without the need for any widening of the standard acquisition window of the CT scan which is associated with increased radiation dose.

This study aimed to evaluate the impact of this free motion-correction (FMC) algorithm on the image quality and interpretability of CT scans compared to the standard reconstruction technique without FMC in a real-world clinical routine population.

Materials and methods

Study population

Consecutive patients referred for clinically indicated contrast-enhanced CCTA were prospectively included. Exclusion criteria were known CAD with a history of revascularization, renal insufficiency with a glomerular filtration rate <60 ml × min−1 × 1.73 m2, pregnancy or breastfeeding, allergy to iodinated contrast, and contraindications to nitroglycerin. Patients with severe arrhythmia in whom CT image acquisition parameters were altered prior to the scan to assure diagnostic image quality were also excluded from the study. The study was approved by the local ethics committee (KEK-ZH-Nr. 214-0632) and all patients provided written informed consent.

Image acquisition

Prior to the examination, all patients received 0.4 mg of isosorbide dinitrate (Isoket, Schwarz Pharma, Monheim, Germany) sublingual. Additionally, up to 30 mg of beta-blocker (Beloc Zok, Astra Zeneca, London, UK) was injected intravenously to reach a target heart rate <65 beats per minute (bpm). All patients underwent a contrast-enhanced CCTA scan on a 256-slice CT scanner (Revolution CT, GE Healthcare, Waukesha, WI, USA) with acquisition during inspiration breath-hold and prospectively ECG-triggered single-beat acquisition at 75% of the R-R-interval. No padding was applied. Iodixanol (Visipaque 320, 320 mg × mL−1, GE Healthcare, Buckinghamshire, UK) was injected into an antecubital vein followed by a 50 mL saline solution based on a body-mass adapted volume and flow rate [5]. Collimation of 256 × 0.625 mm with a z-coverage of 12–16 cm was used with a display field of view of 25 cm. All scans were acquired in high-resolution mode with an in-plane spatial resolution of 0.23 × 0.23 mm. Gantry rotation time was 280 ms.

Image reconstruction

All scans were reconstructed using a high-definition kernel and adaptive statistical iterative reconstruction-veo (ASIR-V) at a level of 70%. In addition, all datasets were reconstructed with the FMC algorithm (SnapShot Freeze, GE Healthcare). The latter exploits the minimal padding inherent to a CT scan due to this scanner type always acquiring at least a fullscan worth of data and due to the short time-interval needed for the tube to reach target currents and voltage. Depending on the HR, “free” multiphase acquisition comprises an additional 3–7% of the RR-interval. Three reconstructions are created from the acquired dataset and are then used to estimate coronary artery motion trajectories and, subsequently, for correction (Central illustration). The FMC algorithm relies on coronary artery centerline tracking, which is performed automatically. However, in case of failed, incomplete, or errant coronary artery vessel centerline tracking, manual adjustments of the centerline is possible where necessary. For the present study, in addition to the completely automated approach (FMCauto), we also manually optimized the centerlines in every dataset (FMCmanual). Thus, three different reconstructions were obtained for each patient: 1) a standard reconstruction without motion correction as per clinical routine (NMC), 2) FMCauto, and 3) FMCmanual.

Central illustration.
Central illustration.

Schematic representation of the “free” motion correction algorithm. This algorithm exploits the short time interval (blue) needed for the tube current to reach target levels and uses this system-inherent padding to create a multi-phase reconstruction which then serves as the basis for motion correction. Illustrated is the tube current profile for single-phase acquisition for a patient with a heart rate of 80 beats per min

Citation: Imaging 14, 2; 10.1556/1647.2022.00060

Image analysis

All reconstructions were independently reviewed by two readers with at least 2 years of experience, blinded to the reconstruction method and patient history. All datasets were viewed on a dedicated workstation (AW 4.6, GE Healthcare, Milwaukee, WI, USA) in a random order to minimize observer bias. Image quality assessment was performed per coronary artery segment, using a 15-segment model, according to the American Heart Association classification [9]. All segments with a diameter of 1,5 mm at their origin were included in the analysis, and the following 4-point Likert-scale was applied: 1 – non-evaluable due to excessive coronary motion or other artifacts, resulting in poor vessel wall delineation, 2 – reduced image quality but evaluable, 3 – good image quality with only minimal motion artifacts, 4 - perfect image quality with no motion artifacts and excellent interpretability (Fig. 1). The average of the results of two reads was subsequently used as a segment score. The mean score was then calculated per patient and per vessel (left anterior descending artery [LAD]: segments 5–10; left circumflex artery [LCX]: segments 11–15; right coronary artery [RCA]: segments 1–4). Image quality derived from NMC was used as the standard of reference.

Fig. 1.
Fig. 1.

Representative examples of subjective image quality assessment. An example is given for each score from 1 (non-evaluable) to 4 (perfect) (A–D, respectively)

Citation: Imaging 14, 2; 10.1556/1647.2022.00060

Radiation dose estimation

Effective radiation dose from CCTA in Millisieverts (mSv) was calculated as the product of dose–length product times a conversion coefficient for the chest (k = 0.014 mSv × [mGy × cm]−1 [3]).

Statistical analysis

Statistical analysis was performed using the statistical software package SPSS 20.0 (IBM, Armonk, NY, USA). Normally distributed continuous variables were expressed as mean ± SD, not-normally distributed continuous variables as median and interquartile range, while categorical variables were represented as percentages. Friedman Test with a Bonferroni post-hoc correction (adjusted P < 0.017 for a per-patient basis comparison and P < 0.005 for a per-vessel basis comparison) was used to compare the average Likert score between the reconstructions. Cochran's Q test with a post hoc multiple McNemar's test with a subsequent manual Bonferroni correction (adjusted P < 0.017) was used to compare the number of evaluable segments and patients with evaluable scans between the reconstructions. Interobserver variability for the assessment of image quality was determined using Cohen's kappa (k) coefficient. A P-value of less than 0.05 was considered statistically significant, if not mentioned otherwise.

Results

Patient characteristics

Ninety consecutive patients were included. The detailed patient characteristics are summarized in Table 1. Three-hundred-and-twenty (31%) segments were either non-existent or excluded from the analysis due to the small diameter (i.e. <1.5 mm at their origin). Hence, a total of 1,030 coronary segments were analyzed. The mean effective radiation dose from CCTA was 1.16 [1.01;1.33] mSv.

Table 1.

Patient characteristics (n = 90)

Age (years)56 ± 12
Male61 (68%)
Body mass index (kg m−2)26 [23;28]
Cardiovascular risk factors
Diabetes3 (3%)
Hypertension38 (42%)
Hypercholesterinemia37 (41%)
Obesity10 (11%)
Smoking35 (39%)
Family history of CAD30 (33%)
CCTA scan characteristics
Contrast volume (mL)40 [30;45]
Tube current (mAs)290 [230;310]
Tube voltage (kVp)100 [100;100]
HR (bpm)58 ± 6, range 66–84

Values given are mean ± standard deviation, median and interquartile range in square brackets or absolute numbers and percentages in round brackets.

CAD = coronary artery disease; CCTA = coronary computed tomography angiography; mL = milliliters; mAs = milliampere-second; kVp = kilovolt peak; bpm = beats per min.

Image quality analysis

There was a good inter-observer agreement for the image quality of coronary segments in the group of NMC as well as in the group of FMCauto and FMCmanual (k = 0.75, 0.77, and 0.71, respectively).

On a per-patient basis, image quality differed significantly between the reconstructions. It was significantly lower for NMC, as compared to FMCauto and FMCmanual (3.58 [3.40; 3.73] vs 3.67 [3.50; 3.81] vs 3.7 [3.55; 3.82], respectively, P = 0.005 and P < 0.001, respectively). By contrast, there was no significant difference in the image quality between FMCauto and FMCmanual (P = 0.08) (Fig. 2A).

Fig. 2.
Fig. 2.

Comparison of the image quality between the different reconstructions per patient (Fig. A) and per vessel (Fig. B) basis.

Citation: Imaging 14, 2; 10.1556/1647.2022.00060

On a per-vessel basis, image quality was significantly lower for the RCA on NMC reconstructions as compared to FMCauto and FMCmanual (3.5 [3.23;3.75] vs 3.75 [3.50; 3.88] vs 3.75 [3.50; 3.88], respectively; P < 0.001 and P < 0.001, respectively). By contrast, image quality did not differ significantly for the LAD (χ2 = 4.94, adjusted P = 0.24) and for the LCX (χ2 = 6.87, adjusted P = 0.09) (Fig. 2B).

Among the 19 (1.8%) segments deemed non-evaluable on the NMC reconstructions, 14 (74%) were attributable to the RCA, 2 (10%) to the LCX, and 3 (16%) to the LAD. The total number of non-interpretable coronary segments decreased progressively by applying motion correction protocol and corresponded to 19 (2%), 9 (0.8%), and 3 (0.3%) segments, respectively, with NMC, FMCauto, and FMCmanual (Fig. 3A). On a per-patient level this resulted in 13 (14%), 7 (8%) and 3 (3%) patients with a non-diagnostic scan (i.e. with at least one non-evaluable coronary segment) for the NMC, FMCauto and FMCmanual reconstructions (Fig. 3B, Fig. 4). Of note, only 4 out of 13 patients with a non-diagnostic scan had an HR of more than 65 bpm during the acquisition, whereas the other 9 patients achieved the target HR and were all in sinus rhythm.

Fig. 3.
Fig. 3.

The number of non-evaluable coronary segments (A) and the number of patients with at least one evaluable coronary segment (B) for each reconstruction algorithm.

Citation: Imaging 14, 2; 10.1556/1647.2022.00060

Fig. 4.
Fig. 4.

Coronary computed tomography angiography of a 55 year-old patient reconstructed without (A) and with (B) motion correction. Note that how, despite a heart rate of only 66 bpm, the right coronary artery shows distinct motion artifacts, hampering its evaluability. By contrast, application of the fully automated motion correction results in good image quality.

Citation: Imaging 14, 2; 10.1556/1647.2022.00060

Discussion

The main finding of our study is a significant improvement of image quality and a higher yield of CCTA exams with diagnostic quality enabled by a motion correction algorithm which is not depending on additional padding but instead exploits the “free” multiphase acquisition inherent to every cardiac scan with this type of device. Of note, the motion correction algorithm can be applied post-hoc and does not result in additional radiation dose exposure. Although statistically significant, the increase in subjective quality conferred by FMC may be considered small and, arguably, clinically negligible. Of note, however, a considerable proportion of segments and patients initially presenting with non-diagnostic image quality were evaluable after the application of FMC. In fact, the use of FMCauto and FMCmanual led to a reduction of non-evaluable patients by 47% and 77%, respectively, as compared to the standard of reference NMC.

Interestingly, among the patients with at least one non-evaluable segment, only a minority did not reach the target HR during image acquisition. Furthermore, the effect of FMC was predominantly conferred by corrections of motion artifacts in the RCA, while the effect on segments of the LAD or LCX was negligible and non-significant, a finding that is generally in line with previous studies [8, 10]. This can be explained by the fact that among all coronary segments, the ones attributable to the RCA feature the highest velocities in three-dimensional space across the R-R interval and, more importantly, exhibit an early and steep incline of velocity in the late diastolic/early systolic phase, rendering the RCA segments particularly prone to motion artifacts even in patients in whom rate control can be achieved through the application of beta-blockers [6]. As these patients cannot be easily identified a priori, the clinical value of a motion correction algorithm that can be applied post-hoc becomes strikingly evident.

Thus, the FMC algorithm may be of particular clinical value in patients where impaired image quality cannot be expected prior to the scan and, hence, no adjustements are made regarding the acquisition technique (e.g. padding). Theoretically, the application of the FMC algorithm can be considered in all patients in clinical routine to improve the overall image quality. However, this leads to increased image reconstruction time, which should be considered in centers with very high patient throughput. Furthermore, with the advent of modern CT scanners with very fast rotation time and/or dual-source technology, CCTA may be less prone to coronary motion artifacts as compared to slower scanners. However, motion artefacts particularly of the RCA occur nevertheless, as evidenced by the present study, where a state-of-the-art CT scanner with a fast rotation time of 280 ms was used.

Several studies have previously reported on the value of motion-correction algorithms for CCTA [7, 8, 10–14]. Among them, Fuchs et al. have reported on the merits of a motion correction algorithm by the same vendor and have demonstrated an increase in overall image quality in 40 patients who did not reach the target HR despite administration of beta-blockers (mean maximum HR during acquisition: 73 bpm) [8]. Hence, the need for motion correction was evident a priori. Furthermore, and contrary to the present study, the motion correction algorithm evaluated by Fuchs et al. and others remains dependent on padding (i.e. a scan time that is 80 ms longer than the minimal acquisition window), resulting in an increased average radiation dose exposure of 2.3 mSv [8]. Similar to Fuchs et al., Fan et al. have provided results for the same motion correction algorithm in 30 patients, demonstrating a substantial increase of interpretability from 78% without versus 93% with motion correction [10]. Finally, Andreini et al. have convincingly demonstrated the beneficial impact on image quality and diagnostic performance in a large multicenter study comprising 230 patients undergoing both CCTA with retrospective gating or prospective ECG-triggering (but with a widened acquisition window) and invasive coronary angiography [14]. Similar to the present study, Andreini et al. have shown a substantial decrease in the proportion of non-evaluable segments, particularly of the RCA, and an overall improvement in diagnostic accuracy of CCTA as compared to the standard of reference.

To the best of our knowledge, the present study is the first to evaluate the impact of a “free” and post-hoc motion correction algorithm. Despite the principles of image quality restoration being similar to previously published motion correction algorithms, the one evaluated in the present study is not dependent on padding, does not lead to increased radiation dose exposure, and, most importantly, can be quickly applied post-hoc whenever deemed necessary in everyday clinical routine, potentially further increasing the rate of scans with diagnostic image quality necessary to fully exploit the potential of CCTA to optimize patient management and reduce downstream resource utilization.

Study limitations

It may be perceived as a limitation that we did not use a standard of reference such as invasive coronary angiography. It was beyond the scope of the present study, however, to re-validate the diagnostic accuracy of the motion correction algorithm per se as data on its validity are already available from Andreini et al. and others. By contrast, the aim of the study was rather to provide qualitative data on its merits if applied post-hoc in a real-world setting by demonstrating a substantial reduction in non-evaluable segments. Obtaining diagnostic image quality in all coronary segments inarguably constitutes the prerequisite for the clinical value of this imaging modality which is conferred through its high sensitivity and negative predictive value in the setting of suspected CAD.

Conclusion

A post-hoc vendor-specific motion-correction algorithm improves image quality and interpretability of prospectively electrocardiogram-triggered CCTA and reduces the proportion of non-evaluable scans without any additional radiation dose exposure.

Authors' contribution

The authors have made substantial contributions to the conception (GD, FM, JV, CG, LSK, RB, DC, CG, APP, PAK, RRB), design of the work (APP, PAK, RRB), the acquisition and analysis (GD, FM, JV, CG, LSK, RB, DC, CG), interpretation of data (GD, FM, LSK, RB, DC, CG), have drafted the work or substantively revised it (GD, FM, JV, CG, LSK, RB, DC, CG, APP, PAK, RRB), have approved the submitted version (GD, FM, JV, CG, LSK, RB, DC, CG, APP, PAK, RRB). All authors agreed to submit it to IMAGING for publication.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Conflicts of interest

RRB has received honoraria from GE Healthcare and Pfizer and he is an associate editor of IMAGING, therefore the submission was handled by a different associate editor. For the remaining authors no personal conflicts of interest were declared. The University Hospital Zurich holds a research agreement with GE Healthcare. No funding was available for the present study.

Abbreviations list

Bpm

Beats per minute

CAD

Coronary artery disease

CCTA

Coronary computed tomography angiography

FMC

Free motion-correction

HR

Heart rate

MC

Motion-correction

NMC

Standart reconstruction without motion-correction

LAD

Left anterior descending artery

LCX

Left circumflex artery

RCA

Right coronary artery

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  • [1]

    Knuuti J, Wijns W, Saraste A, Capodanno D, Barbato E, Funck-Brentano C, et al.: 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J 2020; 41: 407477. https://doi.org/10.1093/eurheartj/ehz425.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [2]

    Pelliccia F, Pasceri V, Evangelista A, Pergolini A, Barillà F, Viceconte N, et al.: Diagnostic accuracy of 320-row computed tomography as compared with invasive coronary angiography in unselected, consecutive patients with suspected coronary artery disease. Int J Cardiovasc Imaging 2013; 29: 443452. https://doi.org/10.1007/s10554-012-0095-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [3]

    Buechel RR, Husmann L, Herzog BA, Pazhenkottil AP, Nkoulou R, Ghadri JR, et al.: Low-dose computed tomography coronary angiography with prospective electrocardiogram triggering: Feasibility in a large population. J Am Coll Cardiol 2011; 57: 332336. https://doi.org/10.1016/j.jacc.2010.08.634.

    • Crossref
    • Search Google Scholar
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Chair of the Editorial Board:
Béla MERKELY (Semmelweis University, Budapest, Hungary)

Editor-in-Chief:
Pál MAUROVICH-HORVAT (Semmelweis University, Budapest, Hungary)

Deputy Editor-in-Chief:
Viktor BÉRCZI (Semmelweis University, Budapest, Hungary)

Executive Editor:
Charles S. WHITE (University of Maryland, USA)

Deputy Editors:
Gianluca PONTONE (Department of Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy)
Michelle WILLIAMS (University of Edinburgh, UK)

Senior Associate Editors:
Tamás Zsigmond KINCSES (University of Szeged, Hungary)
Hildo LAMB (Leiden University, The Netherlands)
Denisa MURARU (Istituto Auxologico Italiano, IRCCS, Milan, Italy)
Ronak RAJANI (Guy’s and St Thomas’ NHS Foundation Trust, London, UK)

Associate Editors:
Andrea BAGGIANO (Department of Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy)
Fabian BAMBERG (Department of Radiology, University Hospital Freiburg, Germany)
Péter BARSI (Semmelweis University, Budapest, Hungary)
Theodora BENEDEK (University of Medicine, Pharmacy, Sciences and Technology, Targu Mures, Romania)
Ronny BÜCHEL (University Hospital Zürich, Switzerland)
Filippo CADEMARTIRI (SDN IRCCS, Naples, Italy) Matteo CAMELI (University of Siena, Italy)
Csilla CELENG (University of Utrecht, The Netherlands)
Edit DÓSA (Semmelweis University, Budapest, Hungary)
Marco FRANCONE (La Sapienza University of Rome, Italy)
Viktor GÁL (OrthoPred Ltd., Győr, Hungary)
Alessia GIMELLI (Fondazione Toscana Gabriele Monasterio, Pisa, Italy)
Tamás GYÖRKE (Semmelweis Unversity, Budapest)
Fabian HYAFIL (European Hospital Georges Pompidou, Paris, France)
György JERMENDY (Bajcsy-Zsilinszky Hospital, Budapest, Hungary)
Pál KAPOSI (Semmelweis University, Budapest, Hungary)
Mihaly KÁROLYI (University of Zürich, Switzerland)
Márton KOLOSSVÁRY (Semmelweis University, Budapest, Hungary)
Lajos KOZÁK (Semmelweis University, Budapest, Hungary)
Mariusz KRUK (Institute of Cardiology, Warsaw, Poland)
Zsuzsa LÉNARD (Semmelweis University, Budapest, Hungary)
Erica MAFFEI (ASUR Marche, Urbino, Marche, Italy)
Robert MANKA (University Hospital, Zürich, Switzerland)
Saima MUSHTAQ (Cardiology Center Monzino (IRCCS), Milan, Italy)
Gábor RUDAS (Semmelweis University, Budapest, Hungary)
Balázs RUZSICS (Royal Liverpool and Broadgreen University Hospital, UK)
Christopher L SCHLETT (Unievrsity Hospital Freiburg, Germany)
Bálint SZILVESZTER (Semmelweis University, Budapest, Hungary)
Richard TAKX (University Medical Centre, Utrecht, The Netherlands)
Ádám TÁRNOKI (National Institute of Oncology, Budapest, Hungary)
Dávid TÁRNOKI (National Institute of Oncology, Budapest, Hungary)
Ákos VARGA-SZEMES (Medical University of South Carolina, USA)
Hajnalka VÁGÓ (Semmelweis University, Budapest, Hungary)
Jiayin ZHANG (Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China)

International Editorial Board:

Gergely ÁGOSTON (University of Szeged, Hungary)
Anna BARITUSSIO (University of Padova, Italy)
Bostjan BERLOT (University Medical Centre, Ljubljana, Slovenia)
Edoardo CONTE (Centro Cardiologico Monzino IRCCS, Milan)
Réka FALUDI (University of Szeged, Hungary)
Andrea Igoren GUARICCI (University of Bari, Italy)
Marco GUGLIELMO (Department of Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy)
Kristóf HISRCHBERG (University of Heidelberg, Germany)
Dénes HORVÁTHY (Semmelweis University, Budapest, Hungary)
Julia KARADY (Harvard Unversity, MA, USA)
Attila KOVÁCS (Semmelweis University, Budapest, Hungary)
Riccardo LIGA (Cardiothoracic and Vascular Department, Università di Pisa, Pisa, Italy)
Máté MAGYAR (Semmelweis University, Budapest, Hungary)
Giuseppe MUSCOGIURI (Centro Cardiologico Monzino IRCCS, Milan, Italy)
Anikó I NAGY (Semmelweis University, Budapest, Hungary)
Liliána SZABÓ (Semmelweis University, Budapest, Hungary)
Özge TOK (Memorial Bahcelievler Hospital, Istanbul, Turkey)
Márton TOKODI (Semmelweis University, Budapest, Hungary)

Managing Editor:
Anikó HEGEDÜS (Semmelweis University, Budapest, Hungary)

Pál Maurovich-Horvat, MD, PhD, MPH, Editor-in-Chief

Semmelweis University, Medical Imaging Centre
2 Korányi Sándor utca, Budapest, H-1083, Hungary
Tel: +36-20-663-2485
E-mail: maurovich-horvat.pal@med.semmelweis-univ.hu

Indexing and Abstracting Services:

  • WoS Emerging Science Citation Index
  • Scopus
  • DOAJ

2021  
Web of Science  
Total Cites
WoS
56
Journal Impact Factor not applicable
Rank by Impact Factor

not applicable

Impact Factor
without
Journal Self Cites
not applicable
5 Year
Impact Factor
not applicable
Journal Citation Indicator 0,10
Rank by Journal Citation Indicator

Medicine, General & Internal 236/329

Scimago  
Scimago
H-index
16
Scimago
Journal Rank
0,226
Scimago Quartile Score Medicine (miscellaneous) (Q4)
Radiological and Ultrasound Technology (Q4)
Radiology, Nuclear Medicine and Imaging (Q4)
Scopus  
Scopus
Cite Score
1,6
Scopus
CIte Score Rank
Medicine (miscellaneous) 175/276 (Q3)
Radiology, Nuclear Medicine and Imaging 209/308 (Q3)
Radiological and Ultrasound Technology 42/60 (Q3)
Scopus
SNIP
0,451

2020  
CrossRef Documents 7
CrossRef Cites 0
CrossRef H-index 1
Days from submission to acceptance 17
Days from acceptance to publication 70
Acceptance Rate 43%

Imaging
Publication Model Gold Open Access
Submission Fee none
Article Processing Charge none
Subscription Information Gold Open Access

Imaging
Language English
Size A4
Year of
Foundation
2020 (2009)
Volumes
per Year
1
Issues
per Year
2
Founder Akadémiai Kiadó
Founder's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2732-0960 (Online)

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