Daniel A. Auger

522 total citations
20 papers, 384 citations indexed

About

Daniel A. Auger is a scholar working on Cardiology and Cardiovascular Medicine, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Daniel A. Auger has authored 20 papers receiving a total of 384 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Cardiology and Cardiovascular Medicine, 17 papers in Radiology, Nuclear Medicine and Imaging and 2 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Daniel A. Auger's work include Cardiovascular Function and Risk Factors (15 papers), Cardiac Imaging and Diagnostics (14 papers) and Advanced MRI Techniques and Applications (13 papers). Daniel A. Auger is often cited by papers focused on Cardiovascular Function and Risk Factors (15 papers), Cardiac Imaging and Diagnostics (14 papers) and Advanced MRI Techniques and Applications (13 papers). Daniel A. Auger collaborates with scholars based in United States, United Kingdom and South Africa. Daniel A. Auger's co-authors include Frederick H. Epstein, Xiaodong Zhong, Bruce Spottiswoode, Michael Salerno, Kenneth C. Bilchick, Christopher M. Kramer, Kenneth Mangion, Colin Berry, Christie McComb and Andrew D. Scott and has published in prestigious journals such as Circulation, European Heart Journal and The American Journal of Cardiology.

In The Last Decade

Daniel A. Auger

20 papers receiving 383 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Daniel A. Auger United States	13	309	257	50	50	35	20	384
Hein W. M. Kayser Netherlands	10	367 1.2×	296 1.2×	48 1.0×	41 0.8×	28 0.8×	14	517
El-Sayed H. Ibrahim United States	10	208 0.7×	194 0.8×	42 0.8×	28 0.6×	33 0.9×	25	301
Pranav Bhagirath Netherlands	11	291 0.9×	199 0.8×	99 2.0×	41 0.8×	45 1.3×	37	454
Kai Lin United States	11	188 0.6×	183 0.7×	75 1.5×	59 1.2×	24 0.7×	48	302
Miguel Silva Vieira Portugal	11	211 0.7×	109 0.4×	96 1.9×	61 1.2×	40 1.1×	38	304
Jonathan M Hasleton United Kingdom	3	388 1.3×	328 1.3×	93 1.9×	16 0.3×	30 0.9×	5	506
Pei G. Chew United Kingdom	12	353 1.1×	285 1.1×	67 1.3×	29 0.6×	22 0.6×	27	444
Eckart Fleck Germany	8	227 0.7×	184 0.7×	99 2.0×	64 1.3×	39 1.1×	16	348
Serge D. Van Kriekinge United States	14	394 1.3×	528 2.1×	57 1.1×	34 0.7×	137 3.9×	27	706
Solenn Toupin France	13	236 0.8×	378 1.5×	48 1.0×	22 0.4×	126 3.6×	60	455

Countries citing papers authored by Daniel A. Auger

Since Specialization

Citations

This map shows the geographic impact of Daniel A. Auger's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daniel A. Auger with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel A. Auger more than expected).

Fields of papers citing papers by Daniel A. Auger

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel A. Auger. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniel A. Auger. The network helps show where Daniel A. Auger may publish in the future.

Co-authorship network of co-authors of Daniel A. Auger

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel A. Auger. A scholar is included among the top collaborators of Daniel A. Auger based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Daniel A. Auger. Daniel A. Auger is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Auger, Daniel A., Xiaoying Cai, Changyu Sun, et al.. (2022). Reproducibility of global and segmental myocardial strain using cine DENSE at 3 T: a multicenter cardiovascular magnetic resonance study in healthy subjects and patients with heart disease. Journal of Cardiovascular Magnetic Resonance. 24(1). 23–23. 18 indexed citations

Auger, Daniel A., Changyu Sun, Christopher A. Hanson, et al.. (2021). Cardiac Magnetic Resonance Assessment of Response to Cardiac Resynchronization Therapy and Programming Strategies. JACC. Cardiovascular imaging. 14(12). 2369–2383. 15 indexed citations

Auger, Daniel A., Xue Feng, Changyu Sun, et al.. (2021). Fully-automated global and segmental strain analysis of DENSE cardiovascular magnetic resonance using deep learning for segmentation and phase unwrapping. Journal of Cardiovascular Magnetic Resonance. 23(1). 20–20. 25 indexed citations

Wang, Vicky Y., Yue Zhang, Henrik Haraldsson, et al.. (2021). A kinematic model‐based analysis framework for 3D Cine‐DENSE—validation with an axially compressed gel phantom and application in sheep before and after antero‐apical myocardial infarction. Magnetic Resonance in Medicine. 86(4). 2105–2121. 1 indexed citations

Mangion, Kenneth, Christopher M. Loughrey, Daniel A. Auger, et al.. (2020). Displacement Encoding With Stimulated Echoes Enables the Identification of Infarct Transmurality Early Postmyocardial Infarction. Journal of Magnetic Resonance Imaging. 52(6). 1722–1731. 2 indexed citations

Tayal, Upasana, Ricardo Wage, Pedro Ferreira, et al.. (2019). The feasibility of a novel limited field of view spiral cine DENSE sequence to assess myocardial strain in dilated cardiomyopathy. Magnetic Resonance Materials in Physics Biology and Medicine. 32(3). 317–329. 8 indexed citations

Löffler, A, Jonathan A. Pan, Pelbreton C. Balfour, et al.. (2019). Frequency of Coronary Microvascular Dysfunction and Diffuse Myocardial Fibrosis (Measured by Cardiovascular Magnetic Resonance) in Patients With Heart Failure and Preserved Left Ventricular Ejection Fraction. The American Journal of Cardiology. 124(10). 1584–1589. 37 indexed citations

Bilchick, Kenneth C., Daniel A. Auger, Roshin C. Mathew, et al.. (2019). CMR DENSE and the Seattle Heart Failure Model Inform Survival and Arrhythmia Risk After CRT. JACC. Cardiovascular imaging. 13(4). 924–936. 22 indexed citations

Mangion, Kenneth, David Carrick, Guillaume Clerfond, et al.. (2019). Predictors of segmental myocardial functional recovery in patients after an acute ST-Elevation myocardial infarction. European Journal of Radiology. 112. 121–129. 19 indexed citations

10.

Ferreira, Pedro, Sònia Nielles‐Vallespin, Andrew D. Scott, et al.. (2017). Evaluation of the impact of strain correction on the orientation of cardiac diffusion tensors with in vivo and ex vivo porcine hearts. Magnetic Resonance in Medicine. 79(4). 2205–2215. 21 indexed citations

11.

Mangion, Kenneth, Christie McComb, Daniel A. Auger, Frederick H. Epstein, & Colin Berry. (2017). Magnetic Resonance Imaging of Myocardial Strain After Acute ST-Segment–Elevation Myocardial Infarction. Circulation Cardiovascular Imaging. 10(8). 53 indexed citations

12.

Auger, Daniel A., Kenneth C. Bilchick, Sophia Cui, et al.. (2017). Imaging left‐ventricular mechanical activation in heart failure patients using cine DENSE MRI: Validation and implications for cardiac resynchronization therapy. Journal of Magnetic Resonance Imaging. 46(3). 887–896. 28 indexed citations

13.

Chen, Xiaohong, Yang Yang, Xiaoying Cai, et al.. (2016). Accelerated two-dimensional cine DENSE cardiovascular magnetic resonance using compressed sensing and parallel imaging. Journal of Cardiovascular Magnetic Resonance. 18(1). 38–38. 18 indexed citations

14.

Mehta, Bhairav, Daniel A. Auger, Jorge A. González, et al.. (2015). Detection of elevated right ventricular extracellular volume in pulmonary hypertension using Accelerated and Navigator-Gated Look-Locker Imaging for Cardiac T1 Estimation (ANGIE) cardiovascular magnetic resonance. Journal of Cardiovascular Magnetic Resonance. 17(1). 110–110. 54 indexed citations

15.

Bilchick, Kenneth C., Bhairav Mehta, Daniel A. Auger, et al.. (2015). Abstract 14921: Right Ventricular Extracellular Volume Fraction by Magnetic Resonance T1 Mapping in Pulmonary Hypertension and Heart Failure. Circulation. 132(suppl_3). 2 indexed citations

16.

Auger, Daniel A., Xiaodong Zhong, Frederick H. Epstein, Ernesta M. Meintjes, & Bruce Spottiswoode. (2014). Semi-automated left ventricular segmentation based on a guide point model approach for 3D cine DENSE cardiovascular magnetic resonance. Journal of Cardiovascular Magnetic Resonance. 16(1). 8–8. 20 indexed citations

17.

Garcia, Damien, et al.. (2013). Blood Velocity Patterns in Healthy Subjects and Patients With Cardiomyopathy: a New Echo Tool Using Doppler Vector Flow Mapping. Canadian Journal of Cardiology. 29(10). S270–S270. 1 indexed citations

18.

Auger, Daniel A., Xiaodong Zhong, Frederick H. Epstein, & Bruce Spottiswoode. (2012). Mapping right ventricular myocardial mechanics using 3D cine DENSE cardiovascular magnetic resonance. Journal of Cardiovascular Magnetic Resonance. 14(1). 13–13. 38 indexed citations

19.

Auger, Daniel A., Xiaodong Zhong, Ernesta M. Meintjes, Frederick H. Epstein, & Bruce Spottiswoode. (2011). Quantifying right ventricular motion and strain using 3D cine DENSE MRI. Journal of Cardiovascular Magnetic Resonance. 13(S1). 1 indexed citations

20.

Auger, Daniel A., Gabe B. Bleeker, Victoria Delgado, et al.. (2010). Long term follow-up of heart failure patients treated with cardiac resynchronization therapy without baseline dyssynchrony. European Heart Journal. 31(10). 13–13. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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