Debra McGivney

1.5k total citations
24 papers, 1.1k citations indexed

About

Debra McGivney is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Debra McGivney has authored 24 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Radiology, Nuclear Medicine and Imaging, 4 papers in Biomedical Engineering and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Debra McGivney's work include Advanced MRI Techniques and Applications (18 papers), Medical Imaging Techniques and Applications (8 papers) and MRI in cancer diagnosis (6 papers). Debra McGivney is often cited by papers focused on Advanced MRI Techniques and Applications (18 papers), Medical Imaging Techniques and Applications (8 papers) and MRI in cancer diagnosis (6 papers). Debra McGivney collaborates with scholars based in United States, Germany and Australia. Debra McGivney's co-authors include Mark A. Griswold, Dan Ma, Vikas Gulani, Yun Jiang, Yong Chen, Eric Y. Pierre, Haris Saybasili, Shivani Pahwa, Nicole Seiberlich and Simone Coppo and has published in prestigious journals such as Radiology, Magnetic Resonance in Medicine and IEEE Transactions on Medical Imaging.

In The Last Decade

Debra McGivney

23 papers receiving 1.1k citations

Peers

Debra McGivney

RNMI

AMPO

SPECT

BIOPH

Jesse Hamilton United States

Ives R. Levesque Canada

A Deshmane Germany

Himanshu Bhat United States

Simone Coppo Switzerland

Christakis Constantinides United States

Jacques A. den Boer Netherlands

Cynthia Wisnieff United States

Ananth J. Madhuranthakam United States

Jesse Hamilton United States

Debra McGivney

1.1k ×1.1

RNMI

99 ×0.8

227 ×2.0

AMPO

69 ×0.8

SPECT

37 ×0.6

BIOPH

Citations per year, relative to Debra McGivney Debra McGivney (= 1×) peers Jesse Hamilton

Countries citing papers authored by Debra McGivney

Since Specialization

Citations

This map shows the geographic impact of Debra McGivney'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 Debra McGivney with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Debra McGivney more than expected).

Fields of papers citing papers by Debra McGivney

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Debra McGivney. 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 Debra McGivney. The network helps show where Debra McGivney may publish in the future.

Co-authorship network of co-authors of Debra McGivney

This figure shows the co-authorship network connecting the top 25 collaborators of Debra McGivney. A scholar is included among the top collaborators of Debra McGivney 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 Debra McGivney. Debra McGivney 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

Hu, Siyuan, Richard J. Adams, Rasim Boyacıoğlu, et al.. (2024). Efficient pulse sequence design framework for high‐dimensional MR fingerprinting scans using systematic error index. Magnetic Resonance in Medicine. 92(4). 1600–1616.

Hu, Siyuan, Stephen P. Jordan, Rasim Boyacıoğlu, et al.. (2023). A fast MR fingerprinting simulator for direct error estimation and sequence optimization. Magnetic Resonance Imaging. 98. 105–114. 4 indexed citations

Dastmalchian, Sara, Debra McGivney, Dan Ma, et al.. (2020). Radiomic analysis of magnetic resonance fingerprinting in adult brain tumors. European Journal of Nuclear Medicine and Molecular Imaging. 48(3). 683–693. 37 indexed citations

Boyacıoğlu, Rasim, et al.. (2020). 3D magnetic resonance fingerprinting with quadratic RF phase. Magnetic Resonance in Medicine. 85(4). 2084–2094. 18 indexed citations

Poorman, Megan, Dan Ma, Debra McGivney, et al.. (2019). Magnetic resonance fingerprinting Part 1: Potential uses, current challenges, and recommendations. Journal of Magnetic Resonance Imaging. 51(3). 675–692. 59 indexed citations

Ma, Dan, Stephen E. Jones, A Deshmane, et al.. (2019). Development of high‐resolution 3D MR fingerprinting for detection and characterization of epileptic lesions. Journal of Magnetic Resonance Imaging. 49(5). 3 indexed citations

McGivney, Debra, Rasim Boyacıoğlu, Yun Jiang, et al.. (2019). Magnetic resonance fingerprinting review part 2: Technique and directions. Journal of Magnetic Resonance Imaging. 51(4). 993–1007. 44 indexed citations

Ma, Dan, Stephen E. Jones, A Deshmane, et al.. (2018). Development of high‐resolution 3D MR fingerprinting for detection and characterization of epileptic lesions. Journal of Magnetic Resonance Imaging. 49(5). 1333–1346. 75 indexed citations

Yang, Mingrui, Dan Ma, Yun Jiang, et al.. (2017). Low rank approximation methods for MR fingerprinting with large scale dictionaries. Magnetic Resonance in Medicine. 79(4). 2392–2400. 52 indexed citations

10.

Deshmane, A, Debra McGivney, Yichuan Jiang, Dan Ma, & Mark A. Griswold. (2017). Proton Density Mapping and Receiver Bias Correction for Absolute Quantification with MR Fingerprinting. MPG.PuRe (Max Planck Society). 2 indexed citations

11.

McGivney, Debra, A Deshmane, Yun Jiang, et al.. (2017). Bayesian estimation of multicomponent relaxation parameters in magnetic resonance fingerprinting. Magnetic Resonance in Medicine. 80(1). 159–170. 41 indexed citations

12.

Ma, Dan, Yun Jiang, Yong Chen, et al.. (2017). Fast 3D magnetic resonance fingerprinting for a whole‐brain coverage. Magnetic Resonance in Medicine. 79(4). 2190–2197. 117 indexed citations

13.

Yu, Alice, Chaitra Badve, Lee Ponsky, et al.. (2017). Development of a Combined MR Fingerprinting and Diffusion Examination for Prostate Cancer. Radiology. 283(3). 729–738. 126 indexed citations

14.

McGivney, Debra, et al.. (2017). Dictionary approach to partial volume estimation with MR Fingerprinting: Validation and application to brain tumor segmentation. MPG.PuRe (Max Planck Society). 2 indexed citations

15.

Deshmane, A, Debra McGivney, Chaitra Badve, et al.. (2016). Accurate Synthetic FLAIR Images Using Partial Volume Corrected MR Fingerprinting. MPG.PuRe (Max Planck Society). 7 indexed citations

16.

McGivney, Debra, A Deshmane, Yanping Jiang, Dianfu Ma, & Mark A. Griswold. (2015). Nonlinear Dimensionality Reduction for Magnetic Resonance Fingerprinting with Applications to Partial Volume. MPG.PuRe (Max Planck Society). 1 indexed citations

17.

Calvetti, Daniela, Paul J. Hadwin, Janne M. J. Huttunen, et al.. (2015). Artificial boundary conditions and domain truncation in electrical impedance tomography. Part I: Theory and preliminary results. Inverse Problems and Imaging. 9(3). 749–766. 13 indexed citations

18.

McGivney, Debra, Eric Y. Pierre, Dan Ma, et al.. (2014). SVD Compression for Magnetic Resonance Fingerprinting in the Time Domain. IEEE Transactions on Medical Imaging. 33(12). 2311–2322. 217 indexed citations

19.

McGivney, Debra, Daniela Calvetti, & Erkki Somersalo. (2012). Quantitative imaging with electrical impedance spectroscopy. Physics in Medicine and Biology. 57(22). 7289–7302. 9 indexed citations

20.

Calvetti, Daniela, Debra McGivney, & Erkki Somersalo. (2012). Left and right preconditioning for electrical impedance tomography with structural information. Inverse Problems. 28(5). 55015–55015. 14 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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