Sarah Englander

850 total citations
21 papers, 673 citations indexed

About

Sarah Englander is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Sarah Englander has authored 21 papers receiving a total of 673 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Radiology, Nuclear Medicine and Imaging, 7 papers in Artificial Intelligence and 4 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Sarah Englander's work include MRI in cancer diagnosis (15 papers), Radiomics and Machine Learning in Medical Imaging (9 papers) and AI in cancer detection (7 papers). Sarah Englander is often cited by papers focused on MRI in cancer diagnosis (15 papers), Radiomics and Machine Learning in Medical Imaging (9 papers) and AI in cancer detection (7 papers). Sarah Englander collaborates with scholars based in United States, Germany and Russia. Sarah Englander's co-authors include Mitchell D. Schnall, Mark Rosen, Shannon C. Agner, Anant Madabhushi, Dinggang Shen, Yuanjie Zheng, Linda White Nunes, Michael D. Feldman, Paul J. Zhang and Carolyn Mies and has published in prestigious journals such as Cancer, Radiology and Magnetic Resonance in Medicine.

In The Last Decade

Sarah Englander

21 papers receiving 660 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sarah Englander United States 13 561 181 77 63 53 21 673
Masoumeh Gity Iran 14 279 0.5× 121 0.7× 91 1.2× 51 0.8× 63 1.2× 67 511
Ayelet Akselrod-Ballin Israel 11 228 0.4× 181 1.0× 162 2.1× 45 0.7× 52 1.0× 20 516
Michael R. Harowicz United States 10 533 1.0× 349 1.9× 37 0.5× 66 1.0× 61 1.2× 19 697
Hanna Piotrzkowska‐Wróblewska Poland 11 330 0.6× 256 1.4× 49 0.6× 21 0.3× 100 1.9× 30 433
Jiejie Zhou China 12 394 0.7× 246 1.4× 23 0.3× 85 1.3× 39 0.7× 31 562
Ulf Neuberger Germany 10 492 0.9× 67 0.4× 79 1.0× 142 2.3× 65 1.2× 15 761
Spiros Kostopoulos Greece 14 306 0.5× 249 1.4× 128 1.7× 28 0.4× 115 2.2× 67 617
Deepa Sheth United States 10 334 0.6× 228 1.3× 17 0.2× 91 1.4× 49 0.9× 23 483
Hadi Tadayyon Canada 15 516 0.9× 224 1.2× 44 0.6× 44 0.7× 273 5.2× 33 670
Etsuo Takada Japan 11 263 0.5× 192 1.1× 65 0.8× 65 1.0× 120 2.3× 33 468

Countries citing papers authored by Sarah Englander

Since Specialization
Citations

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

Fields of papers citing papers by Sarah Englander

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sarah Englander

This figure shows the co-authorship network connecting the top 25 collaborators of Sarah Englander. A scholar is included among the top collaborators of Sarah Englander 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 Sarah Englander. Sarah Englander is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Boss, Michael A., Bradley S. Snyder, Eun‐Hee Kim, et al.. (2022). Repeatability and Reproducibility Assessment of the Apparent Diffusion Coefficient in the Prostate: A Trial of the ECOG‐ACRIN Research Group (ACRIN 6701). Journal of Magnetic Resonance Imaging. 56(3). 668–679. 12 indexed citations
2.
3.
Ou, Yangming, Susan P. Weinstein, Emily F. Conant, et al.. (2014). Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy. Magnetic Resonance in Medicine. 73(6). 2343–2356. 26 indexed citations
4.
Ciunci, Christine, Rodolfo F. Perini, Anjali Narayan Avadhani, et al.. (2013). Phase 1 and pharmacodynamic trial of everolimus in combination with cetuximab in patients with advanced cancer. Cancer. 120(1). 77–85. 21 indexed citations
5.
Yu, Jiangsheng, et al.. (2011). Automatic coil selection for streak artifact reduction in radial MRI. Magnetic Resonance in Medicine. 67(2). 470–476. 35 indexed citations
6.
Agner, Shannon C., et al.. (2011). Spectral embedding based active contour (SEAC): application to breast lesion segmentation on DCE-MRI. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7963. 796305–796305. 5 indexed citations
7.
Agner, Shannon C., Salil Soman, Kathleen M. Thomas, et al.. (2010). Textural Kinetics: A Novel Dynamic Contrast-Enhanced (DCE)-MRI Feature for Breast Lesion Classification. Journal of Digital Imaging. 24(3). 446–463. 96 indexed citations
8.
Zheng, Yuanjie, Sarah Englander, Evangelia I. Zacharaki, et al.. (2009). STEP: Spatiotemporal enhancement pattern for MR‐based breast tumor diagnosis. Medical Physics. 36(7). 3192–3204. 56 indexed citations
9.
Agner, Shannon C., Jun Xu, Hussain Fatakdawala, et al.. (2009). Segmentation and classification of triple negative breast cancers using DCE-MRI. 1227–1230. 13 indexed citations
10.
Xue, Zhong, et al.. (2008). Improving Parenchyma Segmentation by Simultaneous Estimation of Tissue Property T 1 Map and Group-Wise Registration of Inversion Recovery MR Breast Images. Lecture notes in computer science. 11(Pt 1). 342–350. 6 indexed citations
11.
Zheng, Yuanjie, Jingyi Yu, Chandra Kambhamettu, et al.. (2007). De-enhancing the Dynamic Contrast-Enhanced Breast MRI for Robust Registration. Lecture notes in computer science. 10(Pt 1). 933–941. 25 indexed citations
12.
Zheng, Yuanjie, et al.. (2007). Segmentation and Classification of Breast Tumor Using Dynamic Contrast-Enhanced MR Images. Lecture notes in computer science. 10(Pt 2). 393–401. 32 indexed citations
13.
Ou, Yangming, et al.. (2007). SIMULTANEOUS ESTIMATION AND SEGMENTATION OF T1 MAP FOR BREAST PARENCHYMA MEASUREMENT. 332–335. 10 indexed citations
14.
Boston, Raymond C., Mitchell D. Schnall, Sarah Englander, J. Richard Landis, & Peter J. Moate. (2005). Estimation of the content of fat and parenchyma in breast tissue using MRI T1 histograms and phantoms. Magnetic Resonance Imaging. 23(4). 591–599. 37 indexed citations
15.
Nunes, Linda White, et al.. (2002). Optimal post‐contrast timing of breast MR image acquisition for architectural feature analysis. Journal of Magnetic Resonance Imaging. 16(1). 42–50. 9 indexed citations
16.
Miki, Atsushi, Jonathan Raz, Sarah Englander, et al.. (2001). Visual Activation in Functional Magnetic Resonance Imaging at Very High Field (4 Tesla). Journal of Neuro-Ophthalmology. 21(1). 8–11. 14 indexed citations
17.
Schnall, Mitchell D., et al.. (2001). A Combined Architectural and Kinetic Interpretation Model for Breast MR Images. Academic Radiology. 8(7). 591–597. 64 indexed citations
18.
Miki, Atsushi, Grant T. Liu, Sarah Englander, et al.. (2001). Functional Magnetic Resonance Imaging of Eye Dominance at 4 Tesla. Ophthalmic Research. 33(5). 276–282. 10 indexed citations
19.
Miki, Atsushi, Grant T. Liu, Edward J. Modestino, et al.. (2001). Functional magnetic resonance imaging of lateral geniculate nucleus and visual cortex at 4 Tesla in a patient with homonymous hemianopia. Neuro-Ophthalmology. 25(3). 109–114. 5 indexed citations
20.
Englander, Sarah, et al.. (1997). Diffusion imaging of human breast. NMR in Biomedicine. 10(7). 348–352. 67 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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