Sandy Napel

23.0k total citations
190 papers, 9.8k citations indexed

About

Sandy Napel is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Computer Vision and Pattern Recognition. According to data from OpenAlex, Sandy Napel has authored 190 papers receiving a total of 9.8k indexed citations (citations by other indexed papers that have themselves been cited), including 116 papers in Radiology, Nuclear Medicine and Imaging, 62 papers in Pulmonary and Respiratory Medicine and 53 papers in Computer Vision and Pattern Recognition. Recurrent topics in Sandy Napel's work include Radiomics and Machine Learning in Medical Imaging (62 papers), Medical Imaging Techniques and Applications (38 papers) and Advanced X-ray and CT Imaging (32 papers). Sandy Napel is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (62 papers), Medical Imaging Techniques and Applications (38 papers) and Advanced X-ray and CT Imaging (32 papers). Sandy Napel collaborates with scholars based in United States, Canada and Türkiye. Sandy Napel's co-authors include Geoffrey D. Rubin, R. Brooke Jeffrey, Daniel L. Rubin, David S. Paik, Michael D. Dake, Olivier Gevaert, Christopher F. Beaulieu, Ann N. Leung, Charles H. McDonnell and Gary H. Glover and has published in prestigious journals such as SHILAP Revista de lepidopterología, Cancer and Cancer Research.

In The Last Decade

Sandy Napel

184 papers receiving 9.5k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Sandy Napel 5.9k 3.6k 1.9k 1.6k 1.4k 190 9.8k
Issam El Naqa 8.3k 1.4× 6.0k 1.7× 2.4k 1.3× 687 0.4× 1.5k 1.0× 349 14.9k
Bradley J. Erickson 4.5k 0.8× 1.5k 0.4× 1.4k 0.8× 1.4k 0.9× 506 0.4× 280 10.4k
Dimitris Visvikis 9.4k 1.6× 3.2k 0.9× 2.5k 1.4× 592 0.4× 1.4k 1.0× 377 11.4k
Clare M. Tempany 8.0k 1.4× 8.1k 2.3× 4.2k 2.3× 1.9k 1.2× 818 0.6× 273 18.7k
Ahmed Hosny 7.4k 1.3× 2.8k 0.8× 2.3k 1.2× 313 0.2× 1.1k 0.8× 50 10.1k
Zaiyi Liu 6.5k 1.1× 2.6k 0.7× 1.2k 0.7× 558 0.4× 1.8k 1.3× 265 8.6k
Joseph O. Deasy 9.8k 1.7× 8.6k 2.4× 1.9k 1.0× 591 0.4× 1.7k 1.2× 453 17.2k
Di Dong 7.3k 1.2× 4.0k 1.1× 1.7k 0.9× 302 0.2× 1.6k 1.1× 199 9.5k
Carlos A. Perez 2.3k 0.4× 4.2k 1.2× 1.1k 0.6× 1.1k 0.7× 2.1k 1.5× 337 11.9k
G Marchal 4.1k 0.7× 1.5k 0.4× 2.2k 1.2× 2.2k 1.4× 494 0.4× 225 10.5k

Countries citing papers authored by Sandy Napel

Since Specialization
Citations

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

Fields of papers citing papers by Sandy Napel

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sandy Napel

This figure shows the co-authorship network connecting the top 25 collaborators of Sandy Napel. A scholar is included among the top collaborators of Sandy Napel 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 Sandy Napel. Sandy Napel 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.
Selby, Heather M., Pritam Mukherjee, Sachin B. Malik, et al.. (2023). Performance of alternative manual and automated deep learning segmentation techniques for the prediction of benign and malignant lung nodules. Journal of Medical Imaging. 10(4). 44006–44006.
2.
Sá, Rui Carlos, Yuntong Bai, Sandy Napel, et al.. (2023). Machine learning with multimodal data for COVID-19. Heliyon. 9(7). e17934–e17934. 11 indexed citations
3.
Ogunleye, Adeyemi A., Peter Deptula, Suzanne Inchauste, et al.. (2020). The utility of three-dimensional models in complex microsurgical reconstruction. Archives of Plastic Surgery. 47(5). 428–434. 19 indexed citations
4.
Mukherjee, Pritam, Mu Zhou, Edward Lee, et al.. (2020). A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomography image datasets. Nature Machine Intelligence. 2(5). 274–282. 66 indexed citations
5.
Zhang, Weiruo, Mājid Shafiq, Mehran Jamali, et al.. (2018). GFPT2 -Expressing Cancer-Associated Fibroblasts Mediate Metabolic Reprogramming in Human Lung Adenocarcinoma. Cancer Research. 78(13). 3445–3457. 87 indexed citations
6.
Zhou, Mu, Jacob G. Scott, Baishali Chaudhury, et al.. (2017). Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches. American Journal of Neuroradiology. 39(2). 208–216. 272 indexed citations
7.
Hoogi, Assaf, Christopher F. Beaulieu, Guilherme Moura Cunha, et al.. (2017). Adaptive Local Window for Level Set Segmentation of CT and MRI Liver Lesions. PubMed Central. 61 indexed citations
8.
Kurtz, Camille, Christopher F. Beaulieu, Sandy Napel, & Daniel L. Rubin. (2014). A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations. Journal of Biomedical Informatics. 49. 227–244. 21 indexed citations
9.
Nair, Viswam S., Olivier Gevaert, Guido Davidzon, et al.. (2012). Prognostic PET 18F-FDG Uptake Imaging Features Are Associated with Major Oncogenomic Alterations in Patients with Resected Non–Small Cell Lung Cancer. Cancer Research. 72(15). 3725–3734. 103 indexed citations
10.
Napel, Sandy & Daniel L. Rubin. (2010). Imaging Informatics: Toward Capturing and Processing Semantic Information in Radiology Images. Yearbook of Medical Informatics. 19(1). 34–42. 10 indexed citations
11.
12.
Pu, Jiantao, Justus E. Roos, Chin A Yi, et al.. (2008). Adaptive border marching algorithm: Automatic lung segmentation on chest CT images. Computerized Medical Imaging and Graphics. 32(6). 452–462. 157 indexed citations
13.
Sherbondy, Anthony J., Robert F. Dougherty, Sandy Napel, & Brian A. Wandell. (2008). Identifying the human optic radiation using diffusion imaging and fiber tractography. Journal of Vision. 8(10). 12–12. 131 indexed citations
14.
Olcott, Eric W., F. Graham Sommer, & Sandy Napel. (1997). Accuracy of detection and measurement of renal calculi: in vitro comparison of three-dimensional spiral CT, radiography, and nephrotomography.. Radiology. 204(1). 19–25. 92 indexed citations
15.
Napel, Sandy, et al.. (1996). Fast sliding thin slab volume visualization. 79–86. 12 indexed citations
16.
Sumanaweera, Thilaka S., Gary H. Glover, Paul F. Hemler, et al.. (1995). Mr geometric distortion correction for improved frame‐based stereotaxic target localization accuracy. Magnetic Resonance in Medicine. 34(1). 106–113. 39 indexed citations
17.
Sumanaweera, Thilaka S., et al.. (1994). Quantifying MRI geometric distortion in tissue. Magnetic Resonance in Medicine. 31(1). 40–47. 103 indexed citations
18.
Rubin, Geoffrey D., Robert J. Herfkens, Norbert J. Pelc, et al.. (1994). Single Breath-Hold Pulmonary Magnetic Resonance Angiography. Investigative Radiology. 29(8). 766–772. 19 indexed citations
19.
Rubin, Geoffrey D., Michael D. Dake, Sandy Napel, Philip J. Walker, & R. Brooke Jeffrey. (1992). Preoperative and postoperative assessment for abdominal vascular surgery - Spiral CT angiography as an alternative to arteriography. Radiology. 185. 181–181. 7 indexed citations
20.
Rutt, Brian K. & Sandy Napel. (1991). Magnetic resonance techniques for blood-flow measurement and vascular imaging.. PubMed. 42(1). 21–30. 6 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Issam El Naqa Breakdown of academic impact, for papers by Bradley J. Erickson Breakdown of academic impact, for papers by Dimitris Visvikis Breakdown of academic impact, for papers by Clare M. Tempany Breakdown of academic impact, for papers by Ahmed Hosny Breakdown of academic impact, for papers by Zaiyi Liu Breakdown of academic impact, for papers by Joseph O. Deasy Breakdown of academic impact, for papers by Di Dong Breakdown of academic impact, for papers by Carlos A. Perez Breakdown of academic impact, for papers by G Marchal
Rankless by CCL
2026