David M. Hough

4.5k total citations
65 papers, 3.3k citations indexed

About

David M. Hough is a scholar working on Radiology, Nuclear Medicine and Imaging, Surgery and Biomedical Engineering. According to data from OpenAlex, David M. Hough has authored 65 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Radiology, Nuclear Medicine and Imaging, 18 papers in Surgery and 18 papers in Biomedical Engineering. Recurrent topics in David M. Hough's work include Advanced X-ray and CT Imaging (18 papers), Radiation Dose and Imaging (16 papers) and Pancreatic and Hepatic Oncology Research (10 papers). David M. Hough is often cited by papers focused on Advanced X-ray and CT Imaging (18 papers), Radiation Dose and Imaging (16 papers) and Pancreatic and Hepatic Oncology Research (10 papers). David M. Hough collaborates with scholars based in United States, Germany and United Kingdom. David M. Hough's co-authors include Joel G. Fletcher, Jeff L. Fidler, Cynthia H. McCollough, Suresh T. Chari, James E. Huprich, Lifeng Yu, Naoki Takahashi, Hassan Siddiki, Sudhakar K. Venkatesh and Dushyant V. Sahani and has published in prestigious journals such as Journal of Clinical Oncology, Gastroenterology and Hepatology.

In The Last Decade

David M. Hough

63 papers receiving 3.2k citations

Peers

David M. Hough
Wolfgang J. Köstler Austria
Ahmed Ba‐Ssalamah Austria
Alexis Kelekis Greece
Yeon Hyeon Choe South Korea
Francesco Fanfani Italy
Harry Quon United States
G. Brandon Gunn United States
Pier Paolo Mainenti Italy
Medhat Osman United States
Oliver Dudeck Germany
Wolfgang J. Köstler Austria
David M. Hough
Citations per year, relative to David M. Hough David M. Hough (= 1×) peers Wolfgang J. Köstler

Countries citing papers authored by David M. Hough

Since Specialization
Citations

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

Fields of papers citing papers by David M. Hough

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David M. Hough. 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 David M. Hough. The network helps show where David M. Hough may publish in the future.

Co-authorship network of co-authors of David M. Hough

This figure shows the co-authorship network connecting the top 25 collaborators of David M. Hough. A scholar is included among the top collaborators of David M. Hough 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 David M. Hough. David M. Hough 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.
Coppola, Danielle, Rama Melkote, Jaskaran Singh, et al.. (2025). Efficacy and Safety of Paliperidone Extended Release 1.5 mg/day—A Double-blind, Placebo- and Active-Controlled, Study in the Treatment of Patients with Schizophrenia. Psychopharmacology Bulletin. 44(2). 54–72. 3 indexed citations
2.
Hough, David M., Alice R. Mao, Michael G. Aman, et al.. (2023). Randomized clinical trial of low dose suramin intravenous infusions for treatment of autism spectrum disorder. Annals of General Psychiatry. 22(1). 45–45. 4 indexed citations
3.
Marya, Neil B., Suresh T. Chari, Ferga C. Gleeson, et al.. (2020). Utilisation of artificial intelligence for the development of an EUS-convolutional neural network model trained to enhance the diagnosis of autoimmune pancreatitis. Gut. 70(7). 1335–1344. 106 indexed citations
4.
Hecht, Elizabeth M., Gaurav Khatri, Desiree E. Morgan, et al.. (2020). Intraductal papillary mucinous neoplasm (IPMN) of the pancreas: recommendations for Standardized Imaging and Reporting from the Society of Abdominal Radiology IPMN disease focused panel. Abdominal Radiology. 46(4). 1586–1606. 27 indexed citations
5.
Mohammadinejad, Payam, Eric C. Ehman, Sudhakar K. Venkatesh, et al.. (2019). Prior iterative reconstruction (PIR) to lower radiation dose and preserve radiologist performance for multiphase liver CT: a multi-reader pilot study. Abdominal Radiology. 45(1). 45–54. 6 indexed citations
6.
Fletcher, Joel G., Jeff L. Fidler, Sudhakar K. Venkatesh, et al.. (2018). Observer Performance with Varying Radiation Dose and Reconstruction Methods for Detection of Hepatic Metastases. Radiology. 289(2). 455–464. 47 indexed citations
7.
Fletcher, Joel G., Lifeng Yu, Jeff L. Fidler, et al.. (2017). Estimation of Observer Performance for Reduced Radiation Dose Levels in CT. Academic Radiology. 24(7). 876–890. 39 indexed citations
8.
Kambadakone, Avinash, Atif Zaheer, Ott Le, et al.. (2017). Multi-institutional survey on imaging practice patterns in pancreatic ductal adenocarcinoma. Abdominal Radiology. 43(2). 245–252. 7 indexed citations
9.
Venkatesh, Sudhakar K., Tiffany Hennedige, Geoffrey B. Johnson, David M. Hough, & Joel G. Fletcher. (2016). Imaging patterns and focal lesions in fatty liver: a pictorial review. Abdominal Radiology. 42(5). 1374–1392. 20 indexed citations
10.
Wells, Michael, Sudhakar K. Venkatesh, Vishal S. Chandan, et al.. (2015). Biphenotypic hepatic tumors: imaging findings and review of literature. Abdominal Imaging. 40(7). 2293–2305. 39 indexed citations
11.
Ehman, Eric C., Lifeng Yu, Armando Manduca, et al.. (2014). Methods for Clinical Evaluation of Noise Reduction Techniques in Abdominopelvic CT. Radiographics. 34(4). 849–862. 112 indexed citations
12.
Al-Hawary, Mahmoud M., Isaac R. Francis, Suresh T. Chari, et al.. (2013). Pancreatic Ductal Adenocarcinoma Radiology Reporting Template: Consensus Statement of the Society of Abdominal Radiology and the American Pancreatic Association. Radiology. 270(1). 248–260. 338 indexed citations
13.
Fidler, Jeff L. & David M. Hough. (2011). Hepatocyte-specific magnetic resonance Imaging contrast agents. Hepatology. 53(2). 678–682. 29 indexed citations
14.
Fletcher, Joel G., Jeff L. Fidler, David M. Hough, et al.. (2010). Pilot multi-reader study demonstrating potential for dose reduction in dual energy hepatic CT using non-linear blending of mixed kV image datasets. European Radiology. 21(3). 644–652. 20 indexed citations
15.
Fletcher, Joel G., Naoki Takahashi, Robert P. Hartman, et al.. (2009). Dual-Energy and Dual-Source CT: Is There a Role in the Abdomen and Pelvis?. Radiologic Clinics of North America. 47(1). 41–57. 132 indexed citations
16.
Oxentenko, Amy S., Robert A. Vierkant, Darrell S. Pardi, et al.. (2007). Colorectal cancer screening perceptions and practices: Results from a national survey of gastroenterology, surgery and radiology trainees. Journal of Cancer Education. 22(4). 219–226. 5 indexed citations
17.
Gluecker, Thomas M., Eric E. Williamson, Joel G. Fletcher, et al.. (2002). Diseases of the cecum: a CT pictorial review. European Radiology. 13(S06). L51–L61. 12 indexed citations
18.
Bissett, Ian, David M. Hough, Brett R. Cowan, et al.. (2001). Identification of the fascia propria by magnetic resonance imaging and its relevance to preoperative assessment of rectal cancer. Diseases of the Colon & Rectum. 44(2). 259–265. 87 indexed citations
19.
Low, Vincent H. S., Laura E. Heyneman, & David M. Hough. (1998). Inflammatory pseudotumour of the post‐bulbar duodenum. PubMed. 42(4). 370–373. 2 indexed citations
20.

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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