Lars Edenbrandt
About
Lars Edenbrandt is a scholar working on Radiology, Nuclear Medicine and Imaging, Cardiology and Cardiovascular Medicine and Pulmonary and Respiratory Medicine.
According to data from OpenAlex, Lars Edenbrandt has authored 182 papers receiving a total of 4.0k indexed citations (citations by other indexed papers that have themselves been cited), including 114 papers in Radiology, Nuclear Medicine and Imaging, 62 papers in Cardiology and Cardiovascular Medicine and 60 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Lars Edenbrandt's work include Cardiac Imaging and Diagnostics (56 papers), Medical Imaging Techniques and Applications (44 papers) and ECG Monitoring and Analysis (42 papers). Lars Edenbrandt is often cited by papers focused on Cardiac Imaging and Diagnostics (56 papers), Medical Imaging Techniques and Applications (44 papers) and ECG Monitoring and Analysis (42 papers). Lars Edenbrandt collaborates with scholars based in Sweden, Denmark and United States. Lars Edenbrandt's co-authors include Mattias Ohlsson, Carsten Peterson, Leif Sörnmo, Olle Pahlm, M. Lagerholm, Elin Trägårdh, May Sadik, Ralf Rittner, Reza Kaboteh and Olof Enqvist and has published in prestigious journals such as Circulation, Journal of Clinical Oncology and SHILAP Revista de lepidopterología.
In The Last Decade
Lars Edenbrandt
176 papers receiving 3.8k citations
Peers
Lars Edenbrandt
Lampros K. Michalis Greece
Mattias Ohlsson Sweden
R. Todd Hurst United States
Christopher B. Kendall United States
Ashish Sharma United States
Mozziyar Etemadi United States
Katerina Κ. Naka Greece
Zachi I. Attia United States
Zeynettin Akkus United States
Eric K. Oermann United States
Citations per year, relative to Lars Edenbrandt Lars Edenbrandt (= 1×)
peers
Lampros K. Michalis
Countries citing papers authored by Lars Edenbrandt
Since
Specialization
Citations
This map shows the geographic impact of Lars Edenbrandt'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 Lars Edenbrandt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lars Edenbrandt more than expected).
Fields of papers citing papers by Lars Edenbrandt
Since
Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences
This network shows the impact of papers produced by Lars Edenbrandt. 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 Lars Edenbrandt. The network helps show where Lars Edenbrandt may publish in the future.
Co-authorship network of co-authors of Lars Edenbrandt
This figure shows the co-authorship network connecting the top 25 collaborators of Lars Edenbrandt. A scholar is included among the top collaborators of Lars Edenbrandt 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 Lars Edenbrandt. Lars Edenbrandt is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
1.
Trägårdh, Elin, Johannes Ulén, Olof Enqvist, et al.. (2025). A fully automated AI-based method for tumour detection and quantification on [18F]PSMA-1007 PET–CT images in prostate cancer. EJNMMI Physics. 12(1). 78–78.
2.
Ying, Thomas, Pablo Borrelli, Lars Edenbrandt, et al.. (2024). AI-based fully automatic image analysis: Optimal abdominal and thoracic segmentation volumes for estimating total muscle volume on computed tomography scans. SHILAP Revista de lepidopterología. 10(2). 78–83.
3.
Minarik, David, et al.. (2024). High concordance of PET‐CT treatment response evaluation according to PERCIST 1.0 when comparing images reconstructed with OSEM vs. BSREM. Clinical Physiology and Functional Imaging. 45(1). e12907–e12907.
4.
Trägårdh, Elin, Johannes Ulén, Olof Enqvist, Lars Edenbrandt, & Måns Larsson. (2024). Improving sensitivity through data augmentation with synthetic lymph node metastases for AI‐based analysis of PSMA PET‐CT images. Clinical Physiology and Functional Imaging. 44(4). 332–339.
5.
Sadik, May, Sally F. Barrington, Elin Trägårdh, et al.. (2023). Metabolic tumour volume in Hodgkin lymphoma—A comparison between manual and AI‐based analysis. Clinical Physiology and Functional Imaging. 44(3). 220–227.
6.
Trägårdh, Elin, Olof Enqvist, Johannes Ulén, et al.. (2022). Freely Available, Fully Automated AI-Based Analysis of Primary Tumour and Metastases of Prostate Cancer in Whole-Body [18F]-PSMA-1007 PET-CT. Diagnostics. 12(9). 2101–2101.
7.
Trägårdh, Elin, et al.. (2022). Freely available artificial intelligence for pelvic lymph node metastases in PSMA PET-CT that performs on par with nuclear medicine physicians. European Journal of Nuclear Medicine and Molecular Imaging. 49(10). 3412–3418.
8.
Morris, Michael, Peter C. Grayson, Michael T. Collins, et al.. (2021). Artificial Intelligence in Vascular-PET. PET Clinics. 17(1). 95–113.
9.
Trägårdh, Elin, Pablo Borrelli, Reza Kaboteh, et al.. (2020). RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology. EJNMMI Physics. 7(1). 51–51.
10.
Borrelli, Pablo, Måns Larsson, Johannes Ulén, et al.. (2020). Artificial intelligence‐based detection of lymph node metastases by PET/CT predicts prostate cancer‐specific survival. Clinical Physiology and Functional Imaging. 41(1). 62–67.
11.
Borrelli, Pablo, Mads Hvid Poulsen, Oke Gerke, et al.. (2019). Artificial intelligence‐based versus manual assessment of prostate cancer in the prostate gland: a method comparison study. Clinical Physiology and Functional Imaging. 39(6). 399–406.
12.
Sadik, May, Reza Kaboteh, Pablo Borrelli, et al.. (2019). Deep learning‐based quantification of PET/CT prostate gland uptake: association with overall survival. Clinical Physiology and Functional Imaging. 40(2). 106–113.
13.
Borrelli, Pablo, Olof Enqvist, Johannes Ulén, et al.. (2018). Artificial Intelligence Based Method for Automated PET/CT Measurements of Prostate Gland Volume and Choline Uptake. University of Southern Denmark Research Portal (University of Southern Denmark).
14.
Sadik, May, Reza Kaboteh, Olof Enqvist, et al.. (2017). Automated 3D segmentation of the prostate gland in CT images - a first step towards objective measurements of prostate uptake in PET and SPECT images. University of Southern Denmark Research Portal (University of Southern Denmark).
15.
Höglund, Peter, et al.. (2012). Semi-automatic analysis of standard uptake values in serial PET/CT studies in patients with lung cancer and lymphoma. BMC Medical Imaging. 12(1). 6–6.
16.
Riklund, Katrine, Susanna Jakobson Mo, Anne Larsson, et al.. (2010). Semi-Quantification of DAT SPECT Images - Survey of Normal Reference Limits Used at Different Hospitals. European Journal of Nuclear Medicine and Molecular Imaging. 37. 400–400.
17.
Sadik, May, et al.. (2009). Improved Classifications of Planar Whole-Body Bone Scans Using a Computer-Assisted Diagnosis System: A Multicenter, Multiple-Reader, Multiple-Case Study. Journal of Nuclear Medicine. 50(3). 368–375.
18.
Sadik, May, et al.. (2008). Computer-Assisted Interpretation of Planar Whole-Body Bone Scans. Journal of Nuclear Medicine. 49(12). 1958–1965.
19.
Björk, Jonas, et al.. (2006). Comparison between neural networks and multiple logistic regression to predict acute coronary syndrome in the emergency room. Artificial Intelligence in Medicine. 38(3). 305–318.
20.
Hedén, Bo, Mattias Ohlsson, Ralf Rittner, et al.. (1996). Agreement Between Artificial Neural Networks and Experienced Electrocardiographer on Electrocardiographic Diagnosis of Healed Myocardial Infarction11This study was supported by grants from the Swedish Medical Research Council (B95-14X-09893-04B), Stockholm; Swedish National Board for Industrial and Technical Development, Stockholm; the Faculty of Medicine at Lund University, Lund, Sweden; the Göran Gustafsson Foundation for Research in National Science and Medicine, Stockholm; and the Swedish Natural Science Research Council, Stockholm.. Journal of the American College of Cardiology. 28(4). 1012–1016.
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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