Jens Sjölund

1.0k total citations
24 papers, 481 citations indexed

About

Jens Sjölund is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering. According to data from OpenAlex, Jens Sjölund has authored 24 papers receiving a total of 481 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Radiology, Nuclear Medicine and Imaging, 6 papers in Computer Vision and Pattern Recognition and 6 papers in Electrical and Electronic Engineering. Recurrent topics in Jens Sjölund's work include Advanced Neuroimaging Techniques and Applications (6 papers), Advanced MRI Techniques and Applications (5 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). Jens Sjölund is often cited by papers focused on Advanced Neuroimaging Techniques and Applications (6 papers), Advanced MRI Techniques and Applications (5 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). Jens Sjölund collaborates with scholars based in Sweden, Germany and Pakistan. Jens Sjölund's co-authors include Hans Knutsson, Filip Szczepankiewicz, Daniel Forsberg, Manne Andersson, Markus Nilsson, Carl‐Fredrik Westin, Daniel Topgaard, Thomas B. Schön, Ziwei Luo and Fredrik Gustafsson and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and NeuroImage.

In The Last Decade

Jens Sjölund

21 papers receiving 473 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jens Sjölund Sweden 9 347 97 92 57 41 24 481
Éric Van Reeth France 8 184 0.5× 36 0.4× 90 1.0× 47 0.8× 10 0.2× 18 312
Eric K. Gibbons United States 5 281 0.8× 11 0.1× 104 1.1× 90 1.6× 9 0.2× 7 393
Fukai Toyofuku Japan 10 148 0.4× 39 0.4× 79 0.9× 66 1.2× 6 0.1× 53 310
Debashish Pal United States 13 351 1.0× 176 1.8× 18 0.2× 207 3.6× 7 0.2× 38 439
Yan Xia China 10 211 0.6× 83 0.9× 26 0.3× 146 2.6× 14 0.3× 41 292
Juan E. Ortuño Spain 13 229 0.7× 200 2.1× 23 0.3× 63 1.1× 4 0.1× 37 381
Mahmut Yurt United States 6 296 0.9× 26 0.3× 202 2.2× 96 1.7× 9 0.2× 17 497
Muzaffer Özbey Türkiye 7 294 0.8× 21 0.2× 209 2.3× 89 1.6× 10 0.2× 14 532
Junshen Xu United States 8 264 0.8× 39 0.4× 64 0.7× 77 1.4× 70 1.7× 13 360
Yohan Jun South Korea 10 459 1.3× 33 0.3× 81 0.9× 144 2.5× 6 0.1× 21 582

Countries citing papers authored by Jens Sjölund

Since Specialization
Citations

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

Fields of papers citing papers by Jens Sjölund

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jens Sjölund

This figure shows the co-authorship network connecting the top 25 collaborators of Jens Sjölund. A scholar is included among the top collaborators of Jens Sjölund 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 Jens Sjölund. Jens Sjölund 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.
Sjölund, Jens, et al.. (2025). Machine learning for in-situ composition mapping in a self-driving magnetron sputtering system. Materials & Design. 260. 115087–115087. 1 indexed citations
2.
Zhao, Zheng, Ziwei Luo, Jens Sjölund, & Thomas B. Schön. (2025). Conditional sampling within generative diffusion models. Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences. 383(2299). 20240329–20240329. 1 indexed citations
3.
Sjölund, Jens, et al.. (2025). Accelerating aqueous electrolyte design with automated full-cell battery experimentation and Bayesian optimization. Cell Reports Physical Science. 6(5). 102548–102548. 5 indexed citations
4.
Luo, Ziwei, et al.. (2025). Taming diffusion models for image restoration: a review. Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences. 383(2299). 20240358–20240358. 3 indexed citations
5.
Luo, Ziwei, Per Mattsson, Thomas B. Schön, Jens Sjölund, & Ruoqi Zhang. (2024). Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning. 98871–98897.
6.
Johannsmann, Diethelm, et al.. (2024). Exploring Metal Electroplating for Energy Storage by Quartz Crystal Microbalance: A Review. SHILAP Revista de lepidopterología. 3(9). 1 indexed citations
7.
Luo, Ziwei, Fredrik Gustafsson, Zheng Zhao, Jens Sjölund, & Thomas B. Schön. (2024). Photo-Realistic Image Restoration in the Wild with Controlled Vision-Language Models. 6641–6651. 7 indexed citations
8.
Zhao, Zheng, Simo Särkkä, Jens Sjölund, & Thomas B. Schön. (2023). Probabilistic Estimation of Instantaneous Frequencies of Chirp Signals. IEEE Transactions on Signal Processing. 71. 461–476. 2 indexed citations
9.
Zhang, Leiting, Jens Sjölund, Xu Hou, et al.. (2023). Automated electrolyte formulation and coin cell assembly for high-throughput lithium-ion battery research. Digital Discovery. 2(3). 799–808. 19 indexed citations
10.
Luo, Ziwei, Fredrik Gustafsson, Zheng Zhao, Jens Sjölund, & Thomas B. Schön. (2023). Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion Models. 1680–1691. 67 indexed citations
11.
Szczepankiewicz, Filip & Jens Sjölund. (2021). Cross-term-compensated gradient waveform design for tensor-valued diffusion MRI. Journal of Magnetic Resonance. 328. 106991–106991. 11 indexed citations
12.
Szczepankiewicz, Filip, Jens Sjölund, Erica Dall’Armellina, et al.. (2020). Motion‐compensated gradient waveforms for tensor‐valued diffusion encoding by constrained numerical optimization. Magnetic Resonance in Medicine. 85(4). 2117–2126. 25 indexed citations
13.
Szczepankiewicz, Filip, Jens Sjölund, Freddy Ståhlberg, Jimmy Lätt, & Markus Nilsson. (2019). Tensor-valued diffusion encoding for diffusional variance decomposition (DIVIDE): Technical feasibility in clinical MRI systems. PLoS ONE. 14(3). e0214238–e0214238. 69 indexed citations
14.
Sjölund, Jens, et al.. (2018). Bayesian uncertainty quantification in linear models for diffusion MRI. NeuroImage. 175. 272–285. 10 indexed citations
15.
Sjölund, Jens, Anders Eklund, Evren Özarslan, & Hans Knutsson. (2017). Gaussian process regression can turn non-uniform and undersampled diffusion MRI data into diffusion spectrum imaging. arXiv (Cornell University). 778–782. 4 indexed citations
16.
Sjölund, Jens, et al.. (2015). Dose-volume histogram prediction using density estimation. Physics in Medicine and Biology. 60(17). 6923–6936. 32 indexed citations
17.
Sjölund, Jens, Filip Szczepankiewicz, Markus Nilsson, et al.. (2015). Constrained optimization of gradient waveforms for generalized diffusion encoding. Journal of Magnetic Resonance. 261. 157–168. 93 indexed citations
18.
Sjölund, Jens, Daniel Forsberg, Manne Andersson, & Hans Knutsson. (2015). Generating patient specific pseudo-CT of the head from MR using atlas-based regression. Physics in Medicine and Biology. 60(2). 825–839. 119 indexed citations
19.
Sjölund, Jens, et al.. (2014). Skull Segmentation in MRI by a Support Vector Machine Combining Local and Global Features. KTH Publication Database DiVA (KTH Royal Institute of Technology). 3274–3279. 8 indexed citations
20.
Sjölund, Jens. (2012). Dose planning from MRI using machine learning for automatic segmentation of skull and air. KTH Publication Database DiVA (KTH Royal Institute of Technology).

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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