Tobias Rydèn

3.4k total citations · 1 hit paper
55 papers, 1.9k citations indexed

About

Tobias Rydèn is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Tobias Rydèn has authored 55 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Radiology, Nuclear Medicine and Imaging, 14 papers in Artificial Intelligence and 9 papers in Molecular Biology. Recurrent topics in Tobias Rydèn's work include Medical Imaging Techniques and Applications (15 papers), Radiomics and Machine Learning in Medical Imaging (10 papers) and Bayesian Methods and Mixture Models (9 papers). Tobias Rydèn is often cited by papers focused on Medical Imaging Techniques and Applications (15 papers), Radiomics and Machine Learning in Medical Imaging (10 papers) and Bayesian Methods and Mixture Models (9 papers). Tobias Rydèn collaborates with scholars based in Sweden, United States and France. Tobias Rydèn's co-authors include Éric Moulines, Christian P. Robert, D. M. Titterington, Peter Bernhardt, Olivier Cappé, Ludger Overbeck, Martijn van Essen, Mattias Höglund, Vikram Krishnamurthy and Attila Frigyesi and has published in prestigious journals such as The Journal of Chemical Physics, Blood and Bioinformatics.

In The Last Decade

Tobias Rydèn

45 papers receiving 1.8k citations

Hit Papers

Inference in Hidden Markov Models 2010 2026 2015 2020 2010 100 200 300 400 500
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Inference in Hidden Markov Models
    2010 542 citations Éric Moulines, Tobias Rydèn et al. Springer eBooks profile →

Peers

Tobias Rydèn
Dennis D. Cox United States
Faming Liang United States
J. Sunil Rao United States
Maria L. Rizzo United States
Anna Maria Paganoni Italy
Kim‐Anh Do United States
Gabriel Lang United States
Peter Taylor Australia
Alex Gammerman United Kingdom
Garvesh Raskutti United States
Dennis D. Cox United States
Tobias Rydèn
Citations per year, relative to Tobias Rydèn Tobias Rydèn (= 1×) peers Dennis D. Cox

Countries citing papers authored by Tobias Rydèn

Since Specialization
Citations

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

Fields of papers citing papers by Tobias Rydèn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tobias Rydèn

This figure shows the co-authorship network connecting the top 25 collaborators of Tobias Rydèn. A scholar is included among the top collaborators of Tobias Rydèn 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 Tobias Rydèn. Tobias Rydèn 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
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Khan, Jehangir, et al.. (2024). Dosimetric implications of kidney anatomical volume changes in 177Lu-DOTATATE therapy. EJNMMI Physics. 11(1). 71–71.
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Hallqvist, Andreas, et al.. (2021). Optimizing the Schedule of PARP Inhibitors in Combination with 177Lu-DOTATATE: A Dosimetry Rationale. Biomedicines. 9(11). 1570–1570. 9 indexed citations
5.
Baum, Richard P., Aviral Singh, Harshad Kulkarni, et al.. (2021). First-in-Humans Application of 161Tb: A Feasibility Study Using 161Tb-DOTATOC. Journal of Nuclear Medicine. 62(10). 1391–1397. 59 indexed citations
6.
Rydèn, Tobias, et al.. (2020). Deep-Learning Generation of Synthetic Intermediate Projections Improves 177Lu SPECT Images Reconstructed with Sparsely Acquired Projections. Journal of Nuclear Medicine. 62(4). 528–535. 40 indexed citations
7.
Svensson, Johanna, Tobias Rydèn, Martijn van Essen, et al.. (2019). Bone Marrow Absorbed Doses and Correlations with Hematologic Response During 177Lu-DOTATATE Treatments Are Influenced by Image-Based Dosimetry Method and Presence of Skeletal Metastases. Journal of Nuclear Medicine. 60(10). 1406–1413. 54 indexed citations
8.
Rydèn, Tobias, et al.. (2018). Fast GPU-based Monte Carlo code for SPECT/CT reconstructions generates improved 177Lu images. EJNMMI Physics. 5(1). 1–1. 32 indexed citations
9.
Rydèn, Tobias, et al.. (2017). Segmentation of Whole-Body Images into Two Compartments in Model for Bone Marrow Dosimetry Increases the Correlation with Hematological Response in 177 Lu-DOTATATE Treatments. Cancer Biotherapy and Radiopharmaceuticals. 32(9). 335–343. 7 indexed citations
10.
Rydèn, Tobias, et al.. (2016). A novel planar image-based method for bone marrow dosimetry in 177Lu-DOTATATE treatment correlates with haematological toxicity. EJNMMI Physics. 3(1). 21–21. 41 indexed citations
11.
Lindgren, David, Attila Frigyesi, Sigurður Guðjónsson, et al.. (2010). Combined Gene Expression and Genomic Profiling Define Two Intrinsic Molecular Subtypes of Urothelial Carcinoma and Gene Signatures for Molecular Grading and Outcome. Cancer Research. 70(9). 3463–3472. 224 indexed citations
12.
Moulines, Éric, et al.. (2010). Inference in Hidden Markov Models. Springer eBooks. 654–654. 542 indexed citations breakdown →
13.
Lauss, Martin, Attila Frigyesi, Tobias Rydèn, & Mattias Höglund. (2010). Robust assignment of cancer subtypes from expression data using a uni-variate gene expression average as classifier. BMC Cancer. 10(1). 532–532. 7 indexed citations
14.
Rydèn, Tobias, et al.. (2008). Estimating the Total Rate of DNA Replication Using Branching Processes. Bulletin of Mathematical Biology. 70(8). 2177–2194. 11 indexed citations
15.
Rydèn, Tobias, et al.. (2008). Estimating the variation in S phase duration from flow cytometric histograms. Mathematical Biosciences. 213(1). 40–49. 5 indexed citations
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Rydèn, Tobias, et al.. (2007). Estimating the distribution of the G2 phase duration from flow cytometric histograms. Mathematical Biosciences. 211(1). 1–17. 6 indexed citations
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Andersson, Sofia, Tobias Rydèn, & Rolf Johansson. (2002). Linear filtering and state space representations of hidden Markov models. Lund University Publications (Lund University).
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Krishnamurthy, Vikram & Tobias Rydèn. (1999). Consistency and identifiability of autoregressive models with Markov regime. 539. 251–256.
20.
Rydèn, Tobias & D. M. Titterington. (1998). Computational Bayesian Analysis of Hidden Markov Models. Journal of Computational and Graphical Statistics. 7(2). 194–211. 24 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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