Research output per year
Research output per year
Personal profile
Research interests
My main research focus lies in the application of novel machine learning methods to medical images affected by involuntary physiological motion such as respiration.
I am particularly interested in analysing and modelling motion in time series of MR and simultaneous PET/MR images using nonlinear dimensionality reduction techniques.
Biographical details
I was awarded a B.Sc. in Electrical Engineering and Information Technology and a M.Sc. in Biomedical Engineering from the Federal Technical Institute (ETH) in Zurich, Switzerland. Since May 2012 I have been working as a PhD student at King's College London in the Division of Imaging Sciences and Biomedical Engineering.
Research interests (short)
Analysis of medical images affected by physiological motion using novel machine learning methods.
Email: christian.baumgartner [at] kcl.ac.uk
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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- 1 Similar Profiles
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Automated quantification of myocardial tissue characteristics from native T1mapping using neural networks with uncertainty-based quality-control
Puyol-Antón, E., Ruijsink, B., Baumgartner, C. F., Masci, P. G., Sinclair, M., Konukoglu, E., Razavi, R. & King, A. P., 20 Aug 2020, In: Journal of Cardiovascular Magnetic Resonance. 22, 1, 60.Research output: Contribution to journal › Article › peer-review
Open Access34 Citations (Scopus) -
Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks
Sinclair, M., Baumgartner, C. F., Matthew, J., Bai, W., Martinez, J. C., Li, Y., Smith, S., Knight, C. L., Kainz, B., Hajnal, J., King, A. P. & Rueckert, D., 24 Apr 2018, (E-pub ahead of print) In: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference.Research output: Contribution to journal › Article › peer-review
46 Citations (Scopus) -
Autoadaptive motion modelling for MR-based respiratory motion estimation
Baumgartner, C. F., Kolbitsch, C., McClelland, J. R., Rueckert, D. & King, A. P., Jan 2017, In: Medical Image Analysis. 35, p. 83-100 18 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile32 Citations (Scopus)236 Downloads (Pure) -
High-Resolution Self-Gated Dynamic Abdominal MRI Using Manifold Alignment
Chen, X., Usman, M., Baumgartner, C., Balfour, D., Marsden, P., Reader, A., Prieto, C. & King, A. P., 20 Jan 2017, In: IEEE Transactions on Medical Imaging. 36, 4, p. 960-971 12 p., 7828136.Research output: Contribution to journal › Article › peer-review
Open AccessFile18 Citations (Scopus)305 Downloads (Pure) -
Self-Aligning Manifolds for Matching Disparate Medical Image Datasets
Baumgartner, C. F., Gomez, A., Koch, L. M., Housden, J. R., Kolbitsch, C., Mcclelland, J. R., Rueckert, D. & King, A. P., 23 Jun 2015, Information Processing in Medical Imaging. Vol. 90123. p. 363-374 Chapter 28. (Lecture notes in Computer Science).Research output: Chapter in Book/Report/Conference proceeding › Other chapter contribution › peer-review
14 Citations (Scopus)
Thesis
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Manifold Alignment for Imaging and Modelling Respiratory Motion
Baumgartner, C. (Author), King, A. P. (Supervisor), Rueckert, D. (Supervisor) & McClelland, J. (Supervisor), 2016Student thesis: Doctoral Thesis › Doctor of Philosophy
File
Datasets
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Data for publication: Autoadaptive motion modelling for MR-based respiratory motion estimation
Baumgartner, C., Kolbitsch, C., McClelland, J. R., Rueckert, D. & King, A., Zenodo, 10 Jun 2016
DOI: 10.5281/zenodo.55345, https://zenodo.org/record/55345
Dataset
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Randomised high resolution 4D volumes synthesis
Chen, X., King, A., Prieto Vasquez, C., Balfour, D., Reader, A., Marsden, P., Usman, M. & Baumgartner, C., King's College London, 6 Dec 2016
DOI: 10.18742/rdm01-114, https://kcl.figshare.com/articles/dataset/Randomised_high_resolution_4D_volumes_synthesis/16473681
Dataset