Jan Brabec

Brief CV

Education and work experience
2022 - 2023	Post-doctoral research fellow at Johns Hopkins University School of Medicine, USA
2018 - 2022	Ph.D. from Medical Radiation Physics with focus on MRI at Lund University, Sweden
2017	Assistant Data Scientist at Optellum in Oxford, United Kingdom
2015 - 2018	Master in Science (M.Sc.) in general physics at Lund University, Sweden
2014 - 2015	Clinical rotations and surgeon assistant at hospitals of University of Münster, Germany
2009 - 2015	Medical degree (M.D.) at Masaryk University, Brno, Czech Republic

Languages
English	Fluent, C1 certificate
German	Fluent, C1 certificate
Swedish	Fluent, C1 certificate
Czech	Mother tongue

Awards and stipends
Two Summa Cum Laude merit awards (top 5 %) at ISMRM conference (2019 and 2020)
Highlighted abstract at ISMRM conference in diffusion MRI session (2020)
Oral presentation in featured content session at RSNA conference (2020)
ISMRM conference Educational Stipends (2019, 2020 and 2022)
John och Augusta Perssons stiftelse travel stipends (2019, 2020 and 2021)

Other merits
Certified medical doctor in in Norway, Denmark, Sweden and the Czech Republic.
Worked ca 800 hours in operation theatre at 400 surgical operations, ca 75 % as first assistant.
6 first-name and 3 co-authored journal publications, 11 first-name and 2 co-authored conference abstracts and 1 provisional patent application in the USA.
6 presentations at external institutions, 8 at conferences, 1 as panelist, 1 invited educational and 4 peer-reviewed posters at conferences.
Employed in 5 countries.
Moderator at ISMRM (2022).
Reviewer of journal papers and ISMRM abstracts (2020, 2022).
Admitted to MD program as 19th out of 2572 applicants.
During Physics studies, 12 out of 14 courses with the highest grade, 50 % extra courses than minimal requirements.
ESMRMB junior member (2018), ISMRM trainee member (2018–2022), RSNA member-in-training (2020–2022).
Coding skills in Python and MATLAB.
Visual arts education at Art School Blansko, Czech Republic (1998–2008).

Publications

Journal papers

1. Brabec J., Englund, E., Bengzon, J., Szczepankiewicz, F., van Westen, D., Sundgren, P.C. and Nilsson, M., (2023). Coregistered histology sections with diffusion tensor imaging data at 200 μm resolution in meningioma tumors. Data in Brief, p.109261. DOI: 10.1016/j.dib.2023.109261.-[HTML]--[PDF]--[GitHub]-
2. Brabec J., Friedjungova M., Vasata D., Englund E., Bengzon J., Knutsson L., Szczepankiewicz F., Sundgren P-C, and Nilsson M., (2023). Meningioma microstructure assessed by diffusion MRI: an investigation of the source of mean diffusivity and fractional anisotropy by quantitative histology. NeuroImage: Clinical, 37, p.103365. -[HTML]--[PDF]--[GitHub]-
3. Chakwizira A., Westin C-F, Brabec J., Lasic S., Knutsson L., Szczepankiewicz F., Nilsson M., (2022). Diffusion MRI with pulsed and free gradient waveforms: Effects of restricted diffusion and exchange. NMR in Biomedicine. DOI: 10.1002/nbm.4827.-[HTML]--[PDF]-
4. Brabec J., Durmo F., Szczepankiewicz F., Brynolfsson P., Lampinen B., Rydelius A., Knutsson L., Westin C-F, Sundgren P-C, Nilsson M., (2022). Separating glioma hyperintensities from white matter by diffusion-weighted imaging with spherical tensor encoding. Frontiers in Neuroscience. DOI: 10.3389/fnins.2022.842242.-[HTML]--[PDF]--[GitHub]-
5. Brabec J., Szczepankiewicz F., Lennartsson F., Englund E., Pebdani H., Bengzon J., Knutsson L., Westin C-F, Maly Sundgren P.,, Nilsson M., (2022). Histogram analysis of tensor-valued diffusion MRI in meningiomas: Relation to consistency, histological grade and type. NeuroImage: Clinical. DOI: 10.1016/j.nicl.2021.102912.-[HTML]--[PDF]--[GitHub]-
6. Massion P-P, Antic S., Ather S., Arteta C., Brabec J., Chen H., Declerck J., Dufek D., Hickes W., Kadir T., Kunst J., Landman B-A, Munden R-F, Novotny P., Peschl H., Pickup L-C, Santos C., Smith G-T, Talwar A., Gleeson F., (2020). Assessing the accuracy of a deep learning method to risk stratify indeterminate pulmonary nodules. American journal of respiratory and critical care medicine. DOI: 10.1164/rccm.201903-0505OC.-[HTML]--[PDF]-
7. Brabec J., Lasic S., Nilsson M., (2019). Time-dependent diffusion in undulating thin fibers: Impact on axon diameter estimation. NMR in Biomedicine. DOI: 10.1002/nbm.4187.-[HTML]--[PDF]--[GitHub]-
8. Nilsson M., Szczepankiewicz F., Brabec J., Taylor M., Westin C-F., Golby A., Westen D., Maly Sundgren P., (2019). Tensor‐valued diffusion MRI in under 3 minutes: an initial survey of microscopic anisotropy and tissue heterogeneity in intracranial tumors. Magnetic Resonance in Medicine. DOI: 10.1002/mrm.27959.-[HTML]--[PDF]-

Non-peer reviewed publications

• Brabec, J., Szczepankiewicz F., Nilsson, M., Sundgren P-C., (2022). U.S. Provisional Utility Patent No. 63,293,098. Washington, DC: U.S. Patent and Trademark Office. Patent no: 63/293,098.
• Brabec J., Lennartsson F., (2022). Editorial for “Investigation of the inter- and intra-scanner reproducibility and repeatability of Radiomics features in Magnetic Resonance Imaging.” Journal of Magnetic Resonance Imaging. DOI: 10.1002/jmri.28190.-[HTML]-

Conference abstracts

• Brabec J., Friedjungova M., Vasata D., Englund E., Knutsson L., Szczepankiewicz F., Sundgren P-C, Nilsson M. (2022). Explaining variation in DTI parameters with meningioma microscopy: A comparison between a neural network and an image-feature-based approach. International Society for Resonance in Medicine. Digital poster.
• Brabec J., Szczepankiewicz F., Sramek J., Englund E., Bengzon J., Knutsson L., Westin C-F., Sundgren P-C., Nilsson M. (2021). Beyond cellularity: Which microstructural features determine the mesoscopic mean diffusivity in meningiomas? International Society for Resonance in Medicine. Virtual meeting. 4037. Digital poster.
• Brabec J., Durmo F., Szczepankiewicz F., Brynolfsson P., Lampinen B., Ondkova N., Knutsson L., Sundgren C P., Nilsson M. (2020) Diffusion MRI with spherical B-tensor encoding increases glioma tumor conspicuity. Annual meeting of Radiological Society of North America. Virtual meeting. SSNR10-05. Oral presentation in a featured content section.
• Brabec J., Durmo F., Szczepankiewicz F., Lampinen B., Knutsson L., Sundgren C P., Nilsson M. (2020) B-tensor encoding in gliomas: improved tumor grading by the isotropic kurtosis. International Society for Resonance in Medicine. Virtual meeting. 0858. Oral presentation, highlighted in diffusion highlights, summa cum laude award.
• Brabec J., Szczepankiewicz F., Englund E., Bengzon J., Knutsson L., Westin C-F., Sundgren C P. Nilsson M. (2020) Imaging meningioma tumor microstructure: a comparison between quantitative histopathology and high-resolution diffusion tensor imaging. International Society for Resonance in Medicine. Virtual meeting. 4453. Poster.
• Szczepankiewicz F., Juvekar P., Brabec J., Sundgren P-C., Nilsson M., Golby A., Westin C-F. (2020) Investigation of tumor grade and gadolinium enhancement by tensor-valued diffusion encoding and QTI analysis: an exploratory study of gliomas. International Society for Resonance in Medicine. Virtual meeting. Digital poster.
• Nilsson M., Westin C-F., Brabec J., Lasic S., Szczepankiewicz F., (2020) A unified framework for analysis of time-dependent diffusion: numerical validation of a restriction-exchange correlation experiment. International Society for Resonance in Medicine. Virtual meeting. Oral presentation.
• Brabec J., Durmo F., Szczepankiewicz F., Lampinen B., Knutsson L., Sundgren C P., Nilsson M. (2020) Classification of non-enhancing glioma tumors by diffusion MRI with B-tensor encoding. European Congress of Radiology. Virtual meeting. 5875. Oral presentation.
• Brabec J., Szczepankiewicz F., Englund E., Bengzon J., Knutsson L., Westin C-F., Sundgren C P. Nilsson M. (2019) B-tensor encoding in meningiomas: Comparisons with histology, microimaging and tumor consistency. International Society for Resonance in Medicine. Montreal. 1002. Oral presentation, summa cum laude award.
• Brabec J., Szczepankiewicz F., Westin C-F., Golby A., van Westen D., Sundgren P-C., Nilsson M., (2018) B‑tensor encoding is clinically feasible and may add relevant information in the work‑up of patients with brain tumors. Medicinteknikdagarna. Umeå. 108. Oral presentation.
• Brabec J., Lasic S., Nilsson M., (2018) Simulation of diffusion in axons with harmonic and stochastic trajectories. International Society for Resonance in Medicine. Joint Annual Meeting ISMRM-ESMRMB. Paris. 1106. Power pitch.
• Brabec J., Lasic S., Nilsson M., (2017) Diffusion in Undulating Axons: Implications for Axon Diameter Mapping and its Potential Remedy. The European Society for Magnetic Resonance in Medicine and Biology, 34th annual meeting, Barcelona, 514. Oral presentation
• Brabec J., Sramek J., Bernard V. (2013). Texture spectrum and its optimization for ultrasound images. In XXXVI. Dny lekarske biofyziky, Lazne Belohrad. ISBN 978-80-87727-04-1. Poster.

PhD dissertation

I addressed two shortcomings of diffusion MRI: First, I investigated which microstructural features are of relevance to diffusion MRI. I found that the effects of non-straight propagation of axons are indistinguishable from those originating from the axon diameter for typical measurements with a clinical scanner [Paper I]. Furthermore, I found that, quantitively, the interpretation of local variability of mean diffusivity and fractional anisotropy by cell density and tissue anisotropy is valid for fractional anisotropy but not for mean diffusivity in all tumors [Paper II][Data paper].
Second, I studied what contrasts tensor-valued dMRI can add to the imaging routine of patients with intracranial tumors. I found that it can be applied in a short scan time [Paper III], enhances the conspicuity of glioma hyperintensities compared to white matter [Paper IV] and preoperatively may help to classify meningiomas [Paper V]. Furthermore, I was teaching health aspects of non-ionising radiation labs (e.g. wi-fi, cell phones, microwaves) and completed over 10 courses related to radiation physics, computer science or teaching.

Click to read my thesis!

Open-source projects

Analysis of MR images in terms of histology

Wondering what biological features MRI measures? A comparison with histology may help. The MR and histology images can be coregistered (panel A, GitHub repository, journal publication). As an example, we performed analysis explicating diffusion tensor imaging parameters (panel B; GitHub repository, journal publication). The results can also be viewed by a graphical user interface (panel C; GitHub repository).

Diffusion simulations in thin-structures

Interested in diffusional properties of thin-structures and thinking that numerical simulations may provide answer to Your questions? Try two different diffusion simulators. The first one is a "traditional" Monte Carlo simulator (GitHub repository) and the second one is faster and more accurate for this purpose and we refer to it as Gaussian sampling (GitHub repository). Detail description can be found in the journal publication.

Diffusion simulator for educational purposes

Thinking diffusion MRI is difficult? This code will hopefully convince You otherwise. Diffusion is simple because it consists of a diffusion step, which is is all the time the same, and of modulation by the environment. The latter part explains why we see different results in diffsuion MRI (GitHub repository. The simulator is not optimized for computational time but rather for code simplicity. Figure adapted from Figure 2 in my PhD thesis.

Processing pipelines for tensor-valued diffusion MRI data

Do you plan to process tensor-valued diffusion MRI data? Here are two pipeline examples. One applied to meningioma intracranial tumors (GitHub repository, journal publication) and another one to glioma brain tumors (GitHub repository, journal publication).

You can find me here