We are an interdisciplinary team of scientists, physicians and students working on translational medical image analysis. Our goal is the development and application of methods for the analysis of preclinical and clinical multiparametric imaging data of various sources including PET/CT, PET/MRI, MRI, CT, SPECT/CT as well as histology and immunohistichemistry. We use these methods in order to infer information on histological and molecular tissue properties based on imaging data in vivo.

The main focus of our work lies on the translational approach of analyzing medical imaging. To this end we are using well-characterized histological data and preclinical imaging data for the generation of models that describe histological and molecular tissue properties based in imaging data. These models are then adapted and applied to clinical imaging data.

The Translational Image Analysis Group is formed by scientists form the Werner Siemens Imaging Center and the Clinical Department of Radiology.


1: Disselhorst JA, Krueger MA, Ud-Dean SMM, Bezrukov I, Jarboui MA, Trautwein C, Traube A, Spindler C, Cotton JM, Leibfritz D, Pichler BJ. Linking imaging to omics utilizing image-guided tissue extraction. Proc Natl Acad Sci U S A. 2018 Mar 27;115(13):E2980-E2987. doi: 10.1073/pnas.1718304115. Epub 2018 Mar 5. PubMed PMID: 29507209; PubMed Central PMCID: PMC5879681.


2: Katiyar P, Divine MR, Kohlhofer U, Quintanilla-Martinez L, Schölkopf B, Pichler BJ, Disselhorst JA. Spectral Clustering Predicts Tumor Tissue Heterogeneity Using Dynamic (18)F-FDG PET: A Complement to the Standard Compartmental Modeling Approach. J Nucl Med. 2017 Apr;58(4):651-657. doi: 10.2967/jnumed.116.181370. Epub 2016 Nov 3. PubMed PMID: 27811120.


3: Katiyar P, Divine MR, Kohlhofer U, Quintanilla-Martinez L, Schölkopf B, Pichler BJ, Disselhorst JA. A Novel Unsupervised Segmentation Approach Quantifies Tumor Tissue Populations Using Multiparametric MRI: First Results with Histological Validation. Mol Imaging Biol. 2017 Jun;19(3):391-397. doi: 10.1007/s11307-016-1009-y. PubMed PMID: 27734253; PubMed Central PMCID: PMC5332060.


4: Tixier F, Vriens D, Cheze-Le Rest C, Hatt M, Disselhorst JA, Oyen WJ, de Geus-Oei LF, Visser EP, Visvikis D. Comparison of Tumor Uptake Heterogeneity Characterization Between Static and Parametric 18F-FDG PET Images in Non-Small Cell Lung Cancer. J Nucl Med. 2016 Jul;57(7):1033-9. doi: 10.2967/jnumed.115.166918. Epub 2016 Mar 10. PubMed PMID: 26966161.


5: Divine MR, Katiyar P, Kohlhofer U, Quintanilla-Martinez L, Pichler BJ, Disselhorst JA. A Population-Based Gaussian Mixture Model Incorporating 18F-FDG PET and Diffusion-Weighted MRI Quantifies Tumor Tissue Classes. J Nucl Med. 2016  Mar;57(3):473-9. doi: 10.2967/jnumed.115.163972. Epub 2015 Dec 10. PubMed PMID: 26659350.


6: Liebgott A, Küstner T, Strohmeier H, Hepp T, Mangold P, Martirosian P, Bamberg F, Nikolaou K, Yang B, Gatidis S. ImFEATbox: a toolbox for extraction and analysis of medical image features. Int J Comput Assist Radiol Surg. 2018 Dec;13(12):1881-1893. doi: 10.1007/s11548-018-1859-7. Epub 2018 Sep 18. PubMed
PMID: 30229363.


7: Küstner T, Gatidis S, Liebgott A, Schwartz M, Mauch L, Martirosian P, Schmidt H, Schwenzer NF, Nikolaou K, Bamberg F, Yang B, Schick F. A machine-learning framework for automatic reference-free quality assessment in MRI. Magn Reson Imaging. 2018 Nov;53:134-147. doi: 10.1016/j.mri.2018.07.003. Epub 2018 Jul 21. PubMed PMID: 30036653.


8: Zwirner K, Thorwarth D, Winter RM, Welz S, Weiss J, Schwenzer NF, Schmidt H, la Fougère C, Nikolaou K, Zips D, Gatidis S. Voxel-wise correlation of functional imaging parameters in HNSCC patients receiving PET/MRI in an irradiation setup. Strahlenther Onkol. 2018 Aug;194(8):719-726. doi: 10.1007/s00066-018-1292-4. Epub 2018 Mar 21. PubMed PMID: 29564483.


9: Küstner T, Liebgott A, Mauch L, Martirosian P, Bamberg F, Nikolaou K, Yang B, Schick F, Gatidis S. Automated reference-free detection of motion artifacts in magnetic resonance images. MAGMA. 2018 Apr;31(2):243-256. doi: 10.1007/s10334-017-0650-z. Epub 2017 Sep 20. PubMed PMID: 28932991.