Apl. Prof. Dr. Daniela Pfeiffer





Academic Career and Research Areas

Prof. Pfeiffer's (*1982) field of research is the area of the lung imaging and musculoskeletal diagnostics. Her main interest is precise diagnosis of lung diseases such as COPD, lung cancer and infectious diseases (including COVID-19) using novel methods for medical X-Ray and Computed Tomography (CT) Imaging. Her main focus lies in the field of photon counting CT technology, spectral CT applications, dark-field and phase contrast imaging, and machine learning in clinical applications.

Prof. Pfeiffer studied medicine at the University of Regensburg and finished her MD thesis (summa cum laude) in 2008. She has the board certification of Radiology and earned a master’s degree in Business Health Administration. After a research fellowship at the Massachusetts General Hospital (Harvard Medical School) in Boston, she came back as an attending at the Dep. of Radiology at TUM School of Medicine, Klinikum rechts der Isar. She serves as a member of the Ethics Committee at TUM and the Scientific Committee of the RSNA.


    • Röntgen-Award, University of Gießen (2017)
    • Invest in the Youth: Rising Star ECR, Vienna (2012)
    • Scholarship of the Studienstiftung Dr. Heel (2002)

    Urban, T., Gassert, F. T., Frank, M., Willer, K., Noichl, W., Buchberger, P., Schick, R. C., Koehler, T., Bodden, J. H., Fingerle, A. A., Sauter, A. P., Makowski, M. R., Pfeiffer, F., & Pfeiffer, D. (2022). Qualitative and Quantitative Assessment of Emphysema Using Dark-Field Chest Radiography. Radiology, 303(1), 119–127.


    Willer, K., Fingerle, A. A., Noichl, W., De Marco, F., Frank, M., Urban, T., Schick, R., Gustschin, A., Gleich, B., Herzen, J., Koehler, T., Yaroshenko, A., Pralow, T., Zimmermann, G. S., Renger, B., Sauter, A. P., Pfeiffer, D., Makowski, M. R., Rummeny, E. J., Grenier, P. A., … Pfeiffer, F. (2021). X-ray dark-field chest imaging for detection and quantification of emphysema in patients with chronic obstructive pulmonary disease: a diagnostic accuracy study. The Lancet. Digital health, 3(11), e733–e744. 


    Gassert, F. T., Urban, T., Frank, M., Willer, K., Noichl, W., Buchberger, P., Schick, R., Koehler, T., von Berg, J., Fingerle, A. A., Sauter, A. P., Makowski, M. R., Pfeiffer, D., & Pfeiffer, F. (2021). X-ray Dark-Field Chest Imaging: Qualitative and Quantitative Results in Healthy Humans. Radiology, 301(2), 389–395.


    Schultheiss, M., Schmette, P., Bodden, J., Aichele, J., Müller-Leisse, C., Gassert, F. G., Gassert, F. T., Gawlitza, J. F., Hofmann, F. C., Sasse, D., von Schacky, C. E., Ziegelmayer, S., De Marco, F., Renger, B., Makowski, M. R., Pfeiffer, F., & Pfeiffer, D. (2021). Lung nodule detection in chest X-rays using synthetic ground-truth data comparing CNN-based diagnosis to human performance. Scientific reports, 11(1), 15857.


    Deniffel, D., Sauter, A., Fingerle, A., Rummeny, E. J., Makowski, M. R., & Pfeiffer, D. (2021). Improved differentiation between primary lung cancer and pulmonary metastasis by combining dual-energy CT-derived biomarkers with conventional CT attenuation. European radiology, 31(2), 1002–1010.