Neha Vats | Medical Physics | Best Researcher Award

Published on by Life Science

Dr. Neha Vats | Medical Physics | Best Researcher Award

Heidelberg University | Germany

Dr. Neha Vats is a postdoctoral researcher in Medical Imaging and Spectroscopy at the Central Institute of Mental Health, Mannheim, Germany, with a strong research foundation in MRI, CT, and multinuclear MRS. Her expertise lies in quantitative image analysis, medical image processing, and the development of advanced imaging protocols. She has contributed significantly to translational neuroimaging and oncology through the optimization of CT perfusion techniques and neurochemical imaging in psychiatric populations. Her scholarly output includes five peer-reviewed publications in high-impact journals such as Scientific Reports and Magnetic Resonance Imaging, addressing topics like pancreatic CT perfusion standardization, perfusion model development, and machine-learning-based tumor differentiation. According to her Scopus profile, she has 5 documents, an h-index of 4, and 28 citations, reflecting strong research visibility and growing academic impact in biomedical imaging. Dr. Vats’s technical proficiency spans MATLAB, C++, perfusion mathematical modeling, and quantitative neuroimaging, with research interests focusing on advanced spectroscopy methods and metabolic brain characterization. Her ongoing projects on ME/CFS and aggression-related brain changes exemplify her commitment to integrating imaging physics with neuroscience for clinical translation and sustainable advancements in medical diagnostics.

Profiles: Scopus | Orcid | Research Gate 

Featured Publications

Vats, N., Mayer, P., Kortes, F., Klauß, M., Grenacher, L., Stiller, W., Kauczor, H.-U., & Skornitzke, S. Evaluation and timing optimization of CT perfusion first pass analysis in comparison to maximum slope model in pancreatic adenocarcinoma. Scientific Reports, 13, Article 10865.

Skornitzke, S., Vats, N., Mayer, P., Kauczor, H.-U., & Stiller, W. Pancreatic CT perfusion: Quantitative meta-analysis of disease discrimination, protocol development, and effect of CT parameters. Insights into Imaging, 14, Article 1471.

Vats, N., Sengupta, A., Gupta, R. K., Patir, R., Vaishya, S., Ahlawat, S., Saini, J., Agarwal, S., & Singh, A. Differentiation of pilocytic astrocytoma from glioblastoma using a machine-learning framework based upon quantitative T1 perfusion MRI. Magnetic Resonance Imaging, 97, 63–71.

Skornitzke, S., Vats, N., Kopytova, T., Tong, E. W. Y., Hofbauer, T., Weber, T. F., Rehnitz, C., von Stackelberg, O., Maier-Hein, K., & Stiller, W. Asynchronous calibration of quantitative computed tomography bone mineral density assessment for opportunistic osteoporosis screening: Phantom-based validation and parameter influence evaluation. Scientific Reports, 12, Article 20478