Publications of Alberto Merola

Guidi, M., Huber, L., Lampe, L., Merola, A., Ihle, K., & Möller, H. E. (2020). Cortical laminar resting‐state signal fluctuations scale with the hypercapnic blood oxygenation level‐dependent response. Human Brain Mapping, 41, 2014–2027.

Merola, A., Germuska, M. A., Murphy, K., & Wise, R. G. (2018). Assessing the repeatability of absolute CMRO2, OEF and haemodynamic measurements from calibrated fMRI. NeuroImage, 173, 113–126.

Merola, A., Murphy, K., Stone, A. J., Germuska, M. A., Griffeth, V. E. M., Blockley, N. P., … Wise, R. G. (2016). Measurement of oxygen extraction fraction (OEF): An optimized BOLD signal model for use with hypercapnic and hyperoxic calibration. NeuroImage, 129, 159–174.

Pirkl, C. M., Gómez, P. A., Lipp, I., Buonincontri, G., Molina-Romero, M., Sekuboyina, A., … Menze, B. H. (2020). Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting. In Proceedings of Machine Learning Research (pp. 1–17).

Kloenne, M., Niehaus, S., Lampe, L., Merola, A., Reinelt, J., & Scherf, N. (2019). Convolutional neural network stacking for medical image segmentation in CT scans. In 2019 Kidney Tumor Segmentation Challenge. Minneapolis, MN: University of Minnesota Libraries Publishing. doi:10.24926/548719.090

Merola, A. The acute effects of caffeine on brain oxygen metabolism: A dual calibrated FMRI study. Talk_at_event presented at the 24th Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM), Singapore. Retrieved from http://hdl.handle.net/11858/00-001M-0000-002B-7850-F

Merola, A. Acute effects of caffeine on grey matter haemodynamics. Poster presented at the 24th Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM), Singapore. Retrieved from http://hdl.handle.net/11858/00-001M-0000-002B-78E4-1

Kloenne, M., Niehaus, S., Lampe, L., Merola, A., Reinelt, J., & Scherf, N. (2019, September 27). A framework for CT image segmentation inspired by the clinical environment. arXiv. Retrieved from http://hdl.handle.net/21.11116/0000-0004-C770-D

Williams, E., Kienast, M., Medawar, E., Reinelt, J., Merola, A., Klopfenstein, S. A. I., … Niehaus, S. (2022, November 13). FHIR-DHP: A standardized clinical data harmonisation pipeline for scalable AI application deployment. MedRxiv. doi:10.1101/2022.11.07.22281564