Stefan K Piechnik
DSc, PhD, MScEE, FSCMR
Associate Professor of Biomedical Imaging
- Head of Advanced Cardiovascular Image Processing
Over the past 30+ years, I have applied my engineering background to interdisciplinary biomedical research, including mathematical and biophysical modelling, measurement systems and methods design, direct clinical measurements, imaging and estimation using PET, SPECT, and MR, and the clinical and self-assessment approaches. Applications include wide range of clinical specialties in Neurosciences (Neurosurgery, Neurology and Psychiatry), Dermatology, Cardiovascular and Internal Medicine. Over the course of my work I co-authored 7 patent applications, >340 research articles, attracting >13'000 citations, resulting in a h-index of 62 . My most recent focus is in the clinical application of CMR parametric tissue characterisation techniques, in particular T1 mapping, big data and the related applications of artificial intelligence.
Improving robustness of automatic cardiac function quantification from cine magnetic resonance imaging using synthetic image data.
Gheorghiță BA. et al, (2022), Sci Rep, 12
Endogenous T1ρ cardiovascular magnetic resonance in hypertrophic cardiomyopathy.
Thompson EW. et al, (2021), J Cardiovasc Magn Reson, 23
Symptom Persistence Despite Improvement in Cardiopulmonary Health - Insights from longitudinal CMR, CPET and lung function testing post-COVID-19.
Cassar MP. et al, (2021), EClinicalMedicine
Shape registration with learned deformations for 3D shape reconstruction from sparse and incomplete point clouds.
Chen X. et al, (2021), Med Image Anal, 74
Toward Replacing Late Gadolinium Enhancement With Artificial Intelligence Virtual Native Enhancement for Gadolinium-Free Cardiovascular Magnetic Resonance Tissue Characterization in Hypertrophic Cardiomyopathy.
Zhang Q. et al, (2021), Circulation, 144, 589 - 599