26 citations · 46 across the 11 of their papers we have counts for
12 papers
BrainDiff: Longitudinal Report Generation for Multimodal Brain MRI
Krish Patel, Peirong Liu
Neuroradiologists rarely read a brain MRI in isolation, yet automated brain-MRI report generation has been built almost entirely for single studies. Temporal analysis has been expl…
HemoPIC: A Physics-Informed Cerebral Hemodynamics Digital Twin for Brain Perfusion
Yi-Chen Lee, Peirong Liu
Perfusion imaging guides clinical evaluation of stroke and brain tumors by characterizing tissue-level hemodynamics. Routine quantification relies on manual arterial input function…
Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge
Asbjørn Munk, Stefano Cerri, Vardan Nersesjan +81
Clinical deployment of automated brain MRI analysis faces a fundamental challenge: clinical data is heterogeneous and noisy, and high-quality labels are prohibitively costly to obt…
PEPSI: Pathology-Enhanced Pulse-Sequence-Invariant Representations for Brain MRI
Peirong Liu, Oula Puonti, Annabel Sorby-Adams +2
Remarkable progress has been made by data-driven machine-learning methods in the analysis of MRI scans. However, most existing MRI analysis approaches are crafted for specific MR p…
Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI
Pablo Laso, Stefano Cerri, Annabel Sorby-Adams +21
Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis. Automated se…
Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging
Peirong Liu, Oula Puonti, Xiaoling Hu +2
Recent learning-based approaches have made astonishing advances in calibrated medical imaging like computerized tomography (CT), yet they struggle to generalize in uncalibrated mod…
Fluid registration between lung CT and stationary chest tomosynthesis images
Lin Tian, Connor Puett, Peirong Liu +4
Registration is widely used in image-guided therapy and image-guided surgery to estimate spatial correspondences between organs of interest between planning and treatment images. H…
Deep Decomposition for Stochastic Normal-Abnormal Transport
Peirong Liu, Yueh Lee, Stephen Aylward +1
Advection-diffusion equations describe a large family of natural transport processes, e.g., fluid flow, heat transfer, and wind transport. They are also used for optical flow and p…
Accurate Point Cloud Registration with Robust Optimal Transport
Zhengyang Shen, Jean Feydy, Peirong Liu +4
This work investigates the use of robust optimal transport (OT) for shape matching. Specifically, we show that recent OT solvers improve both optimization-based and deep learning m…
Discovering Hidden Physics Behind Transport Dynamics
Peirong Liu, Lin Tian, Yubo Zhang +3
Transport processes are ubiquitous. They are, for example, at the heart of optical flow approaches; or of perfusion imaging, where blood transport is assessed, most commonly by inj…
Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces
Peirong Liu, Zhengwang Wu, Gang Li +2
Charting cortical growth trajectories is of paramount importance for understanding brain development. However, such analysis necessitates the collection of longitudinal data, which…
Local Temperature Scaling for Probability Calibration
Zhipeng Ding, Xu Han, Peirong Liu +1
For semantic segmentation, label probabilities are often uncalibrated as they are typically only the by-product of a segmentation task. Intersection over Union (IoU) and Dice score…