Institution profile

Erasmus Medical Center

Academic institutioneurope · nl
Official website
Research library9linked papers
Opportunities0open roles
Selected work

Representative Papers

The Tensor-Core Beamformer: A High-Speed Signal-Processing Library for Multidisciplinary Use

May 06, 2025

Beamforming in multi-sensor signal processing suffers from high computational density, poor hardware adaptability, and insufficient flexibility in precision. Method: This paper introduces the first tensor-core-oriented general-purpose beamforming acceleration library. It pioneers the generalization of GPU tensor cores for beamforming computation, integrates mixed-precision (FP16/1-bit) design, and achieves cross-platform high-efficiency deployment on both NVIDIA and AMD GPUs via dual-stack heterogeneous optimization using CUDA and HIP. Contributions/Results: The library achieves over 600 TeraOps/s (FP16) on AMD MI300X with near 1 TeraOp/J energy efficiency; on NVIDIA A100, it delivers 3 PetaOps/s in 1-bit mode with >10 TeraOps/J efficiency. It enables, for the first time, ultra-low-precision real-time beamforming and has been successfully deployed in clinical ultrasound and radio astronomy systems.

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Evaluating and Calibrating Diffusion Model-derived Uncertainty for Quantitative MRI Mapping

Aug 12, 2026

This work addresses the lack of interpretable and quantifiable uncertainty estimation in existing deep learning–based quantitative MRI (qMRI) methods. Building upon a data-consistent diffusion model framework, the study generates uncertainty maps through multiple inference passes and, for the first time, systematically evaluates their correlation with ground-truth errors. A novel posterior calibration strategy is introduced, integrating bias correction with uncertainty scaling to substantially enhance the quantitative interpretability of prediction intervals. Experiments demonstrate a strong correlation between estimated uncertainty and actual mapping errors, showing that excluding high-uncertainty voxels significantly reduces error in the retained regions. Furthermore, the calibrated uncertainty intervals exhibit spatially plausible and statistically reliable coverage properties on healthy volunteer data.

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CUPA-T2*: Covariance-Aware Uncertainty Propagation and Alignment for T2* Mapping in Accelerated MRI

Aug 09, 2026

This work addresses the degradation in T2* quantification accuracy in accelerated MRI caused by artifacts and noise from undersampled reconstruction, compounded by the lack of effective modeling of uncertainty propagation to downstream fitting. The study introduces the first explicit covariance-aware uncertainty propagation framework from reconstruction to T2* fitting in accelerated T2* mapping. It estimates voxel-wise multi-echo reconstruction uncertainty via Monte Carlo Dropout, propagates this uncertainty to T2* fitting using a covariance-aware sampling strategy, and aligns predicted variances with reconstruction uncertainties through a heteroscedastic MLP and a correlation-based regularization term. Experiments demonstrate that the proposed method significantly improves T2* fitting performance in white matter under high acceleration in brain MRI, enhances uncertainty consistency, and yields interpretable voxel-level uncertainty maps.

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Recent publications

Latest Papers

Evaluating and Calibrating Diffusion Model-derived Uncertainty for Quantitative MRI Mapping

Aug 12, 2026

This work addresses the lack of interpretable and quantifiable uncertainty estimation in existing deep learning–based quantitative MRI (qMRI) methods. Building upon a data-consistent diffusion model framework, the study generates uncertainty maps through multiple inference passes and, for the first time, systematically evaluates their correlation with ground-truth errors. A novel posterior calibration strategy is introduced, integrating bias correction with uncertainty scaling to substantially enhance the quantitative interpretability of prediction intervals. Experiments demonstrate a strong correlation between estimated uncertainty and actual mapping errors, showing that excluding high-uncertainty voxels significantly reduces error in the retained regions. Furthermore, the calibrated uncertainty intervals exhibit spatially plausible and statistically reliable coverage properties on healthy volunteer data.

0 citationsRead paper

CUPA-T2*: Covariance-Aware Uncertainty Propagation and Alignment for T2* Mapping in Accelerated MRI

Aug 09, 2026

This work addresses the degradation in T2* quantification accuracy in accelerated MRI caused by artifacts and noise from undersampled reconstruction, compounded by the lack of effective modeling of uncertainty propagation to downstream fitting. The study introduces the first explicit covariance-aware uncertainty propagation framework from reconstruction to T2* fitting in accelerated T2* mapping. It estimates voxel-wise multi-echo reconstruction uncertainty via Monte Carlo Dropout, propagates this uncertainty to T2* fitting using a covariance-aware sampling strategy, and aligns predicted variances with reconstruction uncertainties through a heteroscedastic MLP and a correlation-based regularization term. Experiments demonstrate that the proposed method significantly improves T2* fitting performance in white matter under high acceleration in brain MRI, enhances uncertainty consistency, and yields interpretable voxel-level uncertainty maps.

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Do Medical Foundation Models Generalize on the African Brain?

Jul 30, 2026

This study addresses the lack of systematic evaluation of generalization capabilities of existing medical foundation models on brain MRI data from African populations, a gap that raises concerns about potential algorithmic bias. We present the first comprehensive assessment of both general-purpose and segmentation-specific foundation models—including BrainIAC, 3DINO, and MedSAM2—on African (Nigeria Dementia Dataset, BraTS-Africa) and non-African brain MRI datasets across dementia classification and brain tumor segmentation tasks, benchmarking against models trained from scratch. Results show modest gains in classification performance (maximum ROC-AUC of 0.86) but substantially improved segmentation accuracy over baselines (maximum Dice score of 0.86). Model performance was primarily driven by training data scale rather than geographic origin, with no evidence of inherent bias against African populations.

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