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Luuk Jacobs

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YNIMG Journal 2026 Journal Article

Cerebrovascular 5D flow MRI

Luuk Jacobs
Patrick Thurner
Sebastian Kozerke

BACKGROUND: 4D flow MRI facilitates quantification of cardiac phase-resolved blood velocity vector fields and has successfully been deployed to study cerebrovascular flow. Besides cardiac-induced flow pulsation, respiration is known to modulate arterial and venous blood flow in the brain. Quantification of the respiratory flow modulation (RFM) holds potential to further our insights into vascular coupling and improve our understanding of cerebral circulation in general. METHODS: A 5D phase-contrast flow MRI framework was developed to volumetrically quantify RFM by resolving velocity vector fields over the cardiac and respiratory cycle, with high spatial (0.82 mm isotropic) and cardiac (55 ms) resolutions, using two respiratory states whilst accounting for variable physiological RFM delays, with a reasonable acquisition time (20 min at 60 bpm). Recent advances in deep learning-based image reconstruction and analysis methods are incorporated to facilitate the approach. RESULTS: The 5D flow MRI framework was validated in 10 healthy volunteers with reference to fully sampled respiratory-resolved 2D flow MRI orthogonal to the internal carotid artery (ICA), yielding Pearson correlation coefficients of 0.97 and 0.90 and biases of and 0.09 % and 1.77 % for RFM of mean velocity magnitude and amplitude, respectively. The value of cerebrovascular 5D flow MRI is demonstrated using full-field spatially resolved RFM quantification of mean velocity and velocity amplitude, revealing a high physiological intra-subject variability. CONCLUSION: Cerebrovascular 5D flow MRI enables the study of full-field respiratory flow modulation holding potential of furthering our understanding of cerebral circulation.

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AAMAS Conference 2026 Conference Paper

Extending Multi-source Bayesian Optimization With Causality Principles

Luuk Jacobs
Mohammad Ali Javidian

Multi-Source Bayesian Optimization (MSBO) serves as a variant of the traditional Bayesian Optimization (BO) framework applicable to situations involving optimization of an objective black-box function over multiple information sources such as simulations, surrogate models, or real-world experiments. However, traditional MSBO assumes the input variables of the objective function to be independent and identically distributed, limiting its effectiveness in scenarios where causal information is available and interventions can be performed, such as clinical trials or policy-making. In the single-source domain, Causal Bayesian Optimization (CBO) extends standard BO with the principles of causality, enabling better modeling of variable dependencies. This leads to more accurate optimization, improved decision making, and more efficient use of low-cost information sources. In this article, we propose a principled integration of the MSBO and CBO methodologies in the multi-source domain, leveraging the strengths of both to enhance optimization efficiency and reduce computational complexity in higher-dimensional problems. We present the theoretical foundations of both Causal and Multi-Source Bayesian Optimization, and demonstrate how their synergy informs our Multi-Source Causal Bayesian Optimization (MSCBO) algorithm. We compare the performance of MSCBO against its foundational counterparts for both synthetic and real-world datasets with varying levels of noise, highlighting the robustness and applicability of MSCBO. Based on our findings, we conclude that integrating MSBO with the causality principles of CBO facilitates dimensionality reduction and lowers operational costs, ultimately improving convergence speed, performance, and scalability.

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