arXiv subjects

Jannik Stebani

Publications and source records attributed to Jannik Stebani.

2 recordsLinked to original sources

arXiv2026

OpenMRF: A Modular, Vendor-Neutral Open-Source Framework for Reproducible Magnetic Resonance Fingerprinting using Pulseq

Purpose: Widespread adoption and methodological advancement of Magnetic Resonance Fingerprinting (MRF) are limited by the lack of unified, reproducible implementation frameworks and fragmented open-source tools. To address these barriers, we introduce OpenMRF - a comprehensive Pulseq-based solution - designed to enable consistent, reproducible, and transferable MRF research across vendors, sites, and field strengths. Methods: OpenMRF integrates modular Pulseq-based sequence design, Bloch-simulation-based dictionary creation directly from .seq files, and iterative low-rank subspace reconstruction. The framework was evaluated through digital phantom simulations, a multi-site ISMRM/NIST phantom study on Siemens MRI systems at 0.55 T, 1.5 T, and 3 T as well as GE and United Imaging 3 T platforms, and representative in vivo acquisitions in the liver (0.55 T), myocardium (1.5 T), and brain (3 T). Results: Simulations demonstrated high mapping accuracy in an ISMRM/NIST-like digital phantom, with low-rank reconstruction yielding deviations of 0.03+/-0.32 % (T1) and 0.12+/-1.94 % (T2). The multi-site phantom study yielded relaxation times consistent with reference values at all field strengths, with mean deviations of -0.1+/-2.9 % (T1), -1.5+/-8.7 % (T2), and -4.0+/-7.2 % (T1rho). In vivo acquisitions produced high-quality parameter maps across platforms and field strengths. Conclusion: OpenMRF provides a robust, open-source, end-to-end Pulseq-based solution for MRF that enables reproducible sequence implementation, physics-accurate dictionary simulation, and advanced reconstruction across vendors and field strengths. By providing a unified platform for method development, comparison, and multi-site validation, OpenMRF aims to accelerate reproducible and harmonized quantitative MRI research within the community.

physics.med-ph↗

arXiv2026

Controlling spatial correlation in k-space interpolation networks for MRI reconstruction: denoising versus apparent blurring

Purpose: Interpretability is essential for the clinical adoption of state-of-the-art machine learning (ML) methods in magnetic resonance imaging (MRI). Conventional evaluation of ML reconstructions relies heavily on aggregate image metrics that require fully sampled references. These metrics, inherited from classical image processing and natural image ML, often overlook the critical challenge of noise amplification specific to medical image reconstruction. This study aims to analyze the influence of nonlinear activations on spatial noise variance distribution of k-space interpolation networks (RAKI) and to provide a framework for incorporating variance maps during network training. Methods: We present an analytical framework that decomposes pixel-level noise variance into components reflecting linear and nonlinear characteristics of RAKI. By applying automatic differentiation on the image-space equivalent of the network, variance maps are computed during each training iteration, enabling runtime quality assessment beyond data consistency. We introduce apparent blurring, quantifying nonlinear signal mixing without dependence on reference images. By incorporating variance maps into the traning loss as regularizers, our self-informed RAKI architecture (G-factor-informed RAKI, GIF-RAKI) can directly integrate updated noise characteristics during runtime. Results: Experimental results demonstrate that variance components quantitatively explain network behavior. GIF-RAKI outperforms conventional RAKI variants in image fidelity and noise suppression. Conclusion: Our methodology advances practical and theoretical aspects of ML-based MRI reconstruction by reinstating reconstruction noise characterization as a cornerstone for performance evaluation, eliminating the need for fully sampled references. GIF-RAKI also enables optimization of the trade-off between denoising and apparent blurring.

physics.med-ph↗