arXiv subjects

Jonathan Endres

Publications and source records attributed to Jonathan Endres.

4 recordsLinked to original sources

arXiv2026

MR Simulation with Phase Distribution Graphs: Off-Resonant Pulse Response and Slice-Selection

Purpose: Phase Distribution Graphs were introduced as a powerful tool for on-resonant MRI simulations. Herein, we extend the concept beyond the assumptions of hard and on-resonant RF pulses in Phase Distribution Graph simulations, while retaining differentiability across all parameters. The extension into the frequency domain further generalizes the Phase Distribution Graph simulation framework for sequence simulation and optimization. Theory and Methods: Using an effective axis rotation model, the RF operator is generalized to off-resonant rotations, originating from field inhomogeneities, chemical shift, and field gradients. Discretization of the RF pulse shape enables the modeling of the magnetization response to shaped RF pulses and thus slice selection. Results: Including off-resonant pulse response into a PDG based MRI simulation enables the simulation of key MRI features, such as fat suppression, binomial pulses, or Bloch-Siegert shifts. The discretization of RF shapes enables the simulation of their respective frequency response and thus slice selective excitation, refocusing or saturation pulses, including multi-band excitation. In combination with flow, typical inflow artifacts in slice-selective MRI can be simulated. Discussion and Conclusion: Extension for off-resonance removes a major limitation of phase distribution graphs and makes the simulation framework more realistic. As the method is not restricted to specific RF shapes, optimization of RF pulse shapes in amplitude, phase and frequency is now possible in the context of or jointly with specific MR sequences.

physics.med-ph↗

arXiv2026

Agentic MR sequence development: leveraging LLMs with MR skills for automatic physics-informed sequence development

Purpose: Novel MR sequence developments still today allow generation of new diagnostic tools or novel imaging biomarkers. Programming MRI pulse sequences, however, is time-consuming and requires deep expertise in sequence design, restrictions by hardware constraints and MRI physics; even small modifications often require substantial debugging and validation. LLMs can assist when given structured prompts and error feedback, but many generated sequences still exhibit physical inconsistencies. We present Agent4MR, an agent-based framework that automatically generates and refines PyPulseq sequences using a structured, physics-aware validation report. These agents can perform also autonomous research. Methods: We evaluated Agent4MR on a spin-echo EPI task across three state-of-the-art LLMs and compared it to a context-only baseline (LLM4MR) and to a human developer with the same tools. We tested an MR autoresearch on a fluid-suppressed spin-echo EPI challenge for three different model generations. Results: Across all models, Agent4MR consistently produced artifact-free, physically valid sequences in a single user interaction, reducing the number of required interactions below the human baseline while maintaining correct timing and k-space coverage. Autonomous agents could then improve a sequence to match a given target contrast in an autoresearch approach. Conclusion: An appropriate agentic harness with physics-based validation can turn general-purpose LLMs into reliable MRI sequence developers and may ultimately enable non-experts to refine or innovate MR sequences guided by biological or clinical questions, or let swarms of agents realize sequence programming for them. Keywords: MRI; pulse sequence; PyPulseq; large language models; agents; autoresearch, sequence development.

physics.med-ph↗

arXiv2024

MR-zero meets FLASH -- Controlling the transient signal decay in gradient- and rf-spoiled gradient echo sequences

Abstract Purpose The complex signal decay during the transient FLASH MRI readout can lead to artifacts in magnitude and phase images. We show that target-driven optimization of individual rf flip angles and phases can realize near-ideal signal behavior and mitigate artifacts. Methods The differentiable end-to-end optimization framework MR-zero is used to optimize rf trains of the FLASH sequence. We focus herein on minimizing deviations from the ideally spoiled signal by using a mono-exponential Look-Locker target. We first obtain the transient FLASH signal decay substructure, and then minimize the deviation to the Look-Locker decay by optimizing the individual (i) flip angles, (ii) rf phases and (iii) flip angles and rf phases. Comparison between measurement and simulation are performed using Pulseq in 1D and 2D. Results We could reproduce the complex substructure of the transient FLASH signal decay. All three optimization objectives can bring the real FLASH signal closer to the ideal case, with best results when both flip angles and rf phases are adjusted jointly. This solution outperformed all tested conventional quadratic rf cyclings in terms of (i) matching the Look-Locker target signal, (ii) phase stability, (iii) PSF ideality, (iv) robustness against parameter changes, and (v) magnitude and phase image quality. Other target functions for the signal could as well be realized, yet, their response is not as general as for the Look-Locker target and need to be optimized for a specific context. Conclusion Individual flip angle and rf phase optimization improves the transient signal decay of FLASH MRI sequences.

physics.med-ph↗

arXiv2024

MR sequence design to account for non-ideal gradient performance

MRI systems are traditionally engineered to produce close to idealized performance, enabling a simplified pulse sequence design philosophy. An example of this is control of eddy currents produced by gradient fields; usually these are compensated by pre-emphasizing demanded waveforms. This process typically happens invisibly to the pulse sequence designer, allowing them to assume achieved gradient waveforms will be as desired. Whilst convenient, this requires system specifications exposed to the end-user to be substantially down-rated, since pre-emphasis adds an extra overhead to the waveforms. This strategy is undesirable for lower performance or resource-limited hardware. Instead, we propose an optimization-based method to design pre-compensated gradient waveforms that: (i) explicitly respect hardware constraints and (ii) improve imaging performance by correcting k-space samples directly. Gradient waveforms are numerically optimized by including a model for system imperfections. This is investigated in simulation using an exponential eddy current model, then experimentally using an empirical gradient system transfer function on a 7T MRI system. Our proposed method discovers solutions that produce negligible reconstruction errors while satisfying gradient system limits, even when classic pre-emphasis produces infeasible results. Substantial reduction in ghosting artefacts from EPI imaging was observed, including an average reduction of 77% in ghost amplitude in phantoms. This work demonstrates numerical optimization of gradient waveforms, yielding substantially improved image quality when given a model for system imperfections. While the method as implemented has limited flexibility, it could enable more efficient hardware usage, and may prove particularly important for maximizing performance of lower-cost systems.

physics.med-ph↗