OpenULF: A Modular Toolkit Enables Data-Driven Ultra-Low-Field MRI with both Classical and Quantum Sensors

Ultra-low-field (ULF) Magnetic Resonance Imaging (MRI) offers a portable, low-cost alternative to conventional scanners by operating at field strengths below 10 mT. At these fields, two sensor technologies compete: quantum SQUID (Superconducting Quantum Interference Device) sensors, which provide high sensitivity but require cryogenic cooling, and classical induction coils, which are inexpensive and room-temperature but produce weaker signals. With conventional physics-based imaging, both suffer from low signal-to-noise ratios (SNR). Data-driven reconstruction has overcome similar limits in related inverse problems, but its use for ULF-MRI has been hindered by a lack of open datasets, benchmarks, and flexible simulators. To close this gap, we present OpenULF, a modular, open-source simulation toolkit that generates paired training data and supports end-to-end machine-learning (ML) reconstruction. OpenULF integrates a Bloch-equation-validated physics simulator, calibrated SQUID and coil receiver models, reconstruction ML networks, and an automated design-space exploration engine. Architecturally, a receiver abstract base class, a typed HDF5 contract that decouples physics from learning, and a resumable sweep engine make sensors, models, and datasets independently substitutable. Using OpenULF, we train 774 models across four anatomical datasets and conduct three design-space explorations. The effectiveness of ML reconstruction depends strongly on the dataset and the sensor. We identify a SQUID-to-coil crossover near 450 $μ$T and demonstrate that sensor hardware and ML model capacity can be traded off during system design. Sensor and reconstruction choices must therefore be co-designed: the best sensor is set by the dataset and field strength, not by SNR.

Hanhan Wu, Yuchen Yuan, Weiwen Jiang

Leverage4 / 5

A general-purpose tool used across several fields (Adam, ResNet, LoRA, next-generation sequencing).

24% of this desk’s rubric

Magnitude2 / 5

Solid incremental gain on a meaningful problem.

18% of this desk’s rubric

Evidence4 / 5

Strong: large scale, preregistered, independently replicated, or a well-powered randomized trial.

14% of this desk’s rubric

Novelty2 / 5

A new combination of known ideas.

16% of this desk’s rubric

Trajectory2 / 5

Some room to improve with obvious engineering.

18% of this desk’s rubric

Stakes2 / 5

Benefits a professional community (practitioners, clinicians, engineers).

10% of this desk’s rubric

No attention signals recorded yet.

• Reviewed by heuristic-v10 on Nov 3, 2026, 13:38 UTC. Paper type: method.
• Categories: eess.IV, cs.CV, physics.med-ph
• BRIEF, No.4 in the Physics edition of November 4, 2026.