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

Hanhan Wu; Yuchen Yuan; Weiwen Jiang

eess.IVNov 1, 2026

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

Authors: Hanhan Wu, Yuchen Yuan, Weiwen Jiang

Organizations: George Mason University, Fairfax, VA, USA

Abstract

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.

Figures & tables

Feature	MRiLab	JEMRIS	fastMRI	OpenULF
Bloch spin dynamics	✓	✓	–	✓
Pulse sequence sim.	✓	✓	–	✓
ULF sensor models	–	–	–	✓
SQUID + Coil in one	–	–	–	✓
ML reconstruction	–	–	✓	✓
Multi-model library	–	–	✓	✓

TABLE I : Feature comparison of MRI simulation tools (✓ = supported, – = not). “Automated param. sweep” means a built-in resumable, deduplicated sweep engine, not the general scripting that MRiLab and JEMRIS also provide; “Multi-model library” means bundled reconstruction models.

Fig. 1 : Overview of OpenULF. (a) Component library with extensible physics, ML, data, and composition modules. (b) OpenULF toolkit: the co-simulation pipeline (left sub-box) transforms tissue maps through sensor-specific receivers and ML reconstruction; the design-space exploration engine (right sub-box) sweeps $B_{0}\times\text{NEX}\times\text{channels}\times\text{model}\times\text{sensor}$ , training 774 models across four datasets. (c) Design insights: three quantitative design rules plus cross-dataset and compression-ratio validation. The top-level feedback arrow indicates that insights drive iterative extension of the component library.

#	Test description	Error
1	Single-voxel $M_{\perp}$ vs. spin-echo (6 tissues)	0.00%
2	$T_{1}$ contrast: GM/WM ratio vs. TR (5 pts)	0.00%
3	$T_{2}$ decay: signal vs. TE (6 points)	0.00%
4	Prepolarization (12 config combinations)	$<$ 0.01%
5	Image: Parseval, k-space DC, tissue order	0.00%
6	$T_{1}$ field dependence [ 23 ]	0.00%

TABLE II : Numerical-correctness check of the forward engine against closed-form Bloch solutions. Near-zero error is expected of a correct implementation; physical realism comes from measured parameters (Section IV-A ). Here 0.00% denotes error below the two-decimal display threshold.

		SQUID		Coil		Both
Dataset	Model	dB	SSIM	dB	SSIM	dB	SSIM	Gap
BrainWeb	Input	11.0	.16	5.3	.02	–	–	+5.8
	SimpleCNN	18.0	.41	12.2	.16	18.1	.49	+5.9
	DnCNN	18.9	.66	13.8	.37	19.0	.68	+5.2
	UNet	19.8	.80	16.5	.59	19.8	.79	+3.4
	NAFNet	19.8	.78	16.5	.61	19.8	.78	+3.3

TABLE III : Test-set PSNR (dB) and SSIM across 4 datasets, 5 models, and 3 sensor configurations. The Input row reports the raw sensor output (before ML) against the ideal image. Gap = SQUID

-

Coil in PSNR.

Fig. 2 : Reconstruction on a single representative BrainWeb test slice (per-slice PSNR shown; test-set means are in Table III ). Top row: ideal target and noisy inputs from SQUID and coil. Bottom row: UNet outputs from each sensor configuration.

Fig. 3 : Input vs. output SQUID-coil PSNR gap for four datasets (marker: 5-model mean, whisker: min–max across the five networks). The dashed CR

=

1 line is where ML leaves the gap unchanged; above it the gap is amplified, below compressed.

Fig. 4 : PSNR gap (SQUID minus coil) vs.

B_{0}

for three models at NEX = 50. Positive values indicate SQUID is the superior sensor; negative values favor the coil. All three models cross zero near 450

\mu

T; the shaded band (418–473

\mu

T) spans the three models’ individual zero-crossings, confirming that the crossover is driven by sensor physics rather than model choice.

Fig. 5 : Design-space coverage: test PSNR (UNet) over the swept $(B_{0},\text{NEX})$ grid for the SQUID (left) and coil (right) sensors. Brighter is better; gray cells were not trained, exposing the sweep’s goal-directed, non-exhaustive coverage. SQUID leads at low $B_{0}$ and the coil at high $B_{0}$ ; the crossover of Fig. 4 is where they switch.

Fig. 6 : Two faces of hardware–ML substitutability on SQUID (

B_{0}=50\,\mu

T). Left: test PSNR vs. NEX, where UNet at NEX = 4 matches SimpleCNN at NEX = 20, trading model capacity for a

5\times

shorter scan. Right: test PSNR vs. SQUID channel count, where a 2-channel UNet matches SimpleCNN between its 3- and 4-channel points, trading model capacity for sensor channels.

Magnetic Resonance Imaging

Magnetic resonance imaging uses magnetic fields and radio waves to create images of anatomy and tissue function for research and clinical care.

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ULF-Synth: Physics-Guided Ultra-Low-Field MRI Enhancement for Pediatric Neuroimaging

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Kwame Nkrumah University of Science and Technology, Kumasi, Ghana · University of Catania, Catania, Italy · The Hong Kong University of Science and Technology, Hong Kong SAR, China+1

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Ultra-low-field (ULF) MRI offers portable and accessible neuroimaging but suffers from reduced signal-to-noise ratio and limited spatial resolution compared to high-field (HF) systems. Acquiring paired ULF-HF data for supervised enhancement is often difficult, particularly in resource-limited settings. We introduce ULF-Synth, a framework that combines: (i) acquisition-based synthesis of realistic ULF images from HF volumes to create large-scale paired training data, (ii) a spatial-frequency domain objective that prioritizes recovery of high-frequency anatomical detail. This formulation is architecture-agnostic, consistently improving structural similarity and perceptual fidelity across encoder-decoder, adversarial, and diffusion-based translation models. When trained exclusively on synthetic data, the resulting models generalize effectively to real 64mT ULF acquisitions, improving downstream multiclass brain segmentation and achieving higher radiologist preference and diagnostic acceptability in a blinded reader study. These findings demonstrate that synthetic paired supervision provides a practical and scalable pathway for enhancing ULF MRI without requiring real paired acquisitions. Code, Models and Dataset: https://github.com/toufiqmusah/ULF-Synth

Abstract

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