By providing the first openly available end-to-end implementation for phantom-based GIRF measurement, the barrier to routine scanner-specific GIRF characterisation is reduced, facilitating broader adoption of GIRF-based methods across the MRI community.
Abstract
The Gradient Impulse Response Function (GIRF) is widely used to model and correct gradient system imperfections in MRI, but scanner-specific GIRF measurement remains inaccessible to many research groups because existing approaches rely on specialised field monitoring hardware or fragmented and non-reproducible software workflows. To address this limitation, an open-source, end-to-end framework for phantom-based GIRF measurement is presented, providing a reproducible workflow requiring only standard MRI hardware and a spherical water phantom. The framework integrates vendor-independent pulse sequence generation, phantom-based data acquisition, automated data processing, and GIRF estimation. The framework was validated by comparing GIRF-predicted non-Cartesian k-space trajectories with independent measurements acquired using NMR field probes, which served as the gold-standard for trajectory characterization. Accurate prediction of rosette and spiral trajectories was demonstrated across multiple imaging orientations, with substantially lower trajectory error than the corresponding nominal trajectories. By providing the first openly available end-to-end implementation for phantom-based GIRF measurement, the barrier to routine scanner-specific GIRF characterisation is reduced, facilitating broader adoption of GIRF-based methods across the MRI community.
This work enables reproducibility of advanced computational MRI methods within a comprehensive end-to-end open-source framework and proves that quantitative MRI methods consisting of acquisition and reconstruction were successfully implemented in BART.
Daniel Mackner, Philip Schaten, Markus Huemer et al.· arXiv.org· 0 citations
An open-source reference system for portable low-field MRI designed to support replication, reproducibility, benchmarking, and quantitative comparison is presented, aiming to support cross-site comparability, reproducible research, and collaborative development of future portable low-field MRI technologies.
D. Schote, H. Herthum, Umberto Zanovello et al.· 0 citations
This work presents an open-source, optimized solenoid head coil tailored for the 50 mT open-source scanner (OSII ONE v2.1), set the basis for a fully reliable and reproducible component for the open-source OSII ONE MRI scanner.
Umberto Zanovello, Julia Pfitzer, Ariane Ernst et al.· 1 citation
Radiomics features are strongly sensitive to image acquisition, and separating that sensitivity from biological signal usually requires repeated patient scans that cannot be shared. We present an open, fully synthetic framework (radiomics-phantom) that maps and, as a proof of concept, corrects radiomics feature instability without any patient data. Deterministic three-dimensional texture phantoms are generated as anisotropic Gaussian random fields with known ground truth and an optional embedded lesion. An independently implemented feature core aligned with the Image Biomarker Standardization Initiative (IBSI) covers all eleven IBSI-1 feature families and matched all 482 published digital-phantom benchmark values within the applicable tolerances. An image-domain acquisition simulator applies point-spread blur, slice-profile averaging, dose-scaled correlated noise, resampling, and quantisation. Per-feature reproducibility across a sweep of fifteen textures (varying correlation length, anisotropy, and intensity scale) by nine acquisition conditions, with five independent noise realisations per stochastic setting, is summarised by the absolute-agreement intraclass correlation ICC(2,1), with a realisation-aware percentile-bootstrap 95% confidence interval for every estimate; constant features are excluded from estimation. Values span nearly the full range (median 0.13, 95% CI 0.03–0.19), and a hierarchical variance decomposition attributes a median 77% of per-feature variance to the acquisition condition and under 1% to stochastic realisation; the values are interpreted as exploratory rankings within this acquisition envelope. As a proof of concept, intensity variance and grey-level co-occurrence contrast under additive Gaussian noise were normalised using calibrated, invertible response models, returning them to their noiseless values on held-out data (median error below 4% across five textures and repeated noise realisations, and about 11% when the noise level is estimated from the degraded image itself), while features the models cannot describe are refused rather than corrected. All code and a 716-test suite are released openly and archived on Zenodo. The result is a reproducible, patient-data-free testbed for radiomics feature stability.
OBJECTIVE
This study presents an active sensing framework for information-optimal, model-based tracking of de formable anatomy to track the three-dimensional (3D) surface of the heart ventricles using a time sequence of two-dimensional (2D) image slices.
METHODS
A low-order deformable model parameterizes the cardiac surface, while cardiac motion dynamics are modeled by a recursive adaptive filter and tracked using a particle filter. Measurements of the system state are obtained from a magnetic resonance imaging (MRI) system whose slice selection is governed by an entropy-minimizing active sensing method that chooses the sensing action maximizing expected information gain. Performance is compared with cases where the image slice is fixed or randomly selected. The framework operates on general 2D image streams and is validated on multi-slice cine MRI data to enable comparison against ground-truth slice locations. A variable temporal sampling strategy reduces computational load by executing tracking updates at intervals defined by a temporal sampling factor.
RESULTS
The active sensing-based tracking method captured left ventricular (LV) surface points-of-interest within 3 pixels of accuracy at a mean root-mean-square error (RMSE) of 2.93mm, right ventricular (RV) tracking was 4.27mm, for an overall mean RMSE of 3.25mm. For a downsampling factor of 4, the framework maintains an overall RMSE of 3.57mm, a modest degradation relative to fully sampled tracking.
CONCLUSION
The proposed frame work enables a flexible trade-off between computational efficiency and tracking performance.
SIGNIFICANCE
This work demonstrates that information-driven measurement selection can enhance de formable anatomical tracking under sensing constraints.
E. Tuna, D. A. Herzka, M. Cenk Çavuşoğlu· IEEE transactions on bio-med...· 0 citations
Q-space trajectory imaging (QTI) provides promising markers of tissue microstructure, but clinical translation requires shorter acquisitions, faster analysis, and more robust parameter estimation at high spatial resolution. To address these barriers, we trained a voxel-wise multilayer perceptron (MLP) to infer QTI-derived scalar parameters directly from the diffusion signal. We established reference QTI parameters of the brain in 18 healthy subjects using constrained fitting on 50-min QTI scans. The MLP was trained to estimate those reference parameters from a five-minute subset of the diffusion data. We compared the MLP with the constrained fit applied to the same short-protocol input, computing normalized root mean squared error, peak signal-to-noise ratio, and structural similarity with respect to the reference. Here, the MLP consistently achieved better performance metrics, with normalized root mean squared errors up to two-fold lower. For one whole-brain dataset, MLP inference reduced computation time from more than an hour with constrained fitting to a few seconds. Robustness to lower SNR was tested in a separate 1.7 mm isotropic voxel size acquisition of the full protocol, in which the MLP retained lower errors and less visually apparent noise. Finally, we demonstrate qualitative feasibility in two glioma patients scanned with the short protocol. We conclude that a simple MLP can provide high-quality QTI parameter estimates from short tensor-valued diffusion acquisitions. This enables five-minute, high-resolution QTI and may encourage further clinical studies of markers such as microscopic fractional anisotropy.
Oliver Gödicke, Jin-Yang Yu, F. Laun et al.· Magnetic Resonance Imaging· 0 citations
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