Biology subjects

Jezzard, P.

Publications and source records attributed to Jezzard, P..

5 recordsLinked to original sources

bioRxiv2026

Open-Source Framework for Measurement of the Gradient Impulse Response Function

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.

bioengineering↗

bioRxiv2026

From 3D Time-of-Flight Angiography to Accelerated 4D Arterial Spin Labeling Angiography: A Fast Few-Shot Transfer Learning Approach

PurposeTo develop a data-efficient deep learning framework for rapid reconstruction of highly accelerated 4D arterial spin labeling (ASL) magnetic resonance angiography (MRA) with robust generalization using extremely limited acquired data, addressing the challenges of prolonged acquisition and reconstruction time. MethodsA simulation-driven, few-shot transfer learning approach was adopted by leveraging publicly available 3D time-of-flight (TOF)-MRA data to generate realistic multi-coil complex-valued pseudo-ASL k-space datasets for large-scale pre-training. A 3D unrolled reconstruction network was trained on this simulated data using a histogram-weighted loss and subsequently extended to 4D using lightweight temporal fusion modules. Fine-tuning was performed using only two experimentally acquired 4D ASL-MRA datasets. The method was evaluated on retrospectively and prospectively undersampled Cartesian 4D ASL-MRA data acquired at 3T and compared with compressed sensing (CS) and locally low-rank (LLR) reconstructions. ResultsThe proposed method achieved superior reconstruction quality compared with CS and LLR, with improved vessel depiction, particularly in distal branches, and enhanced temporal fidelity. Quantitative evaluation demonstrated higher vessel-masked peak signal-to-noise ratio and structural similarity index measure, along with increased error entropy, indicating reduced noise and structured artifacts. The initial pre-trained model already outperformed conventional methods, while additional 4D fine-tuning further improved performance. Robust reconstruction was demonstrated in prospectively undersampled data and multi-slab acquisitions, enabling large-coverage, time-resolved angiography within clinically feasible scan times (4-6 min). ConclusionsSimulation-driven pre-training combined with few-shot fine-tuning enables accurate and rapid reconstruction of highly accelerated 4D ASL-MRA in data-limited settings. The proposed framework provides a practical pathway toward clinically feasible, non-contrast dynamic cerebrovascular imaging.

neuroscience↗

bioRxiv2026

MR Spectroscopy without Water Suppression using the Gradient Impulse Response Function

PurposeNon-water-suppressed proton spectroscopy, 1H-MRS, is desirable, as retaining the strong water resonance can facilitate automated online data corrections, internal concentration referencing, and monitoring of line narrowing effects in functional MRS. Removal of the water suppression module can also mitigate magnetization transfer effects and slightly reduce the minimum achievable TR and total RF power deposition. However, water suppression is typically considered essential due to eddy current-induced antisymmetric sidebands on the water resonance that distort the spectral baseline and obscure metabolite signals. Theory and MethodsThe Gradient Impulse Response Function (GIRF) was used to predict time-dependent magnetic field perturbations during the FID that generate the artefactual sidebands. The GIRF was measured in a one-time calibration, independent of spectroscopy acquisitions, enabling post-processing correction of the sidebands without sequence modification or additional dedicated hardware. GIRF-corrected non-water-suppressed single-voxel-spectroscopy (SVS) was compared to otherwise identical water-suppressed acquisitions in eight participants at 3T using semi-LASER and MEGA-PRESS sequences. ResultsAcross participants, GIRF correction reduced sideband amplitudes to levels comparable with the spectral baseline, enabling recovery of the underlying metabolite signals. Systematic increases in quantified metabolite concentrations were observed relative to water-suppressed acquisitions, consistent with water-suppression-induced magnetization transfer effects. Total creatine exhibited the largest increase, with enhancement ratios of 1.069{+/-}0.039 for MEGA-PRESS and 1.535{+/-}0.160 for semi-LASER acquisitions. ConclusionGradient-induced artefactual sidebands in non-water-suppressed MR spectroscopy can be effectively corrected using the GIRF to predict time-dependent magnetic field perturbations during the FID. In principle, the approach extends to other SVS sequences and field strengths following appropriate GIRF calibration.

bioengineering↗

bioRxiv2025

Few-shot learning for highly accelerated 3D time-of-flight MRA reconstruction

PurposeTo develop a deep learning-based reconstruction method for highly accelerated 3D time-of-flight magnetic resonance angiography (TOF-MRA) that achieves high-quality reconstruction with robust generalization using extremely limited acquired raw data, addressing the challenge of time-consuming acquisition of high-resolution, whole-head angiograms. MethodsA novel few-shot learning-based reconstruction framework is proposed, featuring a 3D variational network specifically designed for 3D TOF-MRA that is pre-trained on simulated complex-valued, multi-coil raw k-space datasets synthesized from diverse open-source magnitude images and fine-tuned using only two single-slab experimentally acquired datasets. The proposed approach was evaluated against existing methods on acquired retrospectively undersampled in vivo k-space data from five healthy volunteers and on prospectively undersampled data from two additional subjects. ResultsThe proposed method achieved superior reconstruction performance on experimentally acquired in vivo data over comparison methods, preserving most fine vessels with minimal artifacts with up to 8-fold acceleration. Compared to other simulation techniques, the proposed method generated more realistic raw k-space data for 3D TOF-MRA. Consistently high-quality reconstructions were also observed on prospectively undersampled data. ConclusionsBy leveraging few-shot learning, the proposed method enabled highly accelerated 3D TOF-MRA relying on minimal experimentally acquired data, achieving promising results on both retrospective and prospective in vivo data while outperforming existing methods. Given the challenges of acquiring and sharing large raw k-space datasets, this holds significant promise for advancing research and clinical applications in high-resolution, whole-head 3D TOF-MRA imaging.

neuroscience↗

bioRxiv2019

The advantages of radial trajectories for vessel-selective dynamic angiography with arterial spin labeling

ObjectTo demonstrate the advantages of radial k-space trajectories over conventional Cartesian approaches for accelerating the acquisition of vessel-selective arterial spin labeling (ASL) dynamic angiograms, which are conventionally time-consuming to acquire.\n\nMaterials and MethodsVessel-encoded pseudocontinuous ASL was combined with time-resolved balanced steady-state free precession (bSSFP) and spoiled gradient echo (SPGR) readouts to obtain dynamic vessel-selective angiograms arising from the four main brain-feeding arteries. Dynamic 2D protocols with acquisition times of one minute or less were achieved through radial undersampling or a Cartesian parallel imaging approach. For whole-brain dynamic 3D imaging, magnetic field inhomogeneity and the high acceleration factors required rule out the use of bSSFP and Cartesian trajectories, so the feasibility of acquiring 3D radial SPGR angiograms was tested.\n\nResultsThe improved SNR efficiency of bSSFP over SPGR was confirmed for 2D dynamic imaging. Radial trajectories had considerable advantages over a Cartesian approach, including a factor of two improvement in the measured SNR (p<0.00001, N=6), improved distal vessel delineation and the lack of a need for calibration data. The 3D radial approach produced good quality angiograms with negligible artifacts despite the high acceleration factor (R=13).\n\nConclusionRadial trajectories outperform conventional Cartesian techniques for accelerated vessel-selective ASL dynamic angiography.

neuroscience↗