arXiv subjects

Joseph V Hajnal

Publications and source records attributed to Joseph V Hajnal.

11 recordsLinked to original sources

arXiv2025

From Offline to Inline Without Pain: A Practical Framework for Translating Offline MR Reconstructions to Inline Deployment Using the Gadgetron Platform

Purpose: To develop and validate a practical framework to overcome common issues in inline deployment of established offline MR reconstruction, including (1) delay from lengthy reconstructions, (2) limited support for multi-scan input reconstructions, (3) the need to adapt scripts for different raw formats, and (4) limited guidance and experience in retaining scanner reconstructions and applying scanner-based post-processing to custom outputs. Methods: The framework builds upon the Gadgetron platform and includes: (1) an input converter to transform ISMRMRD format raw into a Siemens format raw structure, facilitating reuse of existing code; (2) an asynchronous trigger-and-retrieve mechanism enabling long reconstructions without delaying scanner processes; (3) resource-aware scheduling for parallel execution; (4) integrated file management to support multi-scan inputs; and (5) preservation of scanner-based reconstructions and post-processing. The framework was validated on 2 Siemens scanners for SENSE, AlignedSENSE, and NUFFT reconstructions, and in a large-cohort study. Results: Minimum code modification for inline deployment has been shown, and all reconstructions were successfully executed inline without disrupting scanner workflows. Images were retrieved via automated or retro-reconstruction, with scanner-based post-processing applied to custom outputs. Multi-scan input reconstructions were executed using GPU-aware scheduling, confirming feasibility for routine and large-scale applications. In 480 consecutive examinations, inline reconstructions were retrieved in 99% of cases without disruptions. Conclusion: The framework lowers the technical barrier to inline deployment of offline reconstructions, enabling robust, scalable, and post-processing-compatible integration. It is openly available with documentation and demonstration cases to support reproducibility and community adoption.

physics.med-ph↗

arXiv2025

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↗

arXiv2025

Scanner-based real-time automated volumetry reporting of the fetus, amniotic fluid, placenta and umbilical cord for fetal MRI at 0.55T

Purpose: This work aims to enable real-time automated intra-uterine volumetric reporting and fetal weight estimation for fetal MRI, deployed directly on the scanner. Methods: A multi-region segmentation nnUNet was trained on 146 bSSFP images of 73 fetal subjects (coronal and axial orientations) for the parcellation of the fetal head, fetal body, placenta, amniotic fluid and umbilical cord from whole uterus bSSFP stacks. A reporting tool was then developed to integrate the segmentation outputs into an automated report, providing volumetric measurements, fetal weight estimations, and z-score visualisations. The complete pipeline was subsequently deployed on a 0.55T MRI scanner, enabling real-time inference and fully automated reporting in the duration of the acquisition. Results: The segmentation pipeline was quantitatively and retrospectively evaluated on 36 stacks of 18 fetal subjects and demonstrated sufficient performance for all labels, with high scores (>0.98) for the fetus, placenta and amniotic fluid, and 0.91 for the umbilical cord. The prospective evaluation of the scanner deployment step was successfully performed on 50 cases, with the regional volumetric reports available directly on the scanner. Conclusions: This work demonstrated the feasibility of multi-regional intra-uterine segmentation, fetal weight estimation and automated reporting in real-time. This study provides a robust baseline solution for the integration of fully automated scanner-based measurements into fetal MRI reports.

physics.med-ph↗

arXiv2024

Real-time fetAl brain and placental T2* mapping at 0.55T low-field MRI (RAT)

Purpose: To provide real-time quantitative organ-specific information - specifically placental and brain T2* - to allow optimization of the MR examination to the individual patient. Methods: A FIRE-based real-time setup segmenting placenta and fetal brain in real-time, performing T2* fitting and analysis and calculation of the centile was implemented. A nn-UNet were trained and tested on 2989 datasets for the fetal brain and a second one trained on 210 datasets for the placenta for automatic segmentation. T2* normal curves were obtained from 106 cases and prospective evaluation was performed on 10 cases between 35 and 39 weeks GA. Results: Quantitative brain and placental T2* maps and centiles were available in all prospective cases within 30 seconds. The robustness of the method was shown with intra-scan repeats (mean difference 1.04+/-12.39 ms for fetal brain and -3.15+/-8.88 ms for placenta) and direct validation with vendor-processed offline results (mean difference 1.62+/-4.33 ms for fetal brain and 0.16+/-6.19 ms for placenta). Discussion and Conclusion: Real-time available organ-specific quantitative information enables more personalized MR examinations, selection of the most pertinent sequences and thus the promise of reduced recalls and specific insights into tissue properties. The here enabled placental T2*, demonstrated in multiple recent studies to be a biomarker sensitive to a range of pregnancy complications, and fetal brain T2* will be explored in further studies in pregnancies with pre-eclampsia, growth restriction as a way of enabling future MR-guided fetal interventions.

physics.med-ph↗

arXiv2024

AutOmatic floW planning for fetaL MRI (OWL)

Two subsequent deep learning networks, one localizing the fetal chest and one identifying a set of landmarks on a coronal whole-uterus balanced steady-state free precession scan, were trained on 167 and 71 fetal datasets across field strengths, acquisition parameters, and gestational ages and implemented in a real-time setup. Next, a phase-contrast sequence was modified to use the identified landmarks for planning. The OWL pipeline was evaluated retrospectively in 10 datasets and prospectively in 7 fetal subjects (gestational ages between 36+3 and 39+3 weeks). The prospective cases were additionally manually planned to enable direct comparison both qualitatively, by scoring the planning quality, and quantitatively, by comparing the indexed flow measurements. OWL enabled real-time fully automatic planning of the 2D phase-contrast scans in all but one of the prospective participants. The fetal body localization achieved an overall Dice score of 0.94+-0.05 and the cardiac landmark detection accuracy was 5.77+-2.91 mm for the descending aorta, 4.32+-2.44 mm for the spine, and 4.94+-3.82 mm for the umbilical vein. For the prospective cases, overall planning quality was 2.73/4 for the automated scans, compared to 3.0/4 for manual planning, and the flow quantitative evaluation showed a mean difference of -1.8% (range -14.2% to 14.9%) by comparing the indexed flow measurements obtained from gated automatic and manual acquisitions. Real-time automated planning of 2D phase-contrast MRI was effectively accomplished for 2 major vessels of the fetal vasculature. While demonstrated here on 0.55T, the achieved method has wider implications, and training across multiple field strengths enables generalization. OWL thereby presents an important step towards extending access to this modality beyond specialised centres.

physics.med-ph↗

arXiv2024

Fully automated planning for anatomical fetal brain MRI on 0.55T

Purpose: Widening the availability of fetal MRI with fully automatic real-time planning of radiological brain planes on 0.55T MRI. Methods: Deep learning-based detection of key brain landmarks on a whole-uterus EPI scan enables the subsequent fully automatic planning of the radiological single-shot Turbo Spin Echo acquisitions. The landmark detection pipeline was trained on over 120 datasets from varying field strength, echo times and resolutions and quantitatively evaluated. The entire automatic planning solution was tested prospectively in nine fetal subjects between 20 and 37 weeks. Comprehensive evaluation of all steps, the distance between manual and automatic landmarks, the planning quality and the resulting image quality was conducted. Results: Prospective automatic planning was performed in real-time without latency in all subjects. The landmark detection accuracy was 4.21+-2.56 mm for the fetal eyes and 6.47+-3.23 for the cerebellum, planning quality was 2.44/3 (compared to 2.56/3 for manual planning) and diagnostic image quality was 2.14 compared to 2.07 for manual planning. Conclusions: Real-time automatic planning of all three key fetal brain planes was successfully achieved and will pave the way towards simplifying the acquisition of fetal MRI thereby widening the availability of this modality in non-specialist centres.

physics.med-ph↗

arXiv2023

The Challenge of Fetal Cardiac MRI Reconstruction Using Deep Learning

Dynamic free-breathing fetal cardiac MRI is one of the most challenging modalities, which requires high temporal and spatial resolution to depict rapid changes in a small fetal heart. The ability of deep learning methods to recover undersampled data could help to optimise the kt-SENSE acquisition strategy and improve non-gated kt-SENSE reconstruction quality. In this work, we explore supervised deep learning networks for reconstruction of kt-SENSE style acquired data using an extensive in vivo dataset. Having access to fully-sampled low-resolution multi-coil fetal cardiac MRI, we study the performance of the networks to recover fully-sampled data from undersampled data. We consider model architectures together with training strategies taking into account their application in the real clinical setup used to collect the dataset to enable networks to recover prospectively undersampled data. We explore a set of modifications to form a baseline performance evaluation for dynamic fetal cardiac MRI on real data. We systematically evaluate the models on coil-combined data to reveal the effect of the suggested changes to the architecture in the context of fetal heart properties. We show that the best-performers recover a detailed depiction of the maternal anatomy on a large scale, but the dynamic properties of the fetal heart are under-represented. Training directly on multi-coil data improves the performance of the models, allows their prospective application to undersampled data and makes them outperform CTFNet introduced for adult cardiac cine MRI. However, these models deliver similar qualitative performances recovering the maternal body very well but underestimating the dynamic properties of fetal heart. This dynamic feature of fast change of fetal heart that is highly localised suggests both more targeted training and evaluation methods might be needed for fetal heart application.

eess.IV↗

arXiv2022

3-D Coherent Multi-Transducer Ultrasound Imaging with Sparse Spiral Arrays

Coherent multi-transducer ultrasound (CoMTUS) creates an extended effective aperture through the coherent combination of multiple arrays, which results in images with enhanced resolution, extended field-of-view, and higher sensitivity. The subwavelength localization accuracy of the multiple transducers required to coherently beamform the data is achieved by using the echoes backscattered from targeted points. In this study, CoMTUS is implemented and demonstrated for the first time in 3-D imaging using a pair of 256-element 2-D sparse spiral arrays, which keep the channel-count low and limit the amount of data to be processed. The imaging performance of the method was investigated using both simulations and phantom tests. The feasibility of free-hand operation is also experimentally demonstrated. Results show that, in comparison to a single dense array system using the same total number of active elements, the proposed CoMTUS system improves spatial resolution (up to 10 times) in the direction where both arrays are aligned, contrast-to-noise-ratio (CNR, up to 30%), and generalized CNR (up to 11%). Overall, CoMTUS shows narrower main lobe and higher contrast-to-noise-ratio, which results in an increased dynamic range and better target detectability.

physics.med-ph↗

arXiv2019

Perfusion and apparent oxygenation in the human placenta (PERFOX)

Purpose: To study placental function - both perfusion and an oxygenation surrogate (T2*)-simultaneously and quantitatively in-vivo. Methods: 15 pregnant women were scanned on a 3T MR scanner. For perfusion measurements, a velocity selective arterial spin labelling preparation module was placed before a multi-echo gradient echo EPI readout to integrate T2* and perfusion measurements in one joint perfusion-oxygenation (PERFOX) acquisition. Joint motion correction and quantification were performed to evaluate changes in T2* and perfusion over GA. Results: The optimised integrated PERFOX protocol and post-processing allowed successful visualization and quantification of perfusion and T2* in all subjects. Areas of high T2* and high perfusion appear to correspond to placental sub-units and show a systematic offset in location along the maternal-fetal axis. The areas of highest perfusion are consistently closer to the maternal basal plate and the areas of highest T2* closer to the fetal chorionic plate. Quantitative results show a strong negative correlation of gestational age with T2* and weak negative correlation with perfusion. Conclusion: A strength of the joint sequence is that it provides truly simultaneous and co-registered estimates of local T2* and perfusion, however, to achieve this, the time per slice is prolonged compared to a perfusion only scan which can potentially limit coverage. The achieved interlocking can be particularly useful when quantifying transient physiological effects such as uterine contractions. PERFOX opens a new avenue to elucidate the relationship between maternal supply and oxygen uptake, both of which are central to placental function and dysfunction.

physics.med-ph↗

arXiv2018

Fetal whole-heart 4D imaging using motion-corrected multi-planar real-time MRI

Purpose: To develop a MRI acquisition and reconstruction framework for volumetric cine visualisation of the fetal heart and great vessels in the presence of maternal and fetal motion. Methods: Four-dimensional depiction was achieved using a highly-accelerated multi-planar real-time balanced steady state free precession acquisition combined with retrospective image-domain techniques for motion correction, cardiac synchronisation and outlier rejection. The framework was evaluated and optimised using a numerical phantom, and evaluated in a study of 20 mid- to late-gestational age human fetal subjects. Reconstructed cine volumes were evaluated by experienced cardiologists and compared with matched ultrasound. A preliminary assessment of flow-sensitive reconstruction using the velocity information encoded in the phase of dynamic images is included. Results: Reconstructed cine volumes could be visualised in any 2D plane without the need for highly-specific scan plane prescription prior to acquisition or for maternal breath hold to minimise motion. Reconstruction was fully automated aside from user-specified masks of the fetal heart and chest. The framework proved robust when applied to fetal data and simulations confirmed that spatial and temporal features could be reliably recovered. Expert evaluation suggested the reconstructed volumes can be used for comprehensive assessment of the fetal heart, either as an adjunct to ultrasound or in combination with other MRI techniques. Conclusion: The proposed methods show promise as a framework for motion-compensated 4D assessment of the fetal heart and great vessels.

physics.med-ph↗

arXiv2018

Integrated and efficient diffusion-relaxometry using ZEBRA

The emergence of multiparametric diffusion models combining diffusion and relaxometry measurements provide powerful new ways to explore tissue microstructure with the potential to provide new insights into tissue structure and function. However, their ability to provide rich analyses and the potential for clinical translation critically depends on the availability of efficient, integrated, multi-dimensional acquisitions. We propose a fully integrated sequence simultaneously sampling the acquisition parameter spaces required for T1 and T2* relaxometry and diffusion MRI. Slice-level interleaved diffusion encoding, multiple spin/gradient echoes and slice-shuffling are combined for higher efficiency, sampling flexibility and enhanced internal consistency. In-vivo data was successfully acquired on healthy adult brains. Obtained parametric maps as well as clustering results demonstrate the potential of the technique regarding its ability to provide eloquent data with an acceleration of roughly 20 compared to conventionally used approaches. The proposed integrated acquisition, called ZEBRA, offers significant acceleration and flexibility compared to existing diffusion-relaxometry studies and thus facilitates wider use of these techniques both for research-driven and clinical applications.

physics.med-ph↗