arXiv subjects

Beatrice Lena

Publications and source records attributed to Beatrice Lena.

6 recordsLinked to original sources

arXiv2026

Deep Unrolled Networks in Representation Space Applied to MRI Reconstruction

Deep unrolled networks (DUNs) integrate physical forward models with learned regularization in cascaded network architectures, achieving exceptional performance in inverse problems while maintaining interpretability. While most DUNs operate in the object domain (e.g., image space), recent variants explored representation spaces for improved information flow. However, these methods rely on heuristic methods for data consistency (DC), sacrificing fidelity with measurements. In this work, we introduce DUNE (Deep Unrolled Networks in rEpresentation space), a framework that maintains exact adherence to physical measurements while operating in learned representation spaces. By deriving the DC gradient via the chain rule and implementing it through the Vector-Jacobian Product (VJP), we enable exact backpropagation of measurement residuals into the representation space. This formulation supports diverse architectural backbones, including pre-trained encoders to guide the iterative process. We assess DUNE against state-of-the-art baselines on accelerated MRI reconstruction tasks, demonstrating that exact VJP-based gradients yield superior reconstruction quality and structural fidelity across both single-channel portable low-field and multi-channel clinical high-field MRI acquisitions. The code will be available upon publication at https://github.com/EfeIlicak/DUNE.

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arXiv2026

Physics-Guided Dual-Domain Network with Attention-Based Fusion for Portable MRI Reconstruction

Portable low-field magnetic resonance imaging (MRI) systems have gained renewed interest owing to their cost effectiveness and point-of-care imaging capabilities. Yet, portable MRI systems suffer from relatively low signal-to-noise ratio and limited hardware capabilities. While previous works have proposed the use of deep learning based reconstruction methods to improve low-field image quality, these operated only in the image-domain. Unlike other imaging modalities, MRI directly acquires data in the Fourier-domain (k-space), and exploiting both k-space and image-domain information can improve reconstruction quality. Here, we introduce DUN-DD, a novel physics-guided 3D network for portable MRI reconstruction, with parallel dual-domain branches whose outputs are combined together via an attention-based fusion network. To demonstrate the performance of the proposed method, we present \textit{in vivo} reconstructions obtained from both emulated datasets as well as images acquired with a 47mT Halbach-based portable MRI system. Our results show that DUN-DD outperforms state-of-the-art classical, data-driven, and physics-guided methods on both emulated and real portable MRI acquisitions.

physics.med-ph↗

arXiv2025

Physics-Informed Deep Unrolled Network for Portable MR Image Reconstruction

Magnetic resonance imaging (MRI) is the gold standard imaging modality for numerous diagnostic tasks, yet its usefulness is tempered due to its high cost and infrastructural requirements. Low-cost very-low-field portable scanners offer new opportunities, while enabling imaging outside conventional MRI suites. However, achieving diagnostic-quality images in clinically acceptable scan times remains challenging with these systems. Therefore methods for improving the image quality while reducing the scan duration are highly desirable. Here, we investigate a physics-informed 3D deep unrolled network for the reconstruction of portable MR acquisitions. Our approach includes a novel network architecture that utilizes momentum-based acceleration and leverages complex conjugate symmetry of k-space for improved reconstruction performance. Comprehensive evaluations on emulated datasets as well as 47mT portable MRI acquisitions demonstrate the improved reconstruction quality of the proposed method compared to existing methods.

physics.med-ph↗

arXiv2025

Subject grounding to reduce electromagnetic interference for MRI scanners operating in unshielded environments

Purpose. Portable low-field (< 0.1 T) MRI is increasingly used for point-of-care imaging, but electromagnetic interference (EMI) presents a significant challenge, especially in unshielded environments. EMI can degrade image quality and compromise diagnostic utility. This study investigates whether subject grounding can effectively reduce EMI and improve image quality, comparing different grounding strategies. Methods. Experiments were conducted using a 47 mT Halbach-based MRI scanner with a single receive channel. Reproducibility was evaluated at a second site using a 72 mT scanner with similar geometry. Turbo spin echo sequences were used to image the hand and brain. Subject grounding was implemented using conductive cloth sleeves or ECG electrodes, each connected between the subject's skin and scanner ground. Three EMI conditions were tested: ambient, added single-frequency EMI, and broadband EMI. Signal-to-noise ratios (SNR) were calculated under each configuration. Results. Subject grounding significantly reduced EMI in both hand and brain scans. In hand imaging, conductive sleeves reduced noise from 85x to 1.25x the 50-ohm noise floor. In brain imaging, grounding alone reduced noise from 55x to 25x baseline; when combined with arc RF shields, noise was further reduced to 1.2x baseline, even under added EMI. These results were reproducible across different scanners and locations. Conclusion. Subject grounding is a simple, effective, and reproducible strategy for mitigating EMI in portable low-field MRI. It is especially effective for hand imaging, while brain imaging benefits from additional RF shielding. The approach is robust under various EMI conditions and may complement other denoising techniques.

physics.med-ph↗

arXiv2025

Design and performance of a Toroidal RF Volume Coil with Intrinsic Electromagnetic Interference Rejection for low-field Portable Halbach-Based MRI Systems

Purpose: One of the intrinsic limitations of low-field MRI is low signal-to-noise ratio (SNR), which can be further reduced by electromagnetic interference (EMI) due to the lack of a Faraday shielded room. To address this issue, we propose a novel RF coil design that is inherently less sensitive to EMI while maintaining high receive sensitivity. Methods: The proposed coil structure is based on an anapole (toroidal) design and consists of six rings, each containing four continuous wires wound around an elliptical 3D-printed former. The wire in the entire structure is uninterrupted. This coil is designed for use in systems with an axial B0 field direction. The performance of the proposed RF coil was evaluated on a Halbach array system designed for neuroimaging and operating at 47mT and compared with a widely used spiral head coil. Results: The noise level achieved by the proposed toroidal coil in combination with a belt wrapped around the subjects and grounded to the scanner was comparable to that of the widely used spiral head coil when combined with a grounding belt and passive aluminum shielding. The coil transmit and receive efficiency was comparable to the efficiency of the spiral head coil. Conclusion: The proposed RF coil inherently reduces the effect of EMI, potentially removing or reducing the need for passive shielding or external/internal sensors for EMI reduction in post-processing.

physics.med-ph↗

arXiv2025

Deep learning of personalized priors from past MRI scans enables fast, quality-enhanced point-of-care MRI with low-cost systems

Magnetic resonance imaging (MRI) offers superb-quality images, but its accessibility is limited by high costs, posing challenges for patients requiring longitudinal care. Low-field MRI provides affordable imaging with low-cost devices but is hindered by long scans and degraded image quality, including low signal-to-noise ratio (SNR) and tissue contrast. We propose a novel healthcare paradigm: using deep learning to extract personalized features from past standard high-field MRI scans and harnessing them to enable accelerated, enhanced-quality follow-up scans with low-cost systems. To overcome the SNR and contrast differences, we introduce ViT-Fuser, a feature-fusion vision transformer that learns features from past scans, e.g. those stored in standard DICOM CDs. We show that \textit{a single prior scan is sufficient}, and this scan can come from various MRI vendors, field strengths, and pulse sequences. Experiments with four datasets, including glioblastoma data, low-field ($50mT$), and ultra-low-field ($6.5mT$) data, demonstrate that ViT-Fuser outperforms state-of-the-art methods, providing enhanced-quality images from accelerated low-field scans, with robustness to out-of-distribution data. Our freely available framework thus enables rapid, diagnostic-quality, low-cost imaging for wide healthcare applications.

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