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20242026
most citedImage Reconstruction with B0 Inhomogeneity using an Interpretable Deep Unrolled Network on an Open-bore MRI-Linac

1 citations · 1 across the 7 of their papers we have counts for

collaborators

7 papers

physics.med-ph2026

BEAM3R: Beam's-eye-view architecture with Mamba-3 for implicit dose reconstruction

Chen Cheng, Michael Ferraro, James Grover +2

To enable accurate and rapid photon control point and proton beamlet dose calculation in the DoseRAD2026 challenge, we present BEAM3R, a dose estimation framework operating in beam…

physics.med-ph2026

Closed-loop coupling of personalised and foundation models for real-time treatment guidance with MRI

James Grover, Emily A. Hewson, Andrew Phair +5

Image-guided therapies, including radiotherapy, biopsy and deep brain stimulation, rely on real-time targeting of anatomical structures. However, in the presence of motion, imaging…

cs.CV2026

Enhancing Ultra-low-field MRI with Segmentation-guided Adversarial Learning

James Grover, Andrew Phair, Michael Ferraro +1

Ultra-low-field (ULF) MRI offers portable and low-cost imaging but suffers from poor image quality. To address this, we present our submission to the 2025 ULF Enhancement Challenge…

physics.med-ph2025

Two-stage Respiratory Motion-resolved Radial MR Image Reconstruction Using an Interpretable Deep Unrolled Network

Shanshan Shan, Hongli Chen, Yuhan Wei +12

Due to the prolonged MRI encoding process, respiratory motion can cause undesired artifacts and image blurring, degrading image quality and limiting clinical applications in abdomi…

physics.med-ph2025

Design and Construction of a Dedicated Radiolucent 8-element Flexible Radiofrequency (RF) Torso Coil for the 1.0T Australian MRI-Linac System

Mingyan Li, Ewald Weber, David E. J. Waddington +6

Magnetic resonance imaging-guided linear accelerators (MRI-Linacs) are an emerging treatment technology that enable online soft-tissue visualisation and adaptive radiotherapy. The…

physics.med-ph2024

Accelerating Low-field MRI: From Compressed Sensing to Deep Learning Reconstruction with CNNs and Transformers

Efrat Shimron, Shanshan Shan, James Grover +7

Portable, low-field Magnetic Resonance Imaging (MRI) scanners are increasingly being deployed in clinical settings. However, key barriers to their widespread use include low signal…

physics.med-ph2024★ 1 cited

Image Reconstruction with B0 Inhomogeneity using an Interpretable Deep Unrolled Network on an Open-bore MRI-Linac

Shanshan Shan, Yang Gao, David E. J. Waddington +8

MRI-Linac systems require fast image reconstruction with high geometric fidelity to localize and track tumours for radiotherapy treatments. However, B0 field inhomogeneity distorti…