On Real-time Image Reconstruction with Neural Networks for MRI-guided Radiotherapy

Waddington, David E. J.; Hindley, Nick; Koonjoo, Néha; Chiu, Christopher; Reynolds, Tess; Liu, Paul Z Y; Zhu, Bin; Bhutto, Danyal; Paganelli, Chiara; Keall, P.; Rosen, Matthew S.

doi:10.48550/arxiv.2202.05267

Cited by 3 publications

(3 citation statements)

References 54 publications

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“…Non-Cartesian sequences (e.g., spiral and radial sampling) have recently shown promise for dynamic tumor tracking for radiotherapy. 52,63,64 However, eddy currents are a significant additional challenge for non-Cartesian acquisitions, often deviating k-space trajectories and causing significant image artifact. 30,65 In the future, we will investigate the application of DCReconNet to non-Cartesian reconstruction and distortion-correction problems.…”

Section: Discussionmentioning

confidence: 99%

Distortion‐corrected image reconstruction with deep learning on an MRI‐Linac

Shan

Gao

Liu

et al. 2023

Magnetic Resonance in Med

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Purpose: MRI is increasingly utilized for image-guided radiotherapy due to its outstanding soft-tissue contrast and lack of ionizing radiation. However, geometric distortions caused by gradient nonlinearities (GNLs) limit anatomical accuracy, potentially compromising the quality of tumor treatments. In addition, slow MR acquisition and reconstruction limit the potential for effective image guidance. Here, we demonstrate a deep learning-based method that rapidly reconstructs distortion-corrected images from raw k-space data for MR-guided radiotherapy applications. Methods:We leverage recent advances in interpretable unrolling networks to develop a Distortion-Corrected Reconstruction Network (DCReconNet) that applies convolutional neural networks (CNNs) to learn effective regularizations and nonuniform fast Fourier transforms for GNL-encoding. DCReconNet was trained on a public MR brain dataset from 11 healthy volunteers for fully sampled and accelerated techniques, including parallel imaging (PI) and compressed sensing (CS). The performance of DCReconNet was tested on phantom, brain, pelvis, and lung images acquired on a 1.0T MRI-Linac. The DCReconNet, CS-, PI-and UNet-based reconstructed image quality was measured by structural similarity (SSIM) and RMS error (RMSE) for numerical comparisons. The computation time and residual distortion for each method were also reported. Results: Imaging results demonstrated that DCReconNet better preserves image structures compared to CS-and PI-based reconstruction methods. DCReconNet resulted in the highest SSIM (0.95 median value) and lowest RMSE (<0.04) on simulated brain images with four times acceleration. DCReconNet is over 10-times faster than iterative, regularized reconstruction methods. Conclusions: DCReconNet provides fast and geometrically accurate image reconstruction and has the potential for MRI-guided radiotherapy applications.

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Section: Discussionmentioning

confidence: 99%

Distortion‐corrected image reconstruction with deep learning on an MRI‐Linac

Shan

Gao

Liu

et al. 2023

Magnetic Resonance in Med

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“…The illumination source in WLE is traditionally comprised of either lamps or LEDs which are routed from the proximal end to the distal end through the fiber bundles and onto the target 35 . The acquisition system typically placed at the distal end, contains a couple of channels which include the air, water and biopsy ports, and the imaging optics which consists of a wide-angle objective lens and the image sensor 36 . In SSIE, we retain all the components of a WLE, but add two multi-mode fibers along the insertion tube from an external low power laser.…”

Section: Working Principle Of Ssiementioning

confidence: 99%

Speckle structured illumination endoscopy with enhanced resolution at wide field of view and depth of field

Abraham¹,

Zhou²,

Liu³

2023

OEA

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Structured illumination microscopy (SIM) is one of the most widely applied wide field super resolution imaging techniques with high temporal resolution and low phototoxicity. The spatial resolution of SIM is typically limited to two times of the diffraction limit and the depth of field is small. In this work, we propose and experimentally demonstrate a low cost, easy to implement, novel technique called speckle structured illumination endoscopy (SSIE) to enhance the resolution of a wide field endoscope with large depth of field. Here, speckle patterns are used to excite objects on the sample which is then followed by a blind-SIM algorithm for super resolution image reconstruction. Our approach is insensitive to the 3D morphology of the specimen, or the deformation of illuminations used. It greatly simplifies the experimental setup as there are no calibration protocols and no stringent control of illumination patterns nor focusing optics. We demonstrate that the SSIE can enhance the resolution 2-4.5 times that of a standard white light endoscopic (WLE) system. The SSIE presents a unique route to super resolution in endoscopic imaging at wide field of view and depth of field, which might be beneficial to the practice of clinical endoscopy.

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“…91 However, for most of these accelerated MRI acquisition techniques, the gain in acquisition time results in longer reconstruction times. Fortunately, machine learning approaches that transfer computational processing to offline training of a neural network [99][100][101][102] may overcome long reconstruction times of accelerated acquisitions. In the future, latency for treatment planning and image guidance could be further reduced through use of patient representations composited from models that extract various representative states and their probabilistic variations.…”

Section: Real-time Mrimentioning

confidence: 99%

The future of MRI in radiation therapy: Challenges and opportunities for the MR community

Goodburn

Philippens

Lefebvre

et al. 2022

Magnetic Resonance in Med

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Radiation therapy is a major component of cancer treatment pathways worldwide. The main aim of this treatment is to achieve tumor control through the delivery of ionizing radiation while preserving healthy tissues for minimal radiation toxicity. Because radiation therapy relies on accurate localization of the target and surrounding tissues, imaging plays a crucial role throughout the treatment chain. In the treatment planning phase, radiological images are essential for defining target volumes and organs-at-risk, as well as providing elemental composition (e.g., electron density) information for radiation dose calculations. At treatment, onboard imaging informs patient setup and could be used to guide radiation dose placement for sites affected by motion. Imaging is also an important tool for treatment response assessment and treatment plan adaptation. MRI, with its excellent soft tissue contrast and capacity to probe functional tissue properties, holds great untapped potential for transforming treatment paradigms in radiation therapy. The MR in Radiation Therapy ISMRM Study Group was established to provide a forum within the MR community to discuss the unmet needs and fuel opportunities for further advancement of MRI for radiation therapy applications. During the summer of 2021, the study group organized its first virtual workshop, attended by a diverse international group of clinicians, scientists, and clinical physicists, to explore our predictions for the future of MRI in radiation therapy for the next 25 years. This article reviews the main findings from the event and considers the opportunities and challenges of reaching our vision for the future in this expanding field.

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On Real-time Image Reconstruction with Neural Networks for MRI-guided Radiotherapy

Cited by 3 publications

References 54 publications

Distortion‐corrected image reconstruction with deep learning on an MRI‐Linac

Distortion‐corrected image reconstruction with deep learning on an MRI‐Linac

Speckle structured illumination endoscopy with enhanced resolution at wide field of view and depth of field

The future of MRI in radiation therapy: Challenges and opportunities for the MR community

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