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17 results on '"Liu, Paul Z. Y."'

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

Author

Shan, Shanshan, Gao, Yang, Waddington, David E. J., Chen, Hongli, Whelan, Brendan, Liu, Paul Z. Y., Wang, Yaohui, Liu, Chunyi, Gan, Hongping, Gao, Mingyuan, and Liu, Feng

Subjects
Physics - Medical Physics, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

MRI-Linac systems require fast image reconstruction with high geometric fidelity to localize and track tumours for radiotherapy treatments. However, B0 field inhomogeneity distortions and slow MR acquisition potentially limit the quality of the image guidance and tumour treatments. In this study, we develop an interpretable unrolled network, referred to as RebinNet, to reconstruct distortion-free images from B0 inhomogeneity-corrupted k-space for fast MRI-guided radiotherapy applications. RebinNet includes convolutional neural network (CNN) blocks to perform image regularizations and nonuniform fast Fourier Transform (NUFFT) modules to incorporate B0 inhomogeneity information. The RebinNet was trained on a publicly available MR dataset from eleven healthy volunteers for both fully sampled and subsampled acquisitions. Grid phantom and human brain images acquired from an open-bore 1T MRI-Linac scanner were used to evaluate the performance of the proposed network. The RebinNet was compared with the conventional regularization algorithm and our recently developed UnUNet method in terms of root mean squared error (RMSE), structural similarity (SSIM), residual distortions, and computation time. Imaging results demonstrated that the RebinNet reconstructed images with lowest RMSE (<0.05) and highest SSIM (>0.92) at four-time acceleration for simulated brain images. The RebinNet could better preserve structural details and substantially improve the computational efficiency (ten-fold faster) compared to the conventional regularization methods, and had better generalization ability than the UnUNet method. The proposed RebinNet can achieve rapid image reconstruction and overcome the B0 inhomogeneity distortions simultaneously, which would facilitate accurate and fast image guidance in radiotherapy treatments.

Published
2024

2. Distortion-Corrected Image Reconstruction with Deep Learning on an MRI-Linac

Author

Shan, Shanshan, Gao, Yang, Liu, Paul Z. Y., Whelan, Brendan, Sun, Hongfu, Dong, Bin, Liu, Feng, and Waddington, David E. J.

Subjects
Physics - Medical Physics, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Magnetic resonance imaging (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 nonlinearity (GNL) limit anatomical accuracy, potentially compromising the quality of tumour treatments. In addition, slow MR acquisition and reconstruction limit the potential for real-time image guidance. Here, we demonstrate a deep learning-based method that rapidly reconstructs distortion-corrected images from raw k-space data for real-time MR-guided radiotherapy applications. 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 eleven healthy volunteers for fully sampled and accelerated techniques including parallel imaging (PI) and compressed sensing (CS). The performance of DCReconNet was tested on phantom and volunteer brain data acquired on a 1.0T MRI-Linac. The DCReconNet, CS- and PI-based reconstructed image quality was measured by structural similarity (SSIM) and root-mean-squared error (RMSE) for numerical comparisons. The computation time for each method was also reported. Phantom and volunteer results demonstrated that DCReconNet better preserves image structure when compared to CS- and PI-based reconstruction methods. DCReconNet resulted in highest SSIM (0.95 median value) and lowest RMSE (<0.04) on simulated brain images with four times acceleration. DCReconNet is over 100-times faster than iterative, regularized reconstruction methods. DCReconNet provides fast and geometrically accurate image reconstruction and has potential for real-time MRI-guided radiotherapy applications.

Published
2022

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

Author

Waddington, David E. J., Hindley, Nicholas, Koonjoo, Neha, Chiu, Christopher, Reynolds, Tess, Liu, Paul Z. Y., Zhu, Bo, Bhutto, Danyal, Paganelli, Chiara, Keall, Paul J., and Rosen, Matthew S.

Subjects
Physics - Medical Physics, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

MRI-guidance techniques that dynamically adapt radiation beams to follow tumor motion in real-time will lead to more accurate cancer treatments and reduced collateral healthy tissue damage. The gold-standard for reconstruction of undersampled MR data is compressed sensing (CS) which is computationally slow and limits the rate that images can be available for real-time adaptation. Here, we demonstrate the use of automated transform by manifold approximation (AUTOMAP), a generalized framework that maps raw MR signal to the target image domain, to rapidly reconstruct images from undersampled radial k-space data. The AUTOMAP neural network was trained to reconstruct images from a golden-angle radial acquisition, a benchmark for motion-sensitive imaging, on lung cancer patient data and generic images from ImageNet. Model training was subsequently augmented with motion-encoded k-space data derived from videos in the YouTube-8M dataset to encourage motion robust reconstruction. We find that AUTOMAP-reconstructed radial k-space has equivalent accuracy to CS but with much shorter processing times after initial fine-tuning on retrospectively acquired lung cancer patient data. Validation of motion-trained models with a virtual dynamic lung tumor phantom showed that the generalized motion properties learned from YouTube lead to improved target tracking accuracy. Our work shows that AUTOMAP can achieve real-time, accurate reconstruction of radial data. These findings imply that neural-network-based reconstruction is potentially superior to existing approaches for real-time image guidance applications., Comment: 12 pages, 6 figures, 1 table. v2 has a typo in eqn 1 corrected and references added to the discussion

Published
2022
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6. Open‐source hardware and software for the measurement, characterization, reporting, and correction of geometric distortion in MRI.

Author

Whelan, Brendan M., Liu, Paul Z. Y., Shan, Shanshan, Waddington, David E. J., Dong, Bin, Jameson, Michael G., and Keall, Paul J.

Subjects
*SOFTWARE measurement, *SPHERICAL harmonics, *OPTICAL scanners, *SOFTWARE development tools, *COMPUTED tomography
Abstract

Background: Geometric distortion is a serious problem in MRI, particularly in MRI guided therapy. A lack of affordable and adaptable tools in this area limits research progress and harmonized quality assurance. Purpose: To develop and test a suite of open‐source hardware and software tools for the measurement, characterization, reporting, and correction of geometric distortion in MRI. Methods: An open‐source python library was developed, comprising modules for parametric phantom design, data processing, spherical harmonics, distortion correction, and interactive reporting. The code was used to design and manufacture a distortion phantom consisting of 618 oil filled markers covering a sphere of radius 150 mm. This phantom was imaged on a CT scanner and a novel split‐bore 1.0 T MRI magnet. The CT images provide distortion‐free dataset. These data were used to test all modules of the open‐source software. Results: All markers were successfully extracted from all images. The distorted MRI markers were mapped to undistorted CT data using an iterative search approach. Spherical harmonics reconstructed the fitted gradient data to 1.0 ± 0.6% of the input data. High resolution data were reconstructed via spherical harmonics and used to generate an interactive report. Finally, distortion correction on an independent data set reduced distortion inside the DSV from 5.5 ± 3.1 to 1.6 ± 0.8 mm. Conclusion: Open‐source hardware and software for the measurement, characterization, reporting, and correction of geometric distortion in MRI have been developed. The utility of these tools has been demonstrated via their application on a novel 1.0 T split bore magnet. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Experimental comparison of linear regression and LSTM motion prediction models for MLC‐tracking on an MRI‐linac

Author

Lombardo, Elia, primary, Liu, Paul Z. Y., additional, Waddington, David E. J., additional, Grover, James, additional, Whelan, Brendan, additional, Wong, Esther, additional, Reiner, Michael, additional, Corradini, Stefanie, additional, Belka, Claus, additional, Riboldi, Marco, additional, Kurz, Christopher, additional, Landry, Guillaume, additional, and Keall, Paul J., additional

Published
2023
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14. Real-time motion management in MRI-guided radiotherapy: Current status and AI-enabled prospects.

Author

Lombardo E, Dhont J, Page D, Garibaldi C, Künzel LA, Hurkmans C, Tijssen RHN, Paganelli C, Liu PZY, Keall PJ, Riboldi M, Kurz C, Landry G, Cusumano D, Fusella M, and Placidi L

Subjects
Humans, Motion, Magnetic Resonance Imaging methods, Algorithms, Radiotherapy Planning, Computer-Assisted methods, Artificial Intelligence, Radiotherapy, Image-Guided methods
Abstract

MRI-guided radiotherapy (MRIgRT) is a highly complex treatment modality, allowing adaptation to anatomical changes occurring from one treatment day to the other (inter-fractional), but also to motion occurring during a treatment fraction (intra-fractional). In this vision paper, we describe the different steps of intra-fractional motion management during MRIgRT, from imaging to beam adaptation, and the solutions currently available both clinically and at a research level. Furthermore, considering the latest developments in the literature, a workflow is foreseen in which motion-induced over- and/or under-dosage is compensated in 3D, with minimal impact to the radiotherapy treatment time. Considering the time constraints of real-time adaptation, a particular focus is put on artificial intelligence (AI) solutions as a fast and accurate alternative to conventional algorithms., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Published
2024
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15. Small field correction factors for the IBA Razor.

Author

Liu PZ, Reggiori G, Lobefalo F, Mancosu P, Tomatis S, McKenzie DR, and Suchowerska N

Subjects
Electrical Equipment and Supplies, Monte Carlo Method, Scintillation Counting, Particle Accelerators instrumentation, Radiometry instrumentation
Abstract

The IBA Razor diode supersedes the IBA SFD and is intended for use in small fields. However, its behaviour in small fields has not yet been quantified. In this work, we examine the response of the Razor diode against the air core scintillation dosimeter (FOD) and Gafchromic film in photon beams from three Varian linac beams. Fields between 4mm and 30mm in width were measured, both with and without a flattening filter and at two energies. The Razor exhibited an over-response of up to 4.5% for MLC collimated fields and 7.1% for stereotactic cone collimated fields. The presence of the flattening filter altered the over-response by up to 1.5%. The small field correction factors are tabulated and agree with the mathematical relation of Liu et al. (2014). Four samples of the Razor were used, two having received a significant prior dose. The correction factors for the four samples differed and may depend on their dose history., (Copyright © 2016 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

Published
2016
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16. Small field detector correction factors: effects of the flattening filter for Elekta and Varian linear accelerators.

Author

Tyler MK, Liu PZ, Lee C, McKenzie DR, and Suchowerska N

Subjects
Humans, Radiotherapy Dosage, Radiotherapy Planning, Computer-Assisted methods, Radiotherapy, Intensity-Modulated methods, Filtration instrumentation, Particle Accelerators instrumentation, Phantoms, Imaging, Radiotherapy, Intensity-Modulated instrumentation
Abstract

Flattening filter-free (FFF) beams are becoming the preferred beam type for stereotactic radiosurgery (SRS) and stereotactic ablative radiation therapy (SABR), as they enable an increase in dose rate and a decrease in treatment time. This work assesses the effects of the flattening filter on small field output factors for 6 MV beams generated by both Elekta and Varian linear accelerators, and determines differences between detector response in flattened (FF) and FFF beams. Relative output factors were measured with a range of detectors (diodes, ionization cham-bers, radiochromic film, and microDiamond) and referenced to the relative output factors measured with an air core fiber optic dosimeter (FOD), a scintillation dosimeter developed at Chris O'Brien Lifehouse, Sydney. Small field correction factors were generated for both FF and FFF beams. Diode measured detector response was compared with a recently published mathematical relation to predict diode response corrections in small fields. The effect of flattening filter removal on detector response was quantified using a ratio of relative detector responses in FFF and FF fields for the same field size. The removal of the flattening filter was found to have a small but measurable effect on ionization chamber response with maximum deviations of less than ± 0.9% across all field sizes measured. Solid-state detectors showed an increased dependence on the flattening filter of up to ± 1.6%. Measured diode response was within ± 1.1% of the published mathematical relation for all fields up to 30 mm, independent of linac type and presence or absence of a flattening filter. For 6 MV beams, detector correction factors between FFF and FF beams are interchangeable for a linac between FF and FFF modes, providing that an additional uncertainty of up to ± 1.6% is accepted.

Published
2016
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17. Can small field diode correction factors be applied universally?

Author

Liu PZ, Suchowerska N, and McKenzie DR

Subjects
Monte Carlo Method, Particle Accelerators statistics & numerical data, Radiotherapy Dosage, Radiation Dosage, Radiometry statistics & numerical data, Radiotherapy Planning, Computer-Assisted statistics & numerical data
Abstract

Background and Purpose: Diode detectors are commonly used in dosimetry, but have been reported to over-respond in small fields. Diode correction factors have been reported in the literature. The purpose of this study is to determine whether correction factors for a given diode type can be universally applied over a range of irradiation conditions including beams of different qualities., Materials and Methods: A mathematical relation of diode over-response as a function of the field size was developed using previously published experimental data in which diodes were compared to an air core scintillation dosimeter. Correction factors calculated from the mathematical relation were then compared those available in the literature., Results: The mathematical relation established between diode over-response and the field size was found to predict the measured diode correction factors for fields between 5 and 30 mm in width. The average deviation between measured and predicted over-response was 0.32% for IBA SFD and PTW Type E diodes. Diode over-response was found to be not strongly dependent on the type of linac, the method of collimation or the measurement depth., Conclusions: The mathematical relation was found to agree with published diode correction factors derived from Monte Carlo simulations and measurements, indicating that correction factors are robust in their transportability between different radiation beams., (Copyright © 2014. Published by Elsevier Ireland Ltd.)

Published
2014
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