Contrast-enhanced 4D-MRI for internal target volume generation in treatment planning for liver tumors

Thomas, Horatio; Miao, Xin; Ferguson, D.; Calvin, C.; Krishnamurthy, Uday; Anwar, Mekhail; Feng, Mary; Scholey, Jonathan M.

doi:10.1016/j.radonc.2022.05.037

Cited by 5 publications

(8 citation statements)

References 37 publications

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“…Nonetheless, due to its limited soft tissue contrast, it is difficult for 4D-CT to picture liver tumors. 4D-MRI can similarly provide fundamental motion information for treatment planning along with superior soft-tissue contrast [23] . While this strategy is easy to implement and time saving, it does result in an expansion of the irradiation volume.…”

Section: Discussionmentioning

confidence: 99%

“…The image clarity of simulation CT and MRI images were evaluated using the Clarity Score proposed by Thomas et al [23] , in which images quality were divided into 4 levels.…”

Section: Methodsmentioning

confidence: 99%

“…However, due to the poor soft tissue contrast and quick elimination of intravenous contrast in liver tumors, CRLMs are hardly distinguishable in 4D-CT. Compared to 4D-CT, 4D magnetic resonance imaging (4D-MRI) has demonstrated superior ability in generating anatomical contrast and visualizing liver tumors [23] . Besides, a previous study has demonstrated the feasibility of acquiring T2 weighted 4D-MRI images based on respiratory amplitude, which was able to track tumor motion throughout respiratory cycles [24] .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

4D-MRI assisted stereotactic body radiation therapy for unresectable colorectal cancer liver metastases

Wang,

Zheng,

Sun

et al. 2024

Clinical and Translational Radiation Oncology

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Section: Discussionmentioning

confidence: 99%

“…The image clarity of simulation CT and MRI images were evaluated using the Clarity Score proposed by Thomas et al [23] , in which images quality were divided into 4 levels.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

4D-MRI assisted stereotactic body radiation therapy for unresectable colorectal cancer liver metastases

Wang,

Zheng,

Sun

et al. 2024

Clinical and Translational Radiation Oncology

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“…Additionally, emerging methods of 4D MRI and hybrid MR-linac (MRI + linear accelerator) can track the motion of tumours and other structures in real-time, enabling adaptive treatment and significantly reducing the impact of breathing motion on treatment accuracy [ 82 , 83 , 84 ]. While the use of functional MRI in this context is limited, there is growing evidence to suggest that gadoxetate enhancement can allow the sustained visualisation of tumours that have poor visibility on anatomical MRI [ 85 , 86 ]. However, repeated administration of Gadolinium-based contrast agents has been linked to Gadolinium deposition in the brain [ 87 ], therefore, more research on low-dose gadoxetate MRI as well as other functional MRI techniques such as DWI is required.…”

Section: Current Use Of Functional Mri For Liver Sbrtmentioning

confidence: 99%

Role of Functional MRI in Liver SBRT: Current Use and Future Directions

Tadimalla

Wang

Haworth

2022

Cancers

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Stereotactic body radiation therapy (SBRT) is an emerging treatment for liver cancers whereby large doses of radiation can be delivered precisely to target lesions in 3–5 fractions. The target dose is limited by the dose that can be safely delivered to the non-tumour liver, which depends on the baseline liver functional reserve. Current liver SBRT guidelines assume uniform liver function in the non-tumour liver. However, the assumption of uniform liver function is false in liver disease due to the presence of cirrhosis, damage due to previous chemo- or ablative therapies or irradiation, and fatty liver disease. Anatomical information from magnetic resonance imaging (MRI) is increasingly being used for SBRT planning. While its current use is limited to the identification of target location and size, functional MRI techniques also offer the ability to quantify and spatially map liver tissue microstructure and function. This review summarises and discusses the advantages offered by functional MRI methods for SBRT treatment planning and the potential for adaptive SBRT workflows.

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“…MRI has been increasingly adopted for image-guided liver radiation therapy (RT) owing to its superior soft tissue contrast compared to CT (Hussain and Semelka 2005, Voroney et al 2006, Witt et al 2020. 4D MRI, which is a respiratory-resolved volumetric imaging technique, is especially powerful in quantifying tumor motion (Stemkens et al 2018, Paulson et al 2020, Thomas et al 2022. In a clinical planning workflow for free-breathing liver RT treatments, the liver tumor delineated in individual 4D MR images forms an internal target volume (ITV).…”

Section: Introductionmentioning

confidence: 99%

Paired conditional generative adversarial network for highly accelerated liver 4D MRI

Xu,

Miao,

Liu

et al. 2024

Phys. Med. Biol.

Self Cite

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Purpose: 4D MRI with high spatiotemporal resolution is desired for image-guided liver radiotherapy. Acquiring densely sampled k-space data is time-consuming. Accelerated acquisition with sparse sampling is desirable but often causes degraded image quality or long reconstruction time. We propose the Reconstruct Paired Conditional Generative Adversarial Network (Re-Con-GAN) for shortening the 4D MRI reconstruction time while maintaining the reconstruction quality. Methods: Patients underwent free-breathing liver 4D MRI were included in the study. Fully- and retrospectively under-sampled data at 3, 6 and 10 times (3x, 6x and 10x) were first reconstructed using the nuFFT algorithm. Re-Con-GAN then trained input and output in pairs. Three types of networks, ResNet9, UNet and reconstruction swin transformer, were explored as generators. PatchGAN was selected as the discriminator. Re-Con-GAN processed the data (3D+t) as temporal slices (2D+t). A total of 48 patients with 12332 temporal slices were split into training (37 patients with 10721 slices) and test (11 patients with 1611 slices). Compressed sensing (CS) reconstruction with spatiotemporal sparsity constraint was used as a benchmark. Reconstructed image quality was further evaluated with a liver gross tumor volume (GTV) localization task using Mask-RCNN trained from a separate 3D static liver MRI dataset (70 patients; 103 GTV contours).Results: Re-Con-GAN consistently achieved comparable/better PSNR, SSIM, and RMSE scores compared to CS/UNet models. The inference time of Re-Con-GAN, UNet and CS are 0.15s, 0.16s, and 120s. The GTV detection task showed that Re-Con-GAN and CS, compared to UNet, better improved the dice score (3x Re-Con-GAN 80.98%; 3x CS 80.74%; 3x UNet 79.88%) of unprocessed under-sampled images (3x 69.61%). Conclusion: A generative network with adversarial training is proposed with promising and efficient reconstruction results demonstrated on an in-house dataset. The rapid and qualitative reconstruction of 4D liver MR has the potential to facilitate online adaptive MR-guided radiotherapy for liver cancer.

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Contrast-enhanced 4D-MRI for internal target volume generation in treatment planning for liver tumors

Cited by 5 publications

References 37 publications

4D-MRI assisted stereotactic body radiation therapy for unresectable colorectal cancer liver metastases

4D-MRI assisted stereotactic body radiation therapy for unresectable colorectal cancer liver metastases

Role of Functional MRI in Liver SBRT: Current Use and Future Directions

Paired conditional generative adversarial network for highly accelerated liver 4D MRI

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