Mr. Guillaume Nodari Profile

Guillaume Nodari

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Proceedings Article | 2 April 2024 Poster + Paper

CT-MRI liver registration for selective internal radiation therapy

Adam Fragkiadakis, Felix Quinton, Romain Popoff, Guillaume Nodari, Olivier Lopez, Julie Pellegrinelli, Juan Andres Cisneros Jacome, Olivier Chevallier, Jean-Marc Vrigneaud, Jean-Louis Alberini, Fabrice Meriaudeau, Sarah Leclerc, Benoit Presles

Proceedings Volume 12926, 129261P (2024) https://doi.org/10.1117/12.3003848

KEYWORDS: Liver, Image registration, Image segmentation, Tumors, Magnetic resonance imaging, Computed tomography, Deformation, Deep learning

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Selective internal radiation therapy (SIRT) is a targeted treatment for liver tumors, particularly hepatocellular carcinoma (HCC), that involves the precise delivery of radioactive microspheres to the tumor’s blood supply. Image registration is critical to SIRT, ensuring precise alignment of diagnostic images with the treatment plan to optimize microsphere delivery and minimize side effects. In the context of SIRT, our goal was to develop a fully-automatic hybrid registration pipeline, using liver segmentation masks from a 3D UNet model to achieve performance comparable to expert registrations. The pipeline combines conventional and deep learning methods for automatic alignment of pre-treatment magnetic resonance images (MRI) on single-photon emission computed tomography (SPECT)/computed tomography (CT) images. This hybrid pipeline, which uses conventional global rigid registration and a deep learning-based approach for local deformation, outperformed conventional and manual expert registration. Quantitative assessment on a dataset of 69 HCC patients showed an improved Dice similarity coefficient (DSC) of 0.928 compared to 0.917 with the conventional methods. A subset analysis of 61 patients with expert registrations showed a mean DSC of 0.922, while our proposed method remained at a mean DSC of 0.928. These results demonstrate the effectiveness of our hybrid approach in achieving accurate liver registration, which is critical for precise microsphere delivery during SIRT. The improvement over conventional methods highlights the potential of incorporating deep learning techniques into multimodal liver registration, thereby improving the overall quality and effectiveness of SIRT in the management of HCC. Our method provides clinicians with a reliable and automated registration pipeline that can positively optimize treatment planning and reduce the burden of manual registration. As a result, our hybrid approach holds promise for more accurate and precise registration results.

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