Keywords: IVIM, Lung, Deep learning reconstruction; Lung cancer; Magnetic resonance imaging; Diffusion weighted imaging; Intravoxel incoherent motion.
Motivation: DWI-based IVIM model provides information about tumor microenvironment and is reported useful in discriminating pulmonary lesions. However, lung DWI suffers low SNR and substantial susceptibility artifacts. Recently, a vendor-provided deep-learning reconstruction (DLR), relative to conventional reconstruction (ConR), is provided to improve image quality.
Goal(s): Investigate the impact of DLR on IVIM parameters for distinguishing malignant from benign pulmonary lesion.
Approach: DWI was acquired using FOCUS-MUSE to alleviate susceptibility artifacts and was reconstructed with DLR and ConR, separately. SNR and diagnostic performance of IVIM were compared.
Results: DLR significantly increased DWI image SNR and improved diagnostic performance of IVIM parameters for pulmonary lesion discrimination.
Impact: The application DLR would be beneficial for lung DWI in term of image quality and the performance of IVIM quantitative parameters for distinguishing benign from malignant pulmonary lesion.
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