The impact of deep learning based image reconstruction on IVIM for benign and malignant pulmonary lesion discrimination

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.