Abstract

Motivation: Diffusion-weighted imaging (DWI) is used in endometrial cancer imaging for improved specificity and accuracy in determining the depth of myometrial invasion compared to T2-weighted imaging alone. However, conventional echo planar imaging based DWI, including reduced FOV EPI, is prone to artifacts from field inhomogeneity in the area of endometria and from peristalsis. Goal(s): To improve the diffusion-weighted imaging of endometrial cancerApproach: Propeller DWI is robust to field inhomogeneity and motion. Deep learning (DL) reconstruction is used to mitigate its SNR deficiency and overcome the need for long scan time. Results: DL DW-PROPELLER improved the SNR and in-plane resolution of the conventional DW-PROPELLERImpact: DL DW-PROPELLER improved the SNR and in-plane resolution of the conventional DW-PROPELLER, enabling body DW-PROPELLER in clinically feasible scan time. Compared to the rFOV DW-EPI, DL DW-PROPELLER significantly improves the geometric accuracy and the readability of high b-value images.