The impact of deep learning based image reconstruction on CEST MRI for distinguishing inactive from active thyroid-associated ophthalmopathy

Motivation: Thyroid-associated ophthalmopathy (TAO) is characterized by accumulation of collagen in extraocular muscle. CEST-MRI can evaluate the collagen content by focusing on amide compound. However, CEST effect is small and sensitive to low image SNR. A vendor-provided deep learning reconstruction (DLR) algorithm can dramatically increase image SNR. Goal(s): Investigate if CEST-MRI can distinguish inactive from active TAO and the impact of DLR on its diagnostic performance. Approach: 11 active and 12 inactive TAO were enrolled. CEST imaging was reconstructed with DLR and conventional reconstruction. Results: DLR can significantly increase SNR of CEST imaging and improved the diagnostic performance for discriminating inactive from active TAO. Impact: The treatment of TAO depends on the disease phase. DLR image reconstruction improved the performance of CEST in differentiation between inactive and active TAO. It would help in the evaluation and management of TAO patients.