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September 17, 2025

Feasibility of a deep learning-accelerated T1-w Dixon sequence for kidney MRI

Puntos clave

Deep learning-accelerated MRI resulted in a 24% reduction in acquisition time while improving image quality significantly.
T1DL demonstrated higher sensitivity for detecting renal cysts and microstructures compared to conventional sequences.
Radiologists independently assessed images for cyst presence and classification, ensuring robust evaluations of T1DL.
The findings highlight the potential for deeper lesion detection and re-classification based on improved imaging results.

Resumen

Motivation: Accurate assessment of renal incidental lesions remains challenging in abdominal MRI. Novel deep learning imaging techniques may improve kidney MRI. Goal(s): To evaluate whether a deep learning-accelerated T1-weighted MRI sequence (T1DL) improves detection of renal cysts and cyst classification, utilizing T2w sequence as reference. Approach: 50 patients were included undergoing 3T abdominal MRI with T2, conventional and T1DL sequences, each evaluated by presence of cysts and of microstructures. Two radiologists performed qualitative and quantitative assessments independently. Results: In T1DL, acquisition time was reduced by 24% while image quality was significantly improved. T1DL showed higher sensitivity in detection of renal cysts and microstructures. Impact: The study demonstrates that deep learning-accelerated MRI improves image quality and renal cyst and microstructure detection while reducing scan time. Can it help detecting more renal lesions than conventional T1-w sequences? Can it lead to a re-classification of Bosniak?

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