ArticleAbdominal radiology (New York)2026
Deep learning reconstruction enables accelerated T2-weighted MRI for rectal cancer staging: a prospective study of diagnostic consistency across NEX value reduction.
Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectivesTo evaluate the image quality, interpretation consistency, and scanning efficiency of deep learning-based reconstruction (DLR) algorithm (AIR™ Recon DL; GE Healthcare) compared with conventional reconstruction (ConR) in T2-weighted MRI for rectal cancer across different number of excitations (NEX) values.
methodsThis prospective study enrolled consecutive patients undergoing MRI for primary staging of rectal cancer between July 2022 and April 2023. Each patient underwent T2-weighted MRI with three NEX values (4, 2, and 1), reconstructed using both ConR and DLR methods, generating six image sets. Image quality was assessed quantitatively (signal-to-noise ratio, contrast-to-noise ratio) and qualitatively using a 5-point scale for tissue contrast, edge sharpness, rectal wall morphology, tumor characteristics, overall image quality, and noise. Two radiologists independently evaluated staging parameters including T stage, N stage, extramural vascular invasion (EMVI), and mesorectal fascia (MRF) status. Inter-observer and inter-sequence agreement were analyzed.
resultsThirty-five patients completed all six imaging protocols. Acquisition times were 3 min 55s (NEX = 4), 2 min 1s (NEX = 2, 49% reduction), and 1 min 4s (NEX = 1, 73% reduction). For qualitative assessment, DLR sequences demonstrated significantly superior image quality compared to ConR sequences across all NEX values (all P < 0.001). Among all sequences, FSE
conclusionDLR with reduced NEX values maintains diagnostic consistency in rectal cancer staging. FSE
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