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Deep learning reconstruction enables accelerated T2-weighted MRI for rectal cancer staging: a prospective study of
Weijie Yan1, Shuang Li1, Huilou Liang2
1West China Hospital of Sichuan University, Chengdu, China.
Objectives:
To 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.
Methods:
This 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.
Results:
Thirty-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, FSENEX2-DLR achieved the highest qualitative scores for tissue contrast, edge sharpness, tumor characteristics, and overall image quality. Quantitative analysis showed FSENEX4-DLR demonstrated the highest SNR and CNR values, followed by FSENEX2-DLR, with all DLR sequences significantly outperforming their ConR counterparts (all P < 0.001). Inter-sequence agreement analysis revealed substantial to almost perfect consistency for all staging parameters (mean κ: 0.736-0.800). DLR sequences demonstrated significantly higher inter-sequence agreement (range: 0.715-0.964) compared to ConR sequences (range: 0.505-0.811).
Conclusion:
DLR with reduced NEX values maintains diagnostic consistency in rectal cancer staging. FSENEX2-DLR, achieving 49% scanning time reduction while preserving diagnostic consistency, may offer a reasonable balance between acquisition efficiency and image quality. Future studies with histopathological correlation are needed to validate diagnostic accuracy.
