Deep learning reconstruction versus conventional parallel imaging for sagittal T2-weighted MRI in cervical cancer: a comparison of acceleration factors

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Abstract
Background The optimal acceleration strategy for deep learning reconstruction (DLR) in cervical cancer T2-weighted imaging (T2WI) remains unestablished. Methods Forty-eight patients with primary cervical cancer prospectively underwent 3.0T pelvic MRI. Sagittal T2WI was acquired using conventional parallel imaging (Conv) and DLR at acceleration factors (AF) 2 and 3. Two radiologists evaluated quantitative metrics and six qualitative metrics. Results Compared to conventional imaging at the same acceleration, DLR significantly improved signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) (DL_AF2 vs. Conv_AF2, adjusted p = 0.005; DL_AF3 vs. Conv_AF3, adjusted p < 0.001). No significant quantitative differences were found between AF2 and AF3 for either reconstruction method. Despite statistically comparable objective metrics between DL_AF2 and DL_AF3, DL_AF2 received significantly higher subjective scores across all six qualitative dimensions (all adjusted p < 0.001), with median scores of 5 for all image quality metrics. DL_AF3 demonstrated significantly lower diagnostic confidence scores, suggesting perceptual over-smoothing at higher acceleration. Conclusion At equivalent scan times, DLR with moderate acceleration (AF 2) achieves the optimal balance between objective image quality and subjective anatomical fidelity for sagittal T2WI in cervical cancer evaluation.

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