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Updated: Aug 6, 2026
Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression
Published on: May 7, 2019
Deep learning reconstruction versus conventional parallel imaging for sagittal T2-weighted MRI in cervical cancer: a
Minglei Zhang1, Yifan Lu1, Jiacheng Song1
1Jiangsu Province Hospital, Nanjing, China.
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.
