Deep Learning-Based Gadolinium-Free Virtual Contrast-Enhanced MRI for Predicting Immediate HIFU Ablation Outcomes in Uterine Fibroids.

Authors

Chengwei Li, Hongjian Liao, Jian Liu, Keying Li, Xin Peng, Zhibo Xiao, Jin Gao

Published in

Academic radiology. Nov 05, 2026. Epub Nov 05, 2026.

Abstract

Accurate assessment of microvascular perfusion is essential for predicting high-intensity focused ultrasound (HIFU) ablation outcomes in uterine fibroids. Contrast-enhanced T1-weighted imaging (CE-T1WI) is widely used for vascular evaluation; however, gadolinium-based agents pose safety concerns and may interfere with temperature monitoring. This study aimed to generate synthetic CE-T1WI (Syn-CE-T1WI) from T2-weighted imaging (T2WI) using deep learning for predicting immediate HIFU ablation outcomes.
A mask-guided 3D conditional generative adversarial network (3D-cGAN) was developed using magnetic resonance imaging data from 300 patients (240 training, 60 testing). Syn-CE-T1WI was generated from T2WI. Image quality was assessed using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean absolute error (MAE). Three radiologists performed blinded evaluations of image authenticity and quality. A binary classification task was used to compare Syn-CE-T1WI and Real-CE-T1WI for predicting HIFU outcomes.
In the test set, PSNR was 27.05 ± 2.21 dB, SSIM was 0.825 ± 0.12, and MAE was 0.023. Radiologists achieved a mean accuracy of 52.8% in distinguishing real from synthetic images (all P > 0.05). Overall, 88.9% of Syn-CE-T1WI were rated clinically acceptable. For outcome prediction, Syn-CE-T1WI achieved areas under the receiver operating characteristic curve (AUCs) of 0.713-0.763, comparable to Real-CE-T1WI (0.725-0.775; all P > 0.05). Combining Syn-CE-T1WI with T2WI yielded AUCs of 0.775-0.800, numerically higher than T2WI alone (0.713-0.763), and similar to T2WI + Real-CE-T1WI (0.800-0.813; all P > 0.05).
The mask-guided 3D-cGAN enabled the generation of high-fidelity, gadolinium-free Syn-CE-T1WI that may support noninvasive prediction of immediate HIFU ablation outcomes in uterine fibroids.

PMID:
42988631
Bibliographic data and abstract were imported from PubMed on 06 Nov 2026.

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