A deep learning model to reduce agent dose for contrast-enhanced MRI of the cerebellopontine angle cistern

Clinical Snapshot

Key Findings

• P Value: Not reported for statistical comparisons

• Effect Size: SSIM improved from 0.639±0.113 to 0.993±0.009 across dose ranges; PSNR increased from 21.6±3.73 dB to 41.4±4.84 dB

• Primary Outcome: Image quality restoration measured by structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR)

• Nnt Or Sensitivity: Dice coefficient improvement from 0.673 to 0.734 at 10% dose represents 9.1% relative improvement in segmentation accuracy

• Confidence Interval: Not explicitly reported for primary outcomes

Clinical Application

Requires implementation of deep learning infrastructure in radiology departments, specialized training, and validation on local scanner protocols Relevant to Australian practice where gadolinium safety concerns exist. Would require TGA approval for clinical implementation. Cost-effectiveness analysis needed for PBS consideration of AI-enhanced imaging protocols Patients requiring contrast-enhanced MRI for suspected vestibular schwannoma or other cerebellopontine angle pathology

Abstract

OBJECTIVES: To evaluate a deep learning (DL) model for reducing the agent dose of contrast-enhanced T1-weighted MRI (T1ce) of the cerebellopontine angle (CPA) cistern. MATERIALS AND METHODS: In this multicenter retrospective study, T1 and T1ce of vestibular schwannoma (VS) patients were used to simulate low-dose T1ce with varying reductions of contrast agent dose. DL models were trained to restore standard-dose T1ce from the low-dose simulation. The image quality and segmentation performance of the DL-restored T1ce were evaluated. A head and neck radiologist was asked to rate DL-restored images in multiple aspects, including image quality and diagnostic characterization. RESULTS: 203 MRI studies from 72 VS patients (mean age, 58.51 ± 14.73, 39 men) were evaluated. As the input dose increased, the structural similarity index measure of the restored T1ce increased from 0.639 ± 0.113 to 0.993 ± 0.009, and the peak signal-to-noise ratio increased from 21.6 ± 3.73 dB to 41.4 ± 4.84 dB. At a 10% input dose, using DL-restored T1ce for segmentation improved the Dice from 0.673 to 0.734, the 95% Hausdorff distance from 2.38 mm to 2.07 mm, and the average surface distance from 1.00 mm to 0.59 mm. Both DL-restored T1ce from 10% and 30% input doses showed excellent image quality (3, interquartile range (IQR) [Q3-Q1] = 3-3 and 3, IQR [Q3-Q1] = 4-3), with the latter being considered more informative (4, IQR [Q3-Q1] = 4-3). CONCLUSION: The DL model improved the image quality of low-dose MRI of the CPA cistern, which makes lesion detection and diagnostic characterization possible with 10-30% of the standard dose. KEY POINTS: Question Deep learning models that aid in the reduction of contrast agent dose are not extensively evaluated for MRI of the cerebellopontine angle cistern. Findings Deep learning models restored the low-dose MRI of the cerebellopontine angle cistern, yielding images sufficient for vestibular schwannoma diagnosis and management. Clinical relevance Deep learning models make it possible to reduce the use of gadolinium-based contrast agents for contrast-enhanced MRI of the cerebellopontine angle cistern.

References

1. 1.Chen, Y., Weber, R. A., Neve, O. M., Romeijn, S. R., Hensen, E. F., Wolterink, J. M., Tao, Q., Staring, M., & Verbist, B. M. (2026). A deep learning model to reduce agent dose for contrast-enhanced MRI of the cerebellopontine angle cistern. European Radiology. https://doi.org/10.1148/radiol.2019182953