AI-Assisted Post Contrast Brain MRI: Eighty Percent Reduction in Contrast Dose.

OBJECTIVES

In the context of growing safety concerns regarding the use of gadolinium-based contrast agents in contrast-enhanced MRI, there is a need for dose reduction without compromising diagnostic accuracy. A deep learning (DL) method is proposed and evaluated in this study for predicting full-dose contrast-enhanced T1w images from multiparametric MRI acquired with 20% of the standard dose of gadolinium-based contrast agents.

MATERIALS AND METHODS

This multicentric prospective study leveraged multiparametric brain MRIs acquired between March and July 2024. A total of 101 patients were included. Patients with white matter disease, small vessels disease, tumor or mass, post-operative change and no enhanced lesion were included. Pre-contrast, low-dose, and standard-dose postcontrast T1w sequences were acquired. A DL network was utilized to process pre-contrast and low-dose sequences to generate synthesized full-dose contrast-enhanced T1w images. DL-T1w images and full-dose T1w MRI images were qualitatively and quantitatively compared using both automated voxel-wise metrics and a reader study, in which three neuroradiologists graded the image quality, image SNR, vessel conspicuity and lesion visualization using a 5-point Likert scale.

RESULTS

A comparison of the average reader scores for DL-T1w images and full-dose-T1w images did not show any significant differences in image quality (P = 0.08); however, the image SNR and vessel conspicuity scores were higher for DL-T1w images (P < 0.05). In all 3 reader evaluations, the lower limit of the 95% CI for differences in least square means for border delineation, internal morphology, and contrast enhancement was above the noninferiority margin, showing statistical noninferiority between DL-T1w and full-dose-T1w paired images (≥ -0.26) (P < 0.001). The DL-T1w images obtained an SSIM of 86 ± 12.1% relative to the full-dose-T1w images, and a PSNR of 27 ± 3 dB.

CONCLUSION

The proposed DL method was capable of generating synthesized postcontrast T1-weighted MR images that were comparable to full-dose T1w images, as determined by quantitative analysis and radiologist evaluation.