Automatic AI based Intensity harmonization of MRI prostate images.

Motivation: In multi-centric, multi-scanner datasets differences between images caused by different acquisition protocols and reconstruction techniques are found. These differences introduce biases when developing AI-based solutions that limit their generalization. Goal(s): To develop an algorithm to harmonize intensities on MR medical images independently of the source and artifacts that may be introducing a bias. Approach: Use of the MRI frequency domain to synthetically generate realistic intensity variations simulating differences in acquisition protocols. Image-to-image CNN-based solution to reconstruct any image to a reference dataset. Results: Harmonization of prostate T2w MRI showed a qualitative harmonization of the images and an improvement in AI-based segmentation task. Impact: This methodology helps the harmonization of medical MRI images, enhancing accuracy and efficiency in MRI AI based task. By standardizing image quality and reducing variations, this innovation ensures consistent interpretations across healthcare institutions, improving collaboration among medical and AI professionals.