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Abstract #3239

Generation of 3D Super-resolution FLAIR Images from Heterogeneous 2D FLAIR Acquisitions in Epilepsy Patients

Spencer William Morris1,2, Ting-Yu Su1,2, Andreas Alexopoulos1, Imad Najm1, and Zhong Wang1
1Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, United States, 2Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States

Synopsis

Keywords: Analysis/Processing, Analysis/Processing

Motivation: 3D FLAIR often provides essential information for clinical MRI review, especially for detecting epileptic lesions. 2D FLAIR is still commonly used in many healthcare settings.

Goal(s): We aim to develop a framework for synthesizing high-resolution 3D FLAIR images from heterogenous 2D axial FLAIR acquisitions.

Approach: We used paired 2D and 3D FLAIR images from 31 healthy controls and 17 epilepsy patients to train a generative-adversarial network (GAN). Structural similarity index (SSIM) between the generated images and the original 3D FLAIR images was calculated.

Results: The average SSIM was 0.898±0.020 for our generated 3D FLAIR images, significantly higher than B-spline interpolation (0.845±0.021, p=0.015).

Impact: Our method can assist clinicians to evaluate the existence and the extent of epileptic lesions in data-limited situations in various health care settings. The generated images may also serve as inputs to inform AI models for automated lesion detection.

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Keywords