Overview

The Automated MRI Toolkit streamlines brain structure analysis by generating T2-weighted images directly from T1-weighted (T1w) scans. This approach reduces the need for dual-scan sessions, minimizing patient time in the MRI and lowering costs while maintaining structural contrast and detail.

Design

The toolkit uses machine learning models, including U-Net and CycleGAN architectures, to accurately synthesize T2w images from T1w inputs. For image normalization and intensity standardization, it incorporates Nyul’s method for consistent inputs, while FSL’s FAST segmentation isolates critical brain regions. This integrated process ensures reliable, high-fidelity image synthesis, supporting efficient and comprehensive brain structure analysis.

Results

Tests on diverse datasets show the MRI Toolkit reliably synthesizes T2-weighted images with structural accuracy comparable to traditional scans. Initial evaluations highlight its potential to reduce scan times and enhance data utility in clinical and research workflows. For a detailed report, view the full report.