Field strength-dependent performance variability in deep learning-based analysis of magnetic resonance imaging

TL;DR

This study demonstrates that MRI scanner magnetic field strength significantly affects the performance and generalizability of deep learning segmentation models, especially for soft tissue structures, highlighting the need to consider field strength as a confounding factor.

Contribution

It provides a quantitative analysis of how MRI field strength impacts deep learning segmentation performance across multiple datasets and tasks, emphasizing the importance of field strength in model training and evaluation.

Findings

Higher field strength (3.0T) improves segmentation accuracy for soft tissues.

Models trained on pooled data show reduced performance compared to single-field models.

Field strength influences radiomic features and clustering, affecting model generalizability.

Abstract

This study quantitatively evaluates the impact of MRI scanner magnetic field strength on the performance and generalizability of deep learning-based segmentation algorithms. Three publicly available MRI datasets (breast tumor, pancreas, and cervical spine) were stratified by scanner field strength (1.5T vs. 3.0T). For each segmentation task, three nnU-Net-based models were developed: A model trained on 1.5T data only (m-1.5T), a model trained on 3.0T data only (m-3.0T), and a model trained on pooled 1.5T and 3.0T data (m-combined). Each model was evaluated on both 1.5T and 3.0T validation sets. Field-strength-dependent performance differences were investigated via Uniform Manifold Approximation and Projection (UMAP)-based clustering and radiomic analysis, including 23 first-order and texture features. For breast tumor segmentation, m-3.0T (DSC: 0.494 [1.5T] and 0.433 [3.0T]) significantly outperformed m-1.5T (DSC: 0.411 [1.5T] and 0.289 [3.0T]) and m-combined (DSC: 0.373 [1.5T] and 0.268[3.0T]) on both validation sets (p<0.0001). Pancreas segmentation showed similar trends: m-3.0T achieved the highest DSC (0.774 [1.5T], 0.840 [3.0T]), while m-1.5T underperformed significantly (p<0.0001). For cervical spine, models performed optimally on same-field validation sets with minimal cross-field performance degradation (DSC>0.92 for all comparisons). Radiomic analysis revealed moderate field-strength-dependent clustering in soft tissues (silhouette scores 0.23-0.29) but minimal separation in osseous structures (0.12). These results indicate that magnetic field strength in the training data substantially influences the performance of deep learning-based segmentation models, particularly for soft-tissue structures (e.g., small lesions). This warrants consideration of magnetic field strength as a confounding factor in studies evaluating AI performance on MRI.