UCSF

The presented analysis of multisite, multiplatform clinical oncology trial data sought to enhance quantitative utility of the apparent diffusion coefficient (ADC) metric, derived from diffusion-weighted magnetic resonance imaging, by reducing technical interplatform variability owing to systematic gradient nonlinearity (GNL). This study tested the feasibility and effectiveness of a retrospective GNL correction (GNC) implementation for quantitative quality control phantom data, as well as in a representative subset of 60 subjects from the ACRIN 6698 breast cancer therapy response trial who were scanned on 6 different gradient systems. The GNL ADC correction based on a previously developed formalism was applied to trace-DWI using system-specific gradient-channel fields derived from vendor-provided spherical harmonic tables. For quantitative DWI phantom images acquired in typical breast imaging positions, the GNC improved interplatform accuracy from a median of 6% down to 0.5% and reproducibility of 11% down to 2.5%. Across studied trial subjects, GNC increased low ADC (<1 µm²/ms) tumor volume by 16% and histogram percentiles by 5%-8%, uniformly shifting percentile-dependent ADC thresholds by ∼0.06 µm²/ms. This feasibility study lays the grounds for retrospective GNC implementation in multiplatform clinical imaging trials to improve accuracy and reproducibility of ADC metrics used for breast cancer treatment response prediction.