Journal
TOMOGRAPHY
Volume 6, Issue 2, Pages 86-92Publisher
GRAPHO PUBLICATIONS
DOI: 10.18383/j.tom.2019.00025
Keywords
Nonuniform diffusion weighting; gradient nonlinearity bias; retrospective correction; multi-platform clinical trials; breast cancer therapy response; apparent diffusion coefficient
Funding
- National Institutes of Health [R01CA190299, U01CA166104, U01CA151235, U01CA140204, 5P30CA006973]
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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 mu m(2)/ms) tumor volume by 16% and histogram percentiles by 5%-8%, uniformly shifting percentile-dependent ADC thresholds by similar to 0.06 mu m(2)/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.
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