Additional file 1 of Impact of deep learning image reconstruction on ADC quantification and histogram metrics: a phantom study

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Additional file 1: Fig. S1, Percentage deviation between measured and nominal ADC values across DL reconstruction levels and acquisition sessions, averaged over all inserts. The solid line represents 0% bias, while dashed lines indicate ±5% bias. Fig. S2: Maximum percentage deviation between intra-session measurements for each vial, DL reconstruction level, and type of sequence. Fig. S3. Wasserstein distance quantifying the differences in the entire ADC value distribution between OFF and deep learning acquisition levels (LOW, MEDIUM, and HIGH), pairwise, for each insert using fFOV DWI (a) and rFOV DWI (b). Fig. S4: Trends of percentile-based first-order radiomic features (10th, 25th, 75th, and 90th percentiles) across deep learning (DL) reconstruction levels for fFOV and rFOV acquisitions in the two repeated sessions. Table S1. Estimated fit coefficients and 95% confidence intervals (95% CI) for the ADC–temperature dependence at each PVP concentration. Table S2. Session-specific CV values for each insert and DL reconstruction strength. Table S3 Session-specific Wasserstein distance between OFF and different deep learning acquisition levels (LOW, MEDIUM, and HIGH), pairwise, for each insert using fFOV DWI (a) and rFOV DWI (b). Table S4. Friedman test results for the comparison of ADC histograms across DL levels in the two repeated sessions. Table S5. Session-specific percent differences of first-order radiomic features, between DL-OFF and DL-based reconstructions (LOW, MEDIUM, HIGH) averaged over all phantom inserts, using fFOV DWI (a) and rFOV DWI (b). Table S6. Results of paired Wilcoxon signed-rank test for the comparison of ADC first-order radiomic features across DL levels.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.