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

Clinical Snapshot

Key Findings

• P Value: p < 0.001 for entropy and interquartile range changes

• Effect Size: CV 0.1-1.2% for repeatability; accuracy deviation -2 to 7%

• Primary Outcome: ADC quantification accuracy and repeatability maintained across all DL levels

• Nnt Or Sensitivity: Progressive reduction in histogram dispersion with increasing DL strength, particularly in high-ADC regions

• Confidence Interval: Not reported for primary outcomes

Clinical Application

High feasibility as DL reconstruction is increasingly available on clinical MRI systems Relevant to Australian radiology practices using modern MRI systems; supports TGA-approved DL reconstruction technologies in clinical workflow optimization Patients undergoing diffusion-weighted MRI for oncological applications, particularly tumor characterization

Abstract

OBJECTIVE: Recently, deep learning (DL)-based reconstruction methods have been introduced into clinical magnetic resonance imaging (MRI) systems to enhance image quality and reduce acquisition time. However, their effects on apparent diffusion coefficient (ADC) maps remain unclear. We investigated whether DL-based image reconstruction influences ADC quantification and histogram-based ADC metrics using a calibrated diffusion-weighted imaging (DWI) phantom. MATERIALS AND METHODS: A phantom containing vials with known ADC values was scanned on a 3-T system using full (fFOV) and reduced (rFOV) field-of-view DWI sequences. Each acquisition was performed using conventional (DL-OFF) and three DL-based strength levels (low, medium, high). Median ADC values were analyzed for repeatability (coefficient of variation (CV)) and accuracy. Histogram changes and first-order radiomic features were assessed using the Wasserstein distance, Friedman, and Wilcoxon tests. RESULTS: ADC estimates showed high repeatability (CV 0.1-1.2%) and good accuracy (deviation -2 to 7%) across all DL levels and sequences. DL reconstruction progressively reduced histogram dispersion, particularly in high-ADC vials. Wasserstein distances increased with DL strength, confirming a progressive effect on ADC value distributions, while median ADC values remained unchanged. Entropy and interquartile range decreased significantly (p < 0.001), whereas kurtosis and skewness increased, with differences showing less stable and sequence-dependent statistical significance. CONCLUSION: DL-based reconstruction maintained accurate and repeatable ADC quantification while reducing the dispersion of ADC values. The effect was more evident for high-ADC regions and the rFOV sequence, resulting in narrower distributions of ADC values. Further investigations comparing different DL-based solutions are warranted to assess the generalizability of these findings in clinical settings. RELEVANCE STATEMENT: Over the past decade, ADC histogram analysis has proven valuable for quantifying tumor heterogeneity, differentiating tumor grade, and evaluating early treatment response. Deep learning reconstruction narrows ADC distributions and reduces dispersion, supporting its potential in oncologic DWI, while highlighting the need for patient-based validation studies. KEY POINTS: DL reconstruction preserved ADC accuracy in both full FOV and reduced FOV DWI. ADC repeatability remained high across DL levels for both DWI sequences. Histogram dispersion progressively reduced across DL levels, particularly in high-ADC vials. Entropy and interquartile ranges decreased progressively with increasing DL strength.

References

1. 1.Marzi, S., Bruzzaniti, V., Laganaro, F., Di Giulio, G., Farella, M., Tonnetti, A., Terrenato, I., Castellana, R., & Vidiri, A. (2024). Impact of deep learning image reconstruction on ADC quantification and histogram metrics: a phantom study. European Radiology Experimental, 8, Article 758. https://doi.org/10.1186/s40644-024-00758-9