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April 15, 2026Open Access

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

Puntos clave

Evaluate the effects of deep learning image reconstruction on ADC quantification and histogram metrics.
Conducted a phantom study to assess ADC accuracy across different DL levels.
Analyzed ADC repeatability in both full and reduced field of view (FOV) diffusion-weighted imaging (DWI) sequences.
Measured histogram metrics such as entropy and interquartile ranges at various DL strengths.
ADC accuracy was preserved in full and reduced FOV DWI.
High repeatability of ADC was maintained across different DL levels.
Histogram dispersion decreased progressively with increased DL strength, especially in high-ADC vials.

Resumen

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.

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