Correlations Between MR Apparent Diffusion Coefficients and PET Standard Uptake Values in Simultaneous MR-PET Imaging of Prostate Cancer

TL;DR

This study found that deep learning-based PET imaging correlates better with MRI measurements in prostate cancer than traditional methods.

Contribution

The paper introduces a deep learning approach that improves the correlation between PET SUV and MR ADC values in prostate cancer imaging.

Findings

DL-based SUV values showed stronger correlation with ADC than conventional PET-MR values.

Inverse correlation (ρ = −0.20 to −0.51) was observed between ADC and SUV values in prostate cancer regions.

K-fold cross-validation confirmed the reliability of DL models in improving SUV-ADC correlation.

Abstract

This study evaluated the hypothesis that 68Ga-PSMA-11 PET SUV, obtained via an advanced DL approach, correlates better with MR ADC maps than values from conventional PET-MR. Additionally, we aimed to identify the optimal SUV threshold for maximum correlation with ADC values. A cohort of 32 prostate cancer patients underwent CT and corresponding PET-MR imaging. The dataset underwent K-fold cross-validation, dividing it into four folds. In each fold, 24 patients were used for training, and 8 for validation to create DL models. ADC maps from 27 out of 32 patients were successfully aligned with T2 images for detailed analysis, revealing an inverse correlation (ρ = −0.20 to −0.51) between ADC and SUV values in prostate cancer zones. Statistically significant differences in mean SUV values were observed between PETMRI and PETDL. DL-based SUV values show a stronger correlation with ADC than conventional PET-MR values in our investigation.

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