Impact of deep-learning image reconstruction on multiplexed sensitivity encoding diffusion-weighted imaging in the female pelvis.

BACKGROUND

Diffusion-weighted imaging (DWI) in the female pelvis is degraded by artefacts. The study aimed to evaluate the impact of deep-learning image reconstruction (DLRecon) on the image quality of multiplexed sensitivity encoding (MUSE) DWI in the female pelvis.

METHODS

Female patients scheduled for pelvic magnetic resonance imaging (MRI) with 2-shot MUSE DWI were prospectively recruited. A subset of patients underwent paired 4-shot MUSE DWI. Images were qualitatively reviewed by two radiologists in terms of overall image quality, artefacts, lesions conspicuity and sharpness using a 5-point scale, with and without DLRecon. Intra-observer and inter-observer agreements were evaluated. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and apparent diffusion coefficient (ADC) of normal structures and lesions were quantitatively compared between 2-shot MUSE with and without DLRecon. Statistical analyses were performed using Cohen's kappa, Wilcoxon signed-rank test and paired-sample t-test with Bonferroni correction.

RESULTS

Among the 65 female patients evaluated, 27 malignant lesions and 32 benign lesions were detected, while no pathology was found in 6 patients. DLRecon significantly improved the image quality in all aspects evaluated compared to 2-shot MUSE without DLRecon: overall image quality, artefacts, lesions conspicuity and sharpness (N=65, P<0.001). Furthermore, there was no statistical difference in the image quality between 2-shot MUSE with DLRecon and 4-shot MUSE without DLRecon (N=42, P=0.079-0.225). Inter- and intra-observer agreements were substantial to excellent (κ=0.664-0.815). Both SNR and CNR were increased with DLRecon (P<0.050); while no statistical difference was observed in ADC measurements in gluteus muscle (P=0.194), uterus (P=0.127) and lesions (P=0.414).

CONCLUSIONS

DLRecon improved image quality of MUSE DWI in the female pelvis by reducing impact of artefacts, improving lesion conspicuity and image sharpness, while increasing SNR and CNR but preserving the stability of ADC quantification.