Deep learning-based reconstruction for 5.0T magnetic resonance imaging (MRI) in nasopharyngeal carcinoma: comparison of image quality and diagnostic efficacy.

AIM: To investigate the effect of deep learning-based reconstruction (DLR) technology on image quality and diagnostic efficacy of 5T magnetic resonance imaging (MRI) in nasopharyngeal carcinoma (NPC).

MATERIALS AND

METHODS

This prospective study included 70 NPC patients who underwent 5T MRI examinations. The protocol included axial T2-weighted imaging (T2WI), axial T1-weighted imaging (T1WI), and axial and coronal contrast-enhanced T1WIs. Images of six gear levels (0-5) were reconstructed using the DLR technology. Two radiologists independently evaluated the visibility of lesions, boundary sharpness, artefact presence, and overall image quality using a 5-point Likert scale. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were also calculated for lesions and lateral pterygoid muscles (LPM). T-stage evaluation was performed for both conventional (level = 0) and DLR images, compared to clinical T-stage results.

RESULTS

DLR images (levels 2-5) demonstrated significantly higher SNRs in lesions and LPM compared to conventional images (P < 0.001), with a maximum improvement of 94%. In axial T2WI, and axial and coronal contrast-enhanced T1WI DLR images (levels 2-5), the lesion-to-lateral pterygoid muscle CNR was significantly higher (P < 0.001), with a maximum improvement of 108%. Qualitative analysis revealed that DLR images (levels 2-5) were superior to conventional images (P < 0.05) across all subjective assessment dimensions except artefact reduction. Among the five reconstruction levels of DLR, level 3 yielded the highest overall image quality score. Additionally, the diagnostic performance of T-stage for DLR (level 3) and conventional images exhibited a comparable degree of consistency with the clinical T-stage results (κ = 0.771 and 0.796, respectively).

CONCLUSION

DLR technology improves the quality of conventional nasopharyngeal MRI images without affecting the diagnostic accuracy of T-stage, offering potential clinical value.