Comparison of conventional diffusion-weighted imaging and multiplexed sensitivity-encoding combined with deep learning-based reconstruction in breast magnetic resonance imaging

Purpose

To evaluate the feasibility of multiplexed sensitivity-encoding (MUSE) with deep learning-based reconstruction (DLR) for breast imaging in comparison with conventional diffusion-weighted imaging (DWI) and MUSE alone.

Methods

This study was conducted using conventional single-shot DWI and MUSE data of female participants who underwent breast magnetic resonance imaging (MRI) from June to December 2023. The k-space data in MUSE were reconstructed using both conventional reconstruction and DLR. Two experienced radiologists conducted quantitative analyses of DWI, MUSE, and MUSE-DLR images by obtaining the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR) of lesions and normal tissue and qualitative analyses by using a 5-point Likert scale to assess the image quality. Inter-reader agreement was assessed using the intraclass correlation coefficient (ICC). Image scores, SNR, CNR, and apparent diffusion coefficient (ADC) measurements among the three sequences were compared using the Friedman test, with significance defined at P < 0.05.

Results

In evaluations of the images of 51 female participants using the three sequences, the two radiologists exhibited good agreement (ICC = 0.540–1.000, P < 0.05). MUSE-DLR showed significantly better SNR than MUSE (P < 0.001), while the ADC values within lesions and tissues did not differ significantly among the three sequences (P = 0.924, P = 0.636, respectively). In the subjective assessments, MUSE and MUSE-DLR scored significantly higher than conventional DWI in overall image quality, geometric distortion and axillary lymph node (P < 0.001).

Conclusion

In comparison with conventional DWI, MUSE-DLR yielded improved image quality with only a slightly longer acquisition time.