Deep learning constrained compressed sensing reconstruction for diffusion-weighted imaging in patients with breast cancers: a plot study

Motivation: The challenges such as image quality and long scan time limitations have degraded the diffusion-weighted imaging (DWI) of breast cancer in clinical practice. Goal(s): This study aims to investigate the application of deep learning constrained compressed sensing (CS) reconstruction in DWI to overcome existing limitations. Approach: Quantitative and qualitative image quality of DWI and value apparent diffusion coefficient (ADC) of using CS (DWI-CS) and deep learning constrained CS (DWI-DLCS) were compared. Results: The results of DWI-DLCS exhibited better contrast, contrast-to-noise ratio (CNR), lesion detectability and diagnostic confidence. There were no differences regarding the signal intensity values of the apparent diffusion coefficient (ADC). Impact: Our study showcases the potential of deep learning constrained reconstruction in enhancing the quality and efficiency of DWI. This approach offers a promising clinical implementation to obtain high-quality DWI images while reducing scan time.