Keywords: Diffusion Reconstruction, Diffusion/other diffusion imaging techniques, Breast cancer, Deep learning
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.
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