Rapid progress in this field marks a significant shift from traditional reconstruction methods to data-driven approaches since 2016.
[3] However, these approaches are unsatisfactory in challenging scenarios, such as low-dose CT, fast MRI, metal artifacts, patient motion, and so on.
[15] Deep learning has enabled significant improvements in low-field MRI by enhancing image quality despite lower signal-to-noise ratio (SNR), making these systems clinically viable.
[21] For ultrasound beamforming, deep neural networks, allows superior image quality with limited data at high speed.
Furthermore, deep learning has also enhanced photoacoustic imaging,[26] addressing challenges like high noise, low contrast, and limited resolution.