A Deep Reinforcement Learning Based Region-Specific Beamformer for Sparse Arrays 3-D Ultrasound Imaging.

Area of Science:

• Medical Imaging
• Ultrasound Technology
• Artificial Intelligence in Medicine

Background:

• Sparse arrays in 3-D ultrasound imaging reduce elements but cause sidelobe artifacts, degrading image quality.
• Existing adaptive beamformers can mitigate artifacts but often harm speckle texture, leading to unnatural images.

Purpose of the Study:

• To introduce RSB-Net, a novel region-specific beamformer utilizing deep reinforcement learning (DRL).
• To overcome the limitations of existing methods by adaptively selecting beamformers per image region.

Main Methods:

• RSB-Net employs DRL to differentiate between artifact-prone and texture-rich regions.
• It applies adaptive beamforming to artifact regions and delay-and-sum (DAS) beamforming to preserve speckle texture.
• Validation was performed on simulated and experimental synthetic transmit aperture (STA) RF datasets using a 2-D sparse array prototype.

Main Results:

• RSB-Net significantly improved contrast ratio (CR) by 52.81 dB and generalized contrast-to-noise ratio (gCNR) by 0.65 on simulated data compared to DAS.
• Similar performance gains (51.01 dB CR, 0.64 gCNR) were observed on experimental phantom data.
• The method effectively reduced sidelobe artifacts while preserving essential image texture.

Conclusions:

• RSB-Net offers a robust solution for high-quality B-mode 3-D ultrasound imaging with sparse arrays.
• It has the potential to advance 3-D ultrasound standardization by improving anatomical visualization and measurement accuracy.
• This technique can reduce operator dependency and enhance the detection of lesions and calcifications.