Deep learning-assisted needle artifact suppression for enhanced anatomical visualization in prostate high-dose-rate brachytherapy ultrasound imaging.

PURPOSE: Implanted needles introduce acoustic artifacts that degrade transrectal ultrasound (TRUS) images during high-dose-rate (HDR) prostate brachytherapy, complicating ultrasound-only contouring. We developed an artificial intelligence (AI) needle eraser to remove needles and associated artifacts and facilitate structure delineation. METHODS: TRUS images from 120 patients undergoing HDR prostate brachytherapy were retrospectively collected. Three-dimensional volumes were acquired immediately before and after needle insertion. A Cycle Generative Adversarial Network (CycleGAN) was trained to transform postneedle images into needle-free images. Two physicians independently rated clinical utility for prostate and urethra delineation using a four-point scale (1 = poor; 4 = excellent). Prostate and urethra contours from preneedle and needle-erased images were compared with clinical reference contours using Dice similarity coefficient (DSC). RESULTS: The AI tool suppressed needles and associated artifacts, improving visualization of the prostate and urethral lumen. Mean reader scores were 3.80 ± 0.43 for prostate and 3.75 ± 0.37 for urethra delineation. Compared with preneedle contours, needle-erased contours showed higher agreement with clinical references: prostate DSC increased from 0.91 to 0.93, and urethra DSC increased from 0.70 to 0.89. CONCLUSIONS: A CycleGAN-based needle eraser can generate needle-free ultrasound images from postinsertion scans and improve visualization and contour agreement for physician delineation.