PHOCUS: Physics-Based Deconvolution for Ultrasound Resolution Enhancement
This addresses resolution limitations in medical ultrasound diagnostics, offering a novel approach for enhancing image clarity, though it appears incremental as it builds on existing deconvolution techniques with a new method for a known bottleneck.
The paper tackles the problem of limited resolution in ultrasound imaging by introducing a physics-based deconvolution method that works directly on B-mode images, using Implicit Neural Representations to learn a continuous echogenicity map, resulting in improved metrics like PSNR and SSIM on synthetic data and qualitative enhancements on phantom and in-vivo images.
Ultrasound is widely used in medical diagnostics allowing for accessible and powerful imaging but suffers from resolution limitations due to diffraction and the finite aperture of the imaging system, which restricts diagnostic use. The impulse function of an ultrasound imaging system is called the point spread function (PSF), which is convolved with the spatial distribution of reflectors in the image formation process. Recovering high-resolution reflector distributions by removing image distortions induced by the convolution process improves image clarity and detail. Conventionally, deconvolution techniques attempt to rectify the imaging system's dependent PSF, working directly on the radio-frequency (RF) data. However, RF data is often not readily accessible. Therefore, we introduce a physics-based deconvolution process using a modeled PSF, working directly on the more commonly available B-mode images. By leveraging Implicit Neural Representations (INRs), we learn a continuous mapping from spatial locations to their respective echogenicity values, effectively compensating for the discretized image space. Our contribution consists of a novel methodology for retrieving a continuous echogenicity map directly from a B-mode image through a differentiable physics-based rendering pipeline for ultrasound resolution enhancement. We qualitatively and quantitatively evaluate our approach on synthetic data, demonstrating improvements over traditional methods in metrics such as PSNR and SSIM. Furthermore, we show qualitative enhancements on an ultrasound phantom and an in-vivo acquisition of a carotid artery.