  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.
