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.

翻译：超声在医学诊断中广泛应用，提供便捷且强大的成像能力，但由于衍射和成像系统有限孔径导致的固有分辨率限制，其诊断应用受到制约。超声成像系统的脉冲响应函数称为点扩散函数（PSF），该函数在图像形成过程中与反射体的空间分布进行卷积。通过消除卷积过程引入的图像畸变来恢复高分辨率反射体分布，可显著提升图像清晰度与细节呈现。传统反卷积技术通常直接对射频（RF）数据进行处理，尝试校正成像系统相关的PSF。然而，RF数据往往难以直接获取。为此，我们提出一种基于物理学的反卷积方法，通过建模PSF直接对更普遍可用的B超图像进行处理。借助隐式神经表示（INRs），我们学习从空间位置到对应回声强度值的连续映射，有效补偿离散化图像空间的局限性。本研究的核心贡献在于提出一种创新方法：通过可微分的基于物理学的超声渲染流程，直接从B超图像中重建连续的回声强度分布图，从而实现超声分辨率增强。我们在合成数据上对方法进行了定性与定量评估，结果表明其在PSNR和SSIM等指标上优于传统方法。此外，我们在超声体模和颈动脉活体采集数据中均观察到显著的图像质量提升。