Compressive Deconvolution in Medical Ultrasound Imaging
This work addresses image quality limitations in medical ultrasound imaging, offering a method that combines compressive sampling and deconvolution for potential clinical applications, though it appears incremental as it builds on existing techniques.
The authors tackled the problem of improving ultrasound image quality while reducing data acquisition by proposing a compressive deconvolution framework that reconstructs enhanced radio-frequency images from compressed measurements, achieving joint data volume reduction and image quality improvement as evaluated on simulated and in vivo data.
The interest of compressive sampling in ultrasound imaging has been recently extensively evaluated by several research teams. Following the different application setups, it has been shown that the RF data may be reconstructed from a small number of measurements and/or using a reduced number of ultrasound pulse emissions. Nevertheless, RF image spatial resolution, contrast and signal to noise ratio are affected by the limited bandwidth of the imaging transducer and the physical phenomenon related to US wave propagation. To overcome these limitations, several deconvolution-based image processing techniques have been proposed to enhance the ultrasound images. In this paper, we propose a novel framework, named compressive deconvolution, that reconstructs enhanced RF images from compressed measurements. Exploiting an unified formulation of the direct acquisition model, combining random projections and 2D convolution with a spatially invariant point spread function, the benefit of our approach is the joint data volume reduction and image quality improvement. The proposed optimization method, based on the Alternating Direction Method of Multipliers, is evaluated on both simulated and in vivo data.