Sparse Data Reconstruction and its Application in Spherical Computational Sonography

Supervision: Christoph Hennersperger, Rüdiger Göbl, Prof. Dr. Nassir Navab

Student: Sara Hajmohammadalitorkabadi

Abstract

Ultrasound imaging widely used in clinics because of several advantages compared to other imaging modalities. It provides images in real time, is portable and substantially lower in cost, without requiring harmful radiation. However, to date, it is still highly dependent on a skilled operator and directional information of ultrasound is neglected. Nowadays, the clinical use of three-dimensional ultrasound technology is another area of intense research activity.

State-of-the-art systems mostly perform compounding of the image data prior to further processing and visualization, resulting in 3D volumes of scalar intensities and loss of all directional information. Computational Sonography preserves this directional information of the acquired data, and allows for its exploitation by computational algorithms. In Model Independent Computational Sonography, different models of Computational Sonography are compared to classical scalar compounding for freehand acquisitions, providing both an improved preservation of US directionality as well as improved image quality in 3D.

Two models where proposed to store the directional dependent information, a tensor-CS model and spherical-CS model, however, in both modalities the data is reconstructed sparsely since limited number of segments on sphere contain information. This project aims to overcome this problem and present information for each segment on sphere. In order to achieve the aim, a test bed will be provided to compare different reconstruction schemes, on scattered data distributed on sphere surface and evaluate its application in spherical-CS.