Modeling of Multi-View 3D Freehand Radio Frequency Ultrasound

Abstract

Nowadays ultrasound (US) examinations are typically performed with conventional machines providing two dimensional imagery. However, there exist a multitude of applications where doctors could benefit from three dimensional ultrasound providing better judgment, due to the extended spatial view. 3D freehand US allows acquisition of images by means of a tracking device attached to the ultrasound transducer. Unfortunately, view dependency makes the 3D representation of ultrasound a non-trivial task. To address this we model speckle statistics, in envelope-detected radio frequency (RF) data, using a finite mixture model (FMM), assuming a parametric representation of data, in which the multiple views are treated as components of the FMM. The proposed model is show-cased with registration, using an ultrasound specific distribution based pseudo-distance, and reconstruction tasks, performed on the manifold of Gamma model parameters. Example field of application is neurology using transcranial US, as this domain requires high accuracy and data systematically features low SNR, making intensity based registration difficult. In particular, 3D US can be specifically used to improve differential diagnosis of Parkinson's disease (PD) compared to conventional approaches and is therefore of high relevance for future application.

Intraoperative image-guided transoral robotic surgery: pre-clinical studies

“…Endoscopic and laparoscopic surgical interventions integrating various imaging modalities, such as ultrasound and CBCT , have been applied to similarly high‐risk and complex, minimally invasive procedures. Previously reported work in the context of image‐guided sinus and skull‐base surgery demonstrates operative tools tracked within preoperative images .…”

Toward intraoperative image-guided transoral robotic surgery

“…Enhanced visualization and intraoperative information support has been achieved with various imaging modalities, such as ultrasound [12], CBCT [1, 2], and optical 3D reconstruction using stereoscopic laparoscopes and computational stereo [13]. Visualization and feedback is often accomplished through rendering of virtual or simulated environments [14] or tracking of operative tools within the preoperative image data.…”

Redefining Ultrasound Compounding: Computational Sonography

“…Towards reconstructing more complex models per voxel, Klein et al [12] cluster the samples in a voxel according to their viewing angles but combine them into a volume of mixture models of Nakagami distributions, resulting in a vector containing the distribution parameters per voxel. However, the directionality information is also lost after reconstruction.…”

“…5 shows the sample selection process exemplarily. Based on (12), multiple samples of each scan line could be selected depending on the proximity of a ray to the target position x i . As the main interest for the reconstruction is to collect complementary information, only the nearest sample of each ray is retained in S i .…”