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MUSE
MUSE: Medical Ultrasound Simulator for Education

The medical ultrasound (US) is receiving increased attention despite the fast progress in other, higer resolution imaging modalities. This is due to several facts such as being the safest, the most cost efficient and the most portable medical imaging modality. The challenge in US imaging is that the images are noisy, free-hand slices (2D cross-sections) of the 3D body and furthermore the images are dependent not only on the US device parameters (such as transducer type, the operating frequency, gain, etc.) but also on user actions (such as the pressure applied to the body surface, the amount of gel used, etc.). Hence, even before diagnostic reading of US scans, the ultrasonographers must have been trained on 3D navigation within the body to be able to recognize the anatomical structures in these 2D noisy images.

The conventional way of US training is done on volunteers, which is neither convenient (even not possible in some cases) nor cost efficient. As a solution to this, US training simulators have been developed and are being used in increasing numbers. All of these commercial systems are based on pre-recorded US scans and differ in number and variety of US scans offered as well as their user interfaces. Due to the fact that none of these systems do actually perform US simulation, they are incapable of simulating the effects of the US device parameters and the user actions that affect the images acquired.

The MUSE project aims at developing a real-time, true simulation of US images from 3D virtual patient models built using real volumetric medical image data. The current project involves:

  • 3D Deformable Virtual Patient Model
  • Realistic Haptic Interfaces Simulating Interction With Patient's Body
  • 3D Tracking Based User Interfaces for No-Touch Operation
  • True and Real-Time US Image Simulation from Volumetric Data
This project is in part supported by and run in collaboration with NET Scientific Ltd. Sti., Istanbul and is in part supported by TUBITAK-TEYDEB 1505 Programme grant # 5130002
 
CT Based Deformable Virtual Patient


The VPC (Virtual Patient Creator) application allows interactive virtual patient model building from input abdominal CT volume. VPC provides interactive segmentation (air, soft tissue, bone), virtual patient surface mesh and volume lattice model building. The exported model is used by the MUSE platform, for  3D deformation simulations and 2D arbitrary fan-shaped slicing of input CT volume.

Figure: (In clock-wise order) The virtual patient surface model and the bone mask; The virtual patient and virtual US probe interaction for arbitrary slicing; The fan-shaped CT slice (used as input or CT-to-US conversion)

Real-time Single-ray CT-to-US Conversion with Speckle Model


Real-time CT-to-US conversion is achieved via a ray-based approach. The CT image is used to estimate the acoustic properties of the domain, which are then used to compute a reflection image. The texture of the input CT image is used via a novel scatterer distribution model based on CT texture, which in turn defines the parameters of a speckle image component model. The model follows the basic physics. The shadowing effect, which is generated automatically by the model, can be removed by using an underlying image segmentation mask.
The real-time conversion generates simulated US images dependent on the major US parameters, such as frequency, gain, ultrasound probe geometry, position and orientation. The haptic interface and the deformable virtual patient model allows for realistic deformations and gain dependence on pressure.

Figure: The underlying CT slice/fan and the simulated US images w/o shadowing and suppressed exponential attenuation.

Real-time Multi-ray CT-to-US Conversion without Explicit Speckle Model
Multi-ray CT-to-US conversion Multi-ray parallelizable CT-to-US conversion is based on simulaitons based on multiple acoustic rays per transducer. The image is reconstrcuted by a weighted  and delayed combination of each ray. The delays are related to the transducer-to-point distance. Currently, the model excludes any explicit speckle modeling. Each ray behaves independetly, which allows parallel processing with vritually no extra computational cost.
The method approximates a spherical acoustic wave the propagates with constant speed radially and the phased array reconstruction approach.

Figure: The multi-ray simulated US image, with 25 rays per transducer and sigma 1.32 degrees (weighting parameter). Shadowing has been suppressed.