Ultrasound goes GPU: real-time simulation using CUDA (original) (raw)

Ultrasound goes GPU: real-time simulation using CUDA

Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling, 2009

Despite the increasing adoption of other imaging modalities, ultrasound guidance is widely used for surgical procedures and clinical imaging due to its low cost, non-invasiveness, and real-time visual feedback. Many ultrasound-guided procedures require extensive training and where possible training on simulations should be preferred over patients. Computational resources for existing approaches to ultrasound simulation are usually limited by real-time requirements. Unlike previous approaches we simulate freehand ultrasound images from CT data on the Graphics Processing Unit (GPU). We build upon the method proposed by Wein et al. for estimating ultrasound reflection properties of tissue and modify it to a computationally more efficient form. In addition to previous approaches, we also estimate ultrasound absorption properties from CT data. Using NVIDIA's "Compute Unified Device Architecture" (CUDA), we provide a physically plausible simulation of ultrasound reflection, shadowing artifacts, speckle noise and radial blurring. The same algorithm can be used for simulating either linear or radial imaging, and all parameters of the simulated probe are interactively configurable at runtime, including ultrasound frequency and intensity as well as field geometry. With current hardware we are able to achieve an image width of up to 1023 pixels from raw CT data in real-time, without any pre-processing and without any loss of information from the CT image other than from interpolation of the input data. Visual comparison to real ultrasound images indicates satisfactory results.

Visualization and GPU-accelerated simulation of medical ultrasound from CT images

Computer Methods and Programs in Biomedicine, 2009

c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 9 4 ( 2 0 0 9 ) 250-266 a b s t r a c t We present a fast GPU-based method for simulation of ultrasound images from volumetric CT scans and their visualization. The method uses a ray-based model of the ultrasound to generate view-dependent ultrasonic effects such as occlusions, large-scale reflections and attenuation combined with speckle patterns derived from pre-processing the CT image using a wave-based model of ultrasound propagation in soft tissue. The main applications of the method are ultrasound training and registration of ultrasound and CT images.

Ultrasound Image Simulation with GPU-based Ray Tracing

Abstract: Medical simulators are gaining importance because the experience and skills necessary to perform many of the medical procedures are difficult to obtain due to patient safety and ethical issues. With the development of graphic cards, stereographic and haptic devices, more VR-based simulators are being created. We are developing an interactive ultrasound image simulation that include deformations and needle visualization as part of a training simulator for the application of regional anesthesia.

A GPU-based framework for simulation of medical ultrasound

Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling, 2009

Simulation of ultrasound (US) images from volumetric medical image data has been shown to be an important tool in medical image analysis. However, there is a trade off between the accuracy of the simulation and its realtime performance. In this paper, we present a framework for acceleration of ultrasound simulation on the graphics processing unit (GPU) of commodity computer hardware. Our framework can accommodate ultrasound modeling with varying degrees of complexity. To demonstrate the flexibility of our proposed method, we have implemented several models of acoustic propagation through 3D volumes. We conducted multiple experiments to evaluate the performance of our method for its application in multi-modal image registration and training. The results demonstrate the high performance of the GPU accelerated simulation outperforming CPU implementations by up to two orders of magnitude and encourage the investigation of even more realistic acoustic models.

GPU-based real-time imaging software suite for medical ultrasound

2013

We developed a GPU-based real-time imaging software suite for medical ultrasound imaging to provide a fast real-time imaging platform for various probe geometries and imaging schemes. The imaging software receives raw RF data from a data acquisition system, and processes them on GPU to reconstruct real-time images. The most general-purpose imaging program in the suite displays three cross-sectional images for arbitrary probe geometry and various imaging schemes including conventional beamforming, synthetic beamforming, and planewave compounding. The other imaging programs in the software suite, derived from the general-purpose imaging program, are optimized for their own purposes, such as displaying a rotating B-mode plane and its maximum intensity projection (MIP), photoacoustic imaging, and real-time volume-rendering. Realtime imaging was successfully demonstrated using each of the imaging programs in the software suite.

Real-Time Ultrasound Imaging Simulation

Real-Time Imaging, 1998

Unlike other medical imaging tomography, ultrasound is a non-invasive and non-radiative device. The physician is free to scan the patient's internal organs and concentrate on locations of interest. Ultrasound imaging, however, is rather noisy, blurred and has low spatial and dynamic resolution. The physician has to gain considerable experience before he can diagnose. This paper introduces UltraSim, a computer-based interactive simulator device designed to train physicians and technicians in medical diagnosis using ultrasound systems. Students using the system are able to learn how to identify and diagnose a wide range of medical cases by operating a simulated ultrasound machine on a mannequin, without any need for actual patients.

Focused Ultrasound - Efficient GPU Simulation Methods for Therapy Planning

Over the past years, high intensity focused ultrasound therapy has become a promising therapeutic alternative for non-invasive tumor treatment. The basic idea of this interventional approach is to apply focused ultrasound waves to the tumor tissue such that the cells are heated and hence destroyed. Since it is quite difficult to assess the quality of this non-invasive therapy, there is a dire need for computer support in planning, conduction, and monitoring of such treatments. In this work, we propose efficient simulation techniques for focused ultrasound waves as well as their heat dissemination using current graphics hardware as a numerical co-processor. We achieve speed-ups between 10 and 700 for the single simulation steps compared to an optimized CPU solution, overall resulting in a significant performance gain over previous approaches for simulation of focused ultrasound. Categories and Subject Descriptors (according to ACM CCS): I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling-Physically based modeling I.6.7 [Simulation and Modeling]: Simulation Support Systems-J.3 [Life and Medical Sciences]: Health-

GPU-based real-time generation of large ultrasound volumes from freehand 3D sweeps

Current Directions in Biomedical Engineering, 2015

In the recent past, 3D ultrasound has been gaining relevance in many biomedical applications. One main limitation, however, is that typical ultrasound volumes are either very poorly resolved or only cover small areas. We have developed a GPU-accelerated method for live fusion of freehand 3D ultrasound sweeps to create one large volume. The method has been implemented in CUDA and is capable of generating an output volume with 0.5 mm resolution in real time while processing more than 45 volumes per second, with more than 300.000 voxels per volume. First experiments indicate that large structures like a whole forearm or high-resolution volumes of smaller structures like the hand can be combined efficiently. It is anticipated that this technology will be helpful in pediatric surgery where X-ray or CT imaging is not always possible.

GPU-based real-time small displacement estimation with ultrasound

IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 2000

General purpose computing on graphics processing units has been previously shown to speed up computationally intensive data processing and image reconstruction algorithms for CT, MR, and ultrasound images. Although some algorithms in ultrasound have been converted to GPU processing, many investigative ultrasound research systems still use serial processing on a single CPU. One such ultrasound modality is acoustic radiation force impulse (ARFI) imaging, which investigates the mechanical properties of soft tissue. Traditionally, the raw data are processed offline to estimate the displacement of the tissue after the application of radiation force. It is highly advantageous to process the data in real-time to assess the quality and make modifications during a study. In this paper, we present algorithms for efficient GPU parallel processing of two widely used tools in ultrasound: cubic spline interpolation and Loupas' two-dimensional autocorrelator for displacement estimation. It is shown that a commercially available graphics card can be used for these computations, achieving speed increases up to 40× as compared to single CPU processing. Thus, we conclude that the GPU based data processing approach facilitates realtime (i.e. < 1 second) display of ARFI data and is a promising approach for ultrasonic research systems.

GPU-based reconstruction and display for 4D ultrasound data

… (IUS), 2009 IEEE …, 2009

Due to the required computational effort of 4D ultrasound imaging, such systems depend on low complexity techniques like nearest neighbor interpolation, which affects volume quality. Moreover, more accurate techniques like normalized convolution, backward trilinear interpolation, and forward spherical and ellipsoidal Gaussian kernel, are avoided in real-time imaging because of the tight reconstruction time. The goal of this work is to utilize recent commercial graphics hardware technology of graphics processing unit (GPU) to speed up the reconstruction time while increasing the quality of displayed volume.