3D Quasi-Static Ultrasound Elastography With Plane Wave In Vivo

In biological tissue, an increase in elasticity is often a marker of abnormalities. Techniques such as quasi-static ultrasound elastography have been developed to assess the strain distribution in soft tissues in two dimensions using a quasi-static compression. However, as abnormalities can exhibit very heterogeneous shapes, a three dimensional approach would be necessary to accurately measure their volume and remove operator dependency. Acquisition of volumes at high rates is also critical to performing real-time imaging with a simple freehand compression. In this study, we developed for the first time a 3D quasi-static ultrasound elastography method with plane waves that estimates axial strain distribution in vivo in entire volumes at high volume rate. Acquisitions were performed with a 2D matrix array probe of 2.5MHz frequency and 256 elements. Plane waves were emitted at a volume rate of 100 volumes/s during a continuous motorized and freehand compression. 3D B-mode volumes and 3D cumulative axial strain volumes were successfully estimated in inclusion phantoms and in ex vivo canine liver before and after a high intensity focused ultrasound ablation. We also demonstrated the in vivo feasibility of the method using freehand compression on the calf muscle of a human volunteer and were able to retrieve 3D axial strain volume at a high volume rate depicting the differences in stiffness of the two muscles which compose the calf muscle. 3D ultrasound quasi-static elastography with plane waves could become an important technique for the imaging of the elasticity in human bodies in three dimensions using simple freehand scanning

Quantitative three-dimensional elasticity imaging from quasi-static deformation: a phantom study

We present a methodology to image and quantify the shear elastic modulus of three-dimensional (3D) breast tissue volumes held in compression under conditions similar to those of a clinical mammography system. Tissue phantoms are made to mimic the ultrasonic and mechanical properties of breast tissue. Stiff lesions are created in these phantoms with size and modulus contrast values, relative to the background, that are within the range of values of clinical interest. A two-dimensional ultrasound system, scanned elevationally, is used to acquire 3D images of these phantoms as they are held in compression. From two 3D ultrasound images, acquired at different compressed states, a three-dimensional displacement vector field is measured. The measured displacement field is then used to solve an inverse problem, assuming the phantom material to be an incompressible, linear elastic solid, to recover the shear modulus distribution within the imaged volume. The reconstructed values are then compared to values measured independently by direct mechanical testing.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65094/2/pmb9_3_019.pd

Simulation-Based Joint Estimation of Body Deformation and Elasticity Parameters for Medical Image Analysis

Estimation of tissue stiffness is an important means of noninvasive cancer detection. Existing elasticity reconstruction methods usually depend on a dense displacement field (inferred from ultrasound or MR images) and known external forces. Many imaging modalities, however, cannot provide details within an organ and therefore cannot provide such a displacement field. Furthermore, force exertion and measurement can be difficult for some internal organs, making boundary forces another missing parameter. We propose a general method for estimating elasticity and boundary forces automatically using an iterative optimization framework, given the desired (target) output surface. During the optimization, the input model is deformed by the simulator, and an objective function based on the distance between the deformed surface and the target surface is minimized numerically. The optimization framework does not depend on a particular simulation method and is therefore suitable for different physical models. We show a positive correlation between clinical prostate cancer stage (a clinical measure of severity) and the recovered elasticity of the organ. Since the surface correspondence is established, our method also provides a non-rigid image registration, where the quality of the deformation fields is guaranteed, as they are computed using a physics-based simulation

A CPU-GPU Hybrid Approach for Accelerating Cross-correlation Based Strain Elastography

Elastography is a non-invasive imaging modality that uses ultrasound to estimate the elasticity of soft tissues. The resulting images are called 'elastograms'. Elastography techniques are promising as cost-effective tools in the early detection of pathological changes in soft tissues. The quality of elastographic images depends on the accuracy of the local displacement estimates. Cross-correlation based displacement estimators are precise and sensitive. However cross-correlation based techniques are computationally intense and may limit the use of elastography as a real-time diagnostic tool. This study investigates the use of parallel general purpose graphics processing unit (GPGPU) engines for speeding up generation of elastograms at real-time frame rates while preserving elastographic image quality. To achieve this goal, a cross-correlation based time-delay estimation algorithm was developed in C programming language and was profiled to locate performance blocks. The hotspots were addressed by employing software pipelining, read-ahead and eliminating redundant computations. The algorithm was then analyzed for parallelization on GPGPU and the stages that would map well to the GPGPU hardware were identified. By employing optimization principles for efficient memory access and efficient execution, a net improvement of 67x with respect to the original optimized C version of the estimator was achieved. For typical diagnostic depths of 3-4cm and elastographic processing parameters, this implementation can yield elastographic frame rates in the order of 50fps. It was also observed that all of the stages in elastography cannot be offloaded to the GPGPU for computation because some stages have sub-optimal memory access patterns. Additionally, data transfer from graphics card memory to system memory can be efficiently overlapped with concurrent CPU execution. Therefore a hybrid model of computation where computational load is optimally distributed between CPU and GPGPU was identified as an optimal approach to adequately tackle the speed-quality problem in real-time imaging. The results of this research suggest that use of GPGPU as a co-processor to CPU may allow generation of elastograms at real time frame rates without significant compromise in image quality, a scenario that could be very favorable in real-time clinical elastography

Statistical Analysis of a Three-dimensional Axial Strain and Axial-shear Strain Elastography Algorithm

Pathological phenomena often change the mechanical properties of the tissue. Therefore, estimation of tissue mechanical properties can be of clinical importance. Ultrasound elastography is a well-established strain estimation technique. Until recently, mainly 1D elastography algorithms have been developed. A few 2D algorithms have also been developed in the past. Both of these two types of technique ignore the tissue motion in the elevational direction, which could be a significant source of decorrelation in the RF data. In this thesis, a 3D elastography algorithm that estimates all the three components of tissue displacement is implemented and tested statistically. In this research, displacement fields of mechanical models are simulated. RF signals are then generated based on these displacement fields and used as the input of elastography algorithms. To evaluate the image quality of elastograms, absolute error, SNRe, CNRe and CNRasse are computed. The SNRe, CNRe and CNRasse values are investigated not only under different strain conditions, but also in different frame locations, which forms 3D strain filters. A statistical comparison between image qualities of the 3D technique and 2D technique is also provided. The results of this study show that the 3D elastography algorithm outperforms the 2D elastography algorithm in terms of image quality and robustness, especially under high strain conditions. This is because that the 3D algorithm estimates the elevational displacement, while the 2D technique only estimates the axial and lateral deformation. Since the elevational displacement could be an important source for the decorrelation in the RF data, the 3D technique is more effective and robust compared with the 2D technique

New Image Processing Methods for Ultrasound Musculoskeletal Applications

In the past few years, ultrasound (US) imaging modalities have received increasing interest as diagnostic tools for orthopedic applications. The goal for many of these novel ultrasonic methods is to be able to create three-dimensional (3D) bone visualization non-invasively, safely and with high accuracy and spatial resolution. Availability of accurate bone segmentation and 3D reconstruction methods would help correctly interpreting complex bone morphology as well as facilitate quantitative analysis. However, in vivo ultrasound images of bones may have poor quality due to uncontrollable motion, high ultrasonic attenuation and the presence of imaging artifacts, which can affect the quality of the bone segmentation and reconstruction results. In this study, we investigate the use of novel ultrasonic processing methods that can significantly improve bone visualization, segmentation and 3D reconstruction in ultrasound volumetric data acquired in applications in vivo. Specifically, in this study, we investigate the use of new elastography-based, Doppler-based and statistical shape model-based methods that can be applied to ultrasound bone imaging applications with the overall major goal of obtaining fast yet accurate 3D bone reconstructions. This study is composed to three projects, which all have the potential to significantly contribute to this major goal. The first project deals with the fast and accurate implementation of correlation-based elastography and poroelastography techniques for real-time assessment of the mechanical properties of musculoskeletal tissues. The rationale behind this project is that, iii in the future, elastography-based features can be used to reduce false positives in ultrasonic bone segmentation methods based on the differences between the mechanical properties of soft tissues and the mechanical properties of hard tissues. In this study, a hybrid computation model is designed, implemented and tested to achieve real time performance without compromise in elastographic image quality . In the second project, a Power Doppler-based signal enhancement method is designed and tested with the intent of increasing the contrast between soft tissue and bone while suppressing the contrast between soft tissue and connective tissue, which is often a cause of false positives in ultrasonic bone segmentation problems. Both in-vitro and in-vivo experiments are performed to statistically analyze the performance of this method. In the third project, a statistical shape model based bone surface segmentation method is proposed and investigated. This method uses statistical models to determine if a curve detected in a segmented ultrasound image belongs to a bone surface or not. Both in-vitro and in-vivo experiments are performed to statistically analyze the performance of this method. I conclude this Dissertation with a discussion on possible future work in the field of ultrasound bone imaging and assessment

Development of novel ultrasound techniques for imaging and elastography. From simulation to real-time implementation

Ultrasound techniques offer many advantages, in terms of ease of realization and patients’ safety. The availability of suitable hardware and software tools is condicio sine qua non for new methods testing. This PhD project addresses medical ultrasound signal processing and seeks to achieve two scientific goals: the first is to contribute to the development of an ultrasound research platform, while the second is introducing and validating, through this platform, non-standard methods. During the thesis, the capabilities of the system were improved by creating advanced software tools, such as acoustic field simulators, and by developing echo-signals elaboration programs. In particular, a novel technique for quasi-static elastography was developed, in-vitro tested and implemented in real-time

Simulation-Based Joint Estimation of Body Deformation and Elasticity Parameters for Medical Image Analysis

Elasticity parameter estimation is essential for generating accurate and controlled simulation results for computer animation and medical image analysis. However, finding the optimal parameters for a particular simulation often requires iterations of simulation, assessment, and adjustment and can become a tedious process. Elasticity values are especially important in medical image analysis, since cancerous tissues tend to be stiffer. Elastography is a popular type of method for finding stiffness values by reconstructing a dense displacement field from medical images taken during the application of forces or vibrations. These methods, however, are limited by the imaging modality and the force exertion or vibration actuation mechanisms, which can be complicated for deep-seated organs. In this thesis, I present a novel method for reconstructing elasticity parameters without requiring a dense displacement field or a force exertion device. The method makes use of natural deformations within the patient and relies on surface information from segmented images taken on different days. The elasticity value of the target organ and boundary forces acting on surrounding organs are optimized with an iterative optimizer, within which the deformation is always generated by a physically-based simulator. Experimental results on real patient data are presented to show the positive correlation between recovered elasticity values and clinical prostate cancer stages. Furthermore, to resolve the performance issue arising from the high dimensionality of boundary forces, I propose to use a reduced finite element model to improve the convergence of the optimizer. To find the set of bases to represent the dimensions for forces, a statistical training based on real patient data is performed. I demonstrate the trade-off between accuracy and performance by using different numbers of bases in the optimization using synthetic data. A speedup of more than an order of magnitude is observed without sacrificing too much accuracy in recovered elasticity.Doctor of Philosoph

Robust Displacement Estimation for Ultrasound Elastography and Thermal Imaging

Ultrasound imaging is becoming the modality of choice for many diagnostic and surgical procedures. Besides being inexpensive and safe, ultrasonography is emerging as a quantitative tool able to image tissue properties. In this dissertation we focus on elastography and thermal imaging, which both rely on the measurement of real or apparent motion in ultrasound image sequences. In ultrasound elastography, signal decorrelation is widely viewed as the major limiting factor for adoption of into clinical practice. In this dissertation we focus on improving the robustness of a displacement estimation method based on dynamic programming, addressing multiple weak points. We propose a set of tools which can improve its ability to overcome displacement discontinuities and regions of poorly correlated RF data. The method is further extended to three dimensional data. Phantom, animal and human studies are presented for experimental validation. The addition of robust tools results in an improved ability to achieve repeatable, artifact-free strain maps, without compromising computational speed. In thermal imaging, we focus on the estimation of real and apparent motion while the tissue temperature is increased in an ablation procedure. Estimating heat-induced echo shifts is a very difficult problem because of their very small amplitude, on the order of tens of microns. They can easily be masked by other sources of deformation/movement from the environment such as patient motion or hand tremor. In this dissertation, we build upon the robust displacement estimation method for elastography, with the additional deployment of an iterative motion compensation algorithm. The validation experiments are performed on laboratory induced ablation lesions, where the ultrasound probe is either held by the operator's hand or supported by a robotic arm. We demonstrate the ability to detect and remove non-heat induced tissue motion at every step of the ablation procedure. Our results exceed the state of the art in both the accuracy of temperature estimation as well as the length of time over which temperature estimation can be performed. Previous research in the area of motion compensation resulted in good results for experiments lasting less than 10 seconds. Our experiments lasted close to 20 minutes