Fusion of Color Doppler and Magnetic Resonance Images of the Heart

This study was designed to establish and analyze color Doppler and magnetic resonance fusion images of the heart, an approach for simultaneous testing of cardiac pathological alterations, performance, and hemodynamics. Ten volunteers were tested in this study. The echocardiographic images were produced by Philips IE33 system and the magnetic resonance images were generated from Philips 3.0-T system. The fusion application was implemented on MATLAB platform utilizing image processing technology. The fusion image was generated from the following steps: (1) color Doppler blood flow segmentation, (2) image registration of color Doppler and magnetic resonance imaging, and (3) image fusion of different image types. The fusion images of color Doppler blood flow and magnetic resonance images were implemented by MATLAB programming in our laboratory. Images and videos were displayed and saved as AVI and JPG. The present study shows that the method we have developed can be used to fuse color flow Doppler and magnetic resonance images of the heart. We believe that the method has the potential to: fill in information missing from the ultrasound or MRI alone, show structures outside the field of view of the ultrasound through MR imaging, and obtain complementary information through the fusion of the two imaging methods (structure from MRI and function from ultrasound)

Automatic whole heart segmentation based on image registration

Whole heart segmentation can provide important morphological information of the heart, potentially enabling the development of new clinical applications and the planning and guidance of cardiac interventional procedures. This information can be extracted from medical images, such as these of magnetic resonance imaging (MRI), which is becoming a routine modality for the determination of cardiac morphology. Since manual delineation is labour intensive and subject to observer variation, it is highly desirable to develop an automatic method. However, automating the process is complicated by the large shape variation of the heart and limited quality of the data. The aim of this work is to develop an automatic and robust segmentation framework from cardiac MRI while overcoming these difficulties. The main challenge of this segmentation is initialisation of the substructures and inclusion of shape constraints. We propose the locally affine registration method (LARM) and the freeform deformations with adaptive control point status to tackle the challenge. They are applied to the atlas propagation based segmentation framework, where the multi-stage scheme is used to hierarchically increase the degree of freedom. In this segmentation framework, it is also needed to compute the inverse transformation for the LARM registration. Therefore, we propose a generic method, using Dynamic Resampling And distance Weighted interpolation (DRAW), for inverting dense displacements. The segmentation framework is validated on a clinical dataset which includes nine pathologies. To further improve the nonrigid registration against local intensity distortions in the images, we propose a generalised spatial information encoding scheme and the spatial information encoded mutual information (SIEMI) registration. SIEMI registration is applied to the segmentation framework to improve the accuracy. Furthermore, to demonstrate the general applicability of SIEMI registration, we apply it to the registration of cardiac MRI, brain MRI, and the contrast enhanced MRI of the liver. SIEMI registration is shown to perform well and achieve significantly better accuracy compared to the registration using normalised mutual information

Registration of 3D Ultrasound Volumes with Applications in Neurosurgery and Prostate Radiotherapy

Brain tissue deforms significantly after opening the dura and during tumor resection, invalidating pre-operative imaging data. Ultrasound is a popular imaging modality for providing the neurosurgeon with real-time updated images of brain tissue. Interpretation of post-resection ultrasound images is difficult due to large brain shift and tissue resection. Furthermore, several factors degrade the quality of post-resection ultrasound images such as strong reflection of waves at the interface of saline water and brain tissue in resection cavities, air bubbles and the application of blood-clotting agents around the edges of resection. Image registration allows comparison of post-resection ultrasound images with higher quality pre-resection images, assists in interpretation of post-resection images and may help identify residual tumor, and as such, is of significant clinical importance. Prostate motion is known to reduce the precision of prostate radiotherapy. This motion can be categorized into intrafraction and interfraction. Interfraction motion introduces large systematic errors into the treatment and is the largest contributor to prostate planning treatment volume (PTV) margins. Conventional solutions to interfraction motion all have respective drawbacks. Clarity Autoscan system provides continuous ultrasound imaging of the prostate for interfraction motion correction, however it is time-consuming and can have large interobserver errors. The intension of accurately targeting the prostate and reducing the side effects in treatment requests a faster and more accurate registration framework for interfraction motion correction. In this thesis, we first propose a registration framework called Nonrigid Symmetric Registration (NSR) for accurate alignment of pre- and post-resection volumetric ultrasound images in near real-time. An outlier detection algorithm is proposed and utilized in this framework to identify non-corresponding regions (outliers) and therefore improve the robustness and accuracy of registration. We use an Efficient Second-order Minimization (ESM) method for fast and robust optimization. A symmetric and inverse-consistent method is exploited to generate realistic deformation fields. The results show that NSR significantly improves the quality of alignment between pre- and post-resection ultrasound images. Then based on this framework, we develop a rigid registration framework called Prostate Registration Framework (PRF) for alignment of the prosate region in simulation and treatment volumes. PRF is trained using 2 3D transperineal ultrasound (TPUS) images of an ultrasound prostate phantom and 20 3D TPUS images from 11 patients receiving Clarity Autoscan. Algorithm performance is evaluated using further 21 TPUS images from a total of 8 patients by comparison of the PRF with manual matching of landmarks and Clarity-based estimation of interfraction motion performed by three observers. The results show that PRF outputs more accurate alignment of the prosate region in simulation and treatment volumes than Clarity, and further, provides the reposition of the prostate in treatment images efficiently and accurately

Fast 4D Ultrasound Registration for Image Guided Liver Interventions

Liver problems are a serious health issue. The common liver problems are hepatitis, fatty liver, liver cancer and liver damage caused by alcohol abuse. Continuous, long term disease may cause a condition of the liver known as the Liver Cirrhosis. Liver cirrhosis makes the liver scarred and hardened up causing portal hypertension. In such a situation the collateral vessels try to bypass the liver as blood cannot freely flow through the liver; causing internal bleeding. One of the treatments of portal hypertension is Transjugular intrahepatic portosystemic shunt (TIPS). In a TIPS procedure a tract in the liver is created that shortcuts two veins in the liver, reducing the portal hypertension. Radiofrequency ablation (RFA) is use for the treatment of liver cancer. In RFA, a needle electrode is placed through the skin into the liver tumor. High-frequency electrical currents are passed through the electrode, creating heat that destroys the cancer cells, without damaging the surrounding liver tissues. TIPS and RFA are minimally invasive procedures, where small incisions are made to perform the surgery and are alternative to open surgery. A minimally invasive alternative has large potential in reducing complication rates, minimizing surgical trauma and reducing hospital stay. However, in these procedures, due to lack of direct eyesight, three-dimensional imaging information about the anatomy and instruments during the intervention is required. The most difficult part of these procedures is the interpretation and selection of oblique views for needle/instrument insertion and target visualization. In our work we develop and evaluate techniques that enable the effective use of 3D ultrasound for image guided interventions. Ultrasound is low cost, mobile and unlike CT and X-rays does not use any harmful radiation in the imaging process. During these procedures, breathing shifts the region of interest and makes it difficult to constantly focus on a region of interest. We provide an approach to correct for the motion due to breathing. Additionally, we propose a method for image fusion of interventional ultrasound and preoperative imaging modalities such as CT for cases where the lesions are visible in CT but not visible in ultrasound. Incorporating CT data during intervention additionally adds greater definition and precision to the ultrasound based navigation system. Concluding, in this thesis, we presented methods and evaluated their accuracies that demonstrate the use of real-time 3D US and its fusion with CT in potentially improving image guidance in minimally invasive US guided liver interventions

Post formation processing of cardiac ultrasound data for enhancing image quality and diagnostic value

Cardiovascular diseases (CVDs) constitute a leading cause of death, including premature death, in the developed world. The early diagnosis and treatment of CVDs is therefore of great importance. Modern imaging modalities enable the quantification and analysis of the cardiovascular system and provide researchers and clinicians with valuable tools for the diagnosis and treatment of CVDs. In particular, echocardiography offers a number of advantages, compared to other imaging modalities, making it a prevalent tool for assessing cardiac morphology and function. However, cardiac ultrasound images can suffer from a range of artifacts reducing their image quality and diagnostic value. As a result, there is great interest in the development of processing techniques that address such limitations. This thesis introduces and quantitatively evaluates four methods that enhance clinical cardiac ultrasound data by utilising information which until now has been predominantly disregarded. All methods introduced in this thesis utilise multiple partially uncorrelated instances of a cardiac cycle in order to acquire the information required to suppress or enhance certain image features. No filtering out of information is performed at any stage throughout the processing. This constitutes the main differentiation to previous data enhancement approaches which tend to filter out information based on some static or adaptive selection criteria. The first two image enhancement methods utilise spatial averaging of partially uncorrelated data acquired through a single acoustic window. More precisely, Temporal Compounding enhances cardiac ultrasound data by averaging partially uncorrelated instances of the imaged structure acquired over a number of consecutive cardiac cycles. An extension to the notion of spatial compounding of cardiac ultrasound data is 3D-to-2D Compounding, which presents a novel image enhancement method by acquiring and compounding spatially adjacent (along the elevation plane), partially uncorrelated, 2D slices of the heart extracted as a thin angular sub-sector of a volumetric pyramid scan. Data enhancement introduced by both approaches includes the substantial suppression of tissue speckle and cavity noise. Furthermore, by averaging decorrelated instances of the same cardiac structure, both compounding methods can enhance tissue structures, which are masked out by high levels of noise and shadowing, increasing their corresponding tissue/cavity detectability. The third novel data enhancement approach, referred as Dynamic Histogram Based Intensity Mapping (DHBIM), investigates the temporal variations within image histograms of consecutive frames in order to (i) identify any unutilised/underutilised intensity levels and (ii) derive the tissue/cavity intensity threshold within the processed frame sequence. Piecewise intensity mapping is then used to enhance cardiac ultrasound data. DHBIM introduces cavity noise suppression, enhancement of tissue speckle information as well as considerable increase in tissue/cavity contrast and detectability. A data acquisition and analysis protocol for integrating the dynamic intensity mapping along with spatial compounding methods is also investigated. The linear integration of DHBIM and Temporal Compounding forms the fourth and final implemented method, which is also quantitatively assessed. By taking advantage of the benefits and compensating for the limitations of each individual method, the integrated method suppresses cavity noise and tissue speckle while enhancing tissue/cavity contrast as well as the delineation of cardiac tissue boundaries even when heavily corrupted by cardiac ultrasound artifacts. Finally, a novel protocol for the quantitative assessment of the effect of each data enhancement method on image quality and diagnostic value is employed. This enables the quantitative evaluation of each method as well as the comparison between individual methods using clinical data from 32 patients. Image quality is assessed using a range of quantitative measures such as signal-to-noise ratio, tissue/cavity contrast and detectability index. Diagnostic value is assessed through variations in the repeatability level of routine clinical measurements performed on patient cardiac ultrasound scans by two experienced echocardiographers. Commonly used clinical measures such as the wall thickness of the Interventricular Septum (IVS) and the Left Ventricle Posterior Wall (LVPW) as well as the cavity diameter of the Left Ventricle (LVID) and Left Atrium (LAD) are employed for assessing diagnostic value

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

Multiview RT3D Echocardiography Image Fusion

Real-time three-dimensional echocardiography (RT3DE) permits the acquisition and visualization of the beating heart in 3D. However, its actual utility is limited due to missing anatomical structures and limited field-of-view (FOV). We present an automatic two-stage registration and fusion method to integrate multiple single-view RT3DE images. The registration scheme finds a rigid transformation by using a multiresolution algorithm. The fusion is based on the 3D wavelet transform, utilizing the separation of the image into low- and high-frequency wavelet subbands. The qualitative and quantitative results, from 12 subjects, demonstrate that the proposed fusion framework helps in: (i) filling-in missing anatomical information, (ii) extending the FOV, and (iii) increasing the structural information and image contrast. © 2009 Springer Berlin Heidelberg