635 research outputs found
Elevational Spatial Compounding for enhancing image quality in Echocardiography
INTRODUCTION: Echocardiography is commonly used in clinical practice for the real-time assessment of cardiac morphology and function. Nevertheless, due to the nature of the data acquisition, cardiac ultrasound images are often corrupted by a range of acoustic artefacts, including acoustic noise, speckle and shadowing. Spatial compounding techniques have long been recognised for their ability to suppress common ultrasound artefacts, enhancing the imaged cardiac structures. However, they require extended acquisition times as well as accurate spatio-temporal alignment of the compounded data. Elevational spatial compounding acquires and compounds adjacent partially decorrelated planes of the same cardiac structure. METHODS: This paper employs an anthropomorphic left ventricle phantom to examine the effect of acquisition parameters, such as inter-slice angular displacement and 3D sector angular range, on the elevational spatial compounding of cardiac ultrasound data. RESULTS AND CONCLUSION: Elevational spatial compounding can produce substantial noise and speckle suppression as well as visual enhancement of tissue structures even for small acquisition sector widths (2.5° to 6.5°). In addition, elevational spatial compounding eliminates the need for extended acquisition times as well as the need for temporal alignment of the compounded datasets. However, moderate spatial registration may still be required to reduce any tissue/chamber blurring side effects that may be introduced
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
Ultrafast Ultrasound Imaging
Among medical imaging modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), ultrasound imaging stands out due to its temporal resolution. Owing to the nature of medical ultrasound imaging, it has been used for not only observation of the morphology of living organs but also functional imaging, such as blood flow imaging and evaluation of the cardiac function. Ultrafast ultrasound imaging, which has recently become widely available, significantly increases the opportunities for medical functional imaging. Ultrafast ultrasound imaging typically enables imaging frame-rates of up to ten thousand frames per second (fps). Due to the extremely high temporal resolution, this enables visualization of rapid dynamic responses of biological tissues, which cannot be observed and analyzed by conventional ultrasound imaging. This Special Issue includes various studies of improvements to the performance of ultrafast ultrasoun
Echocardiography
The book "Echocardiography - New Techniques" brings worldwide contributions from highly acclaimed clinical and imaging science investigators, and representatives from academic medical centers. Each chapter is designed and written to be accessible to those with a basic knowledge of echocardiography. Additionally, the chapters are meant to be stimulating and educational to the experts and investigators in the field of echocardiography. This book is aimed primarily at cardiology fellows on their basic echocardiography rotation, fellows in general internal medicine, radiology and emergency medicine, and experts in the arena of echocardiography. Over the last few decades, the rate of technological advancements has developed dramatically, resulting in new techniques and improved echocardiographic imaging. The authors of this book focused on presenting the most advanced techniques useful in today's research and in daily clinical practice. These advanced techniques are utilized in the detection of different cardiac pathologies in patients, in contributing to their clinical decision, as well as follow-up and outcome predictions. In addition to the advanced techniques covered, this book expounds upon several special pathologies with respect to the functions of echocardiography
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
A Markov Random Field Based Approach to 3D Mosaicing and Registration Applied to Ultrasound Simulation
A novel Markov Random Field (MRF) based method for the mosaicing of 3D ultrasound volumes is presented in this dissertation. The motivation for this work is the production of training volumes for an affordable ultrasound simulator, which offers a low-cost/portable training solution for new users of diagnostic ultrasound, by providing the scanning experience essential for developing the necessary psycho-motor skills. It also has the potential for introducing ultrasound instruction into medical education curriculums. The interest in ultrasound training stems in part from the widespread adoption of point-of-care scanners, i.e. low cost portable ultrasound scanning systems in the medical community.
This work develops a novel approach for producing 3D composite image volumes and validates the approach using clinically acquired fetal images from the obstetrics department at the University of Massachusetts Medical School (UMMS). Results using the Visible Human Female dataset as well as an abdominal trauma phantom are also presented. The process is broken down into five distinct steps, which include individual 3D volume acquisition, rigid registration, calculation of a mosaicing function, group-wise non-rigid registration, and finally blending. Each of these steps, common in medical image processing, has been investigated in the context of ultrasound mosaicing and has resulted in improved algorithms. Rigid and non-rigid registration methods are analyzed in a probabilistic framework and their sensitivity to ultrasound shadowing artifacts is studied.
The group-wise non-rigid registration problem is initially formulated as a maximum likelihood estimation, where the joint probability density function is comprised of the partially overlapping ultrasound image volumes. This expression is simplified using a block-matching methodology and the resulting discrete registration energy is shown to be equivalent to a Markov Random Field. Graph based methods common in computer vision are then used for optimization, resulting in a set of transformations that bring the overlapping volumes into alignment. This optimization is parallelized using a fusion approach, where the registration problem is divided into 8 independent sub-problems whose solutions are fused together at the end of each iteration. This method provided a speedup factor of 3.91 over the single threaded approach with no noticeable reduction in accuracy during our simulations. Furthermore, the registration problem is simplified by introducing a mosaicing function, which partitions the composite volume into regions filled with data from unique partially overlapping source volumes. This mosaicing functions attempts to minimize intensity and gradient differences between adjacent sources in the composite volume.
Experimental results to demonstrate the performance of the group-wise registration algorithm are also presented. This algorithm is initially tested on deformed abdominal image volumes generated using a finite element model of the Visible Human Female to show the accuracy of its calculated displacement fields. In addition, the algorithm is evaluated using real ultrasound data from an abdominal phantom. Finally, composite obstetrics image volumes are constructed using clinical scans of pregnant subjects, where fetal movement makes registration/mosaicing especially difficult.
Our solution to blending, which is the final step of the mosaicing process, is also discussed. The trainee will have a better experience if the volume boundaries are visually seamless, and this usually requires some blending prior to stitching. Also, regions of the volume where no data was collected during scanning should have an ultrasound-like appearance before being displayed in the simulator. This ensures the trainee\u27s visual experience isn\u27t degraded by unrealistic images. A discrete Poisson approach has been adapted to accomplish these tasks. Following this, we will describe how a 4D fetal heart image volume can be constructed from swept 2D ultrasound. A 4D probe, such as the Philips X6-1 xMATRIX Array, would make this task simpler as it can acquire 3D ultrasound volumes of the fetal heart in real-time; However, probes such as these aren\u27t widespread yet.
Once the theory has been introduced, we will describe the clinical component of this dissertation. For the purpose of acquiring actual clinical ultrasound data, from which training datasets were produced, 11 pregnant subjects were scanned by experienced sonographers at the UMMS following an approved IRB protocol. First, we will discuss the software/hardware configuration that was used to conduct these scans, which included some custom mechanical design. With the data collected using this arrangement we generated seamless 3D fetal mosaics, that is, the training datasets, loaded them into our ultrasound training simulator, and then subsequently had them evaluated by the sonographers at the UMMS for accuracy. These mosaics were constructed from the raw scan data using the techniques previously introduced. Specific training objectives were established based on the input from our collaborators in the obstetrics sonography group. Important fetal measurements are reviewed, which form the basis for training in obstetrics ultrasound. Finally clinical images demonstrating the sonographer making fetal measurements in practice, which were acquired directly by the Philips iU22 ultrasound machine from one of our 11 subjects, are compared with screenshots of corresponding images produced by our simulator
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