3 research outputs found
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