Second harmonic imaging is currently adopted as standard in commercial echographic systems. A new imaging
technique, coined as superharmonic imaging (SHI), combines the 3rd till the 5th harmonics, arising during
nonlinear sound propagation. It could further enhance resolution and quality of echographic images. To meet
the bandwidth requirement for SHI a dedicated phased array has been developed: a low frequency subarray,
intended for transmission, interleaved with a high frequency subarray, used in reception. As the bandwidth of the
elements is limited, the spectral gaps in between the harmonics cause multiple reflection artifacts. Recently, we
introduce a dual-pulse frequency compounding (DPFC) method to suppress those artifacts at price of a reduced
frame rate. In this study we investigate the feasibility of performing the frequency compounding protocol within
a single transmission.
The traditional DPFC method constructs each trace in a post-processing stage by summing echoes from two
emitted pulses, the second slightly frequency-shifted compared to the first. In the newly proposed method, the
transmit aperture is divided into two parts: the first half is used to send a pulse at the lower center frequency,
while the other half simultaneously transmits at the higher center frequency. The suitability of the protocol
for medical imaging applications in terms of the steering capabilities was performed in a simulation study using
the FIELD II toolkit. Moreover, an experimental study was performed to deduce the optimal parametric set
for implementation of the clinical imaging protocol. The latter was subsequently used to obtain the images of
a tissue mimicking phantom containing strongly reflecting wires. For in-vitro acquisitions the SHI probe with
interleaved phased array (44 odd elements at 1MHz and 44 even elements at 3.7MHz elements, optimized for
echocardiography) was connected to a fully programmable ultrasound system.
The results of the Field II simulations demonstrated that the angle between the main and grating lobe
amounted to 90°. The difference in the fundamental pressure level between those lobes was equal to -26.8 dB.
Those results suggest that the superharmonic content in the grating lobe was acceptably low. A considerable
improvement in the axial resolution of the SHI component (0.73 mm) at -6 dB in comparison with the 3rd
harmonic (2.23 mm) was observed. A similar comparison in terms of the lateral resolution slightly favored the
superharmonic component by 0.2 mm. Additionally, the images of the tissue mimicking phantom exhibited an
absence of the multiple reflection artifacts in the focal and post-focal regions.
The new method is equally effective in eliminating the ripple artifacts associated with SHI as the dual pulse
technique, while the full frame rate is maintained.
The analysis of echocardiograms, whether visual or automated, is often hampered by ultrasound artifacts which
obscure the moving myocardial wall. In this study, a probabilistic framework for tracking the endocardial surface
in 3D ultrasound images is proposed, which distinguishes between visible and artifact-obscured myocardium.
Motion estimation of visible myocardium relies more using a local, data-driven tracker, whereas tracking of
obscured myocardium is assisted by a global, statistical model of cardiac motion. To make this distinction, the
expectation-maximization algorithm is applied in a stationary and dynamic frame-of-reference. Evaluation on
35 three-dimensional echocardiographic sequences shows that this artifact-aware tracker gives better results than
when no distinction is made. In conclusion, the proposed tracker is able to reduce the influence of artifacts,
potentially improving quantitative analysis of clinical quality echocardiograms.
For obtaining quantitative and objective functional parameters from three-dimensional (3D) echocardiographic sequences, automated segmentation methods may be preferable to cumbersome manual delineation of 3D borders. In this study, a novel optical-flow based tracking method is proposed for propagating 3D endocardial contours of the left ventricle throughout the cardiac cycle. To take full advantage of the time-continuous nature of cardiac motion, a statistical motion model was explicitly embedded in the optical flow solution. The cardiac motion was modeled as frame-to-frame affine transforms, which were extracted using Procrustes analysis on a set of training contours. Principal component analysis was applied to obtain a compact model of cardiac motion throughout the whole cardiac cycle. The parameters of this model were resolved in an optical flow manner, via spatial and temporal gradients in image intensity. The algorithm was tested on 36 noncontrast and 28 contrast enhanced 3D echocardiographic sequences in a leave-one-out manner. Good results were obtained using a combination of the proposed motion-guided method and a purely data-driven optical flow approach. The improvement was particularly noticeable in areas where the LV wall was obscured by image artifacts. In conclusion, the results show the applicability of the proposed method in clinical quality echocardiograms.
Intravascular Ultrasound (IVUS) palpography is a techniques that depicts the distribution of the mechanical
strain over the luminal surface of coronary arteries. It utilizes conventional radiofrequency (RF) signals acquired
at two different levels of a compressional load. The signals are cross-correlated to obtain the microscopic tissue
displacements, which can be directly translated into local strain of the vessel wall. However, (apparent) tissue
motion and nonuniform deformation of the vessel wall due to catheter jolting and rotation reduce signal correlation
and result in void strain estimates. Implications of probe motion were studied on the tissue-mimicking
phantom. The measured circumferential tissue displacement and level of the speckle decorrelation amounted to
12° and 0.58 for the catheter displacement of 800 μm, respectively. To compensate for the motion artifacts in
IVUS palpography, a novel method, based on the feature-based scale-space Optical Flow (OF) was employed.
The computed OF vector field quantifies the amount of the local tissue misalignment in consecutive frames.
Subsequently, the extracted motion pattern is used to realign the signals prior to the cross-correlation analysis,
reducing signal decorrelation and increasing the number of valid strain estimates. The advantage of applying the
motion compensation algorithms was demonstrated in a mid-scale validation study on 14 in-vivo pullbacks. Both
methods substantially increase the number of valid strain estimates in the partial and compounded palpograms.
A mean relative improvement amounts to 28% and 14%, respectively. Implementation of motion compensation
method increase the diagnostic value of IVUS palpography.
The quantitative assessment of and compensation for catheter rotation in Intravascular Ultrasound (IVUS) images
presents a fundamental problem for noninvasive characterization of the mechanical properties of the coronary
arteries. A method based on the scale-space optical flow algorithm with a feature-based weighting scheme is
proposed to account for the aforementioned artifact. The computed vector field, describing the transformation
between two consecutive frames, allows the quantitative assessment of the amount of vessel wall tissue motion,
which is directly related to the catheter rotation. Algorithm accuracy and robustness were demonstrated on a
tissue-mimicking phantom, subjected to controlled amount of angular deviation. The proposed method shows a
great reliability in prediction of catheter rotational motion up to 4°.
This paper describes a method for automatic contour detection in reformatted short-axis (SA) cardiac computed tomography (CT) using
a virtual exploring robot. The robot is a tricycle with a steering
front wheel. Its motion obeys a set of kinematic equations and is
subject to the non-holonomic constraints. The robot is designed to
navigate in the binary representation of a cardiac image, consisting of the allowed navigational and obstacle spaces. It is initially positioned inside the allowed navigational space. Avoiding obstacles, the robot autonomously cruises through the navigational space and collects information about the location of the left ventricular (LV) boundaries. Consequently, the obtained information is used to reconstruct the endocardial and epicardial contours. Validation of the method was performed on in-vivo multislice multiphase short-axis cardiac CT images of ten subjects. Results showed good correlation between the quantitative parameters, computed from manual and automatic segmentation: for end-diastolic volume (EDV) r=0.99, for end-systolic volume (ESV) r=0.98, ejection fraction (EF) r=0.83, and LV mass (LVM) r=0.95.
Manual quantitative analysis of cardiac left ventricular function using multi-slice CT is labor intensive because of the large datasets. We present an automatic, robust and intrinsically three-dimensional segmentation method for cardiac CT images, based on 3D Active Shape Models (ASMs). ASMs describe shape and shape variations over a population as a mean shape and a number of eigenvariations, which can be extracted by e.g. Principal Component Analysis (PCA). During the iterative ASM matching process, the shape deformation is restricted within statistically plausible constraints (±3σ). Our approach has two novel aspects: the 3D-ASM application to volume data of arbitrary planar orientation, and the application to image data from another modality than which was used to train the model, without the necessity of retraining it. The 3D-ASM was trained on MR data and quantitatively evaluated on 17 multi-slice cardiac CT data sets, with respect to calculated LV volume (blood pool plus myocardium) and endocardial volume. In all cases, model matching was convergent and final results showed a good model performance. Bland-Altman analysis however, showed that bloodpool volume was slightly underestimated and LV volume was slightly overestimated by the model. Nevertheless, these errors remain within clinically acceptable margins. Based on this evaluation, we conclude that our 3D-ASM combines robustness with clinically acceptable accuracy. Without retraining for cardiac CT, we could adapt a model trained on cardiac MR data sets for application in cardiac CT volumes, demonstrating the flexibility and feasibility of our matching approach. Causes for the systematic errors are edge detection, model constraints, or image data reconstruction. For all these categories, solutions are discussed.