In Vivo Blood Velocity Vector Imaging Using Adaptive Velocity Compounding in the Carotid Artery Bifurcation.

Visualization and quantification of blood flow are considered important for early detection of atherosclerosis and patient-specific diagnosis and intervention. As conventional Doppler imaging is limited to 1-D velocity estimates, 2-D and 3-D techniques are being developed. We introduce an adaptive velocity compounding technique that estimates the 2-D velocity vector field using predominantly axial displacements estimated by speckle tracking from dual-angle plane wave acquisitions. Straight-vessel experiments with a 7.8-MHz linear array transducer connected to a Verasonics Vantage ultrasound system revealed that the technique performed with a maximum velocity magnitude bias and angle bias of -3.7% (2.8% standard deviation) and -0.16° (0.41° standard deviation), respectively. In vivo, complex flow patterns were visualized in two healthy and three diseased carotid arteries and quantified using a vector complexity measure that increased with increasing wall irregularity. This measure could potentially be a relevant clinical parameter which might aid in early detection of atherosclerosis.

Simultaneous Vascular Strain and Blood Vector Velocity Imaging Using High-Frequency Versus Conventional-Frequency Plane Wave Ultrasound: A Phantom Study.

Plaque strain and blood vector velocity imaging of stenosed arteries are expected to aid in diagnosis and prevention of cerebrovascular disease. Ultrafast plane wave imaging enables simultaneous strain and velocity estimation. Multiple ultrasound vendors are introducing high-frequency ultrasound probes and systems. This paper investigates whether the use of high-frequency ultrafast ultrasound is beneficial for assessing blood velocities and strain in arteries. The performance of strain and blood flow velocity estimation was compared between a high-frequency transducer (MS250, fc = 21 MHz) and a clinically utilized transducer (L12-5, fc = 9 MHz). Quantitative analysis based on straight tube phantom experiments revealed that the MS250 outperformed the L12-5 in the superficial region: low velocities near the wall were more accurately estimated and wall strains were better resolved. At greater than 2-cm echo depth, the L12-5 performed better due to the high attenuation of the MS250 probe. Qualitative comparison using a perfused patient-specific carotid bifurcation phantom confirmed these findings. Thus, in conclusion, for strain and blood velocity estimation for depths up to ~2 cm, a high-frequency probe is recommended.

Three-Dimensional Model-Based Segmentation in Echocardiography Using High Temporal Tissue and Blood Flow Information.

Accurate 3-D surface segmentation is a challenging task in echocardiography because of the relatively low image quality. We introduce a new method for 3-D segmentation of the endocardium involving temporal decorrelation of echo signals originating from tissue and blood using radiofrequency (RF) signals acquired in 3-D Doppler mode. Temporal features were extracted in 3-D Doppler mode, where a sequence of RF lines is recorded for each image line. Each set of RF lines is highly correlated because of the high pulse repetition frequency. However, for high blood flow, the RF signals will decorrelate over time in contrast to the endocardium, which will remain relatively highly correlated over time. These decorrelation features permit differentiation between myocardial tissue and blood flow. We describe an implementation of a 3-D segmentation model in which temporal information is used as external constraint. The model was validated in a phantom and in vivo in healthy volunteers (n = 5). The phantom study revealed that the model successfully segmented the artificial blood lumen even for low flow velocity and illustrated the sensitivity of the segmentations to flow rate. In healthy volunteers, high Dice similarity indices indicate that 3-D segmentation of the endocardial border in vivo is feasible.

Robust cardiac motion estimation using ultrafast ultrasound data: a low-rank topology-preserving approach.

Cardiac motion estimation is an important diagnostic tool for detecting heart diseases and it has been explored with modalities such as MRI and conventional ultrasound (US) sequences. US cardiac motion estimation still presents challenges because of complex motion patterns and the presence of noise. In this work, we propose a novel approach to estimate cardiac motion using ultrafast ultrasound data. Our solution is based on a variational formulation characterized by the L 2-regularized class. Displacement is represented by a lattice of b-splines and we ensure robustness, in the sense of eliminating outliers, by applying a maximum likelihood type estimator. While this is an important part of our solution, the main object of this work is to combine low-rank data representation with topology preservation. Low-rank data representation (achieved by finding the k-dominant singular values of a Casorati matrix arranged from the data sequence) speeds up the global solution and achieves noise reduction. On the other hand, topology preservation (achieved by monitoring the Jacobian determinant) allows one to radically rule out distortions while carefully controlling the size of allowed expansions and contractions. Our variational approach is carried out on a realistic dataset as well as on a simulated one. We demonstrate how our proposed variational solution deals with complex deformations through careful numerical experiments. The low-rank constraint speeds up the convergence of the optimization problem while topology preservation ensures a more accurate displacement. Beyond cardiac motion estimation, our approach is promising for the analysis of other organs that exhibit motion.

A Comparison Between Compounding Techniques Using Large Beam-Steered Plane Wave Imaging for Blood Vector Velocity Imaging in a Carotid Artery Model.

Conventional color Doppler imaging is limited, since it only provides velocity estimates along the ultrasound beam direction for a restricted field of view at a limited frame rate. High-frame-rate speckle tracking, using plane wave transmits, has shown potential for 2-D blood velocity estimation. However, due to the lack of focusing in transmit, image quality gets reduced, which hampers speckle tracking. Although ultrafast imaging facilitates improved clutter filtering, it still remains a major challenge in blood velocity estimation. Signal dropouts and poor velocity estimates are still present for high beam-to-flow angles and low blood flow velocities. In this paper, ultrafast plane wave imaging was combined with multiscale speckle tracking to assess the 2-D blood velocity vector in a common carotid artery (CCA) flow field. A multiangled plane wave imaging sequence was used to compare the performance of displacement compounding, coherent compounding, and compound speckle tracking. Zero-degree plane wave imaging was also evaluated. The performance of the methods was evaluated before and after clutter filtering for the large range of velocities (0-1.5 m/s) that are normally present in a healthy CCA during the cardiac cycle. An extensive simulation study was performed, based on a sophisticated model of the CCA, to investigate and evaluate the performance of the methods at different pulse repetition frequencies and signal-to-noise levels. In vivo data were acquired of a healthy carotid artery bifurcation to support the simulation results. In general, methods utilizing compounding after speckle tracking, i.e., displacement compounding and compound speckle tracking, were least affected by clutter filtering and provided the most robust and accurate estimates for the entire velocity range. Displacement compounding, which uses solely axial information to estimate the velocity vector, provided most accurate velocity estimates, although it required sufficiently high pulse repetition frequencies in high blood velocity phases and reliable estimates for all acquisition angles. When this latter requirement was not met, compound speckle tracking was most accurate, because it uses the possibility to discard angular velocity estimates corrupted by clutter filtering. Similar effects were observed for in vivo data obtained at the carotid artery bifurcation. Investigating a combination of these two compounding techniques is recommended for future research.

2-D Versus 3-D Cross-Correlation-Based Radial and Circumferential Strain Estimation Using Multiplane 2-D Ultrafast Ultrasound in a 3-D Atherosclerotic Carotid Artery Model.

Three-dimensional (3-D) strain estimation might improve the detection and localization of high strain regions in the carotid artery (CA) for identification of vulnerable plaques. This paper compares 2-D versus 3-D displacement estimation in terms of radial and circumferential strain using simulated ultrasound (US) images of a patient-specific 3-D atherosclerotic CA model at the bifurcation embedded in surrounding tissue generated with ABAQUS software. Global longitudinal motion was superimposed to the model based on the literature data. A Philips L11-3 linear array transducer was simulated, which transmitted plane waves at three alternating angles at a pulse repetition rate of 10 kHz. Interframe (IF) radio-frequency US data were simulated in Field II for 191 equally spaced longitudinal positions of the internal CA. Accumulated radial and circumferential displacements were estimated using tracking of the IF displacements estimated by a two-step normalized cross-correlation method and displacement compounding. Least-squares strain estimation was performed to determine accumulated radial and circumferential strain. The performance of the 2-D and 3-D methods was compared by calculating the root-mean-squared error of the estimated strains with respect to the reference strains obtained from the model. More accurate strain images were obtained using the 3-D displacement estimation for the entire cardiac cycle. The 3-D technique clearly outperformed the 2-D technique in phases with high IF longitudinal motion. In fact, the large IF longitudinal motion rendered it impossible to accurately track the tissue and cumulate strains over the entire cardiac cycle with the 2-D technique.

Automated Assessment of Right Ventricular Volumes and Function Using Three-Dimensional Transesophageal Echocardiography.

Assessment of right ventricular (RV) function is known to be of diagnostic value in patients with RV dysfunction. Because of its complex anatomic shape, automated determination of the RV volume is difficult and strong reliance on geometric assumptions is not desired. A method for automated RV assessment was developed using three-dimensional (3-D) echocardiography without relying on a priori knowledge of the cardiac anatomy. A 3-D adaptive filtering technique that optimizes the discrimination between blood and myocardium was applied to facilitate endocardial border detection. Filtered image data were incorporated in a segmentation model to automatically detect the endocardial RV border. End-systolic and end-diastolic RV volumes, as well as ejection fraction, were computed from the automatically segmented endocardial surfaces and compared against reference volumes manually delineated by two expert cardiologists. The results reported good performance in terms of correlation and agreement with the results from the reference volumes.

Correlation-based discrimination between cardiac tissue and blood for segmentation of the left ventricle in 3-D echocardiographic images.

For automated segmentation of 3-D echocardiographic images, incorporation of temporal information may be helpful. In this study, optimal settings for calculation of temporal cross-correlations between subsequent time frames were determined, to obtain the maximum cross-correlation (MCC) values that provided the best contrast between blood and cardiac tissue over the entire cardiac cycle. Both contrast and boundary gradient quality measures were assessed to optimize MCC values with respect to signal choice (radiofrequency or envelope data) and axial window size. Optimal MCC values were incorporated into a deformable model to automatically segment the left ventricular cavity. MCC values were tested against, and combined with, filtered, demodulated radiofrequency data. Results reveal that using envelope data in combination with a relatively small axial window (0.7-1.25 mm) at fine scale results in optimal contrast and boundary gradient between the two tissues over the entire cardiac cycle. Preliminary segmentation results indicate that incorporation of MCC values has additional value for automated segmentation of the left ventricle.

New developments in paediatric cardiac functional ultrasound imaging.

Ultrasound imaging can be used to estimate the morphology as well as the motion and deformation of tissues. If the interrogated tissue is actively deforming, this deformation is directly related to its function and quantification of this deformation is normally referred as 'strain imaging'. Tissue can also be deformed by applying an internal or external force and the resulting, induced deformation is a function of the mechanical tissue characteristics. In combination with the load applied, these strain maps can be used to estimate or reconstruct the mechanical properties of tissue. This technique was named 'elastography' by Ophir et al. in 1991. Elastography can be used for atherosclerotic plaque characterisation, while the contractility of the heart or skeletal muscles can be assessed with strain imaging. Rather than using the conventional video format (DICOM) image information, radio frequency (RF)-based ultrasound methods enable estimation of the deformation at higher resolution and with higher precision than commercial methods using Doppler (tissue Doppler imaging) or video image data (2D speckle tracking methods). However, the improvement in accuracy is mainly achieved when measuring strain along the ultrasound beam direction, so it has to be considered a 1D technique. Recently, this method has been extended to multiple directions and precision further improved by using spatial compounding of data acquired at multiple beam steered angles. Using similar techniques, the blood velocity and flow can be determined. RF-based techniques are also beneficial for automated segmentation of the ventricular cavities. In this paper, new developments in different techniques of quantifying cardiac function by strain imaging, automated segmentation, and methods of performing blood flow imaging are reviewed and their application in paediatric cardiology is discussed.

Three-dimensional cardiac strain imaging in healthy children using RF-data.

In this study, a new radio-frequency (RF)-based, three-dimensional (3-D) strain imaging technique is introduced and applied to 3-D full volume ultrasound data of the heart of healthy children. Continuing advances in performance of transducers for 3-D ultrasound imaging have boosted research on 3-D strain imaging. In general, speckle tracking techniques are used for strain imaging. RF-based strain imaging has the potential to yield better performance than speckle- based methods because of the availability of phase information but such a system output is commercially not available. Furthermore, the relatively low frame rate of 3-D ultrasound data has limited broad application of RF-based cardiac strain imaging. In this study, the previously reported two-dimensional (2-D) strain methodology was extended to the third dimension. Three-dimensional RF-data were acquired in 13 healthy children, in the age range of 6-15 years, at a relatively low frame rate of 38-51 Hz. A 3-D, free-shape, coarse-to-fine displacement and strain estimation algorithm was applied to the RF-data. The heart was segmented using 3-D ellipsoid fitting. Strain was estimated in the radial (R), circumferential (C) and longitudinal directions (L). Our preliminary results reveal the applicability of the 3-D strain estimation technique on full volume 3-D RF-data. The technique enabled 3-D strain imaging of all three strain components. The average strains for all children were in the lateral wall R = 37 ± 10% (infero-lateral) and R = 32% ± 10% (antero-lateral), C = -9% ± 4% (antero-lateral) and C = -9% ± 4% (infero-lateral), L = -18% ± 6 % (antero-lateral) and L = -15% ± 4% (infero-lateral). In the septum, strains were found to be R = 24% ± 10% (antero-septal) and R = 13% ± 5% (infero-septal), C = -13% ± 5% (antero-septal) and -13% ± 5% (infero-septal) and L = -13% ± 3% (antero-septal) and L = -16% ± 5% (infero-septal). Strain in the anterior and inferior walls seemed underestimated, probably caused by the low (in-plane) resolution and poor image quality. The field-of-view as well as image quality were not always sufficient to image the entire left ventricle. It is concluded that 3-D strain imaging using RF-data is feasible, but validation with other modalities and with conventional 3-D speckle tracking techniques will be necessary.

Correlation based 3-D segmentation of the left ventricle in pediatric echocardiographic images using radio-frequency data.

Clinical diagnosis of heart disease might be substantially supported by automated segmentation of the endocardial surface in three-dimensional (3-D) echographic images. Because of the poor echogenicity contrast between blood and myocardial tissue in some regions and the inherent speckle noise, automated analysis of these images is challenging. A priori knowledge on the shape of the heart cannot always be relied on, e.g., in children with congenital heart disease, segmentation should be based on the echo features solely. The objective of this study was to investigate the merit of using temporal cross-correlation of radio-frequency (RF) data for automated segmentation of 3-D echocardiographic images. Maximum temporal cross-correlation (MCC) values were determined locally from the RF-data using an iterative 3-D technique. MCC values as well as a combination of MCC values and adaptive filtered, demodulated RF-data were used as an additional, external force in a deformable model approach to segment the endocardial surface and were tested against manually segmented surfaces. Results on 3-D full volume images (Philips, iE33) of 10 healthy children demonstrate that MCC values derived from the RF signal yield a useful parameter to distinguish between blood and myocardium in regions with low echogenicity contrast and incorporation of MCC improves the segmentation results significantly. Further investigation of the MCC over the whole cardiac cycle is required to exploit the full benefit of it for automated segmentation.

Dynamic imaging of skeletal muscle contraction in three orthogonal directions.

In this study, a multidimensional strain estimation method using biplane ultrasound is presented to assess local relative deformation (i.e., local strain) in three orthogonal directions in skeletal muscles during induced and voluntary contractions. The method was tested in the musculus biceps brachii of five healthy subjects for three different types of muscle contraction: 1) excitation of the muscle with a single electrical pulse via the musculocutaneous nerve, resulting in a so-called "twitch" contraction; 2) a train of five pulses at 10 Hz and 20 Hz, respectively, to obtain a submaximum tetanic contraction; and 3) voluntary contractions at 30, 60, and 100% of maximum contraction force. Results show that biplane ultrasound strain imaging is feasible. The method yielded adequate performance using the radio frequency data in tracking the tissue motion and enabled the measurement of local deformation in both the vertical direction (orthogonal to the arm) and in the horizontal directions (parallel and perpendicular to direction of the arm) in two orthogonal cross sections of the muscle. The twitch experiments appeared to be reproducible in all three directions, and high strains in vertical (25 to 30%) and horizontal (-20% to -10%) directions were measured. Visual inspection of both the ultrasound data, as well as the strain data, revealed a relaxation that was significantly slower than the force decay. The pulse train experiments nicely illustrated the performance of our technique: 1) similar patterns of force and strain waveforms were found; and 2) each stimulation frequency yielded a different strain pattern, e.g., peak vertical strain was 40% during 10-Hz stimulation and 60% during 20-Hz stimulation. The voluntary contraction patterns were found to be both practically feasible and reproducible, which will enable muscles and more natural contraction patterns to be examined without the need of electrical stimulation.

Performance of two dimensional displacement and strain estimation techniques using a phased array transducer.

The goal of this study was to investigate the applicability of conventional 2-D displacement and strain imaging techniques to phased array radiofrequency (RF) data. Furthermore, the possible advantages of aligning and stretching techniques for the reduction of decorrelation artefacts was examined. Data from both realistic simulations and phantoms were used in this study. Recently, the used processing concepts were successfully applied to linear array data. However, their applicability to sector scan data is not trivial because of the polar grid. Homogeneous and inhomogeneous tissue phantoms were simulated at a range of strains (0 to 5%) using Field II((c)). The inhomogeneous phantom, a commonly used tumor/lesion model, was also constructed using gelatin/agar solutions. A coarse-to-fine displacement algorithm was applied, using aligning and stretching to enhance re-correlation. Vertical and horizontal strains were reconstructed from the axial and lateral displacements. Results revealed that the error on displacement estimates was lower when using 2-D data windows rather than 1-D windows. For regions at large depths and large insonification angles, the allowed lateral window size was limited. Still, 1-D windows resulted in larger errors. The re-correlation techniques resulted in a significant increase in the elastographic signal-to-noise ratio (SNRe) and elastographic contrast-to-noise ratio (CNRe) of the vertical and horizontal strain components. An increase of the SNRe of 5-20 dB was observed over a range of strains (0.5 to 5.0%). In the inhomogeneous phantom, a vertical SNRe of 27.7 dB and a horizontal SNRe of 16.7 dB were measured in the background. The vertical and horizontal CNRe were 35 dB and 23.1 dB, respectively. For the experimental data, lower SNRe (vertical: 19.1 dB; horizontal: 11.4 dB) and CNRe (vertical: 33.3 dB; horizontal: 12.5 dB) were found. In conclusion, 2-D window matching of sector scan data is feasible and outperforms 1-D window matching. Furthermore, the use of re-correlation techniques enhances both precision and contrast of strain images.

Comparison of one-dimensional and two-dimensional least-squares strain estimators for phased array displacement data.

In this study, the performances of one-dimensional and two-dimensional least-squares strain estimators (LSQSE) are compared. Furthermore, the effects of kernel size are examined using simulated raw frequency data of a widely-adapted hard lesion/soft tissue model. The performances of both methods are assessed in terms of root-mean-squared errors (RMSE), elastographic signal-to-noise ratio (SNRe) and contrast-to-noise ratio (CNRe). RMSE analysis revealed that the 2D LSQSE yields better results for phased array data, especially for larger insonification angles. Using a 2D LSQSE enabled the processing of unfiltered displacement data, in particular for the lateral/horizontal strain components. The SNRe and CNRe analysis showed an improvement in precision and almost no loss in contrast using 2D LSQSE. However, the RMSE images for different kernel sizes revealed that the optimal 2D kernel size depends on the region-of-interest and showed that the LSQ kernel size should be limited to avoid loss in resolution.

Performance evaluation of methods for two-dimensional displacement and strain estimation using ultrasound radio frequency data.

In elastography, several methods for 2-D strain imaging have been introduced, based on both raw frequency (RF) data and speckle-tracking. Although the precision and lesion detectability of axial strain imaging in terms of elastographic signal-to-noise ratio (SNRe) and elastographic contrast-to-noise ratio (CNRe) have been reported extensively, analysis of lateral precision is still lacking. In this paper, the performance of different 2-D correlation RF- and envelope-based strain estimation methods was evaluated using simulation data and phantom experiments. Besides window size and interpolation methods for subsample displacement estimation, the influence of recorrelation techniques was examined. Precision and contrast of the measured displacements and strains were assessed using the difference between modeled and measured displacements, SNRe and CNRe. In general, a 2-D coarse-to-fine displacement estimation method is favored, using envelope data for window sizes exceeding the theoretical upper bound for strain estimation. Using 2-D windows of RF data resulted in better displacement estimates for both the axial and lateral direction than 1-D RF-based or envelope-based techniques. Obtaining subsample lateral displacement estimates by fitting a predefined shape through the cross-correlation function (CCF) yielded results similar to those obtained with up-sampling of RF data in the lateral direction. Using a CCF model was favored because of the decreased computation time. Local aligning and stretching of the windows (recorrelation) resulted in an increase of 2-17 and 6-7 dB in SNRe for axial and lateral strain estimates, respectively, over a range of strains (0.5 to 5.0%). For a simulated inhomogeneous phantom (2.0% applied strain), the measured axial and lateral SNRes were 29.2 and 20.2 dB, whereas the CNRes were 50.2 dB and 31.5 dB, respectively. For the experimental data, lower SNRe (axial: 28.5 dB; lateral: 17.5 dB) and CNRe (axial: 39.3 dB; lateral: 31 dB) were found. In conclusion, a coarse-to-fine approach is favored using RF data on a fine scale. The use of 2D parabolic interpolation is favored to obtain subsample displacement estimates. Recorrelation techniques, such as local aligning and stretching, increase SNRe and CNRe in both directions.

3D cardiac segmentation using temporal correlation of radio frequency ultrasound data.

Semi-automatic segmentation of the myocardium in 3D echographic images may substantially support clinical diagnosis of heart disease. Particularly in children with congenital heart disease, segmentation should be based on the echo features solely since a priori knowledge on the shape of the heart cannot be used. Segmentation of echocardiographic images is challenging because of the poor echogenicity contrast between blood and the myocardium in some regions and the inherent speckle noise from randomly backscattered echoes. Phase information present in the radio frequency (rf) ultrasound data might yield useful, additional features in these regions. A semi-3D technique was used to determine maximum temporal cross-correlation values locally from the rf data. To segment the endocardial surface, maximum cross-correlation values were used as additional external force in a deformable model approach and were tested against and combined with adaptive filtered, demodulated rf data. The method was tested on full volume images (Philips, iE33) of four healthy children and evaluated by comparison with contours obtained from manual segmentation.

Modeling envelope statistics of blood and myocardium for segmentation of echocardiographic images.

The objective of this study was to investigate the use of speckle statistics as a preprocessing step for segmentation of the myocardium in echocardiographic images. Three-dimensional (3D) and biplane image sequences of the left ventricle of two healthy children and one dog (beagle) were acquired. Pixel-based speckle statistics of manually segmented blood and myocardial regions were investigated by fitting various probability density functions (pdf). The statistics of heart muscle and blood could both be optimally modeled by a K-pdf or Gamma-pdf (Kolmogorov-Smirnov goodness-of-fit test). Scale and shape parameters of both distributions could differentiate between blood and myocardium. Local estimation of these parameters was used to obtain parametric images, where window size was related to speckle size (5 x 2 speckles). Moment-based and maximum-likelihood estimators were used. Scale parameters were still able to differentiate blood from myocardium; however, smoothing of edges of anatomical structures occurred. Estimation of the shape parameter required a larger window size, leading to unacceptable blurring. Using these parameters as an input for segmentation resulted in unreliable segmentation. Adaptive mean squares filtering was then introduced using the moment-based scale parameter (sigma(2)/mu) of the Gamma-pdf to automatically steer the two-dimensional (2D) local filtering process. This method adequately preserved sharpness of the edges. In conclusion, a trade-off between preservation of sharpness of edges and goodness-of-fit when estimating local shape and scale parameters is evident for parametric images. For this reason, adaptive filtering outperforms parametric imaging for the segmentation of echocardiographic images.

Segmentation of the heart muscle in 3-D pediatric echocardiographic images.

This study aimed to show segmentation of the heart muscle in pediatric echocardiographic images as a preprocessing step for tissue analysis. Transthoracic image sequences (2-D and 3-D volume data, both derived in radiofrequency format, directly after beam forming) were registered in real time from four healthy children over three heart cycles. Three preprocessing methods, based on adaptive filtering, were used to reduce the speckle noise for optimizing the distinction between blood and myocardium, while preserving the sharpness of edges between anatomical structures. The filtering kernel size was linked to the local speckle size and the speckle noise characteristics were considered to define the optimal filter in one of the methods. The filtered 2-D images were thresholded automatically as a first step of segmentation of the endocardial wall. The final segmentation step was achieved by applying a deformable contour algorithm. This segmentation of each 2-D image of the 3-D+time (i.e., 4-D) datasets was related to that of the neighboring images in both time and space. By thus incorporating spatial and temporal information of 3-D ultrasound image sequences, an automated method using image statistics was developed to perform 3-D segmentation of the heart muscle.

Quantitative ultrasound imaging of healthy and reconstructed cleft lip: a feasibility study.

OBJECTIVE: To investigate the feasibility of echographic imaging of healthy and reconstructed cleft lip and to estimate tissue dimensions and normalized echo level. METHODS: Echographic images of the upper lip were made on three healthy subjects and two patients using a linear array transducer (7 to 11 MHz bandwidth) and a noncontact gel coupling. Tissue dimensions were measured using calipers. Echo levels were calibrated and were corrected for beam characteristics, gel path, and tissue attenuation using a tissue-mimicking phantom. RESULTS: At the central position of the philtrum, mean thickness (SD) of lip loose connective tissue layer, orbicularis oris muscle, and dense connective layer was 4.0 (0.1) mm, 2.3 (0.7) mm, and 2.2 (0.7) mm, respectively, in healthy lip at rest; and 4.1 (0.9) mm, 3.8 (1.7) mm, and 2.6 (0.6) mm, respectively, in contracted lip. Mean (SD) echo level of muscle and dense connective tissue layer with respect to echo level of lip loose connective tissue layer was -19.3 (0.6) dB and -10.7 (4.0) dB, respectively, in relaxed condition and -20.7 (1.5) dB and -7.7 (2.3) dB, respectively, in contracted state. Color mode echo images were calculated, showing lip tissues in separate colors and highlighting details like discontinuity of the orbicularis oris muscle and presence of scar tissue. CONCLUSIONS: Quantitative assessment of thickness and echo level of various lip tissues is feasible after proper echographic equipment calibration. Diagnostic potentials of this method for noninvasive evaluation of cleft lip reconstruction outcome are promising.