A dynamic cardiac phantom can play a significant role in the evaluation and development of ultrasound and cardiac
magnetic resonance (MR) motion tracking and registration methods. A four chamber multimodal cardiac phantom has
been designed and built to simulate normal and pathologic hearts with different degrees of “infarction” and “scar
tissues”. In this set up, cardiac valves have been designed and modeled as well. The four-chamber structure can simulate
the asymmetric ventricular, atrial and valve motions. Poly Vinyl Alcohol (PVA) is used as the principal material since it
can simulate the shape, elasticity, and MR and ultrasound properties of the heart. The cardiac shape is simulated using a
four-chamber mold made of polymer clay. An additional pathologic heart phantom containing stiff inclusions has been
manufactured in order to simulate an infracted heart. The stiff inclusions are of different shapes and different degrees of
elasticity and are able to simulate abnormal cardiac segments. The cardiac elasticity is adjusted based on freeze-thaw
cycles of the PVA cryogel for normal and scarred regions. Ultrasound and MRI markers were inserted in the cardiac
phantom as landmarks for validations. To the best of our knowledge, this is the first multimodal phantom that models a
dynamic four-chamber human heart including the cardiac valve.
Quantitative motion analysis of echocardiographic images helps clinicians with the diagnosis and therapy of patients
suffering from cardiac disease. Quantitative analysis is usually based on TDI (Tissue Doppler Imaging) or speckle
tracking. These methods are based on two independent techniques – the Doppler Effect and image registration,
respectively. In order to increase the accuracy of the speckle tracking technique and cope with the angle dependency of
TDI, herein, a combined approach dubbed TDIOF (Tissue Doppler Imaging Optical Flow) is proposed. TDIOF is
formulated based on the combination of B-mode and Doppler energy terms in an optical flow framework and minimized
using algebraic equations. In this paper, we report on validations with simulated, physical cardiac phantom, and in-vivo
patient data. It is shown that the additional Doppler term is able to increase the accuracy of speckle tracking, the basis for
several commercially available echocardiography analysis techniques.
KEYWORDS: Arteries, Magnetic resonance imaging, Computer programming, Independent component analysis, Phase contrast, Blood circulation, Simulation of CCA and DLA aggregates, Phase measurement, Data acquisition, Signal to noise ratio
Use of Phase contrast (PC) MRI in measurement of blood flow has significant clinical importance. In this
paper, we compare the accuracy of the conventional approach to flow imaging to two de novo approaches in 3 normal
subjects in the common, internal, and external carotid arteries and discuss and demonstrate the advantages and
disadvantages of each method.
The conventional PC sequence adopts a Cartesian read-out in k-space and requires longer acquisitions but exhibits
flow artifacts in the setting of stenotic and disturbed flow. Spiral PC collects k-space data using spiral readout and is
capable of reducing the TR and TE in order to minimize the total imaging time. Despite its efficiency in scan time, in the
single shot mode, this technique suffers from off-resonance and inconsistent data artifacts. Use of multiple short spiral
arms for providing k-space coverage resolves these issues. Ultra short TE (UTE) PC MRI is a novel technique which
adopts a radial trajectory and provides improvements to the standard radial acquisition by reducing the echo time to less
than 1 ms through combination of flow encoding and slice select gradients and by immediate sampling of the FID during
readout. The ultra-short echo times, improves on intravoxel spin dephasing due to fluid mixing observed in imaging of
disturbed flow and stenotic jets. Despite its capability of achieving the shortest TE, this method is hindered by longer
acquisition times and phase corruption errors. We mitigate this by a novel 3-D acquisition which includes a phase
correction step.
All three approaches were found to be able to quantify the normal Carotid flow waveform with high accuracy.
Two-dimensional echocardiography continues to be the most widely used modality for the assessment of cardiac function
due to its effectiveness, ease of use, and low costs. Echocardiographic images are derived from the mechanical interaction
between the ultrasound field and the contractile heart tissue. Previously, in [6], based on B-mode echocardiographic
simulations, we showed that motion estimation errors are significantly higher in shift-varying simulations when compared to
shift-invariant simulations. In order to ascertain the effect of the spatial variance of the Ultrasonic field point spread function
(PSF) and the transducer geometry on motion estimation, in the current paper, several simple canonical cardiac motions such
as translation in axial and horizontal direction, and out-of-plane motion were simulated and the motion estimation errors
were calculated. For axial motions, the greatest angular errors occurred within the lateral regions of the image, irrespective of
the motion estimation technique that was adopted. We hypothesize that the transducer geometry and the PSF spatial-variance
were the underlying sources of error for the motion estimation methods. No similar conclusions could be made regarding
motion estimation errors for azimuthal and out-of-plane ultrasound simulations.
In this paper, we combine a ventricular kinematic model and an ultrasound simulation model in order to simulate the
echocardiographic imaging process. In addition to its capability to generate raw RF data, when compared to previous
echocardiography simulation models, the result achieves more realistic B-Mode images. Several echocardiography
parameters were taken into account including central frequency, apodization, number of elements in the array, speed of
sound, and number of scatterers. The proposed improvements are due to the use of a shift-variant Point Spread Function
(PSF) and more accurate cardiac motion assumptions. One attribute of the simulator is also that it provides the groundtruth
vector field of actual "ventricular deformations'' which may be used to strictly validate motion estimation and
myocardial elastography algorithms.
The paper presents the first application of optical flow to normalized data in piece-wise segments of RF images.
Different optical flow motion estimation techniques such as Lucas-Kanade, Horn-Schunck, Brox et al., Black and
Anandan, and Block Matching (BM) were applied to the simulated B-mode images and RF data. The estimated motion
fields from the RF data as well as the B-mode images were validated with the ground-truth motion fields derived from
the simulator.
The validation results show that the Brox et al. method performs better than other motion estimation techniques when
applied to B-Mode and RF data. Also, as intuitively expected, use of RF data results in more accurate displacement
fields than when B-mode images alone are used.
Recently Strain and strain rate imaging have proved their superiority with respect to classical motion estimation
methods in myocardial evaluation as a novel technique for quantitative analysis of myocardial function.
Here in this paper, we propose a novel strain rate imaging algorithm using a new optical flow technique which is more
rapid and accurate than the previous correlation-based methods. The new method presumes a spatiotemporal constancy
of intensity and Magnitude of the image. Moreover the method makes use of the spline moment in a multiresolution
approach. Moreover cardiac central point is obtained using a combination of center of mass and endocardial tracking.
It is proved that the proposed method helps overcome the intensity variations of ultrasound texture while preserving
the ability of motion estimation technique for different motions and orientations. Evaluation is performed on simulated,
phantom (a contractile rubber balloon) and real sequences and proves that this technique is more accurate and faster
than the previous methods.
Here in this paper a combined method of pixel based and region based mass detection is proposed. In the first step, the
background and pectoral muscle are filtered from mammography images and the image contrast is enhanced using an
adaptive density weighted approach. Then, in a coarse level, suspected regions are extracted based on mathematical
morphology and adaptive thresholding methods. Finally, to reduce the false positives produced in the coarse stage, a
useful feature vector based on ranklet transform is obtained and fed into a support vector machine classifier to detect
masses. MIAS (Mammographic Image Analysis Society) and Imam Hospital databases were used to evaluate the
performance of the algorithm. The sensitivity and specificity of the proposed method are 74% and 91% respectively. The
proposed algorithm shows a high degree of robustness in detecting masses of different shapes.
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