Imaging phantom

About: Imaging phantom is a research topic. Over the lifetime, 28170 publications have been published within this topic receiving 510003 citations. The topic is also known as: phantom.

Spherical navigator echoes for full 3D rigid body motion measurement in MRI.

Abstract: We developed a 3D spherical navigator (SNAV) echo technique that can measure rigid body motion in all six degrees of freedom simultaneously by sampling a spherical shell in k-space. 3D rotations of an imaged object simply rotate the data on this shell and can be detected by registration of k-space magnitude values. 3D translations add phase shifts to the data on the shell and can be detected with a weighted least-squares fit to the phase differences at corresponding points. MRI pulse sequences were developed to study k-space sampling strategies on such a shell. Data collected with a computer-controlled motion phantom with known rotational and translational motions were used to evaluate the technique. The accuracy and precision of the technique depend on the sampling density. Roughly 2000 sample points were necessary for accurate detection to within the error limits of the motion phantom when using a prototype time-intensive sampling method. This number of samples can be captured in an approximately 27-ms double excitation SNAV pulse sequence with a 3D helical spiral trajectory. Preliminary results with the helical SNAV are encouraging and indicate that accurate motion measurement suitable for retrospective or prospective correction should be feasible with SNAV echoes.

Strain rate imaging using two-dimensional speckle tracking

Abstract: Strain rate images (SRI) of the beating heart have been proposed to identify non-contracting regions of myocardium. Initial attempts used spatial derivatives of tissue velocity (Doppler) signals. Here, an alternate method is proposed based on two-dimensional phase-sensitive speckle tracking applied to very high frame rate, real-time images. This processing can produce high resolution maps of the time derivative of the strain magnitude (i.e., square root of the strain intensity). Such images complement traditional tissue velocity images (TVI), providing a more complete description of cardiac mechanics. To test the proposed approach, SRI were both simulated and measured on a thick-walled, cylindrical, tissue-equivalent phantom modeling cardiac deformations. Real-time ultrasound images were captured during periodic phantom deformation, where the period was matched to the data capture rate of a commercial scanner mimicking high frame rate imaging of the heart. Simulation results show that SRI with spatial resolution between 1 and 2 mm are possible with an array system operating at 5 MHz. Moreover, these images are virtually free of angle-dependent artifacts present in TVI and simple strain rate maps derived from these images. Measured results clearly show that phantom regions of low deformation, which are difficult to identify on tissue velocity-derived SRI, are readily apparent with SRI generated from two-dimensional phase-sensitive speckle tracking.

Tracking Myocardial Motion From Cine DENSE Images Using Spatiotemporal Phase Unwrapping and Temporal Fitting

Abstract: Displacement encoding with stimulated echoes (DENSE) encodes myocardial tissue displacement into the phase of the MR image. Cine DENSE allows for rapid quantification of myocardial displacement at multiple cardiac phases through the majority of the cardiac cycle. For practical sensitivities to motion, relatively high displacement encoding frequencies are used and phase wrapping typically occurs. In order to obtain absolute measures of displacement, a two-dimensional (2-D) quality-guided phase unwrapping algorithm was adapted to unwrap both spatially and temporally. Both a fully automated algorithm and a faster semi-automated algorithm are proposed. A method for computing the 2-D trajectories of discrete points in the myocardium as they move through the cardiac cycle is introduced. The error in individual displacement measurements is reduced by fitting a time series to sequential displacement measurements along each trajectory. This improvement is in turn reflected in strain maps, which are derived directly from the trajectories. These methods were validated both in vivo and on a rotating phantom. Further measurements were made to optimize the displacement encoding frequency and to estimate the baseline strain noise both on the phantom and in vivo. The fully automated phase unwrapping algorithm was successful for 767 out of 800 images (95.9%), and the semi-automated algorithm was successful for 786 out of 800 images (98.3%). The accuracy of the tracking algorithm for typical cardiac displacements on a rotating phantom is 0.24plusmn0.15mm. The optimal displacement encoding frequency is in the region of 0.1 cycles/mm, and, for 2 scans of 17-s duration, the strain noise after temporal fitting was estimated to be 2.5plusmn3.0% at end-diastole, 3.1plusmn3.1% at end-systole, and 5.3plusmn5.0% in mid-diastole. The improvement in intra-myocardial strain measurements due to temporal fitting is apparent in strain histograms, and also in identifying regions of dysfunctional myocardium in studies of patients with infarcts

Multiple-bandwidth photoacoustic tomography

Abstract: Photoacoustic tomography, also referred to as optoacoustic tomography, employs short laser pulses to generate ultrasonic waves in biological tissues. The reconstructed images can be characterized by the convolution of the structure of samples, the laser pulse and the impulse response of the ultrasonic transducer used for detection. Although the laser-induced ultrasonic waves cover a wide spectral range, a single transducer can receive only part of the spectrum because of its limited bandwidth. To systematically analyse this problem, we constructed a photoacoustic tomographic system that uses multiple ultrasonic transducers simultaneously, each at a different central frequency. The photoacoustic images associated with the different transducers were compared and analysed. The system was tested by imaging both mouse brains and phantom samples. The vascular vessels in the brain were revealed by all of the transducers, but the image resolutions differed. The higher frequency detectors provided better image resolution while the lower frequency detectors delineated the major structural traits with a higher signal–noise ratio.