Quantification of 3-D field effects during 2-D microwave imaging

Two-dimensional (2-D) approaches to microwave imaging have dominated the research landscape primarily due to the moderate levels of measurement data, data-acquisition time, and computational costs required. Three-dimensional (3-D) approaches have been investigated in simulation, phantom, and animal experiments. While 3-D approaches are certainly important in terms of the potential to improve image quality, their associated costs are significant at this time. In addition, benchmarks are needed to evaluate these new generation systems as more 3-D methods begin to appear. In this paper, we present a systematic series of experiments which assess the capability of our 2-D system to image classical 3-D geometries. We demonstrate where current methods suffer from 3-D effects but also identify situations where they remain quite useful. Comparisons between reconstructions utilizing phantom measurements and simulated 3-D data are also shown to validate the results. These findings suggest that for certain biomedical applications, 2-D approaches remain quite attractive.     

3-D radar imaging using range migration techniques

An imaging system with three-dimensional (3-D) capability can be implemented by using a stepped frequency radar which synthesizes a two-dimensional (2-D) planar aperture. A 3-D image can be formed by coherently integrating the backscatter data over the measured frequency band and the two spatial coordinates of the 2-D synthetic aperture. This paper presents a near-field 3-D synthetic aperture radar (SAR) imaging algorithm. This algorithm is an extension of the 2-D range migration algorithm (RMA). The presented formulation is justified by using the method of the stationary phase (MSP). Implementation aspects including the sampling criteria, resolutions, and computational complexity are assessed. The high computational efficiency and accurate image reconstruction of the algorithm are demonstrated both with numerical simulations and measurements using an outdoor linear SAR system     

Brain shift estimation in image-guided neurosurgery using 3-D ultrasound

Intraoperative brain deformation is one of the most important causes affecting the overall accuracy of image-guided neurosurgical procedures. One option for correcting for this deformation is to acquire three-dimensional (3-D) ultrasound data during the operation and use this data to update the information provided by the preoperatively acquired MR data. For 12 patients 3-D ultrasound images have been reconstructed from freehand sweeps acquired during neurosurgical procedures. Ultrasound data acquired prior to and after opening the dura, but prior to surgery, have been quantitatively compared to the preoperatively acquired MR data to estimate the rigid component of brain shift at the first stages of surgery. Prior to opening the dura the average brain shift measured was 3.0 mm parallel to the direction of gravity, with a maximum of 7.5 mm, and 3.9 mm perpendicular to the direction of gravity, with a maximum of 8.2 mm. After opening the dura the shift increased on average 0.2 mm parallel to the direction of gravity and 1.4 mm perpendicular to the direction of gravity. Brain shift can be detected by acquiring 3-D ultrasound data during image-guided neurosurgery. Therefore, it can be used as a basis for correcting image data and preoperative planning for intraoperative deformations.     

Passive markers for tracking surgical instruments in real-time 3-D ultrasound imaging

A family of passive echogenic markers is presented by which the position and orientation of a surgical instrument can be determined in a 3-D ultrasound volume, using simple image processing. Markers are attached near the distal end of the instrument so that they appear in the ultrasound volume along with the instrument tip. They are detected and measured within the ultrasound image, thus requiring no external tracking device. This approach facilitates imaging instruments and tissue simultaneously in ultrasound-guided interventions. Marker-based estimates of instrument pose can be used in augmented reality displays or for image-based servoing. Design principles for marker shapes are presented that ensure imaging system and measurement uniqueness constraints are met. An error analysis is included that can be used to guide marker design and which also establishes a lower bound on measurement uncertainty. Finally, examples of marker measurement and tracking algorithms are presented along with experimental validation of the concepts.     

A multichannel pipeline analog-to-digital converter for an integrated 3-D ultrasound imaging system

An 8-channel 10-bit pipeline analog-to-digital converter, designed for use in an integrated three-dimensional ultrasound imaging system, has been implemented in a 0.25-/spl mu/m CMOS technology. Two parallel multiplexing sample-and-hold stages are employed to multiplex a total of eight adjacent ultrasound channels, each sampled at 20 MHz. The sampled and multiplexed signals are fed into two parallel time-interleaved pipeline paths, each operating at 80 MHz. The two parallel pipelines are subsequently multiplexed into a single pipeline operating at 160 MHz to conserve area and reduce complexity. An experimental prototype of the proposed architecture occupies less than 4 mm/sup 2/ of active silicon area and shows a peak signal-to-noise-plus-distortion ratio more than 54 dB for a 2.1-MHz input signal, while dissipating only 20 mW of analog power per input channel from a 2.5-V supply.     

Analysis of 3-D integral imaging displays using the Wigner distribution

Integral imaging is a promising technology for 3-D TV and 3-D display. In this paper, a theoretical analysis of 3-D integral imaging systems is performed in the frame of the Wigner distribution formalism. It is shown that the entire intensity distribution in the pick-up image plane of these systems can be obtained from a single 2-D Wigner distribution function of a single lenslet pupil. This result reveals the Wigner distribution function as a powerful tool for analysis of 3-D integral imaging systems with different pupil functions. As an example, the extension of the depth of field of an integral imaging system with lenslets having amplitude modulation (central obscuration) is proposed.     

Computer-aided lesion diagnosis in automated 3-D breast ultrasound using coronal spiculation

A computer-aided diagnosis (CAD) system for the classification of lesions as malignant or benign in automated 3-D breast ultrasound (ABUS) images, is presented. Lesions are automatically segmented when a seed point is provided, using dynamic programming in combination with a spiral scanning technique. A novel aspect of ABUS imaging is the presence of spiculation patterns in coronal planes perpendicular to the transducer. Spiculation patterns are characteristic for malignant lesions. Therefore, we compute spiculation features and combine them with features related to echotexture, echogenicity, shape, posterior acoustic behavior and margins. Classification experiments were performed using a support vector machine classifier and evaluation was done with leave-one-patient-out cross-validation. Receiver operator characteristic (ROC) analysis was used to determine performance of the system on a dataset of 201 lesions. We found that spiculation was among the most discriminative features. Using all features, the area under the ROC curve (A(z)) was 0.93, which was significantly higher than the performance without spiculation features (A(z)=0.90, p=0.02). On a subset of 88 cases, classification performance of CAD (A(z)=0.90) was comparable to the average performance of 10 readers (A(z)=0.87).     

Practical considerations for 3-D image reconstruction using spherically symmetric volume elements

Spherically symmetric volume elements with smooth tapering of the values near their boundaries are alternatives to the more conventional voxels for the construction of volume images in the computer. Their use, instead of voxels, introduces additional parameters which enable the user to control the shape of the volume element (blob) and consequently to control the characteristics of the images produced by iterative methods for reconstruction from projection data. For images composed of blobs, efficient algorithms have been designed for the projection and discrete back-projection operations, which are the crucial parts of iterative reconstruction methods. The authors have investigated the relationship between the values of the blob parameters and the properties of images represented by the blobs. Experiments show that using blobs in iterative reconstruction methods leads to substantial improvement in the reconstruction performance, based on visual quality and on quantitative measures, in comparison with the voxel case. The images reconstructed using appropriately chosen blobs are characterized by less image noise for both noiseless data and noisy data, without loss of image resolution.     

Deformable segmentation of 3-D ultrasound prostate images using statistical texture matching method

This paper presents a novel deformable model for automatic segmentation of prostates from three-dimensional ultrasound images, by statistical matching of both shape and texture. A set of Gabor-support vector machines (G-SVMs) are positioned on different patches of the model surface, and trained to adaptively capture texture priors of ultrasound images for differentiation of prostate and nonprostate tissues in different zones around prostate boundary. Each G-SVM consists of a Gabor filter bank for extraction of rotation-invariant texture features and a kernel support vector machine for robust differentiation of textures. In the deformable segmentation procedure, these pretrained G-SVMs are used to tentatively label voxels around the surface of deformable model as prostate or nonprostate tissues by a statistical texture matching. Subsequently, the surface of deformable model is driven to the boundary between the tentatively labeled prostate and non-prostate tissues. Since the step of tissue labeling and the step of label-based surface deformation are dependent on each other, these two steps are repeated until they converge. Experimental results by using both synthesized and real data show the good performance of the proposed model in segmenting prostates from ultrasound images.     

Unambiguous measurement of volume flow using ultrasound

A new modality for transcutaneous measurement of blood flow is described. Two ultrasonic pulsed Doppler techniques (Doppler imaging and average Doppler shift detection) are combined to measure net flow across an arbitrary sample plane. This estimate of volume flow is independent of lumen shape, orientation, and velocity profile.     

Image guidance of breast cancer surgery using 3-D ultrasound imagesand augmented reality visualization

This paper describes augmented reality visualization for the guidance of breast-conservative cancer surgery using ultrasonic images acquired in the operating room just before surgical resection. By combining an optical three-dimensional (3-D) position sensor, the position and orientation of each ultrasonic cross section are precisely measured to reconstruct geometrically accurate 3-D tumor models from the acquired ultrasonic images. Similarly, the 3-D position and orientation of a video camera are obtained to integrate video and ultrasonic images in a geometrically accurate manner. Superimposing the 3-D tumor models onto live video images of the patient's breast enables the surgeon to perceive the exact 3-D position of the tumor, including irregular cancer invasions which cannot be perceived by touch, as if it were visible through the breast skin. Using the resultant visualization, the surgeon can determine the region for surgical resection in a more objective and accurate manner, thereby minimizing the risk of a relapse and maximizing breast conservation. The system was shown to be effective in experiments using phantom and clinical data     

A time-shared ultrasound Doppler measurement and 2-D imaging system

Methods for obtaining image-guided Doppler blood velocity measurements are briefly reviewed. Preference is given to a time-sharing scheme in which the Doppler measurement is turned off during the data-acquisition period for a 2-D image frame (typically 20 ms). The signal dropout that occurs during the image-updating period is removed from the Doppler audio by inserting a synthetic signal segment. The synthetic signal is generated by passing white noise through a discrete-time FIR (finite-impulse response) filter with filter coefficients that are a windowed version of the Doppler signal measured immediately prior to the imaging interrupt. It is shown that the artificial signal has spectral properties (and thus audible sound) similar to those of the real Doppler signal segment on which it is based. The time-sharing method is analyzed and evaluated experimentally, using dedicated hardware. The proposed algorithm allows for the design of time-shared Doppler/imaging systems that carry out pulsed or continuous Doppler measurements with essentially real-time imaging guidance     

Rapid elastic image registration for 3-D ultrasound

A Subvolume-based algorithm for elastic Ultrasound REgistration (SURE) was developed and evaluated. Designed primarily to improve spatial resolution in three-dimensional compound imaging, the algorithm registers individual image volumes nonlinearly before combination into compound volumes. SURE works in one or two stages, optionally using MIAMI Fuse software first to determine a global affine registration before iteratively dividing the volume into subvolumes and computing local rigid registrations in the second stage. Connectivity of the entire volume is ensured by global interpolation using thin-plate splines after each iteration. The performance of SURE was quantified in 20 synthetically deformed in vivo ultrasound volumes, and in two phantom scans, one of which was distorted at acquisition by placing an aberrating layer in the sound path. The aberrating layer was designed to induce beam aberrations reported for the female breast. Synthetic deformations of 1.5-2.5 mm were reduced by over 85% when SURE was applied to register the distorted image volumes with the original ones. Registration times were below 5 min on a 500-MHz CPU for an average data set size of 13 MB. In the aberrated phantom scans, SURE reduced the average deformation between the two volumes from 1.01 to 0.30 mm. This was a statistically significant (P = 0.01) improvement over rigid and affine registration transformations, which produced reductions to 0.59 and 0.50 mm, respectively.     

Medical ultrasound imaging using pulse compression

Pulse compression techniques can play a major role in improving image quality in medical ultrasound. A prototype imaging and digital signal processing system incorporating this technique is described. Feasibility studies were performed and system resolution was evaluated with experiments on tissue mimicking phantoms.<>     

Segmentation of skin lesions in 2-D and 3-D ultrasound images using a spatially coherent generalized Rayleigh mixture model

This paper addresses the problem of jointly estimating the statistical distribution and segmenting lesions in multiple-tissue high-frequency skin ultrasound images. The distribution of multiple-tissue images is modeled as a spatially coherent finite mixture of heavy-tailed Rayleigh distributions. Spatial coherence inherent to biological tissues is modeled by enforcing local dependence between the mixture components. An original Bayesian algorithm combined with a Markov chain Monte Carlo method is then proposed to jointly estimate the mixture parameters and a label-vector associating each voxel to a tissue. More precisely, a hybrid Metropolis-within-Gibbs sampler is used to draw samples that are asymptotically distributed according to the posterior distribution of the Bayesian model. The Bayesian estimators of the model parameters are then computed from the generated samples. Simulation results are conducted on synthetic data to illustrate the performance of the proposed estimation strategy. The method is then successfully applied to the segmentation of in vivo skin tumors in high-frequency 2-D and 3-D ultrasound images.     

3-D object recognition using 2-D views

We consider the problem of recognizing 3-D objects from 2-D images using geometric models and assuming different viewing angles and positions. Our goal is to recognize and localize instances of specific objects (i.e., model-based) in a scene. This is in contrast to category-based object recognition methods where the goal is to search for instances of objects that belong to a certain visual category (e.g., faces or cars). The key contribution of our work is improving 3-D object recognition by integrating Algebraic Functions of Views (AFoVs), a powerful framework for predicting the geometric appearance of an object due to viewpoint changes, with indexing and learning. During training, we compute the space of views that groups of object features can produce under the assumption of 3-D linear transformations, by combining a small number of reference views that contain the object features using AFoVs. Unrealistic views (e.g., due to the assumption of 3-D linear transformations) are eliminated by imposing a pair of rigidity constraints based on knowledge of the transformation between the reference views of the object. To represent the space of views that an object can produce compactly while allowing efficient hypothesis generation during recognition, we propose combining indexing with learning in two stages. In the first stage, we sample the space of views of an object sparsely and represent information about the samples using indexing. In the second stage, we build probabilistic models of shape appearance by sampling the space of views of the object densely and learning the manifold formed by the samples. Learning employs the Expectation-Maximization (EM) algorithm and takes place in a "universal," lower-dimensional, space computed through Random Projection (RP). During recognition, we extract groups of point features from the scene and we use indexing to retrieve the most feasible model groups that might have produced them (i.e., hypothesis generation). The likelihood of each hypothesis is then computed using the probabilistic models of shape appearance. Only hypotheses ranked high enough are considered for further verification with the most likely hypotheses verified first. The proposed approach has been evaluated using both artificial and real data, illustrating promising performance. We also present preliminary results illustrating extensions of the AFoVs framework to predict the intensity appearance of an object. In this context, we have built a hybrid recognition framework that exploits geometric knowledge to hypothesize the location of an object in the scene and both geometrical and intesnity information to verify the hypotheses.     

Registration of real-time 3-D ultrasound images of the heart for novel 3-D stress echocardiography

Stress echocardiography is a routinely used clinical procedure to diagnose cardiac dysfunction by comparing wall motion information in prestress and poststress ultrasound images. Incomplete data, complicated imaging protocols and misaligned prestress and poststress views, however, are known limitations of conventional stress echocardiography. We discuss how the first two limitations are overcome via the use of real-time three-dimensional (3-D) ultrasound imaging, an emerging modality, and have called the new procedure "3-D stress echocardiography." We also show that the problem of misaligned views can be solved by registration of prestress and poststress 3-D image sequences. Such images are misaligned because of variations in placing the ultrasound transducer and stress-induced anatomical changes. We have developed a technique to temporally align 3-D images of the two sequences first and then to spatially register them to rectify probe placement error while preserving the stress-induced changes. The 3-D spatial registration is mutual information-based. Image registration used in conjunction with 3-D stress echocardiography can potentially improve the diagnostic accuracy of stress testing.     

Multidimensional tomographic imaging using volume holography

We propose the application of volume holography to four-dimensional (4-D) spatiospectral imaging. The proposed systems use materials and techniques developed for holographic data storage and interconnections to capture three-dimensional (3-D) spatial and one-dimensional (1-D) spectral information about a remote light source or scatterer. We analyze case studies of simple architectures using spherical-reference volume holograms as imaging elements in a fluorescence confocal microscope arrangement and demonstrate the equivalence of the holographic degeneracies with a slicing operation on the reconstructing incoherent source. We develop a general theoretical framework for the diffraction of random fields from volume holograms and show that the formulation can be used as an imaging design tool. Applications and future directions are also discussed     

水下激光距离选通三维成像方法

基于水下距离选通激光成像技术,利用选通成像中回波强度相变化特性中包含的距离信息,提出了一种针对水下目标的三维成像方法。结合实验室现有的水下距离选通激光成像系统,对15 m处的水下目标进行了三维成像。这一方法有效抑制了水下成像系统中存在的目标表面材质、水体衰减以及目标各点法线方向与入射激光脉冲方向夹角不同等因素对于三维成像造成的不良影响,同时仅需要从单一方向对目标进行成像,减少了所需图像采集的次数,简化了三维重构的过程。