An automatic technique for finding and localizing externallyattached markers in CT and MR volume images of the head

An image processing technique is presented for finding and localizing the centroids of cylindrical markers externally attached to the human head in computed tomography (CT) and magnetic resonance (MR) image volumes. The centroids can be used as control points for image registration. The technique, which is fast, automatic, and knowledge-based, has two major steps. First, it searches the entire image volume to find one voxel inside each marker-like object. The authors call this voxel a “candidate” voxel, and they call the object a candidate marker. Second, it classifies the voxels in a region surrounding the candidate voxel as marker or nonmarker voxels using knowledge-based rules and calculates an intensity-weighted centroid for each true marker. The authors call this final centroid the “fiducial” point of the marker. The technique was developed on 42 scans of six patients-one CT and six MR scans per patient. There are four markers attached to each patient for a total of 168 marker images. For the CT images the false marker rate was zero. For MR the false marker rate was 1.4% (Two out of 144 markers). To evaluate the accuracy of the fiducial points, CT-MR registration was performed after correcting the MR images for geometrical distortion. The fiducial registration accuracy averaged 0.4 mm and was better than 0.6 mm for each of the eighteen image pairs     

Dynamics of MRI-Guided thermal ablation of VX2 tumor in paraspinal muscle of rabbits

This study combines fast magnetic resonance imaging (MRI) and model simulation of tissue thermal ablation for monitoring and predicting the dynamics of lesion size for tumor destruction. In vivo experiments were conducted using radiofrequency (RF) thermal ablation in paraspinal muscle of rabbit with a VX2 tumor. Before ablation, turbo-spin echo (TSE) images visualized the 3-D tumor (necrotic core and tumor periphery) and surrounding normal tissue. MR gradient-recalled echo (GRE) phase and magnitude images were acquired repeatedly in 3.3 s at 30-s intervals during and after thermal ablation to follow tissue temperature distribution dynamics and lesion development in tumor and surrounding normal tissue. Final lesion sizes estimated from GRE magnitude, post-ablation TSE, and stained histologic images were compared. Model simulations of temperature distribution and lesion development dynamics closely corresponded to the experimental data from MR images in tumor and normal tissue. The combined use of MR image monitoring and model simulation has the potential for improving pretreatment planning and real-time prediction of lesion-size dynamics for guidance of thermal ablation of tumors.     

Regularized field map estimation in MRI

In fast magnetic resonance (MR) imaging with long readout times, such as echo-planar imaging (EPI) and spiral scans, it is important to correct for the effects of field inhomogeneity to reduce image distortion and blurring. Such corrections require an accurate field map, a map of the off-resonance frequency at each voxel. Standard field map estimation methods yield noisy field maps, particularly in image regions with low spin density. This paper describes regularized methods for field map estimation from two or more MR scans having different echo times. These methods exploit the fact that field maps are generally smooth functions. The methods use algorithms that decrease monotonically a regularized least-squares cost function, even though the problem is highly nonlinear. Results show that the proposed regularized methods significantly improve the quality of field map estimates relative to conventional unregularized methods.      

Diffeomorphic image registration of diffusion MRI using spherical harmonics

Nonrigid registration of diffusion magnetic resonance imaging (MRI) is crucial for group analyses and building white matter and fiber tract atlases. Most current diffusion MRI registration techniques are limited to the alignment of diffusion tensor imaging (DTI) data. We propose a novel diffeomorphic registration method for high angular resolution diffusion images by mapping their orientation distribution functions (ODFs). ODFs can be reconstructed using q-ball imaging (QBI) techniques and represented by spherical harmonics (SHs) to resolve intra-voxel fiber crossings. The registration is based on optimizing a diffeomorphic demons cost function. Unlike scalar images, deforming ODF maps requires ODF reorientation to maintain its consistency with the local fiber orientations. Our method simultaneously reorients the ODFs by computing a Wigner rotation matrix at each voxel, and applies it to the SH coefficients during registration. Rotation of the coefficients avoids the estimation of principal directions, which has no analytical solution and is time consuming. The proposed method was validated on both simulated and real data sets with various metrics, which include the distance between the estimated and simulated transformation fields, the standard deviation of the general fractional anisotropy and the directional consistency of the deformed and reference images. The registration performance using SHs with different maximum orders were compared using these metrics. Results show that the diffeomorphic registration improved the affine alignment, and registration using SHs with higher order SHs further improved the registration accuracy by reducing the shape difference and improving the directional consistency of the registered and reference ODF maps.     

Volume registration using needle paths and point landmarks for evaluation of interventional MRI treatments

We created a method for three-dimensional (3-D) registration of medical images (e.g., magnetic resonance imaging (MRI) or computed tomography) to images of physical tissue sections or to other medical images and evaluated its accuracy. Our method proved valuable for evaluation of animal model experiments on interventional-MRI guided thermal ablation and on a new localized drug delivery system. The method computes an optimum set of rigid body registration parameters by minimization of the Euclidean distances between automatically chosen correspondence points, along manually selected fiducial needle paths, and optional point landmarks, using the iterative closest point algorithm. For numerically simulated experiments, using two needle paths over a range of needle orientations, mean voxel displacement errors depended mostly on needle localization error when the angle between needles was at least 20 degrees. For parameters typical of our in vivo experiments, the mean voxel displacement error was < 0.35 mm. In addition, we determined that the distance objective function was a useful diagnostic for predicting registration quality. To evaluate the registration quality of physical specimens, we computed the misregistration for a needle not considered during the optimization procedure. We registered an ex vivo sheep brain MR volume with another MR volume and tissue section photographs, using various combinations of needle and point landmarks. Mean registration error was always < or = 0.54 mm for MR-to-MR registrations and < or = 0.52 mm for MR to tissue section registrations. We also applied the method to correlate MR volumes of radio-frequency induced thermal ablation lesions with actual tissue destruction. In this case, in vivo rabbit thigh volumes were registered to photographs of ex vivo tissue sections using two needle paths. Mean registration errors were between 0.7 and 1.36 mm over all rabbits, the largest error less than two MR voxel widths. We conclude that our method provides sufficient spatial correspondence to facilitate comparison of 3-D image data with data from gross pathology tissue sections and histology.     

Multiscale modeling for image analysis of brain tumor studies

Image-based modeling of tumor growth combines methods from cancer simulation and medical imaging. In this context, we present a novel approach to adapt a healthy brain atlas to MR images of tumor patients. In order to establish correspondence between a healthy atlas and a pathologic patient image, tumor growth modeling in combination with registration algorithms is employed. In a first step, the tumor is grown in the atlas based on a new multiscale, multiphysics model including growth simulation from the cellular level up to the biomechanical level, accounting for cell proliferation and tissue deformations. Large-scale deformations are handled with an Eulerian approach for finite element computations, which can operate directly on the image voxel mesh. Subsequently, dense correspondence between the modified atlas and patient image is established using nonrigid registration. The method offers opportunities in atlas-based segmentation of tumor-bearing brain images as well as for improved patient-specific simulation and prognosis of tumor progression.     

Partial volume tissue classification of multichannel magnetic resonance images-a mixel model

A single volume element (voxel) in a medical image may be composed of a mixture of multiple tissue types. The authors call voxels which contain multiple tissue classes mixels. A statistical mixel image model based on Markov random field (MRF) theory and an algorithm for the classification of mixels are presented. The authors concentrate on the classification of multichannel magnetic resonance (MR) images of the brain although the algorithm has other applications. The authors also present a method for compensating for the gray-level variation of MR images between different slices, which is primarily caused by the inhomogeneity of the RF field produced by the imaging coil.     

Optimization of tag thickness for measuring position with magnetic resonance imaging

Using magnetic resonance (MR) tagging, it is possible to track tissue motion by accurate detection of tag line positions. The authors show that with a least squares estimation algorithm, it is possible to define the position of the tag lines with a precision on the order of a tenth of a pixel. They calculate the Cramer-Rao bound for the tag position estimation error as a function of the tag thickness, the shape of the tag profile and line spread function of the MR imaging system. The tag thickness that minimizes tag position estimation error is between 0.8 to 1.5 pixels depending on the shape of the tag and the line spread function. In addition, tag position estimation error is inversely proportional to contrast-to-noise ratio between the tag and the background tissue. The theoretical results obtained in this study were verified by experiments performed on a whole-body 1.5 T MR imager and Monte-Carlo simulation studies.     

Interpolation artifacts in multimodality image registration based on maximization of mutual information

Mutual information (MI) is an increasingly popular match metric for multimodality image registration. However, its value is affected by interpolation, which may limit registration accuracy. The purpose of this study was to characterize the artifacts from eight interpolators and to investigate efficient strategies to overcome these artifacts. The interpolators were: 1) nearest neighbor; 2) linear; 3) cubic Catmull-Rom; 4) Hamming-windowed sinc; 5) partial volume; 6) NN with jittered sampling (JIT); 7) NN with histogram blurring (BLUR); and 8) NN with JIT and BLUR. The impact of interpolation on MI was evaluated in two dimensions over different translational and rotational misregistration. Interpolation caused spurious fluctuations in MI whenever the voxel grids had coinciding periodicities and were nearly aligned. The artifacts did not lessen by using intensity interpolators with wider support (e.g., cubic Catmull-Rom, Hamming-windowed sinc). PV could lead to either arch artifacts or inverted-arch artifacts, depending on the relative voxel sizes. Several strategies reduced artifacts and improved registration robustness: JIT, BLUR, avoiding an extreme number of intensity bins, and resampling the images in a rotated orientation with different relative voxel sizes (e.g., pi/3). These findings also apply to related methods, including normalized MI, joint entropy, and Hill's third moment.     

Determining correspondence in 3-D MR brain images using attribute vectors as morphological signatures of voxels

Finding point correspondence in anatomical images is a key step in shape analysis and deformable registration. This paper proposes an automatic correspondence detection algorithm for intramodality MR brain images of different subjects using wavelet-based attribute vectors (WAVs) defined on every image voxel. The attribute vector (AV) is extracted from the wavelet subimages and reflects the image structure in a large neighborhood around the respective voxel in a multiscale fashion. It plays the role of a morphological signature for each voxel, and our goal is, therefore, to make it distinctive of the respective voxel. Correspondence is then determined from similarities of AVs. By incorporating the prior knowledge of the spatial relationship among voxels, the ability of the proposed algorithm to find anatomical correspondence is further improved. Experiments with MR images of human brains show that the algorithm performs similarly to experts, even for complex cortical structures.     

Likelihood maximization approach to image registration

A likelihood maximization approach to image registration is developed in this paper. It is assumed that the voxel values in two images in registration are probabilistically related. The principle of maximum likelihood is then exploited to find the optimal registration: the likelihood that given image f, one has image g and given image g, one has image f is optimized with respect to registration parameters. All voxel pairs in the overlapping volume or a portion of it can be used to compute the likelihood. A knowledge-based method and a self-consistent technique are proposed to obtain the probability relation. In the knowledge-based method, prior knowledge of the distribution of voxel pairs in two registered images is assumed, while such knowledge is not required in the self-consistent method. The accuracy and robustness of the likelihood maximization approach is validated by single modality registration of single photon emission computed tomographic (SPECT) images and magnetic resonance (MR) images and by multimodality registration (MR/SPECT). The results demonstrate that the performance of the likelihood maximization approach is comparable to that of the mutual information maximization technique. Finally the relationship between the likelihood approach and the entropy, conditional entropy, and mutual information approaches is discussed.     

Automated detection of prostatic adenocarcinoma from high-resolution ex vivo MRI

Prostatic adenocarcinoma is the most commonly occurring cancer among men in the United States, second only to skin cancer. Currently, the only definitive method to ascertain the presence of prostatic cancer is by trans-rectal ultrasound (TRUS) directed biopsy. Owing to the poor image quality of ultrasound, the accuracy of TRUS is only 20%-25%. High-resolution magnetic resonance imaging (MRI) has been shown to have a higher accuracy of prostate cancer detection compared to ultrasound. Consequently, several researchers have been exploring the use of high resolution MRI in performing prostate biopsies. Visual detection of prostate cancer, however, continues to be difficult owing to its apparent lack of shape, and the fact that several malignant and benign structures have overlapping intensity and texture characteristics. In this paper, we present a fully automated computer-aided detection (CAD) system for detecting prostatic adenocarcinoma from 4 Tesla ex vivo magnetic resonance (MR) imagery of the prostate. After the acquired MR images have been corrected for background inhomogeneity and nonstandardness, novel three-dimensional (3-D) texture features are extracted from the 3-D MRI scene. A Bayesian classifier then assigns each image voxel a "likelihood" of malignancy for each feature independently. The "likelihood" images generated in this fashion are then combined using an optimally weighted feature combination scheme. Quantitative evaluation was performed by comparing the CAD results with the manually ascertained ground truth for the tumor on the MRI. The tumor labels on the MR slices were determined manually by an expert by visually registering the MR slices with the corresponding regions on the histology slices. We evaluated our CAD system on a total of 33 two-dimensional (2-D) MR slices from five different 3-D MR prostate studies. Five slices from two different glands were used for training. Our feature combination scheme was found to outperform the individual texture features, and also other popularly used feature combination methods, including AdaBoost, ensemble averaging, and majority voting. Further, in several instances our CAD system performed better than the experts in terms of accuracy, the expert segmentations being determined solely from visual inspection of the MRI data. In addition, the intrasystem variability (changes in CAD accuracy with changes in values of system parameters) was significantly lower than the corresponding intraobserver and interobserver variability. CAD performance was found to be very similar for different training sets. Future work will focus on extending the methodology to guide high-resolution MRI-assisted in vivo prostate biopsies.     

一种三维数据场多表面显示方法

本文提出了一种基于体绘制的三维数据场多表面显示方法.首先,采用灰度梯度加权提取出三维数据场中不同物质间的边界,根据显示的需要只对这些边界上的体元赋予相应的阻光度并进行光亮度合成计算,因而可大大减少计算量,提高显示的速度;将边界上的体元作为不同物质的混合体,采用与方向有关的三线性插值来计算视线方向与体素内等值面的交点,根据交点的法向量进行光照效应计算以提高显示图像的质量;最后用投影成像法显示最终的图像.本文对医学CT图像做了相关的实验,取得了较好的效果.     

Simultaneous ultrasound and MRI system for breast biopsy: compatibility assessment and demonstration in a dual modality phantom

Simultaneous capturing of ultrasound (US) and magnetic resonance (MR) images allows fusion of information obtained from both modalities. We propose an MR-compatible US system where MR images are acquired in a known orientation with respect to the US imaging plane and concurrent real-time imaging can be achieved. Compatibility of the two imaging devices is a major issue in the physical setup. Tests were performed to quantify the radio frequency (RF) noise introduced in MR and US images, with the US system used in conjunction with MRI scanner of different field strengths (0.5 T and 3 T). Furthermore, simultaneous imaging was performed on a dual modality breast phantom in the 0.5 T open bore and 3 T close bore MRI systems to aid needle-guided breast biopsy. Fiducial based passive tracking and electromagnetic based active tracking were used in 3 T and 0.5 T, respectively, to establish the location and orientation of the US probe inside the magnet bore. Our results indicate that simultaneous US and MR imaging are feasible with properly-designed shielding, resulting in negligible broadband noise and minimal periodic RF noise in both modalities. US can be used for real time display of the needle trajectory, while MRI can be used to confirm needle placement.     

Out-of-plane motion compensation in multislice spin-echo MRI

In magnetic resonance imaging (MRI), it is well-known that patient motion plays a significant role in the degradation of image quality. Although the case of translational in-plane motion (x-y-motion) has been studied by several researchers, the effect of rigid, translational out-of-plane motion (z-motion) has not yet been completely analyzed due to its more complex nature. Out-of-plane motion introduces blurring along the slice-selection direction in addition to motion artifacts. Here, the authors present a model to represent the effect of out-of-plane motion on multislice MR data. The inversion of this model not only results in the correction of the artifacts due to out-of-plane motion, but also reduces blurring in the slice-selection direction, yielding higher resolution images. Because of the shift-varying nature of the authors' model, they propose to use a nonlinear postprocessing method, projection onto convex sets (POCS), for its inversion, provided that the motion kernel and the slice-selection profile are known. The proposed method has been tested on simulated data and then applied to actual MR data to demonstrate the feasibility of the technique in real imaging situations.     

Accounting for signal loss due to dephasing in the correction of distortions in gradient-echo EPI via nonrigid registration

Gradient-echo (GE) echo planar imaging (EPI) is susceptible to both geometric distortions and signal loss. This paper presents a retrospective correction approach based on nonrigid image registration. A new physics-based intensity correction factor derived to compensate for intravoxel dephasing in GE EPI images is incorporated into a previously reported nonrigid registration algorithm. Intravoxel dephasing causes signal loss and thus intensity attenuation in the images. The new rephasing factor we introduce, which changes the intensity of a voxel in images during the registration, is used to improve the accuracy of the intensity-based nonrigid registration method and mitigate the intensity attenuation effect. Simulation-based experiments are first used to evaluate the method. A magnetic resonance (MR) simulator and a real field map are used to generate a realistic GE EPI image. The geometric distortion computed from the field map is used as the ground truth to which the estimated nonrigid deformation is compared. We then apply the algorithm to a set of real human brain images. The results show that, after registration, alignment between EPI and multi-shot, spin-echo images, which have relatively long acquisition times but negligible distortion, is improved and that signal loss caused by dephasing can be recovered.     

Monte Carlo simulation of light-tissue interaction: three-dimensional simulation for trans-illumination-based imaging of skin lesions

Three-dimensional, voxel-based, and wavelength-dependent skin lesion models are developed and simulated using Monte Carlo techniques. The optical geometry of the Nevoscope with trans-illumination is used in the simulations for characterizing the lesion thickness. Based on the correlation analysis between the lesion thickness and the diffuse reflectance, optical wavelengths are selected for multispectral imaging of skin lesions using the Nevoscope. Tissue optical properties reported by various researchers are compiled together to form a voxel library. Tissue models used in the simulations are developed using the voxel library which offers flexibility in updating the optical properties and adding new media types into the models independent of the Monte Carlo simulation code.     

地基光学望远镜对目标飞行器成像的仿真与验证

以目标飞行器地基光学观测与姿态监测需求为背景，研究了地基光学望远镜成像仿真模型与方法，通过目标建模、轨道确定和光学成像建模，结合目标表面材质光散射特性和望远镜成像性能，基于OpenGL（Open Graphics Library）仿真生成目标飞行器在轨飞行光学图像序列。根据理论分析比对、第三方仿真软件STK（Satellite Tool Kit）比对、实测光学图像序列比对多种途径，验证了仿真方法和结果的正确性。可为目标飞行器观测任务制定与在轨姿态监控、空间目标姿态判别和目标识别提供参考和依据。     

Measuring the progression of foreign-body reaction to siliconeimplants using in vivo MR microscopy

We used in vivo magnetic resonance (MR) microscopy to follow the growth of fibrous capsule as a foreign body reaction to silicone implants in rats. Anesthetized rats were imaged 1, 7, 14, and 28 days after silicone-coated MR imaging coils were sutured to their neck muscles. On the twenty eighth day, rats were sacrificed and coils and adjacent tissues were removed en bloc and fixed in formalin, reimaged with MR, and sectioned for conventional histology. Three-dimensional (3-D) spin-echo [3DFT] acquisition gave in-plane resolution of 32×32 μm in vivo and 16×16 μm ex vivo. All MR images showed a diffuse band of elevated signal intensity between the silicone of the coil and adjacent tissue. The border of the hyperintense band was thin and not well defined at seven days post-implantation. From 7-28 days, the band showed relatively homogeneous signal intensity and its thickness increased 44% on the rectus muscle side and 78% on the subcutaneous side. The capsule thickness determined either by MR in vivo and ex vivo microscopy or conventional histology was not significantly different, and there was a significant correlation between thickness measurements among those methods. MR in vivo microscopy provides sufficient resolution and spatial information to serially evaluate the growth of the foreign body fibrous capsule over time, thus achieving greater accuracy and consistency in measurements     

Semi-automatic tracking of myocardial motion in MR tagged images

Tissue tagging using magnetic resonance (MR) imaging has enabled quantitative noninvasive analysis of motion and deformation in vivo. One method for MR tissue tagging is Spatial Modulation of Magnetization (SPAMM). Manual detection and tracking of tissue tags by visual inspection remains a time-consuming and tedious process. The authors have developed an interactively guided semi-automated method of detecting and tracking tag intersections in cardiac MR images. A template matching approach combined with a novel adaptation of active contour modeling permits rapid analysis of MR images. The authors have validated their technique using MR SPAMM images of a silicone gel phantom with controlled deformations. Average discrepancy between theoretically predicted and semi-automatically selected tag intersections was 0.30 mm+/-0.17 [mean+/-SD, NS (P<0.05)]. Cardiac SPAMM images of normal volunteers and diseased patients also have been evaluated using the authors' technique.