Proc. SPIE. 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging
KEYWORDS: Image processing algorithms and systems, Signal to noise ratio, Image segmentation, Image resolution, 3D modeling, Image registration, Bone, In vivo imaging, Spatial resolution, 3D image processing
Recently, micro-magnetic resonance imaging (μMRI) in conjunction with micro-finite element analysis has shown great
potential in estimating mechanical properties - stiffness and elastic moduli - of bone in patients at risk of osteoporosis.
Due to limited spatial resolution and signal-to-noise ratio achievable in vivo, the validity of estimated properties is often
established by comparison to those derived from high-resolution micro-CT (μCT) images of cadaveric specimens. For
accurate comparison of mechanical parameters derived from μMR and μCT images, analyzed 3D volumes have to be
closely matched. The alignment of the micro structure (and the cortex) is often hampered by the fundamental differences
of μMR and μCT images and variations in marrow content and cortical bone thickness. Here we present an intensity
cross-correlation based registration algorithm coupled with segmentation for registering 3D tibial specimen images
acquired by μMRI and μCT in the context of finite-element modeling to assess the bone's mechanical constants. The
algorithm first generates three translational and three rotational parameters required to align segmented μMR and CT
images from sub regions with high micro-structural similarities. These transformation parameters are then used to
register the grayscale μMR and μCT images, which include both the cortex and trabecular bone. The intensity crosscorrelation
maximization based registration algorithm described here is suitable for 3D rigid-body image registration
applications where through-plane rotations are known to be relatively small. The close alignment of the resulting images
is demonstrated quantitatively based on a voxel-overlap measure and qualitatively using visual inspection of the micro
Spin noise is inherent in magnetic resonance. It is caused by incomplete cancellation of spin moments when
the external static magnetic field is absent or by their small but finite fluctuations when the magnetic field is
applied. Spin noise is viewed as the variation of thermal equilibrium macroscopic magnetization (TEMM),
and can be described statistically. For MRI, TEMM is shown to be characterized by a Binomial distribution
and is well approximated by a Gaussian. Parameters of this Gaussian distribution are determined by the
spin density and the ratio of population difference over the total population of spins in a unit volume of
the sample. Statistics of spin noise not only confirm Bloch's prediction of spin noise in the absence of the
external magnetic field, but also give a more accurate account of its behavior under various conditions. These
statistics also provide a new insight into the limits of spatial resolution in magnetic resonance microscopy
(MRM) and are consistent with Glover and Mansfield' corresponding conclusions based on their experiments.
Independent of overall bone density, 3D trabecular bone (TB) architecture has been shown to play an important role in
conferring strength to the skeleton. Advances in imaging technologies such as micro-computed tomography (CT) and
micro-magnetic resonance (MR) now permit in vivo imaging of the 3D trabecular network in the distal extremities.
However, various experimental factors preclude a straightforward analysis of the 3D trabecular structure on the basis of
these in vivo images. For MRI, these factors include blurring due to patient motion, partial volume effects, and
measurement noise. While a variety of techniques have been developed to deal with the problem of patient motion, the
second and third issues are inherent limitations of the modality. To address these issues, we have developed a series of
robust processing steps to be applied to a 3D MR image and leading to a 3D skeleton that accurately represents the
trabecular bone structure. Here we describe the algorithm, provide illustrations of its use with both specimen and in vivo
micro-MR images, and discuss the accuracy and quantify the relationship between the original bone structure and the
resulting 3D skeleton volume.
Registration of 3D images acquired from different imaging modalities such as micro-magnetic resonance imaging (µMRI) and micro-computed tomography (µCT) are of interest in a number of medical imaging applications. Most general-purpose multimodality registration algorithms tend to be computationally intensive and do not take advantage of the shape of the imaging volume. Multimodality trabecular bone (TB) images of cylindrical cores, for example, tend to be misaligned along and around the axial direction more than that around other directions. Additionally, TB images acquired by µMRI can differ substantially from those acquired by µCT due to apparent trabecular thickening from magnetic susceptibility boundary effects and non-linear intensity correspondence. However, they share very similar contrast characteristics since the images essentially represent a binary tomographic system. The directional misalignment and the fundamental similarities of the two types of images can be exploited to achieve fast 3D registration. Here we present an intensity cross-correlation based 3D registration algorithm for registering 3D specimen images from cylindrical cores of cadaveric TB acquired by µMRI and µCT in the context of finite-element modeling to assess the bone's mechanical constants. The algorithm achieves the desired registration by first coarsely approximating the three translational and three rotational parameters required to align the µMR images to the µCT scan coordinate frame and fine-tuning the parameters in the neighborhood of the approximate solution. The algorithm described here is suitable for 3D rigid-body image registration applications where through-plane rotations are known to be relatively small. The accuracy of the technique is constrained by the image resolution and in-plane angular increments used.
Osteoporosis is the cause of over 1.5 million bone fractures annually. Most of these fractures occur in sites rich in trabecular bone, a complex network of bony struts and plates found throughout the skeleton. The three-dimensional structure of the trabecular bone network significantly determines mechanical strength and thus fracture resistance. Here we present a data acquisition and processing system that allows efficient noninvasive assessment of trabecular bone structure through a "virtual bone biopsy". High-resolution MR images are acquired from which the trabecular bone network is extracted by estimating the partial bone occupancy of each voxel. A heuristic voxel subdivision increases the effective resolution of the bone volume fraction map and serves a basis for subsequent analysis of topological and orientational parameters. Semi-automated registration and segmentation ensure selection of the same anatomical location in subjects imaged at different time points during treatment. It is shown with excerpts from an ongoing clinical study of early post-menopausal women, that significant reduction in network connectivity occurs in the control group while the structural integrity is maintained in the hormone replacement group. The system described should be suited for large-scale studies designed to evaluate the efficacy of therapeutic intervention in subjects with metabolic bone disease.
Atherosclerotic cerebrovascular disease leads to formation of lipid-laden plaques that can form emboli when ruptured causing blockage to cerebral vessels. The clinical manifestation of this event sequence is stroke; a leading cause of disability and death. In vivo MR imaging provides detailed image of vascular architecture for the carotid artery making it suitable for analysis of morphological features. Assessing the status of carotid arteries that supplies blood to the brain is of primary interest to such investigations. Reproducible quantification of carotid artery dimensions in MR images is essential for plaque analysis. Manual segmentation being the only method presently makes it time consuming and sensitive to inter and intra observer variability. This paper presents a deformable model for lumen and vessel wall segmentation of carotid artery from MR images. The major challenges of carotid artery segmentation are (a) low signal-to-noise ratio, (b) background intensity inhomogeneity and (c) indistinct inner and/or outer vessel wall. We propose a new, effective object-class uncertainty based deformable model with additional features tailored toward this specific application. Object-class uncertainty optimally utilizes MR intensity characteristics of various anatomic entities that enable the snake to avert leakage through fuzzy boundaries. To strengthen the deformable model for this application, some other properties are attributed to it in the form of (1) fully arc-based deformation using a Gaussian model to maximally exploit vessel wall smoothness, (2) construction of a forbidden region for outer-wall segmentation to reduce interferences by prominent lumen features and (3) arc-based landmark for efficient user interaction. The algorithm has been tested upon T1- and PD-weighted images. Measures of lumen area and vessel wall area are computed from segmented data of 10 patient MR images and their accuracy and reproducibility are examined. These results correspond exceptionally well with manual segmentation completed by radiology experts. Reproducibility of the proposed method is estimated for both intra- and inter-operator studies.
Trabecular bone (TB) consists of a network of interconnected struts and plates occurring near the joints of long bones and in the axial skeleton. In response to mechanical stresses it remodels such that trabeculae are aligned with the major stress lines, thus leading to a highly anisotropic network. Beside bone volume fraction, anisotropy and topological indices are known to be strong predictor of the TB mechanical competence. In osteoporosis, the most common bone disorder, the remodeling balance is perturbed due to increased resorption, resulting in net bone loss accompanied by architectural deterioration, leading to fragile bone and increased fracture risk. In vertebral osteoporosis, preferential loss of transverse trabeculae leads to increased anisotropy and change in topology, hence exact measurements of these parameters are of paramount interest. Current in vivo imaging yields voxel size comparable to TB thickness, thus resulting in inherently fuzzy representations. The commonly used methods for anisotropy require binarization which is difficult to achieve in the limited spatial resolution regime where the intensity histogram is mono-modal. Here, we present a new tensor scale (t-scale) based TB architectural measures that (1) obviates binarization, and (2) yields localized measures. We evaluate the performance of this method on micro-CT images of vertebral bone and test the hypothesis that the method, along with BMD and other structural parameters, allows prediction of TB’s mechanical competence. Toward this goal, we estimate Young’s modulus (YM) of (13mm)3 vertebral TB samples under uniaxial loading and examine linear correlation of different t-scale parameters computed via micro-CT imaging .
The spatial autocorrelation analysis method represents a powerful, new approach to quantitative characterization of structurally quasi-periodic anisotropic materials such as trabecular bone (TB). The method is applicable to grayscale images and thus does not require any preprocessing, such as segmentation which is difficult to achieve in the limited resolution regime of in vivo imaging. The 3D autocorrelation function (ACF) can be efficiently calculated using the Fourier transform. The resulting trabecular thickness and spacing measurements are robust to the presence of noise and produce values within the expected range as determined by other methods from μCT and μMRI datasets. TB features found from the ACF are shown to correlate well with those determined by the Fuzzy Distance transform (FDT) in the transverse plane, i.e. the plane orthogonal to bone’s major axis. The method is further shown to be applicable to in-vivo μMRI data. Using the ACF, we examine data acquired in a previous study aimed at evaluating the structural implications of male hypogonadism characterized by testosterone deficiency and reduced bone mass. Specifically, we consider the hypothesis that eugonadal and hypogonadal men differ in the anisotropy of their trabecular networks. The analysis indicates a significant difference in trabecular bone thickness and longitudinal spacing between the control group and the testosterone deficient group. We conclude that spatial autocorrelation analysis is able to characterize the 3D structure and anisotropy of trabecular bone and provides new insight into the structural changes associated with osteoporotic trabecular bone loss.
Object segmentation is of paramount interest in many medical imaging applications. Among others, "snake"-an "active contour"-is a popular boundary-based segmentation framework where a spline is continuously deformed to lock onto an object boundary. The dynamics of a snake is governed by different internal and external forces. A major limitation of this framework has been the difficulty in using object-intensity driven features into snake dynamics which may prevent uncontrolled expansion/contraction once the snake leaks through a weak boundary region. In this paper, object-intensity force is effectively introduced into the snake-model using class uncertainty theory. Given a priori knowledge of object/background intensity distributions, class uncertainty yields object/background class of any location and establishes the confidence level of the classification. This confidence level has previously been demonstrated to be high inside the object/background regions and low near boundaries with intermediate intensities. This class uncertainty information adds an expanding (outward) force at locations pertaining to intensity-based object class and a squeezing (inward) force inside background regions. Consequently, the method possesses potential to resist an uncontrolled expansion of the snake (for an expanding type) into the background through a weak boundary while reducing the effect of this force near the boundary using the confidence information. The theory of object class uncertainty induced snake is developed and an implementation with efficient graphical interface is achieved. Preliminary results of application of the proposed snake approach on different images are presented and comparisons with conventional snake approaches are demonstrated.
Trabecular bone (TB) is a network of interconnected bony struts and plates mostly occurring near the joints of long bones and in the axial skeleton. Several bone diseases including osteoporosis are characterized by fragile bone and increased fracture risk and most fractures occur at locations rich in TB. The mechanical competence of this type of bone can only be partly explained by variations in the bone’s mass density (BMD), and there is now compelling evidence for the role of TB architecture in conferring skeletal strength. Our previous studies have demonstrated that a reduction in BMD is accompanied by a greatly magnified topological process that involves the conversion of trabeculae from plates to struts. Current in vivo technologies yield voxel sizes comparable to TB thickness resulting in inherently fuzzy representations and thus making in vivo assessment of TB architecture challenging. Most existing methods require binarization of an image into bone and non-bone regions and thus are associated with significant errors. Here, a new approach is presented for assessing TB architecture (e.g. classification of plates versus struts) using 3D tensor scale - a local morphometric index - that (1) obviates the need for binary segmentation and is applicable to grayscale bone volume fraction images, and (2) provides precise topological classification over the continuum between a perfect rod and a perfect plate. At any TB location p, tensor scale is the parametric representation of the largest ellipsoid that is centered at p and contained inside the bone region. Accuracy and reproducibility of the method under varying voxel size, and image rotation is presented and its applicability on TB images at in vivo MR resolution is demonstrated.
Trabecular bone (TB) is a network of interconnected struts and plates that constantly remodels to adapt dynamically to the stresses to which it is subjected in such a manner that the trabeculae are oriented along the major stress lines (Wolf's Law). Structural anisotropy can be expressed in terms of the fabric tensor. Next to bone density, TB has been found to be the largest determinant of bone biomechanical behavior. Existing methods, including mean intercept length (MIL), provide only a global statistical average of TB anisotrophy and, generally, require a large sample volume. In this paper, we present a new method, based on the recently conceived notion of tensor scale, which provides regional information of TB orientation and anisotropy. Preliminary evaluation of the method in terms of its sensitivity to resolution and image rotation is reported. The characteristic differences between TB anisotropy computed from transverse and longitudinal sections have been studied and potential applications of the method to in vivo MR imaging are demonstrated. Finally, the ongoing extension of three-dimensional tensor scale in quantitative analysis of tissue morphology is discussed.
This paper describes the theory and algorithms of fuzzy distance transform (FDT). Fuzzy distance is defined as the length of the shortest path between two points. The length of a path in a fuzzy subset is defined as the integration of fuzzy membership values of the points along the path. The shortest path between two points is the one with the minimum length among all (infinitely many) paths between the two points. It is demonstrated that, unlike in the binary case, the shortest path in a fuzzy subset is not necessarily a straight-line segment. The support of a fuzzy subset is the set of points with nonzero membership values. It is shown that, for any fuzzy subset, fuzzy distance is a metric for the interior of its support. FDT is defined as the process on a fuzzy subset that assigns at each point the smallest fuzzy distance from the boundary of the support. The theoretical framework of FDT in continuous space is extended to digital spaces and a dynamic programming-based algorithm is presented for its computation. Several potential medical imaging applications are presented including the quantification of blood vessels and trabecular bone thickness in the regime of limited special resolution.
The aim of this work was to develop a reliable semi-automatic method for quantifying carotid atherosclerotic lesion burden using black-blood high-resolution MR images. Vessel wall volume was quantified by measuring its cross-sectional area in adjacent slices. Two methods for obtaining this measure are presented. The first method approximates the outer boundary of the vessel on a slice-by-slice basis by fitting an ellipse to user-identified points and automatically identifying the lumen through examination of the histogram obtained from a local region of interest (ROI). The second, method identifies the lumen and wall throughout the entire volume based upon user-selected points in a single slice. Radially directed intensity profiles are examined in order to automatically locate points on the outer boundary, and the same histogram-based method is used for lumen delineation. The measure of wall area provided by the manual outer boundary selection has an intra-class correlation coefficient (ICC) of 0.83 for test-retest comparisons, but the ICC values for the inter-observer comparisons (0.84, 0.65) suggest that user bias remains a potential source of error. A susceptibility to low image signal-to-noise ratio (SNR) may present a limitation on the usefulness of the automated outer boundary selection method for use on whole image volumes.
The authors report on the Signa Tutor, a magnetic resonance imaging (MRI) simulator. Their goal was to cost-effectively teach MRI scanning techniques to technologists and physicians using the simulator, off-line from the General Electric (GE) Signa scanner. They implemented the scanner's user-interface for image acquisition, using HyperCard together with an Oracle database to maintain images and scan-parameters. The software operates on a standard Apple Macintosh-II computer. Two aspects of the project are reported in this paper. First, enhancements to the Signa Tutor, including Signa 4.0 pulse sequences and new images, are presented. Second, a time and motion study that compares image acquisition learning time using Signa Tutor verses traditional technologist training on the GE-Signa console is described. In this study, an expediency test was performed which compares the speed at which an operator performs a single scan sequence on both devices.
The increasing use of magnetic resonance imaging (MRI) as a clinical modality has put an enormous
burden on medical institutions to cost-effectively teach Mill scanning techniques to
technologists and physicians. Since MRI scanner time is a scarce resource, it would be ideal if the
teaching could be effectively performed off-line. In order to meet this goal, the Radiology Department
has designed and developed a Magnetic Resonance Imaging Simulator. The Simulator in its
current implementation mimics the General Electric Signa scanner's user-interface for image acquisition.
The design is general enough to be applied to other MRI scanners. One unique feature
of the simulator is its incorporation of an image-synthesis module which permits the user to derive
images for any arbitrary combination of pulsing parameters for spin-echo, gradient-echo, and inversion
recovery pulse sequences. These images are computed in five seconds. The development
platform chosen is a standard Apple Macintosh-Il computer with no specialized hardware peripherals.
The user-interface is implemented in HyperCard. All other software development including
synthesis and display functions are implemented under the MPW 'C' environment. The scan parameters,
demographics and images are tracked using an Oracle database. Images are currently
stored on magnetic disk but could be stored on optical media with minimal effort.