The publication data currently available has been vetted by Vanderbilt faculty, staff, administrators and trainees. The data itself is retrieved directly from NCBI's PubMed and is automatically updated on a weekly basis to ensure accuracy and completeness.
If you have any questions or comments, please contact us.
The purpose of this study was to evaluate the effects of system parameters (focal spot size, tube voltage, geometry, detector resolution, and image noise) and object characteristics (edge gradient/ shape, composition, thickness, and overlying attenuating material) upon the edge enhancement effect in phase-contrast radiography. Each variable of interest was adjusted and images of a 3 mm lucite phantom were obtained with the other variables remaining constant. A microfocus x-ray source coupled to a CCD camera with an intensifying screen was used to acquire the digital images. Two parameters of image analysis were used to quantify the effects. The edge enhancement index (EEI) was used to measure the absolute degree of edge enhancement, while the edge enhancement to noise ratio (EE/N) was used to measure the conspicuity of the edge enhancement relative to image noise. Little effect on EEI was seen from tube voltage, object thickness, overlying attenuating material, while focal spot size and system geometry demonstrated measurable effects upon the degree of edge enhancement. It was also shown that while the edge enhancement effect over straight edges is highly dependent upon how the edge aligns with the x-ray beam, rounded edges, which better model biological objects, do not suffer from this dependence and the EEI reaches its maximal level at any alignment. Decreasing detector resolution diminished the EEI slightly, but even with pixel sizes of 0.360 x 0.360 mm edge enhancement effects were readily visible. The effect of image noise on EE/N was evaluated using different exposure times showing an expected improvement with longer exposure time with EE/N approaching a plateau at 5 min. Many of the parameters that will go into the design of a future PC-R imaging system have been quantified in terms of their effect on the degree of edge enhancement in the acquired image. These results, taken together, indicate that either a specimen or even clinical breast imaging system could be created with currently available technology. The major limitation to a clinical system would be the low x-ray flux from the microfocal x-ray source.
Occluding surfaces frequently obstruct the object of interest yet are easily dealt with by the visual system. Here, we test whether neural areas known to participate in motion perception and eye movements are regions that also process occluded motion. Functional magnetic resonance imaging (fMRI) was used to assess brain activation while subjects watched a moving ball become occluded. Areas activated during occluded motion included the intraparietal sulcus (IPS) as well as middle temporal (MT) regions analogous to monkey MT/MST. A second experiment showed that these results were not due to motor activity. These findings suggest that human cortical regions involved in perceiving occluded motion are similar to regions that process real motion and regions responsible for eye movements. The intraparietal sulcus may be involved in predicting the location of an unseen target for future hand or eye movements.
Nonrigid registration of medical images is important for a number of applications such as the creation of population averages, atlas-based segmentation, or geometric correction of functional magnetic resonance imaging (fMRI) images to name a few. In recent years, a number of methods have been proposed to solve this problem, one class of which involves maximizing a mutual information (MI)-based objective function over a regular grid of splines. This approach has produced good results but its computational complexity is proportional to the compliance of the transformation required to register the smallest structures in the image. Here, we propose a method that permits the spatial adaptation of the transformation's compliance. This spatial adaptation allows us to reduce the number of degrees of freedom in the overall transformation, thus speeding up the process and improving its convergence properties. To develop this method, we introduce several novelties: 1) we rely on radially symmetric basis functions rather than B-splines traditionally used to model the deformation field; 2) we propose a metric to identify regions that are poorly registered and over which the transformation needs to be improved; 3) we partition the global registration problem into several smaller ones; and 4) we introduce a new constraint scheme that allows us to produce transformations that are topologically correct. We compare the approach we propose to more traditional ones and show that our new algorithm compares favorably to those in current use.
The present study describes the in vivo detection and imaging of tumour-associated MMP-7 (matrix metalloproteinase-7 or matrilysin) activity using a novel polymer-based fluorogenic substrate PB-M7VIS, which serves as a selective 'proteolytic beacon' (PB) for this metalloproteinase. PB-M7VIS is built on a PAMAM (polyamido amino) dendrimer core of 14.2 kDa, covalently coupled with an Fl (fluorescein)-labelled peptide Fl(AHX)RPLALWRS(AHX)C (where AHX stands for aminohexanoic acid) and with TMR (tetramethylrhodamine). PB-M7VIS is efficiently and selectively cleaved by MMP-7 with a k (cat)/ K (m) value of 1.9x10(5) M(-1).s(-1) as measured by the rate of increase in Fl fluorescence (up to 17-fold for the cleavage of an optimized PB-M7VIS) with minimal change in the TMR fluorescence. The K (m) value for PB-M7VIS is approx. 0.5 microM, which is approx. two orders of magnitude lower when compared with that for an analogous soluble peptide, indicating efficient interaction of MMP-7 with the synthetic polymeric substrate. With MMP-2 or -3, the k (cat)/ K (m) value for PB-M7VIS is approx. 56- or 13-fold lower respectively, when compared with MMP-7. In PB-M7VIS, Fl(AHX)RPLALWRS(AHX)C is a selective optical sensor of MMP-7 activity and TMR serves to detect both the uncleaved and cleaved reagents. Each of these can be visualized as subcutaneous fluorescent phantoms in a mouse and optically discriminated based on the ratio of green/red (Fl/TMR) fluorescence. The in vivo specificity of PB-M7VIS was tested in a mouse xenograft model. Intravenous administration of PB-M7VIS gave significantly enhanced Fl fluorescence from MMP-7-positive tumours, but not from control tumours ( P <0.0001), both originally derived from SW480 human colon cancer cells. Prior systemic treatment of the tumour-bearing mice with an MMP inhibitor BB-94 ([4-( N -hydroxyamino)-2 R -isobutyl-3 S -(thienylthiomethyl)-succinyl]-L-phenylalanine- N -methylamide), markedly decreased the Fl fluorescence over the MMP-7-positive tumour by approx. 60%. Thus PB-M7VIS functions as a PB for in vivo detection of MMP-7 activity that serves to light this optical beacon and is, therefore, a selective in vivo optical molecular imaging contrast reagent.
The purpose of this study was to evaluate dual focal spot imaging as a method for extracting the phase component from a phase-contrast radiography image. All measurements were performed using a microfocus tungsten-target x-ray tube with an adjustable focal-spot size (0.01 mm to 0.045 mm). For each object, high-resolution digital radiographs were obtained with two different focal spot sizes to produce matched image pairs in which all other geometric variables as well as total exposure and tube kVp were held constant. For each image pair, a phase extraction was performed using pixel-wise division. The phase-extracted image resulted in an image similar to the standard image processing tool commonly referred to as "unsharp masking" but with the additional edge-enhancement produced by phase-contrast effects. The phase-extracted image illustrates the differences between the two images whose imaging parameters differ only in focal spot size. The resulting image shows effects from both phase contrast as well as geometric unsharpness. In weakly attenuating materials the phase-contrast effect predominates, while in strongly attenuating materials the phase effects are so small that they are not detectable. The phase-extracted image in the strongly attenuating object reflects differences in geometric unsharpness. The degree of phase extraction depends strongly on the size of the smallest focal spot used. This technique of dual-focal spot phase-contrast radiography provides a simple technique for phase-component (edge) extraction in phase-contrast radiography. In strongly attenuating materials the phase-component is overwhelmed by differences in geometric unsharpness. In these cases the technique provides a form of unsharp masking which also accentuates the edges. Thus, the two effects are complimentary and may be useful in the detection of small objects.
In this paper, a method of acquiring intraoperative data using a laser range scanner (LRS) is presented within the context of model-updated image-guided surgery. Registering textured point clouds generated by the LRS to tomographic data is explored using established point-based and surface techniques as well as a novel method that incorporates geometry and intensity information via mutual information (SurfaceMI). Phantom registration studies were performed to examine accuracy and robustness for each framework. In addition, an in vivo registration is performed to demonstrate feasibility of the data acquisition system in the operating room. Results indicate that SurfaceMI performed better in many cases than point-based (PBR) and iterative closest point (ICP) methods for registration of textured point clouds. Mean target registration error (TRE) for simulated deep tissue targets in a phantom were 1.0 +/- 0.2, 2.0 +/- 0.3, and 1.2 +/- 0.3 mm for PBR, ICP, and SurfaceMI, respectively. With regard to in vivo registration, the mean TRE of vessel contour points for each framework was 1.9 +/- 1.0, 0.9 +/- 0.6, and 1.3 +/- 0.5 for PBR, ICP, and SurfaceMI, respectively. The methods discussed in this paper in conjunction with the quantitative data provide impetus for using LRS technology within the model-updated image-guided surgery framework.
As image guided surgical procedures become increasingly diverse, there will be more scenarios where point-based fiducials cannot be accurately localized for registration and rigid body assumptions no longer hold. As a result, procedures will rely more frequently on anatomical surfaces for the basis of image alignment and will require intraoperative geometric data to measure and compensate for tissue deformation in the organ. In this paper we outline methods for which a laser range scanner may be used to accomplish these tasks intraoperatively. A laser range scanner based on the optical principle of triangulation acquires a dense set of three-dimensional point data in a very rapid, noncontact fashion. Phantom studies were performed to test the ability to link range scan data with traditional modes of image-guided surgery data through localization, registration, and tracking in physical space. The experiments demonstrate that the scanner is capable of localizing point-based fiducials to within 0.2 mm and capable of achieving point and surface based registrations with target registration error of less than 2.0 mm. Tracking points in physical space with the range scanning system yields an error of 1.4 +/- 0.8 mm. Surface deformation studies were performed with the range scanner in order to determine if this device was capable of acquiring enough information for compensation algorithms. In the surface deformation studies, the range scanner was able to detect changes in surface shape due to deformation comparable to those detected by tomographic image studies. Use of the range scanner has been approved for clinical trials, and an initial intraoperative range scan experiment is presented. In all of these studies, the primary source of error in range scan data is deterministically related to the position and orientation of the surface within the scanner's field of view. However, this systematic error can be corrected, allowing the range scanner to provide a rapid, robust method of acquiring anatomical surfaces intraoperatively.
Historically, increased mechanical stiffness during tissue palpation exams has been associated with assessing organ health as well as with detecting the growth of a potentially life-threatening cell mass. As such, techniques to image elasticity parameters (i.e., elastography) have recently become of great interest to scientists. In this work, a new method of elastography will be introduced within the context of mammographic imaging. The elastography method proposed represents a non-rigid iterative image registration algorithm that varies material properties within a finite element model to improve registration. More specifically, regional measures of image similarity are used within an objective function minimization framework to reconstruct elasticity images of tissue stiffness. Numerical simulations illustrate: (1) the encoding of stiffness information within the context of a regional image similarity criterion, (2) the methodology for an iterative elastographic imaging framework and (3) elasticity reconstruction simulations. The real strength in this approach is that images from any modality (e.g., magnetic resonance, computed tomography, ultrasound. etc) that have sufficient anatomically-based intensity heterogeneity and remain consistent from a pre- to a post-deformed state could be used in this paradigm.
This work presents a robust and comprehensive approach for the in vivo automated segmentation and quantitative tissue volume measurement of normal brain composition from multispectral magnetic resonance imaging (MRI) data. Statistical pattern recognition methods based on a finite mixture model are used to partition the intracranial volume into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) spaces. A masking algorithm initially extracts the brain volume from surrounding extrameningeal tissue. Radio frequency (RF) field inhomogeneity effects in the images are then removed using a recursive method that adapts to the intrinsic local tissue contrast. Our technique supports heterogeneous data with multispectral MR images of different contrast and intensity weighting acquired at varying spatial resolution and orientation. The proposed image segmentation methods have been tested using multispectral T1-, proton density-, and T2-weighted MRI data from young and aged non-human primates as well as from human subjects.