We are investigating three-dimensional (3D) to two-dimensional (2D) registration methods for computed tomography (CT) and dual-energy digital radiography (DR) for the detection of coronary artery calcification. CT is an established tool for the diagnosis of coronary artery diseases (CADs). Dual-energy digital radiography could be a cost-effective alternative for screening coronary artery calcification. In order to utilize CT as the "gold standard" to evaluate the ability of DR images for the detection and localization of calcium, we developed an automatic intensity-based 3D-to-2D registration method for 3D CT volumes and 2D DR images. To generate digital rendering radiographs (DRR) from the CT volumes, we developed three projection methods, i.e. Gaussian-weighted projection, threshold-based projection, and average-based projection. We tested normalized cross correlation (NCC) and normalized mutual information (NMI) as similarity measurement. We used the Downhill Simplex method as the search strategy. Simulated projection images from CT were fused with the corresponding DR images to evaluate the localization of cardiac calcification. The registration method was evaluated by digital phantoms, physical phantoms, and clinical data sets. The results from the digital phantoms show that the success rate is 100% with mean errors of less 0.8 mm and 0.2 degree for both NCC and NMI. The registration accuracy of the physical phantoms is 0.34 ± 0.27 mm. Color overlay and 3D visualization of the clinical data show that the two images are registered well. This is consistent with the improvement of the NMI values from 0.20 ± 0.03 to 0.25 ± 0.03 after registration. The automatic 3D-to-2D registration method is accurate and robust and may provide a useful tool to evaluate the dual-energy DR images for the detection of coronary artery calcification.
With a dedicated breast CT system using a quasi-monochromatic x-ray source and flat-panel digital detector, the 2D and 3D scatter to primary ratios (SPR) of various geometric phantoms having different densities were characterized in detail. Projections were acquired using geometric and anthropomorphic breast phantoms. Each phantom was filled with 700ml of 5 different water-methanol concentrations to simulate effective boundary densities of breast compositions from 100% glandular (1.0g/cm(3)) to 100% fat (0.79g/cm(3)). Projections were acquired with and without a beam stop array. For each projection, 2D scatter was determined by cubic spline interpolating the values behind the shadow of each beam stop through the object. Scatter-corrected projections were obtained by subtracting the scatter, and the 2D SPRs were obtained as a ratio of the scatter to scatter-corrected projections. Additionally the (un)corrected data were individually iteratively reconstructed. The (un)corrected 3D volumes were subsequently subtracted, and the 3D SPRs obtained from the ratio of the scatter volume-to-scatter-corrected (or primary) volume. Results show that the 2D SPR values peak in the center of the volumes, and were overall highest for the simulated 100% glandular composition. Consequently, scatter corrected reconstructions have visibly reduced cupping regardless of the phantom geometry, as well as more accurate linear attenuation coefficients. The corresponding 3D SPRs have increased central density, which reduces radially. Not surprisingly, for both 2D and 3D SPRs there was a dependency on both phantom geometry and object density on the measured SPR values, with geometry dominating for 3D SPRs. Overall, these results indicate the need for scatter correction given different geometries and breast densities that will be encountered with 3D cone beam breast CT.
Recent studies have shown an increase in the occurrence of deformational plagiocephaly and brachycephaly in children. This increase has coincided with the "Back to Sleep" campaign that was introduced to reduce the risk of Sudden Infant Death Syndrome (SIDS). However, there has yet to be an objective quantification of the degree of severity for these two conditions. Most diagnoses are done on subjective factors such as patient history and physician examination. The existence of an objective quantification would help research in areas of diagnosis and intervention measures, as well as provide a tool for finding correlation between the shape severity and cognitive outcome. This paper describes a new shape severity quantification and localization method for deformational plagiocephaly and brachycephaly. Our results show that there is a positive correlation between the new shape severity measure and the scores entered by a human expert.
We present a 3D non-rigid registration algorithm for the potential use in combining PET/CT and transrectal ultrasound (TRUS) images for targeted prostate biopsy. Our registration is a hybrid approach that simultaneously optimizes the similarities from point-based registration and volume matching methods. The 3D registration is obtained by minimizing the distances of corresponding points at the surface and within the prostate and by maximizing the overlap ratio of the bladder neck on both images. The hybrid approach not only capture deformation at the prostate surface and internal landmarks but also the deformation at the bladder neck regions. The registration uses a soft assignment and deterministic annealing process. The correspondences are iteratively established in a fuzzy-to-deterministic approach. B-splines are used to generate a smooth non-rigid spatial transformation. In this study, we tested our registration with pre- and post-biopsy TRUS images of the same patients. Registration accuracy is evaluated using manual defined anatomic landmarks, i.e. calcification. The root-mean-squared (RMS) of the difference image between the reference and floating images was decreased by 62.6±9.1% after registration. The mean target registration error (TRE) was 0.88±0.16 mm, i.e. less than 3 voxels with a voxel size of 0.38×0.38×0.38 mm(3) for all five patients. The experimental results demonstrate the robustness and accuracy of the 3D non-rigid registration algorithm.
Properly constructed stereoscopic images are aligned vertically on the display screen, so on-screen binocular disparities are strictly horizontal. If the viewer's inter-ocular axis is also horizontal, he/she makes horizontal vergence eye movements to fuse the stereoscopic image. However, if the viewer's head is rolled to the side, the on-screen disparities now have horizontal and vertical components at the eyes. Thus, the viewer must make horizontal and vertical vergence movements to binocularly fuse the two images. Vertical vergence movements occur naturally, but they are usually quite small. Much larger movements are required when viewing stereoscopic images with the head rotated to the side. We asked whether the vertical vergence eye movements required to fuse stereoscopic images when the head is rolled cause visual discomfort. We also asked whether the ability to see stereoscopic depth is compromised with head roll. To answer these questions, we conducted behavioral experiments in which we simulated head roll by rotating the stereo display clockwise or counter-clockwise while the viewer's head remained upright relative to gravity. While viewing the stimulus, subjects performed a psychophysical task. Visual discomfort increased significantly with the amount of stimulus roll and with the magnitude of on-screen horizontal disparity. The ability to perceive stereoscopic depth also declined with increasing roll and on-screen disparity. The magnitude of both effects was proportional to the magnitude of the induced vertical disparity. We conclude that head roll is a significant cause of viewer discomfort and that it also adversely affects the perception of depth from stereoscopic displays.
Early detection of prostate cancer is critical in maximizing the probability of successful treatment. Current systematic biopsy approach takes 12 or more randomly distributed core tissue samples within the prostate and can have a high potential, especially with early disease, for a false negative diagnosis. The purpose of this study is to determine the accuracy of a 3D ultrasound-guided biopsy system. Testing was conducted on prostate phantoms created from an agar mixture which had embedded markers. The phantoms were scanned and the 3D ultrasound system was used to direct the biopsy. Each phantom was analyzed with a CT scan to obtain needle deflection measurements. The deflection experienced throughout the biopsy process was dependent on the depth of the biopsy target. The results for markers at a depth of less than 20 mm, 20-30 mm, and greater than 30 mm were 3.3 mm, 4.7 mm, and 6.2 mm, respectively. This measurement encapsulates the entire biopsy process, from the scanning of the phantom to the firing of the biopsy needle. Increased depth of the biopsy target caused a greater deflection from the intended path in most cases which was due to an angular incidence of the biopsy needle. Although some deflection was present, this system exhibits a clear advantage in the targeted biopsy of prostate cancer and has the potential to reduce the number of false negative biopsies for large lesions.
We developed a three-dimensional (3D) segmentation method for transrectal ultrasound (TRUS) images, which is based on longitudinal image registration and machine learning. Using longitudinal images of each individual patient, we register previously acquired images to the new images of the same subject. Three orthogonal Gabor filter banks were used to extract texture features from each registered image. Patient-specific Gabor features from the registered images are used to train kernel support vector machines (KSVMs) and then to segment the newly acquired prostate image. The segmentation method was tested in TRUS data from five patients. The average surface distance between our and manual segmentation is 1.18 ± 0.31 mm, indicating that our automatic segmentation method based on longitudinal image registration is feasible for segmenting the prostate in TRUS images.
The goal of this study was to characterize the image quality of our dedicated, quasi-monochromatic spectrum, cone beam breast imaging system under scatter corrected and non-scatter corrected conditions for a variety of breast compositions. CT projections were acquired of a breast phantom containing two concentric sets of acrylic spheres that varied in size (1-8mm) based on their polar position. The breast phantom was filled with 3 different concentrations of methanol and water, simulating a range of breast densities (0.79-1.0g/cc); acrylic yarn was sometimes included to simulate connective tissue of a breast. For each phantom condition, 2D scatter was measured for all projection angles. Scatter-corrected and uncorrected projections were then reconstructed with an iterative ordered subsets convex algorithm. Reconstructed image quality was characterized using SNR and contrast analysis, and followed by a human observer detection task for the spheres in the different concentric rings. Results show that scatter correction effectively reduces the cupping artifact and improves image contrast and SNR. Results from the observer study indicate that there was no statistical difference in the number or sizes of lesions observed in the scatter versus non-scatter corrected images for all densities. Nonetheless, applying scatter correction for differing breast conditions improves overall image quality.
We are developing a molecular image-directed, 3D ultrasound-guided, targeted biopsy system for improved detection of prostate cancer. In this paper, we propose an automatic 3D segmentation method for transrectal ultrasound (TRUS) images, which is based on multi-atlas registration and statistical texture prior. The atlas database includes registered TRUS images from previous patients and their segmented prostate surfaces. Three orthogonal Gabor filter banks are used to extract texture features from each image in the database. Patient-specific Gabor features from the atlas database are used to train kernel support vector machines (KSVMs) and then to segment the prostate image from a new patient. The segmentation method was tested in TRUS data from 5 patients. The average surface distance between our method and manual segmentation is 1.61 ± 0.35 mm, indicating that the atlas-based automatic segmentation method works well and could be used for 3D ultrasound-guided prostate biopsy.
Brown adipose tissue (BAT) plays an important role in whole body metabolism and could potentially mediate weight gain and insulin sensitivity. Although some imaging techniques allow BAT detection, there are currently no viable methods for continuous acquisition of BAT energy expenditure. We present a non-invasive technique for long term monitoring of BAT metabolism using microwave radiometry.
A multilayer 3D computational model was created in HFSS™ with 1.5 mm skin, 3-10 mm subcutaneous fat, 200 mm muscle and a BAT region (2-6 cm(3)) located between fat and muscle. Based on this model, a log-spiral antenna was designed and optimized to maximize reception of thermal emissions from the target (BAT). The power absorption patterns calculated in HFSS™ were combined with simulated thermal distributions computed in COMSOL® to predict radiometric signal measured from an ultra-low-noise microwave radiometer. The power received by the antenna was characterized as a function of different levels of BAT metabolism under cold and noradrenergic stimulation.
The optimized frequency band was 1.5-2.2 GHz, with averaged antenna efficiency of 19%. The simulated power received by the radiometric antenna increased 2-9 mdBm (noradrenergic stimulus) and 4-15 mdBm (cold stimulus) corresponding to increased 15-fold BAT metabolism.
Results demonstrated the ability to detect thermal radiation from small volumes (2-6 cm(3)) of BAT located up to 12 mm deep and to monitor small changes (0.5 °C) in BAT metabolism. As such, the developed miniature radiometric antenna sensor appears suitable for non-invasive long term monitoring of BAT metabolism.
Spinal needle injections are technically demanding procedures. The use of ultrasound image guidance without prior CT and MR imagery promises to improve the efficacy and safety of these procedures in an affordable manner.
We propose to create a statistical shape model of the lumbar spine and warp this atlas to patient-specific ultrasound images during the needle placement procedure. From CT image volumes of 35 patients, statistical shape model of the L3 vertebra is built, including mean shape and main modes of variation. This shape model is registered to the ultrasound data by simultaneously optimizing the parameters of the model and its relative pose. Ground-truth data was established by printing 3D anatomical models of 3 patients using a rapid prototyping. CT and ultrasound data of these models were registered using fiducial markers.
Pairwise registration of the statistical shape model and 3D ultrasound images led to a mean target registration error of 3.4 mm, while 81% of all cases yielded clinically acceptable accuracy below the 3.5 mm threshold.
The current definitive diagnosis of prostate cancer is transrectal ultrasound (TRUS) guided biopsy. However, the current procedure is limited by using 2D biopsy tools to target 3D biopsy locations. This paper presents a new method for automatic segmentation of the prostate in three-dimensional transrectal ultrasound images, by extracting texture features and by statistically matching geometrical shape of the prostate. A set of Wavelet-based support vector machines (W-SVMs) are located and trained at different regions of the prostate surface. The WSVMs capture texture priors of ultrasound images for classification of the prostate and non-prostate tissues in different zones around the prostate boundary. In the segmentation procedure, these W-SVMs are trained in three sagittal, coronal, and transverse planes. The pre-trained W-SVMs are employed to tentatively label each voxel around the surface of the model as a prostate or non-prostate voxel by the texture matching. The labeled voxels in three planes after post-processing is overlaid on a prostate probability model. The probability prostate model is created using 10 segmented prostate data. Consequently, each voxel has four labels: sagittal, coronal, and transverse planes and one probability label. By defining a weight function for each labeling in each region, each voxel is labeled as a prostate or non-prostate voxel. Experimental results by using real patient data show the good performance of the proposed model in segmenting the prostate from ultrasound images.
Statistical imaging atlases allow for integration of information from multiple patient studies collected across different image scales and modalities, such as multi-parametric (MP) MRI and histology, providing population statistics regarding a specific pathology within a single canonical representation. Such atlases are particularly valuable in the identification and validation of meaningful imaging signatures for disease characterization in vivo within a population. Despite the high incidence of prostate cancer, an imaging atlas focused on different anatomic structures of the prostate, i.e. an anatomic atlas, has yet to be constructed. In this work we introduce a novel framework for MRI atlas construction that uses an iterative, anatomically constrained registration (AnCoR) scheme to enable the proper alignment of the prostate (Pr) and central gland (CG) boundaries. Our current implementation uses endorectal, 1.5T or 3T, T2-weighted MRI from 51 patients with biopsy confirmed cancer; however, the prostate atlas is seamlessly extensible to include additional MRI parameters. In our cohort, radical prostatectomy is performed following MP-MR image acquisition; thus ground truth annotations for prostate cancer are available from the histological specimens. Once mapped onto MP-MRI through elastic registration of histological slices to corresponding T2-w MRI slices, the annotations are utilized by the AnCoR framework to characterize the 3D statistical distribution of cancer per anatomic structure. Such distributions are useful for guiding biopsies toward regions of higher cancer likelihood and understanding imaging profiles for disease extent in vivo. We evaluate our approach via the Dice similarity coefficient (DSC) for different anatomic structures (delineated by expert radiologists): Pr, CG and peripheral zone (PZ). The AnCoR-based atlas had a CG DSC of 90.36%, and Pr DSC of 89.37%. Moreover, we evaluated the deviation of anatomic landmarks, the urethra and veromontanum, and found 3.64 mm and respectively 4.31 mm. Alternative strategies that use only the T2-w MRI or the prostate surface to drive the registration were implemented as comparative approaches. The AnCoR framework outperformed the alternative strategies by providing the lowest landmark deviations.
We have developed a dose-tracking system (DTS) that calculates the radiation dose to the patient's skin in real-time by acquiring exposure parameters and imaging-system-geometry from the digital bus on a Toshiba Infinix C-arm unit. The cumulative dose values are then displayed as a color map on an OpenGL-based 3D graphic of the patient for immediate feedback to the interventionalist. Determination of those elements on the surface of the patient 3D-graphic that intersect the beam and calculation of the dose for these elements in real time demands fast computation. Reducing the size of the elements results in more computation load on the computer processor and therefore a tradeoff occurs between the resolution of the patient graphic and the real-time performance of the DTS. The speed of the DTS for calculating dose to the skin is limited by the central processing unit (CPU) and can be improved by using the parallel processing power of a graphics processing unit (GPU). Here, we compare the performance speed of GPU-based DTS software to that of the current CPU-based software as a function of the resolution of the patient graphics. Results show a tremendous improvement in speed using the GPU. While an increase in the spatial resolution of the patient graphics resulted in slowing down the computational speed of the DTS on the CPU, the speed of the GPU-based DTS was hardly affected. This GPU-based DTS can be a powerful tool for providing accurate, real-time feedback about patient skin-dose to physicians while performing interventional procedures.
We have applied image analysis methods in the assessment of human kidney perfusion based on 3D dynamic contrast-enhanced (DCE) MRI data. This approach consists of 3D non-rigid image registration of the kidneys and fuzzy C-mean classification of kidney tissues. The proposed registration method reduced motion artifacts in the dynamic images and improved the analysis of kidney compartments (cortex, medulla, and cavities). The dynamic intensity curves show the successive transition of the contrast agent through kidney compartments. The proposed method for motion correction and kidney compartment classification may be used to improve the validity and usefulness of further model-based pharmacokinetic analysis of kidney function.
Multi-parametric MRI is emerging as a promising method for prostate cancer diagnosis. prognosis and treatment planning. However, the localization of in-vivo detected lesions and pathologic sites of cancer remains a significant challenge. To overcome this limitation we have developed and tested a system for co-registration of in-vivo MRI, ex-vivo MRI and histology.
Three men diagnosed with localized prostate cancer (ages 54-72, PSA levels 5.1-7.7 ng/ml) were prospectively enrolled in this study. All patients underwent 3T multi-parametric MRI that included T2W, DCE-MRI, and DWI prior to robotic-assisted prostatectomy. Ex-vivo multi-parametric MRI was performed on fresh prostate specimen. Excised prostates were then sliced at regular intervals and photographed both before and after fixation. Slices were perpendicular to the main axis of the posterior capsule, i.e., along the direction of the rectal wall. Guided by the location of the urethra, 2D digital images were assembled into 3D models. Cancer foci, extra-capsular extensions and zonal margins were delineated by the pathologist and included in 3D histology data. A locally-developed software was applied to register in-vivo, ex-vivo and histology using an over-determined set of anatomical landmarks placed in anterior fibro-muscular stroma, central. transition and peripheral zones. The mean root square distance across corresponding control points was used to assess co-registration error.
Two specimens were pT3a and one pT2b (negative margin) at pathology. The software successfully fused in-vivo MRI. ex-vivo MRI fresh specimen and histology using appropriate (rigid and affine) transformation models with mean square error of 1.59 mm. Coregistration accuracy was confirmed by multi-modality viewing using operator-guided variable transparency.
The method enables successful co-registration of pre-operative MRI, ex-vivo MRI and pathology and it provides initial evidence of feasibility of MRI-guided surgical planning.
Numerical estimation of the size of the kidney is useful in evaluating conditions of the kidney, especially, when serial MR imaging is performed to evaluate the kidney function. This paper presents a new method for automatic segmentation of the kidney in three-dimensional (3D) MR images, by extracting texture features and statistical matching of geometrical shape of the kidney. A set of Wavelet-based support vector machines (W-SVMs) is trained on the MR images. The W-SVMs capture texture priors of MRI for classification of the kidney and non-kidney tissues in different zones around the kidney boundary. In the segmentation procedure, these W-SVMs are trained to tentatively label each voxel around the kidney model as a kidney or non-kidney voxel by texture matching. A probability kidney model is created using 10 segmented MRI data. The model is initially localized based on the intensity profiles in three directions. The weight functions are defined for each labeled voxel for each Wavelet-based, intensity-based, and model-based label. Consequently, each voxel has three labels and three weights for the Wavelet feature, intensity, and probability model. Using a 3D edge detection method, the model is re-localized and the segmented kidney is modified based on a region growing method in the model region. The probability model is re-localized based on the results and this loop continues until the segmentation converges. Experimental results with mouse MRI data show the good performance of the proposed method in segmenting the kidney in MR images.
Single molecule tracking in three dimensions (3D) in a live cell environment promises to reveal important new insights into cell biological mechanisms. However, classical microscopy techniques suffer from poor depth discrimination which severely limits single molecule tracking in 3D with high temporal and spatial resolution. We introduced a novel imaging modality, multifocal plane microscopy (MUM) for the study of subcellular dynamics in 3D. We have shown that MUM provides a powerful approach with which single molecules can be tracked in 3D in live cells. MUM allows for the simultaneous imaging at different focal planes, thereby ensuring that trajectories can be imaged continuously at high temporal resolution. A critical requirement for 3D single molecule tracking as well as localization based 3D super-resolution imaging is high 3D localization accuracy. MUM overcomes the depth discrimination problem of classical microscopy based approaches and supports high accuracy 3D localization of singe molecule/particles. In this way, MUM opens the way for high precision 3D single molecule tracking and 3D super-resolution imaging within a live cell environment. We have used MUM to reveal complex intracellular pathways that could not be imaged with classical approaches. In particular we have tracked quantum dot labeled antibody molecules in the exo/endocytic pathway from the cell interior to the plasma membrane at the single molecule level. Here, we present a brief review of these results.
Systematic transrectal ultrasound (TRUS)-guided biopsy is the standard method for a definitive diagnosis of prostate cancer. However, this biopsy approach uses two-dimensional (2D) ultrasound images to guide biopsy and can miss up to 30% of prostate cancers. We are developing a molecular image-directed, three-dimensional (3D) ultrasound image-guided biopsy system for improved detection of prostate cancer. The system consists of a 3D mechanical localization system and software workstation for image segmentation, registration, and biopsy planning. In order to plan biopsy in a 3D prostate, we developed an automatic segmentation method based wavelet transform. In order to incorporate PET/CT images into ultrasound-guided biopsy, we developed image registration methods to fuse TRUS and PET/CT images. The segmentation method was tested in ten patients with a DICE overlap ratio of 92.4% ± 1.1 %. The registration method has been tested in phantoms. The biopsy system was tested in prostate phantoms and 3D ultrasound images were acquired from two human patients. We are integrating the system for PET/CT directed, 3D ultrasound-guided, targeted biopsy in human patients.
4D image-guided radiation therapy (IGRT) for free-breathing lungs is challenging due to the complicated respiratory dynamics. Effective modeling of respiratory motion is crucial to account for the motion affects on the dose to tumors. We propose a shape-correlated statistical model on dense image deformations for patient-specic respiratory motion estimation in 4D lung IGRT. Using the shape deformations of the high-contrast lungs as the surrogate, the statistical model trained from the planning CTs can be used to predict the image deformation during delivery verication time, with the assumption that the respiratory motion at both times are similar for the same patient. Dense image deformation fields obtained by diffeomorphic image registrations characterize the respiratory motion within one breathing cycle. A point-based particle optimization algorithm is used to obtain the shape models of lungs with group-wise surface correspondences. Canonical correlation analysis (CCA) is adopted in training to maximize the linear correlation between the shape variations of the lungs and the corresponding dense image deformations. Both intra- and inter-session CT studies are carried out on a small group of lung cancer patients and evaluated in terms of the tumor location accuracies. The results suggest potential applications using the proposed method.
Bilateral filtration has proven an effective tool for denoising CT data. The classic filter utilizes Gaussian domain and range weighting functions in 2D. More recently, other distributions have yielded more accurate results in specific applications, and the bilateral filtration framework has been extended to higher dimensions. In this study, brute-force optimization is employed to evaluate the use of several alternative distributions for both domain and range weighting: Andrew's Sine Wave, El Fallah Ford, Gaussian, Flat, Lorentzian, Huber's Minimax, Tukey's Bi-weight, and Cosine. Two variations on the classic bilateral filter which use median filtration to reduce bias in range weights are also investigated: median-centric and hybrid bilateral filtration. Using the 4D MOBY mouse phantom reconstructed with noise (stdev. ~ 65 HU), hybrid bilateral filtration, a combination of the classic and median-centric filters, with Flat domain and range weighting is shown to provide optimal denoising results (PSNRs: 31.69, classic; 31.58 median-centric; 32.25, hybrid). To validate these phantom studies, the optimal filters are also applied to in vivo, 4D cardiac micro-CT data acquired in the mouse. In a constant region of the left ventricle, hybrid bilateral filtration with Flat domain and range weighting is shown to provide optimal smoothing (stdev: original, 72.2 HU; classic, 20.3 HU; median-centric, 24.1 HU; hybrid, 15.9 HU). While the optimal results were obtained using 4D filtration, the 3D hybrid filter is ultimately recommended for denoising 4D cardiac micro-CT data because it is more computationally tractable and less prone to artifacts (MOBY PSNR: 32.05; left ventricle stdev: 20.5 HU).
Laser removal of dental hard tissue can be combined with optical, spectral or acoustic feedback systems to selectively ablate dental caries and restorative materials. Near-infrared (NIR) imaging has considerable potential for the optical discrimination of sound and demineralized tissue. The objective of this study was to test the hypothesis that two-dimensional NIR images of demineralized tooth surfaces can be used to guide CO(2) laser ablation for the selective removal of artificial caries lesions. Highly patterned artificial lesions were produced by submerging 5 × 5 mm(2) bovine enamel samples in demineralized solution for a 9-day period while sound areas were protected with acid resistant varnish. NIR imaging and polarization sensitive optical coherence tomography (PS-OCT) were used to acquire depth-resolved images at a wavelength of 1310-nm. An imaging processing module was developed to analyze the NIR images and to generate optical maps. The optical maps were used to control a CO(2) laser for the selective removal of the lesions at a uniform depth. This experiment showed that the patterned artificial lesions were removed selectively using the optical maps with minimal damage to sound enamel areas. Post-ablation NIR and PS-OCT imaging confirmed that demineralized areas were removed while sound enamel was conserved. This study successfully demonstrated that near-IR imaging can be integrated with a CO(2) laser ablation system for the selective removal of dental caries.
The level set method is a popular technique used in medical image segmentation; however, the numerics involved make its use cumbersome. This paper proposes an approximate level set scheme that removes much of the computational burden while maintaining accuracy. Abandoning a floating point representation for the signed distance function, we use integral values to represent the signed distance function. For the cases of 2D and 3D, we detail rules governing the evolution and maintenance of these three regions. Arbitrary energies can be implemented in the framework. This scheme has several desirable properties: computations are only performed along the zero level set; the approximate distance function requires only a few simple integer comparisons for maintenance; smoothness regularization involves only a few integer calculations and may be handled apart from the energy itself; the zero level set is represented exactly removing the need for interpolation off the interface; and evolutions proceed on the order of milliseconds per iteration on conventional uniprocessor workstations. To highlight its accuracy, flexibility and speed, we demonstrate the technique on intensity-based segmentations under various statistical metrics. Results for 3D imagery show the technique is fast even for image volumes.
Diffusion weighted imaging (DWI) is widely used to study microstructural characteristics of the brain. High angular resolution diffusion imaging (HARDI) samples diffusivity at a large number of spherical angles, to better resolve neural fibers that mix or cross. Here, we implemented a framework for advanced mathematical analysis of mouse 5-shell HARDI (b=1000, 3000, 4000, 8000, 12000 s/mm(2)), also known as hybrid diffusion imaging (HYDI). Using q-ball imaging (QBI) at ultra-high field strength (7 Tesla), we computed diffusion and fiber orientation distribution functions (dODF, fODF) to better detect crossing fibers. We also computed a quantitative anisotropy (QA) index, and deterministic tractography, from the peak orientation of the fODFs. We found that the signal to noise ratio (SNR) of the QA was significantly higher in single and multi-shell reconstructed data at the lower b-values (b=1000, 3000, 4000 s/mm(2)) than at higher b-values (b=8000, 12000 s/mm(2)); the b=1000 s/mm(2) shell increased the SNR of the QA in all multi-shell reconstructions, but when used alone or in <5-shell reconstruction, it led to higher angular error for the major fibers, compared to 5-shell HYDI. Multi-shell data reconstructed major fibers with less error than single-shell data, and was most successful at reducing the angular error when the lowest shell was excluded (b=1000 s/mm(2)). Overall, high-resolution connectivity mapping with 7T HYDI offers great potential for understanding unresolved changes in mouse models of brain disease.
Ventral hernias (VHs) are abnormal openings in the anterior abdominal wall that are common side effects of surgical intervention. Repair of VHs is the most commonly performed procedure by general surgeons worldwide, but VH repair outcomes are not particularly encouraging (with recurrence rates up to 43%). A variety of open and laparoscopic techniques are available for hernia repair, and the specific technique used is ultimately driven by surgeon preference and experience. Despite routine acquisition of computed tomography (CT) for VH patients, little quantitative information is available on which to guide selection of a particular approach and/or optimize patient-specific treatment. From anecdotal interviews, the success of VH repair procedures correlates with hernia size, location, and involvement of secondary structures. Herein, we propose an image labeling protocol to segment the anterior abdominal area to provide a geometric basis with which to derive biomarkers and evaluate treatment efficacy. Based on routine clinical CT data, we are able to identify inner and outer surfaces of the abdominal walls and the herniated volume. This is the first formal presentation of a protocol to quantify these structures on abdominal CT. The intra- and inter rater reproducibilities of this protocol are evaluated on 4 patients with suspected VH (3 patients were ultimately diagnosed with VH while 1 was not). Mean surfaces distances of less than 2mm were achieved for all structures.
The treatment of ventral hernias (VH) has been a challenging problem for medical care. Repair of these hernias is fraught with failure; recurrence rates ranging from 24-43% have been reported, even with the use of biocompatible mesh. Currently, computed tomography (CT) is used to guide intervention through expert, but qualitative, clinical judgments; notably, quantitative metrics based on image-processing are not used. We propose that image segmentation methods to capture the three-dimensional structure of the abdominal wall and its abnormalities will provide a foundation on which to measure geometric properties of hernias and surrounding tissues and, therefore, to optimize intervention. To date, automated segmentation algorithms have not been presented to quantify the abdominal wall and potential hernias. In this pilot study with four clinically acquired CT scans on post-operative patients, we demonstrate a novel approach to geometric classification of the abdominal wall and essential abdominal features (including bony landmarks and skin surfaces). Our approach uses a hierarchical design in which the abdominal wall is isolated in the context of the skin and bony structures using level set methods. All segmentation results were quantitatively validated with surface errors based on manually labeled ground truth. Mean surface errors for the outer surface of the abdominal wall was less than 2mm. This approach establishes a baseline for characterizing the abdominal wall for improving VH care.
Immersive virtual environments use a stereoscopic head-mounted display and data glove to create high fidelity virtual experiences in which users can interact with three-dimensional models and perceive relationships at their true scale. This stands in stark contrast to traditional PACS-based infrastructure in which images are viewed as stacks of two-dimensional slices, or, at best, disembodied renderings. Although there has substantial innovation in immersive virtual environments for entertainment and consumer media, these technologies have not been widely applied in clinical applications. Here, we consider potential applications of immersive virtual environments for ventral hernia patients with abdominal computed tomography imaging data. Nearly a half million ventral hernias occur in the United States each year, and hernia repair is the most commonly performed general surgery operation worldwide. A significant problem in these conditions is communicating the urgency, degree of severity, and impact of a hernia (and potential repair) on patient quality of life. Hernias are defined by ruptures in the abdominal wall (i.e., the absence of healthy tissues) rather than a growth (e.g., cancer); therefore, understanding a hernia necessitates understanding the entire abdomen. Our environment allows surgeons and patients to view body scans at scale and interact with these virtual models using a data glove. This visualization and interaction allows users to perceive the relationship between physical structures and medical imaging data. The system provides close integration of PACS-based CT data with immersive virtual environments and creates opportunities to study and optimize interfaces for patient communication, operative planning, and medical education.
Radiofrequency ablation is a promising procedure for treating atrial fibrillation (AF) that relies on accurate lesion delivery in the left atrial (LA) wall for success. Late Gadolinium Enhancement MRI (LGE MRI) at three months post-ablation has proven effective for noninvasive assessment of the location and extent of scar formation, which are important factors for predicting patient outcome and planning of redo ablation procedures. We have developed an algorithm for automatic classification in LGE MRI of scar tissue in the LA wall and have evaluated accuracy and consistency compared to manual scar classifications by expert observers. Our approach clusters voxels based on normalized intensity and was chosen through a systematic comparison of the performance of multivariate clustering on many combinations of image texture. Algorithm performance was determined by overlap with ground truth, using multiple overlap measures, and the accuracy of the estimation of the total amount of scar in the LA. Ground truth was determined using the STAPLE algorithm, which produces a probabilistic estimate of the true scar classification from multiple expert manual segmentations. Evaluation of the ground truth data set was based on both inter- and intra-observer agreement, with variation among expert classifiers indicating the difficulty of scar classification for a given a dataset. Our proposed automatic scar classification algorithm performs well for both scar localization and estimation of scar volume: for ground truth datasets considered easy, variability from the ground truth was low; for those considered difficult, variability from ground truth was on par with the variability across experts.
Over 50% of HIV+ individuals show significant impairment in psychomotor functioning, processing speed, working memory and attention [1, 2]. Patients receiving combination antiretroviral therapy may still have subcortical atrophy, but the profile of HIV-associated brain changes is poorly understood. With parametric surface-based shape analyses, we mapped the 3D profile of subcortical morphometry in 63 elderly HIV+ subjects (4 female; age=65.35 ± 2.21) and 31 uninfected elderly controls (2 female; age=64.68 ± 4.57) scanned with MRI as part of a San Francisco Bay Area study of elderly people with HIV. We also investigated whether morphometry was associated with nadir CD4+ (T-cell) counts, viral load and illness duration among HIV+ participants. FreeSurfer was used to segment the thalamus, caudate, putamen, pallidum, hippocampus, amygdala, accumbens, brainstem, callosum and ventricles from brain MRI scans. To study subcortical shape, we analyzed: (1) the Jacobian determinant (JD) indexed over structures' surface coordinates and (2) radial distances (RD) of structure surfaces from a medial curve. A JD less than 1 reflects regional tissue atrophy and greater than 1 reflects expansion. The volumes of several subcortical regions were found to be associated with HIV status. No regional volumes showed detectable associations with CD4 counts, viral load or illness duration. The shapes of numerous subcortical regions were significantly linked to HIV status, detectability of viral RNA and illness duration. Our results show subcortical brain differences in HIV+ subjects in both shape and volumetric domains.
Changes in myocardial function signatures such as wall motion and thickening are typically computed separately from myocardial perfusion SPECT (MPS) stress and rest studies to assess for stress-induced function abnormalities. The standard approach may suffer from the variability in contour placements and image orientation when subtle changes between stress and rest scans in motion and thickening are being evaluated. We have developed a new measure of regional change of function signature (motion and thickening) computed directly from registered stress and rest gated MPS data. In our novel approach, endocardial surfaces at the end-diastolic and end-systolic frames for stress and rest studies were registered by matching ventricular surfaces. Furthermore, we propose a new global registration method based on finding the optimal rotation for myocardial best ellipsoid fit to minimize the indexing disparities between two surfaces between stress and rest studies. Myocardial stress-rest function changes were computed and normal limits of change were determined as the mean and standard deviation of the training set for each polar sample. Normal limits were utilized to quantify the stress-rest function change for each polar map sample and the accumulated quantified function signature values were used for abnormality assessments in territorial regions. To evaluate the effectiveness of our novel method, we examined the agreements of our results against visual scores for motion change on vessel territorial regions obtained by human experts on a test group with 623 cases and were able to show that our detection method has a improved sensitivity on per vessel territory basis, compared to those obtained by human experts utilizing gated MPS data.
Neuron optical excitations are important for brain-circuitry explorations and sensory-neuron-stimulation applications. To optimize the stimulation, we identify neuron mid-IR absorption peaks in this study and discuss their meanings and delivery methods of mid-IR photons.
Swallowing accelerometry is a promising tool for non-invasive assessment of swallowing difficulties. A recent contribution showed that swallowing accelerometry signals for healthy swallows and swallows indicating laryngeal penetration or tracheal aspiration have different time-frequency structures, which may be problematic for compressive sensing schemes based on time-frequency dictionaries. In this paper, we examined the effects of different swallows on the accuracy of a compressive sensing scheme based on modulated discrete prolate spheroidal sequences. We utilized tri-axial swallowing accelerometry signals recorded from four patients during routinely schedule videofluoroscopy exams. In particular, we considered 77 swallows approximately equally distributed between healthy swallows and swallows presenting with some penetration/aspiration. Our results indicated that the swallow type does not affect the accuracy of a considered compressive sensing scheme. Also, the results confirmed previous findings that each individual axis contributes different information. Our findings are important for further developments of a device which is to be used for long-term monitoring of swallowing difficulties.
Prolonged use of conventional stereo displays causes viewer discomfort and fatigue because of the vergence-accommodation conflict. We used a novel volumetric display to examine how viewing distance, the sign of the vergence-accommodation conflict, and the temporal properties of the conflict affect discomfort and fatigue. In the first experiment, we presented a fixed conflict at short, medium, and long viewing distances. We compared subjects' symptoms in that condition and one in which there was no conflict. We observed more discomfort and fatigue with a given vergence-accommodation conflict at the longer distances. The second experiment compared symptoms when the conflict had one sign compared to when it had the opposite sign at short, medium, and long distances. We observed greater symptoms with uncrossed disparities at long distances and with crossed disparities at short distances. The third experiment compared symptoms when the conflict changed rapidly as opposed to slowly. We observed more serious symptoms when the conflict changed rapidly. These findings help define comfortable viewing conditions for stereo displays.
Prolonged use of conventional stereo displays causes viewer discomfort and fatigue because of the vergence-accommodation conflict. We used a novel volumetric display to examine how viewing distance and the sign of the vergence-accommodation conflict affect discomfort and fatigue. In the first experiment, we presented a fixed conflict at short, medium, and long viewing distances. We compared subjects' symptoms in that condition and one in which there was no conflict. We observed more discomfort and fatigue with a given vergence-accommodation conflict at the longer distances. The second experiment compared symptoms when the conflict had one sign compared to when it had the opposite sign at short, medium, and long distances. We observed greater symptoms with uncrossed disparities at long distances and with crossed disparities at short distances. These findings help define comfortable viewing conditions for stereo displays.
The vergence-accommodation conflict associated with viewing stereoscopic 3D (S3D) content can cause visual discomfort. Previous studies of vergence and accommodation have shown that the coupling between the two responses is driven by a fast, phasic component. We investigated how the temporal properties of vergence-accommodation conflicts affect discomfort. Using a unique volumetric display, we manipulated the stimulus to vergence and the stimulus to accommodation independently. There were two experimental conditions: 1) natural viewing in which the stimulus to vergence was perfectly correlated with the stimulus to accommodation; and 2) conflict viewing in which the stimulus to vergence varied while the stimulus to accommodation remained constant (thereby mimicking S3D viewing). The stimulus to vergence (and accommodation in natural viewing) varied at one of three temporal frequencies in those conditions. The magnitude of the conflict was the same for all three frequencies. The young adult subjects reported more visual discomfort when vergence changes were faster, particularly in the conflict condition. Thus, the temporal properties of the vergence-accommodation conflict in S3D media affect visual discomfort. The results can help content creators minimize discomfort by making conflict changes sufficiently slow.
The electron-multiplying charge-coupled device (EMCCD) is a popular technology for imaging under extremely low light conditions. It has become widely used, for example, in single molecule microscopy experiments where few photons can be detected from the individual molecules of interest. Despite its important role in low light microscopy, however, little has been done in the way of determining how accurately parameters of interest (e.g., location of a single molecule) can be estimated from an image that it produces. Here, we develop the theory for calculating the Fisher information matrix, and hence the Cramer-Rao lower bound-based limit of the accuracy, for estimating parameters from an EMCCD image. An EMCCD operates by amplifying a weak signal that would otherwise be drowned out by the detector's readout noise as in the case of a conventional charge-coupled device (CCD). The signal amplification is a stochastic electron multiplication process, and is modeled here as a geometrically multiplied branching process. In developing our theory, we also introduce a "noise coefficient" which enables the comparison of the Fisher information of different data models via a scalar quantity. This coefficient importantly allows the selection of the best detector (e.g., EMCCD or CCD), based on factors such as the signal level, and regardless of the specific estimation problem at hand. We apply our theory to the problem of localizing a single molecule, and compare the calculated limits of the localization accuracy with the standard deviations of maximum likelihood location estimates obtained from simulated images of a single molecule.
We report a quantitative evaluation of the clinical accuracy of a MRI-guided robotic prostate biopsy system that has been in use for over five years at the U.S. National Cancer Institute. A two-step rigid volume registration using mutual information between the pre and post needle insertion images was performed. Contour overlays of the prostate before and after registration were used to validate the registration. A total of 20 biopsies from 5 patients were evaluated. The maximum registration error was 2 mm. The mean biopsy target displacement, needle placement error, and biopsy error was 5.4 mm, 2.2 mm, and 5.1 mm respectively. The results show that the pre-planned biopsy target did dislocate during the procedure and therefore causing biopsy errors.
Single molecule fluorescence microscopy is a relatively novel technique that is used, for example, to study the behavior of individual biomolecules in cells. Since a single molecule can move in all three dimensions in a cellular environment, the three dimensional tracking of single molecules can provide valuable insights into cellular processes. It is therefore of importance to know the accuracy with which the location of a single molecule can be determined with a fluorescence microscope. We study this performance limit of a fluorescence microscope from a statistical point of view by deriving the Fisher information matrix for the estimation problem of the location of the single molecule. In this way we obtain a lower bound on the standard deviation of any reasonable (unbiased) estimation method of the location parameters. This lower bound provides a fundamental limit on the accuracy with which a single molecule can be localized using a fluorescence microscope and is given in terms of such quantities as the photon detection rate of the single molecule, the acquisition time, the numerical aperture of the objective lens etc. We also present results that show how factors such as noise sources, detector size and pixelation deteriorate the fundamental limit of the localization accuracy. The present results can be used to evaluate and optimize experimental setups in order to carry out three dimensional single molecule tracking experiments and provide guidelines for experimental design.
Clot elastic modulus (CEM) has recently been shown to correlate with various hemostatic and thrombotic disorders and may be an important diagnostic parameter in cardiovascular diseases. Current methods of CEM measurement lack repeatability and require large sample volume. We present a novel method named resonant acoustic spectroscopy with optical vibrometry (RASOV) that has the potential to assess CEM with higher accuracy and speed, and lower sample volume. To validate RASOV, we measured the acoustic spectrum of agarose gel with varied concentrations in open-faced rectangular wells. Results showed a linear relationship between the natural resonant frequency and agarose content within a concentration range of 4 to 12 mg/mL. Furthermore, we observed that the resonant frequencies decrease with increasing transducer mass. As a highly accurate, resonance-based method, RASOV has great potential for biomechanical properties measurement, especially for human blood.
Intensity normalization is an important preprocessing step in magnetic resonance (MR) image analysis. In MR images (MRI), the observed intensities are primarily dependent on (1) intrinsic magnetic resonance properties of the tissues such as proton density (PD ), longitudinal and transverse relaxation times (T 1 and T 2 respectively), and (2) the scanner imaging parameters like echo time (TE), repeat time (TR), and flip angle (α). We propose a method which utilizes three co-registered images with different contrast mechanisms (PD-weighted, T2-weighted and T1-weighted) to first estimate the imaging parameters and then estimate PD , T 1, and T 2 values. We then normalize the subject intensities to a reference by simply applying the pulse sequence equation of the reference image to the subject tissue parameters. Previous approaches to solve this problem have primarily focused on matching the intensity histograms of the subject image to a reference histogram by different methods. The fundamental drawback of these methods is their failure to respect the underlying imaging physics and tissue biology. Our method is validated on phantoms and we show improvement of normalization on real images of human brains.
We present the image processing upgrades implemented on a Graphics Processing Unit (GPU) in the Control, Acquisition, Processing, and Image Display System (CAPIDS) for the custom Micro-Angiographic Fluoroscope (MAF) detector. Most of the image processing currently implemented in the CAPIDS system is pixel independent; that is, the operation on each pixel is the same and the operation on one does not depend upon the result from the operation on the other, allowing the entire image to be processed in parallel. GPU hardware was developed for this kind of massive parallel processing implementation. Thus for an algorithm which has a high amount of parallelism, a GPU implementation is much faster than a CPU implementation. The image processing algorithm upgrades implemented on the CAPIDS system include flat field correction, temporal filtering, image subtraction, roadmap mask generation and display window and leveling. A comparison between the previous and the upgraded version of CAPIDS has been presented, to demonstrate how the improvement is achieved. By performing the image processing on a GPU, significant improvements (with respect to timing or frame rate) have been achieved, including stable operation of the system at 30 fps during a fluoroscopy run, a DSA run, a roadmap procedure and automatic image windowing and leveling during each frame.
Due to the high-resolution needs of angiographic and interventional vascular imaging, a Micro-Angiographic Fluoroscope (MAF) detector with a Control, Acquisition, Processing, and Image Display System (CAPIDS) was installed on a detector changer which was attached to the C-arm of a clinical angiographic unit. The MAF detector provides high-resolution, high-sensitivity, and real-time imaging capabilities and consists of a 300 μm-thick CsI phosphor, a dual stage micro-channel plate light image intensifier (LII) coupled to a fiber optic taper (FOT), and a scientific grade frame-transfer CCD camera, providing an image matrix of 1024×1024 35 μm square pixels with 12 bit depth. The Solid-State X-Ray Image Intensifier (SSXII) is an EMCCD (Electron Multiplying charge-coupled device) based detector which provides an image matrix of 1k×1k 32 μm square pixels with 12 bit depth. The changer allows the MAF or a SSXII region-of-interest (ROI) detector to be inserted in front of the standard flat-panel detector (FPD) when higher resolution is needed during angiographic or interventional vascular imaging procedures. The CAPIDS was developed and implemented using LabVIEW software and provides a user-friendly interface that enables control of several clinical radiographic imaging modes of the MAF or SSXII including: fluoroscopy, roadmapping, radiography, and digital-subtraction-angiography (DSA). The total system has been used for image guidance during endovascular image-guided interventions (EIGI) using prototype self-expanding asymmetric vascular stents (SAVS) in over 10 rabbit aneurysm creation and treatment experiments which have demonstrated the system's potential benefits for future clinical use.
With traditional 2-color Förster Resonance Energy Transfer (FRET) microscopy, valuable quantitative analyses can be conducted. Correlations of donor (D), acceptor (A) and their ratios (D:A) with energy transfer efficiency (E%) or distance (r) allows measurement of changes between control and experimental samples; also, clustered vs. random assembly of cellular components can be differentiated. Essentially, only the above three parameters D, A and D:A vs. E% are the basis for these deductions. 3-color FRET uses the same basic parameters, but exponentially expands the opportunities to quantify interrelationships among 3 cellular components. We investigated a number of questions based on the results of a triple combination (F1-F2-F3) of TFP-NWASP/Venus-IQGAP1/mCherry-Actin - all involved in the nucleation of actin - to apply the extensive analysis assay possible with 3-color FRET. How do changing N-WASP or IQGAP1 fluorescence levels affect actin fluorescence? What is the effect on E% of NWASP-actin by IQGAP1 or E% of IQGAP1-actin by N-WASP? These and other questions are explored in the context of all proteins of interest being in FRET distance vs. any two in the absence of the third. 4 cases are compared based on bleed-through corrected FRET: (1) all 3 interact, (2) only F1-F3 and F2-F3 [not F1-F2], (3) only F1-F2 and F2-F3 interact [not F1-F3], (4) only F1-F2 and F1-F3 interact [not F2-F3]. Other than describing the methodology in detail, several biologically relevant results are presented showing how E% (i.e. distance), fluorescence levels and ratios are affected in each of the cases. These correlations can only be observed in a 3-fluorophore combination. 3-color FRET will greatly expand the investigative range of quantitative analysis for the life-science researcher.
Increased complexity of scientific research poses new challenges to scientific data management. Meanwhile, scientific collaboration is becoming increasing important, which relies on integrating and sharing data from distributed institutions. We develop SciPort, a Web-based platform on supporting scientific data management and integration based on a central server based distributed architecture, where researchers can easily collect, publish, and share their complex scientific data across multi-institutions. SciPort provides an XML based general approach to model complex scientific data by representing them as XML documents. The documents capture not only hierarchical structured data, but also images and raw data through references. In addition, SciPort provides an XML based hierarchical organization of the overall data space to make it convenient for quick browsing. To provide generalization, schemas and hierarchies are customizable with XML-based definitions, thus it is possible to quickly adapt the system to different applications. While each institution can manage documents on a Local SciPort Server independently, selected documents can be published to a Central Server to form a global view of shared data across all sites. By storing documents in a native XML database, SciPort provides high schema extensibility and supports comprehensive queries through XQuery. By providing a unified and effective means for data modeling, data access and customization with XML, SciPort provides a flexible and powerful platform for sharing scientific data for scientific research communities, and has been successfully used in both biomedical research and clinical trials.
Distributed video coding (DVC) is rapidly increasing in popularity by the way of shifting the complexity from encoder to decoder, whereas no compression performance degrades, at least in theory. In contrast with conventional video codecs, the inter-frame correlation in DVC is explored at decoder based on the received syndromes of Wyner-Ziv (WZ) frame and side information (SI) frame generated from other frames available only at decoder. However, the ultimate decoding performances of DVC are based on the assumption that the perfect knowledge of correlation statistic between WZ and SI frames should be available at decoder. Therefore, the ability of obtaining a good statistical correlation estimate is becoming increasingly important in practical DVC implementations. Generally, the existing correlation estimation methods in DVC can be classified into two main types: pre-estimation where estimation starts before decoding and on-the-fly (OTF) estimation where estimation can be refined iteratively during decoding. As potential changes between frames might be unpredictable or dynamical, OTF estimation methods usually outperforms pre-estimation techniques with the cost of increased decoding complexity (e.g., sampling methods). In this paper, we propose a low complexity adaptive DVC scheme using expectation propagation (EP), where correlation estimation is performed OTF as it is carried out jointly with decoding of the factor graph-based DVC code. Among different approximate inference methods, EP generally offers better tradeoff between accuracy and complexity. Experimental results show that our proposed scheme outperforms the benchmark state-of-the-art DISCOVER codec and other cases without correlation tracking, and achieves comparable decoding performance but with significantly low complexity comparing with sampling method.
The UNC-Utah NA-MIC DTI framework represents a coherent, open source, atlas fiber tract based DTI analysis framework that addresses the lack of a standardized fiber tract based DTI analysis workflow in the field. Most steps utilize graphical user interfaces (GUI) to simplify interaction and provide an extensive DTI analysis framework for non-technical researchers/investigators.
We illustrate the use of our framework on a 54 directional DWI neuroimaging study contrasting 15 Smokers and 14 Controls.
At the heart of the framework is a set of tools anchored around the multi-purpose image analysis platform 3D-Slicer. Several workflow steps are handled via external modules called from Slicer in order to provide an integrated approach. Our workflow starts with conversion from DICOM, followed by thorough automatic and interactive quality control (QC), which is a must for a good DTI study. Our framework is centered around a DTI atlas that is either provided as a template or computed directly as an unbiased average atlas from the study data via deformable atlas building. Fiber tracts are defined via interactive tractography and clustering on that atlas. DTI fiber profiles are extracted automatically using the atlas mapping information. These tract parameter profiles are then analyzed using our statistics toolbox (FADTTS). The statistical results are then mapped back on to the fiber bundles and visualized with 3D Slicer.
This framework provides a coherent set of tools for DTI quality control and analysis.
This framework will provide the field with a uniform process for DTI quality control and analysis.
Adipose-derived stem cells (ADSCs) are adult stem cells isolated from lipoaspirates. They are a good candidate for autologuous cell therapy and tissue engineering. For these applications, label-free imaging could be critical to assess noninvasively the efficiency of stem cell (SC) differentiation. We report on the development and application of a multimodal microscope to monitor and quantify ADSC differentiation into osteoblasts and adipocytes.
In stereo displays, binocular disparity creates a striking impression of depth. However, such displays present focus cues-blur and accommodation-that specify a different depth than disparity, thereby causing a conflict. This conflict causes several problems including misperception of the 3D layout, difficulty fusing binocular images, and visual fatigue. To address these problems, we developed a display that preserves the advantages of conventional stereo displays, while presenting correct or nearly correct focus cues. In our new stereo display each eye views a display through a lens that switches between four focal distances at very high rate. The switches are synchronized to the display, so focal distance and the distance being simulated on the display are consistent or nearly consistent with one another. Focus cues for points in-between the four focal planes are simulated by using a depth-weighted blending technique. We will describe the design of the new display, discuss the retinal images it forms under various conditions, and describe an experiment that illustrates the effectiveness of the display in maximizing visual performance while minimizing visual fatigue.
Different techniques have been advocated for estimating single molecule locations from microscopy images. The question arises as to which technique produces the most accurate results. Various factors, e.g. the stochastic nature of the photon emission/detection process, extraneous additive noise, pixelation, etc., result in the estimated single molecule location deviating from its true location. Here, we review the results presented by [Abraham et. al, Optics Express, 2009, 23352-23373], where the performance of the maximum likelihood and nonlinear least squares estimators for estimating single molecule locations are compared. Our results show that on average both estimators recover the true single molecule location in all scenarios. Comparing the standard deviations of the estimates, we find that in the absence of noise and modeling inaccuracies, the maximum likelihood estimator is more accurate than the non-linear least squares estimator, and attains the best achievable accuracy for the sets of experimental and imaging conditions tested. In the presence of noise and modeling inaccuracies, the maximum likelihood estimator produces results with consistent accuracy across various model mismatches and misspecifications. At high noise levels, neither estimator has an accuracy advantage over the other. We also present new results regarding the performance of the maximum likelihood estimator with respect to the objective function used to fit data containing both additive Gaussian and Poisson noise. Comparisons were also carried out between two localization accuracy measures derived previously. User-friendly software packages were developed for single molecule location estimation (EstimationTool) and localization accuracy calculations (FandPLimitTool).
One hour before nanoparticle hyperthermia, CDDP chemotherapy (5mg/kg of body mass) was delivered intraperitoneally (IP). Iron oxide nanoparticles, 7.5mg of iron per gram of tumor, were injected into MTGB flank tumors in female C3H mice immediately before activation. A 170 KHz, 400-450 Oe alternating magnetic field (AMF) was used to induce particle heating. A comparison of nanoparticle induced hyperthermia to non-nanoparticle induced hyperthermia was also made using a 915 MHz microwave generator. Treatment duration was determined by the use of the cumulative equivalent minutes (CEM) algorithm. A CEM 60 was selected as the thermal dose for all experimental groups.
1) Preliminary mNP hyperthermia/cisplatinum results have shown a tumor growth delay greater than either modality alone at comparable doses. 2) mNP hyperthermia delivered 10 minutes post mNP injection and microwave hyperthermia, with the same thermal dose, demonstrate similar treatment efficacy.