In this paper we present a generic system for fast and accurate retrieval of the best matching frame from Ultrasound
video clips given a reference Ultrasound image. It is challenging to build a generic system to handle various lesion types
without any prior information of the anatomic structures of the Ultrasound data. We propose to solve the problem based
on both spatial and temporal saliency maps calculated from the Ultrasound images, which implicitly analyze the
semantics of images and emphasize the anatomic regions of interest. The spatial saliency map describes the importance
of the pixels of the reference image while the temporal saliency map further distinguishes the subtle changes of the
anatomic structure in a video. A hierarchical comparison scheme based on a novel similarity measure is employed to
locate the most similar frames quickly and precisely. Our system ensures the robustness, accuracy and efficiency.
Experiments show that our system achieves more accurate results with fast speed.
PURPOSE: Detection of coronary artery calcifications (CAC) using conventional chest radiographs has a high positive
predictive value but low sensitivity for coronary artery disease. We investigated the role of dual energy imaging to
enhance reader performance in the detection of CAC, indicative of atherosclerotic plaques.
METHODS: A sample of 53 patients with CT documented CAC and 23 patients without CT evidence of CAC, was
imaged using a dual energy protocol on an amorphous silicon flat panel system (Revolution XR/d, GE Medical
Systems). The acquisition sequence consisted of a 60kVp ("low energy") exposure, followed by a 120 kVp ("high
energy") exposure with a time separation of 150ms. Subsequent image processing yielded conventional PA and lateral
radiographs and a subtracted PA "bone image". For all patients and both data sets, CAC were evaluated by two
experienced board-certified thoracic radiologists via Likert scale measurement (1-5 score).
RESULTS: Sensitivity for CAC detection, using conventional radiographs, was 34.0% and 56.6% while specificity was
96.6% and 91.3%, for the two readers respectively. Using the "bone images", sensitivity was 92.4% and 83.0% while
specificity was 100% and 91.3%. For patients with verified CAC, "bone images" resulted in at least a one Likert score
increase in 73.6% and 54.7% of cases for the two readers.
CONCLUSION: We conclude that using dual energy technology, "bone images" may allow higher sensitivity in
detecting CAC compared with conventional radiographs, without decreased specificity. Thus, we believe our findings
are useful in defining a role for dual energy subtraction radiography in improved detection of coronary artery disease.
Solid tumors and other pathologies are being treated using radio-frequency (RF) ablation under interventional magnetic resonance imaging (iMRI) guidance. In animal experiments, we are investigating the ability of MR to monitor ablation treatments by comparing MR images of thermal lesions to histologically assayed cellular damage. We developed a new methodology using three-dimensional registration for making spatial correlations. A low-field, open MRI system was used to guide an ablation probe into the thigh muscle of 10 rabbits and acquire MR volumes post ablation. After the in vivo MR and histology images were aligned with a registration accuracy of 1.32 +/- 0.39 mm (mean ± SD), a boundary of necrosis identified in histology images was compared with manually segmented boundaries of the elliptical hyperintense region in MR images. For 14 MR images, we determined that the outer boundary of the hyperintense region in MR closely corresponds to the region of cell death, with a mean absolute distance between boundaries of 0.97 mm. Since this distance may be less than our ability to measure such differences, boundaries may match perfectly. This is good evidence that MR lesion images can localize the region of cell death during RF ablation treatments.
KEYWORDS: Image segmentation, 3D modeling, Magnetic resonance imaging, Tissues, In vivo imaging, Data modeling, Animal model studies, Visual process modeling, Electrodes, Natural surfaces
Radiofrequency current energy can be used to ablate pathologic tissue. Through magnetic resonance imaging (MRI), real-time guidance and control of the procedure is feasible. For many tissues, resulting lesions have a characteristic appearance with two boundaries enclosing an inner hypo-intense region and an outer hyper-intense margin, in both contrast enhanced T1 and T2 weighted MR images. We created a model having two quadric surfaces and twelve-parameters to describe both lesion surfaces. Parameter estimation was performed using iterative optimization such that the sum of the squared shortest distances from segmented points to the model surface was minimized. The method was applied to in vivo image volumes of lesions in a rabbit thigh model. For all in vivo lesions, the mean signed distance from the model surface to segmented boundaries, accounting for the interior or exterior location of points, was approximately zero with standard deviations less than a voxel width (0.7 mm). For all in vivo lesions, the median absolute distance from the model surface to data was <= 0.6 mm for both surfaces. We conclude our model provides a good approximation of actual lesion geometry and should prove useful for three-dimensional lesion visualization, volume estimation, automated segmentation, and volume registration.
KEYWORDS: Image segmentation, 3D modeling, Magnetic resonance imaging, Tissues, Electrodes, In vivo imaging, Visualization, Data modeling, Animal model studies, Thermal modeling
We are investigating magnetic resosance imaging-guided radiofrequency ablation of pathologic tissue. For many tissues, resulting lesions have a characteristic two-boundary appearance featuring an inner region and an outer hyper-intense margin in both contrast enchanced (CE) T1 and T2 weighted MR images. We created a twelve-parameter, three-dimensional, globally deformable model with two quadratic surfaces that describe both lesion zones. We present an energy minimization approach to automatically fit the model to a grayscale MR image volume. We applied the automatic model to in vivo lesions (n = 5) in a rabbit thigh model, using CE T1 and T2 weighted MR images, and compared the results to multi-operator manually segmented boundaries. For all lesions, the median error was <1.0mm for both the inner and outer regions, values that favorably compare to a voxel width of 0.7 mm. These results suggest that our method provides a precise, automatic approximation of lesion shape. We believe that the method has applications in lesion visualization, volume estimation, image quantification, and volume registration.
KEYWORDS: Image segmentation, 3D modeling, Tissues, Magnetic resonance imaging, Electrodes, 3D image processing, In vivo imaging, Laser ablation, Data modeling, Radiofrequency ablation
Using magnetic resonance imaging (MRI), real-time guidance is feasible for radiofrequency (RF) current ablation of pathologic tissue. Lesions have a characteristic two-zone appearance: an inner core (Zone I) surrounded by a hyper-intense rim (Zone II). A better understanding of both the immediate (hyper-acute) and delayed (sub-acute) physiological response of the target tissue will aid development of minimally invasive tumor treatment strategies. We performed in vivo RF ablations in a rabbit thigh model and characterized the tissue response to treatment through contrast enhanced (CE) T1 and T2 weighted MR images at two time points. We measured zonal grayscale changes as well as zone volume changes using a 3D computationally fitted globally deformable parametric model. Comparison over time demonstrated an increase in the volume of both the inner necrotic core (mean 56.5% increase) and outer rim (mean 16.8% increase) of the lesion. Additionally, T2 images of the lesion exhibited contrast greater than or equal to CE T1 (mean 35% improvement). This work establishes a foundation for the clinical use of T2 MR images coupled with a geometric model of the ablation for noninvasive lesion monitoring and characterization.
KEYWORDS: Image registration, Tissues, Magnetic resonance imaging, Photography, Brain, Tissue optics, Medical imaging, In vivo imaging, 3D image processing, Natural surfaces
We created a method for three-dimensional registration of medical scanner image volumes to images of physical tissue sections or other volumes, and evaluated its accuracy. The method is applicable for many animal experiments, and we are applying it to evaluate interventional MRI imaging of thermal ablation and to quantify in vivo drug release from a new device for localized, controlled release. The method computes an optimum set of rigid body registration parameters by iterative minimization of the Euclidean distances between automatically generated correspondence points, along manually selected fiducial needle paths, and optional point landmarks. For numerically simulated registrations, using two needle paths over a range of needle orientations, median voxel displacement errors depended only on needle localization error when the angle between needles was at least 15 degrees. For parameters typical of our in vivo experiments, the median error was <EQ0.18 mm. In addition, we determined that the distance objective function was a useful diagnostic for predicting registration quality. To evaluate the registration quality of physical specimens, we computed the misregistration for a needle not considered during the optimization procedure. We registered an ex vivo sheep brain MR volume with another MR volume and tissue section photographs, using various combinations of needle and point landmarks. Registration error was always <EQ0.65 mm for MR-to-MR registrations and <EQ0.9 mm for MR to tissue section registrations. We conclude that our method provides sufficient spatial correspondence to facilitate comparison of 3D image data with data from gross pathology tissue sections and histology.
KEYWORDS: Magnetic resonance imaging, Tissues, Tissue optics, Photography, Image registration, Birefringence, In vivo imaging, 3D image processing, Tumors, Image segmentation
We are treating tumors using radiofrequency (RF) ablation under interventional MRI (iMRI) guidance. We investigated the ability of MR to monitor the treated region by comparing MR thermal lesion images to cellular damage as seen histologically. Our new methodology allows 3D registration that should enable more accurate correlation than previous 2D methods. Using a low-field (0.2T) open magnet iMRI system for probe guidance, we applied RF ablation to the thigh muscle of four New Zealand White rabbits. To relate in vivo MR and histology images, we obtained intermediate ex vivo MR images and pictures of thick tissue slices obtained using a specially designed apparatus. Registration was done with a computer algorithm that matches tracks of needle fiducials placed near the tissue of interest. After registration, we determined the region inside the circular, hyperintense rim in MR closely corresponds to the region of necrosis as determined by histology on animals sacrificed 30 minutes after ablation. This is good evidence that iMRI images can be used for real-time feedback during thermal RF ablation treatments.
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