Abstract: Techniques for registration of multiple measurement modes of a body include receiving first and second data from different modes. Each includes measured values with coordinate values. For two mechanically aligned modes, any non-rigid registration is performed. For some modes, the non-rigid registration includes a coarse transformation and multiple fine scale transformations. The coarse transformation maximizes a coarse similarity measure. The second data is sub-divided into contiguous sub-regions. Fine transformations are determined between the sub-regions and corresponding portions of the first data to maximize a fine similarity measure. Sub-dividing and determining fine transformations repeats until stop conditions are satisfied. Transformations between the last-divided sub-regions are interpolated.

Abstract: Medical imaging often involves the collective use of information presented in multiple images of an individual, such as images generated through different imaging modalities (X-ray, CT, PET, etc.) The use of a composite of these images may involve image registration to adjust for the variable position and orientation discrepancies of the individual during imaging. However, registration may be complicated by soft tissue deformation between images, and implementations (particularly pure software implementations) of the mathematical models used in image registration may be computationally complex and may require up to several hours. Hardware architectures are presented that apply the mathematical techniques in an accelerated manner, thereby providing near-realtime image registration that may be of particular use for the short timeframe requirements of surgical environments.

Abstract: Techniques for registration of multiple measurement modes of a body include receiving first and second data from different modes. Each includes measured values with coordinate values. For two mechanically aligned modes, any non-rigid registration is performed. For some modes, the non-rigid registration includes a coarse transformation and multiple fine scale transformations. The coarse transformation maximizes a coarse similarity measure. The second data is sub-divided into contiguous sub-regions. Fine transformations are determined between the sub-regions and corresponding portions of the first data to maximize a fine similarity measure. Sub-dividing and determining fine transformations repeats until stop conditions are satisfied. Transformations between the last-divided sub-regions are interpolated.

Abstract: Techniques for registration of multiple measurement modes of a body include receiving first and second data from different modes. Each includes measured values with coordinate values. For two mechanically aligned modes, any non-rigid registration is performed. For some modes, the non-rigid registration includes a coarse transformation and multiple fine scale transformations. The coarse transformation maximizes a coarse similarity measure. The second data is sub-divided into contiguous sub-regions. Fine transformations are determined between the sub-regions and corresponding portions of the first data to maximize a fine similarity measure. Sub-dividing and determining fine transformations repeats until stop conditions are satisfied. Transformations between the last-divided sub-regions are interpolated.

Abstract: Techniques for registration of multiple measurement modes of a body include receiving first and second data from different modes. Each includes measured values with coordinate values. For two mechanically aligned modes, any nonrigid registration is performed. For some modes, the nonrigid registration includes a coarse transformation and multiple fine scale transformations. The coarse transformation maximizes a coarse similarity measure. The second data is subdivided into contiguous subregions. Fine transformations are determined between the subregions and corresponding portions of the first data to maximize a fine similarity measure. Subdividing and determining fine transformations repeats until stop conditions are satisfied. Transformations between the last divided subregions are interpolated.

Abstract: Medical imaging often involves the collective use of information presented in multiple images of an individual, such as images generated through different imaging modalities (X-ray, CT, PET, etc.) The use of a composite of these images may involve image registration to adjust for the variable position and orientation discrepancies of the individual during imaging. However, registration may be complicated by soft tissue deformation between images, and implementations (particularly pure software implementations) of the mathematical models used in image registration may be computationally complex and may require up to several hours. Hardware architectures are presented that apply the mathematical techniques in an accelerated manner, thereby providing near-realtime image registration that may be of particular use for the short timeframe requirements of surgical environments.

Abstract: Techniques for segmenting data include receiving reference segmentation data and target data. The reference segmentation data defines a mesh indicating a boundary of a physical component in a reference body. The target data includes measured values at coordinates within a target body. Coordinates for vertices of the mesh are moved toward nearby edges in values of the target data. The adjustment also may be based on deviations from adjacent vertices or from a three dimensional generalized gradient vector field. The mesh may be initially transformed by a particular transformation that automatically maximizes a similarity measure between the target data and reference data. The reference data includes measured values within the reference body. The reference segmentation also may define a second mesh that indicates a second boundary in the reference body, and the adjustment is also based on an adjusted distance between the mesh and the second mesh.

Abstract: Techniques for segmenting data include receiving reference segmentation data and target data. The reference segmentation data defines a mesh indicating a boundary of a physical component in a reference body. The target data includes measured values at coordinates within a target body. Coordinates for vertices of the mesh are moved toward nearby edges in values of the target data. The adjustment also may be based on deviations from adjacent vertices or from a three dimensional generalized gradient vector field. The mesh may be initially transformed by a particular transformation that automatically maximizes a similarity measure between the target data and reference data. The reference data includes measured values within the reference body. The reference segmentation also may define a second mesh that indicates a second boundary in the reference body, and the adjustment is also based on an adjusted distance between the mesh and the second mesh.

Abstract: Stress test analysis is facilitated through the acquired and manipulated use of a sequence of volumetric data regarding the heart (and may particularly comprise the left ventricle) for the assessment of the health state of the heart. Several provided and illustrated examples specifically relate to ultrasound volumetric data, but the volumetric data may be obtained through the use of any imaging modality (e.g., CT, MRI, X-ray, PET, SPECT, etc.) or combination thereof, and may be used to compute one or more functional quantitative metrics (e.g., ejection fraction.) The volumetric data may also be used to render one or more views of the heart, and particularly of the left ventricle. This disclosure relates to these and other uses of such volumetric data, and to some various implementations thereof, such as methods, systems, and graphical user interfaces.

Abstract: Techniques for accelerated elastic registration include receiving reference scan data and floating scan data, and a first transformation for mapping coordinates of scan elements from the first scan to coordinates of scan elements in the second scan. A subset of contiguous scan elements is determined. At least one of several enhancements is implemented. In one enhancement cubic spline interpolation is nested by dimensions within a subset. In another enhancement, a local joint histogram of mutual information based on the reference scan data and the floating scan data for the subset is determined and subtracted from an overall joint histogram to determine a remainder joint histogram. Each subset is then transformed, used to compute an updated local histogram, and added to the remainder joint histogram to produce an updated joint histogram. In another enhancement, a measure of similarity other than non-normalized mutual information is derived from the updated joint histogram.

Abstract: Techniques for registration of multiple measurement modes of a body include receiving first and second data from different modes. Each includes measured values with coordinate values. For two mechanically aligned modes, any nonrigid registration is performed. For some modes, the nonrigid registration includes a coarse transformation and multiple fine scale transformations. The coarse transformation maximizes a coarse similarity measure. The second data is subdivided into contiguous subregions. Fine transformations are determined between the subregions and corresponding portions of the first data to maximize a fine similarity measure. Subdividing and determining fine transformations repeats until stop conditions are satisfied. Transformations between the last divided subregions are interpolated.

Abstract: A system architecture is disclosed that facilitates rapid execution of 3D registration or alignment algorithms. A first image module (e.g., RAM) is included to store data corresponding to images to be registered. A processor coupled to the first storage module accesses the data and determined mutual histogram (MH) values which are then used to compute mutual information (MI) between the images. The processor accumulates the MH values in a second image module. The second storage module is accessible so that registered images can be displayed. The architecture is scalable facilitating distributed calculations to speed-up the registration process.

Abstract: Techniques for indicating arrangement of moving target tissue in a living body include receiving first scan data based at least in part on a first mode of measuring with high spatial resolution over a first duration at a first time. Also received is second scan data representing a scan of the living body based at least in part on a second mode of measuring at a second time. The second mode can be different with a second duration and a repeat rate greater than a repeat rate for the first scan data. An elastic transform is determined that registers the first scan data elastically to the second scan data. A particular spatial arrangement of the moving target tissue is indicted based on the elastic transform. These techniques can be used to update a pre-intervention plan and highlight target detail by registering pre-intervention data to second scan data during the intervention.

Abstract: The present invention relates to laser ablation patterns to correct refractive errors of the eye (60) such as nearsightedness, farsightedness, astigmatism, and higher order aberrations of the eye (60). The laser ablation patterns used to control the laser (10) prevent induced aberrations by compensating for post-procedure epithelial smoothing. The position of laser pulses (12) is also controlled to optimize the achievement of the intended ablation pattern.

Abstract: Systems and methods provide convexity map data associated with captured surface image data of a cornea The convexity map data is determined by transforming an elevation map data set into a convexity map data set. Convexity is computed as the negative of the Laplacian of the local elevation. One or more statistical parameters can be associated with the convexity map data set and employed to derive indices. The indices can be utilized to diagnosis a corneal condition.

Abstract: An intravascular ultrasound (IVUS) analysis system and method is provided which determines luminal and medial-adventitial boundaries of a blood vessel. Ultrasonic data is acquired by a rotating transducer mounted to a tip of a catheter which is inserted into the blood vessel. An intravascular image is reconstructed from the ultrasound data. To determine the luminal boundary of a vessel, a user selects boundary points on the image believed to be locations of the luminal boundary. A boundary contour is generated based on the boundary points. The boundary contour is then optimized by adjusting the boundary points based on a radially determined edge of the luminal boundary performed on the image in polar format. Once the final luminal boundary contour is generated, the process is repeated to determine the medial-adventitial boundary contour. With the contour data, properties of the blood vessel are analyzed including determining the area of the lumen and percent of occlusion caused by plaque.

Abstract: A needle biopsy system (10) includes a biopsy needle (210), and a needle support assembly (200). The needle support assembly (200) holds the biopsy needle (210) and manipulates the biopsy needle (210) in response to received control signals. A needle simulator (250) having an input device (252) generates the control signals in response to manipulation of the input device (252) by an operator. The operator, in turn, receives feedback from the needle simulator (250) in accordance with forces experienced by the biopsy needle (210). In a preferred embodiment, the feedback received by the operator includes tactile sensations experienced by the operator as the operator manipulates the input device (252). The tactile sensations mimic those the operator would have received had the operator directly manipulated the biopsy needle (210). Optionally, a curved needle guide (280) is employed to restrict the biopsy needle's progression longitudinally therethrough.

Abstract: A cardiac gated spiral CT scanner (10) has a source of penetrating radiation (20) arranged for rotation about an examination region (14) having a central axis extending in a z direction. The source (20) emits a beam of radiation (22) that passes through the examination region (14) as the source (20) rotates. A patient support (30) holds a patient within the examination region (14) and translates the patient through the examination region (14) in the z direction while this source (20) is rotated such that the source (20) follows a helical path relative to the patient. A control processor (90) implements a patient-specific scan protocol in response to measured patient characteristics (for example, the patient's heart rate, the patient's breath hold time, and/or the range of coverage in the z direction based on the patient's anatomy) and scanner characteristics (for example, the number of detector rings, and/or the scan rate).