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: 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: 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: 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 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 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.