Patent Application
20100214293
The present disclosure relates generally to a system and method for user interaction in a direct, iterative reconstruction from image data using an adaptive mesh grid.
The data gathered from (molecular) imaging modalities such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) scanners can be used to reconstruct model parameters, describing the concentration of tracer chemicals (e.g., the dynamic behavior of the concentration) in the body. Such parameters are described on a ‘voxel-by-voxel’ basis, where a voxel is a small volume element inside a three dimensional (3-D) grid that is super-imposed on the studied object. The size of the voxels inside this grid determines the spatial accuracy or resolution with which the distribution of the model parameters can be estimated. Several reconstruction methods (e.g., direct reconstruction from the list mode data and maximum a posteriori estimation) allow irregular voxel grids, e.g., grids that contain voxels of various shapes and sizes. The optimal choice for the layout of such a voxel grid is still an open issue.
State-of-the-art for reconstruction of image data includes reconstruction on “irregular” voxel grids with local variations in resolution (e.g., voxel size) and includes a static grid with higher resolutions in regions of interest, as indicated manually before reconstruction, for example, on a preliminary reconstruction, or a reconstruction based on another modality (e.g., CT-scan). State-of-the-art also includes content-adaptive mesh generation for image reconstruction, where resolution is increased automatically in regions of high spatial variation.
Mesh modeling of an image involves partitioning the image domain into a collection of nonoverlapping (generally polygonal) patches, called mesh elements, (here triangles are used as illustrated in
High resolution, which is implemented through a very fine voxel grid, requires overly long computation times. Lower resolution, which is implemented with a coarser voxel grid, leads to a loss of spatial information and less accurate system output (e.g., parameter maps). Further, an optimal compromise between speed and high resolution is influenced by aspects including, for example, regions of interest, spatial variation, availability of sufficient statistics and availability or requirement of computation time. The regions of interest under consideration influence the requirements for higher resolution. Certain areas of the studied object may be of more importance than other areas, and subsequently require higher resolution. Also, higher resolution in the regions of “lesser” interest (e.g., background) yields no additional information of value, but still slows down the reconstruction process. Regarding spatial variation, model parameters may feature strong spatial variation from voxel-to-voxel in one area, yet vary more slowly in other areas. In areas where the variation of model parameters is relatively slow, only limited resolution is required, whereas areas of strong model parameter variation are best modeled through a finely meshed grid.
The estimation of model parameters for each voxel relies on a sufficient number of events (e.g., detector measurements) that are related to this particular voxel. If there are too few events due to too small a voxel size, for example, a poor signal-to-noise ratio (SNR) is implied, subsequently resulting in poor estimation. Thus, it can be seen that the availability of sufficient statistics influences an optimal compromise between speed and high resolution.
Of course, when unlimited time is available, a small voxel size throughout the entire grid would always be preferable, provided there is a sufficient number of detected events at the smaller voxel size. However, physicians have limited time available, and prefer not to wait for results. The relative importance of speed and accuracy therefore has a direct influence on the choice of resolution.
Therefore, it would be desirable to provide control over the reconstruction process to minimize computation time to prevent unnecessary waiting and ensure maximum resolution of the regions of interest within the boundaries of clinically acceptable reconstruction times.
The present disclosure relates to a method for iterative reconstruction with user interaction in data-driven, adaptive mesh generation for reconstruction of model parameters from imaging data. In an exemplary embodiment, the method includes reading input (both a priori (110) and on-line (115)) from a user and checking reconstructed parameters (130) for convergence after each iteration. A required computation time is estimated (130) after each iteration based on a current mesh grid and expected number of iterations and the mesh grid is subsequently updated (140). An on-line representation of the reconstructed parameters and an adapted mesh grid is displayed during the reconstruction (170) and a next iteration of the reconstruction is based on the adapted mesh grid (145).
In another exemplary embodiment, a system for iterative reconstruction with user interaction in data-driven, adaptive mesh generation for reconstruction of model parameters from imaging data is disclosed. The system includes a reconstructor configured to check reconstructed parameters for convergence after each iteration and estimate a required computation time after each iteration based on a current mesh grid and expected number of iterations. A user interface is configured to accept user input for the reconstructor to read and a display means 17 displays an on-line representation of the reconstructed parameters and an adapted mesh grid during the reconstruction updating the mesh grid 14, wherein a next iteration of the reconstruction is based on the adapted mesh grid.
In yet another exemplary embodiment, a computer software product for iterative reconstruction with user interaction in data-driven, adaptive mesh generation for reconstruction of model parameters from imaging data is disclosed. The product includes a computer-readable medium, in which program instructions are stored, which instructions, when read by a computer, cause the computer to read input (both a priori (110) and on-line (115)) from a user input device and check reconstructed parameters (130) for convergence after each iteration. The computer estimates a required computation time (130) after each iteration based on a current mesh grid and expected number of iterations and updates the mesh grid (140). The computer then directs an on-line representation of the reconstructed parameters and an adapted mesh grid to be displayed on a display means during the reconstruction (170) and bases a next iteration of the reconstruction on the adapted mesh grid (145).
Additional features, functions and advantages associated with the disclosed system and method will be apparent from the detailed description which follows, particularly when reviewed in conjunction with the figures appended hereto.
To assist those of ordinary skill in the art in making and using the disclosed system and method, reference is made to the appended figures, wherein:
As set forth herein, the present disclosure advantageously provides a direct, iterative reconstruction method that uses an adaptive mesh grid. The grid layout is determined by a priori indication of regions of interest and a state of the reconstruction process. Early iterations, where parameter estimates are still coarse, feature low resolution grids. The resolution is increased with each iteration, reaching its peak when parameter estimates start to converge. The grid layout is also determined by available data per voxel. In regions of little activity, voxels are merged (e.g., pooled) to form a coarse grid, with a better signal-to-noise ratio for each voxel. Spatial variation of the reconstructed parameters is also used to determine the grid layout. Areas of high variation are overlaid with a finer voxel grid. The grid layout is further determined by selection of a maximum computation time allowed. Before reconstruction starts, the user defines a maximum computation time. After each iteration the remaining computation time is estimated, and the grid resolution is adapted (e.g., made coarser or finer) depending on whether the allowed computation time will be exceeded or met (e.g., easily). Other user interaction is also used to determine the grid layout as discussed more fully below.
Referring to
During the reconstruction process (e.g., on-line), the user sees the currently used (3-D) grid 14 and the reconstructed model parameter values that are intensity coded per voxel at 16 and define a reconstructed parameter map 15. The user views both on a display 17 that shows the current estimation of the reconstructed parameter map 15, along with the mesh grid 14 that is currently used. By navigating a 3-D cursor 19 through the grid 14 with arrow buttons 18 and resizing the grid 14 with sizing buttons 20 in which the user can select a region to increase or decrease the resolution. The entire image can also be rotated around three axes using a respective button 22. The buttons indicated generally at the left of the GUI 10 are present for global action indicated generally at 24, a log message window 26 indicates reconstruction progress and feedback relative to the user's actions. In addition, the log message window 26 provides information concerning the convergence of the estimated parameters, estimated time left, and current resolution, also based on the user's actions. The user can also choose to increase or decrease the overall resolution, as well as to increase or decrease the speed of the reconstruction parameter process using buttons 28 and 30, respectively.
Referring now to
The user receives information about the current parameter estimates (θn), indicated with broken line 160, and mesh grid 14, indicated with broken line 150, via display 17 at block 170. The user may actively influence the mesh grid 14 at blocks 110 and 115 as discussed above. When the reconstruction is completed or the reconstruction cycle has converged, indicated with line 175, the reconstructed image is output at block 180. It will be recognized by one skilled in the pertinent art that although the display 17 is shown as part of the GUI 10, that the display 17 may be an independent display separate from the user input buttons located on the lower and left-hand sides of the display 17, as illustrated in
Each estimation of the required computation time depends on the current mesh grid and the expected number of iterations. The latter is easily calculated in the so-called one-pass algorithms, where all data is seen exactly once, or other algorithms with a fixed number of iterations. Algorithms that depend explicitly on the convergence of the reconstructed parameter estimates, need to estimate the number of iterations that are left based on convergence statistics.
Reconstruction algorithms are known in the art that yield an updated θn for the model parameters after each event, after a subset of the complete set of events or after an iteration that includes all events. To ensure proper user interaction, the number of events that is used per iteration (e.g., for each parameter update) must be chosen small enough to give the user the chance to interact at reasonable intervals.
It should be noted that user interaction may not only take place during the reconstruction, but also after the reconstruction has finished. When the reconstruction cycle has converged, the state of the system (e.g., the currently used voxel grid and the estimated model parameter values) are stored in the computer memory. The graphical user interface 10 still allows the user to increase the spatial resolution in areas of interest, based on the reconstructed image, whereafter the reconstruction cycle may continue. An example of the use of this feature would be to make an initial “quick reconstruction”, increase the resolution in, possibly patient specific, regions of interest, and then to allow the system to continue with the “main reconstruction”.
Advantageously, embodiments of the present disclosure enable a user of the system, method and computer software product to visually inspect an on-line representation of the reconstructed parameters and mesh grid during reconstruction. Further, the system, method and computer software product of the present disclosure facilitates on-line user interaction with the reconstruction process through manual adaptation of the local and global mesh grid resolution and uses the estimated remaining computation time as a determining factor in mesh adaptation.
Other advantages include a smaller dependence on a priori availability of reconstructed data. For example, a coarse first indication of regions of interest may be refined on-line, as soon as reconstructed data becomes available. The system, method and computer software product of the present disclosure also provides more control over the reconstruction process. For example, an automatic, data-driven mesh segmentation may differ from the choices of a human expert. Although the option to trust the reconstruction algorithm with the choice of which areas deserve high resolution still exists, the user interface adds the option to make human expert knowledge an active part of the decision process. Interesting features that arise unexpectedly in the reconstructed parameter map may be examined “more closely” (e.g., under a higher resolution) as soon as the features of interest start to show up in the reconstruction. Thus, there is no need to complete the entire reconstruction, add or change regions of interest, and re-run the reconstruction. Another advantage provided by the above described system, method and computer software product of the present disclosure includes the option to set a maximum computation time to prevent unnecessary waiting, and ensuring maximum resolution within the boundaries of the allowed time.
Although the method, system and software product of the present disclosure have been described with reference to exemplary embodiments thereof, the present disclosure is not limited to such exemplary embodiments. Rather, the method, system and software product disclosed herein are susceptible to a variety of modifications, enhancements and/or variations, without departing from the spirit or scope hereof. Accordingly, the present disclosure embodies and encompasses such modifications, enhancements and/or variations within the scope of the claims appended hereto.
| Filing Document | Filing Date | Country | Kind | 371c Date |
|---|---|---|---|---|
| PCT/IB2006/054267 | 11/15/2006 | WO | 00 | 5/30/2008 |
| Number | Date | Country | |
|---|---|---|---|
| 60741739 | Dec 2005 | US |
