1. A method for cone beam computed tomography, the method comprising:
moving a digital radiation detector along at least a portion of a detector path, the at least a portion of the detector path extending so that the digital radiation detector is configured to move at least partially around a first extremity of a patient, the detector path having a distance D1 that is sufficiently long to allow the first extremity to be positioned within the detector path;
moving a radiation source along at least a portion of a source path outside the detector path, the source path having a distance D2 greater than the distance D1, the distance D2 being sufficiently long to allow adequate radiation exposure of the first extremity for an image capture by the digital radiation detector;
moving the radiation source at a source position along the source path in correspondence to a detector position for the detector along the detector path during the image capture; and
providing a first gap in the detector path.
2. The method according to claim 1, where the detector path, the detector, the source path and the radiation source are within a housing.
3. The method according to claim 1, comprising providing a second gap in the source path sized to allow a second, adjacent extremity to be positioned in the second gap during the image capture, the first gap having a circumferential length sufficient to allow the first extremity to pass through the first gap.
4. The method according to claim 1, further comprising a foot support that is adjustable for foot placement at an angular position that is horizontal, vertical, or at some angle that lies between horizontal and vertical, where the foot support is positioned operatively adjacent to the detector path.
5. The method according to claim 1, further comprising moving the source and detector along the first extremity or rotating the detector path and source path to a vertical or other angular orientation.
6. The method according to claim 1, further comprising revolving the source path and independently revolving the detector path, where the detector path can revolve to align the first gap with a second gap in the source path.
7. The method according to claim 6, where the first and second gaps each extend approximately 180 degrees plus the fan angle determined by radiation source and detector geometry and distance.
8. The method according to claim 1, where detector path and the source path are rigidly connected or movably connected.
9. An apparatus for cone beam computed tomography (CBCT), the apparatus comprising:
a digital radiation detector;
a first device to move the detector along at least a portion of a detector path, the at least a portion of the detector path extending so that the detector is configured to move at least partially around an imaging position of the CBCT apparatus, the detector path having a distance D1 that is sufficiently long to allow the imaging position of the CBCT apparatus to be positioned within the detector path;
a radiation source;
a second device to move the source along at least a portion of a source path outside the detector path, the source path having a distance D2 greater than the distance D1, the distance D2 being sufficiently long to allow adequate radiation exposure of the imaging position of the CBCT apparatus for an image capture by the detector; and
a first gap in the detector path.
10. The apparatus according to claim 9, where the detector path and the detector are within a first housing, and where the source path and the radiation source are within a second housing.
11. The apparatus according to claim 9, comprising a second gap in the source path sized to allow a second, adjacent extremity to be positioned in the second gap during the image capture, the first gap having a circumferential length sufficient to allow a first extremity of the patient to pass through the first gap into the imaging position of the CBCT apparatus.
12. The apparatus according to claim 9, wherein the first and second devices maintain the radiation source at a source position along the source path in correspondence to a detector position for the detector along the detector path during the image capture.
13. The apparatus according to claim 9, further comprising a foot support that is adjustable for foot placement at an angular position that is horizontal, vertical, or at some angle between horizontal and vertical, where the foot support is operatively adjacent the detector path.
14. The apparatus according to claim 9, further comprising a third device configured to move the source and detector along the first extremity and configured to rotate the detector path and source path to a vertical or other angular orientation.
15. The apparatus according to claim 9, where the first device is revolvable, where the second device is revolvable, and when the second device is revolved to one position a second gap in the source path is configured to align with the first gap.
16. The apparatus according to claim 15, where the first and second gaps each extend approximately 180 degrees plus the fan angle determined by radiation source and detector geometry and distance.
17. The apparatus according to claim 9, where the first device and the second device are the same device or the first device and the second device are movably connected.
18. The apparatus according to claim 9, comprising a second gap in the source path sized to allow a first extremity of the patient to pass through the second gap and a housing for the source path to correspond to the second gap, where the first gap has a circumferential length sufficient to allow the first extremity to pass through the first gap.
19. The apparatus according to claim 9, further comprising a third device configured to adjust the source and detector along the first extremity or to adjust the detector path and source path to a vertical or other angular orientation.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. An intra-prediction module for use in a video encoder that encodes a video signal including picture data, the intra-prediction module comprising:
an intra-prediction preprocessor configured to process a plurality of pixels of blocks of the picture data to determine edge strength data and edge angle range data corresponding to the plurality of pixels of the blocks, and further to generate intra-prediction candidate data, wherein the intra-prediction candidate data indicates a first subset selected from a plurality of intra-prediction partitions and a second subset selected from a plurality of intra-prediction modes generated from the edge strength data and the edge angle range data; and
a final intra-prediction processor, coupled to the intra-prediction preprocessor, that determines final intra-prediction data, based on rate distortion based costs of the intra-prediction candidate data.
2. The intra-prediction module of claim 1 wherein the intra-prediction preprocessor selects the first subset based on an accumulation of edge strength data for each pixel having a common one of a plurality of possible edge angle range values.
3. The intra-prediction module of claim 2 wherein the intra-prediction preprocessor evaluates a plurality of prediction block sizes.
4. The intra-prediction module of claim 3 wherein the intra-prediction preprocessor determines, for a selected one of the plurality of prediction block sizes, an edge angle range value of the plurality of possible edge angle range values having a second highest accumulation of edge strength data, compares the second highest accumulation of edge strength data to a threshold and excludes a partitioning of the selected one of the plurality of prediction block sizes from the first subset when the second highest accumulation of edge strength data compares favorably to the threshold.
5. The intra-prediction module of claim 3 wherein the intra-prediction preprocessor determines, for a selected one of the plurality of prediction block sizes and for each partitioned subblock of the selected one of the plurality of prediction block sizes, an edge angle range value of the plurality of possible edge angle range values having a highest accumulation of edge strength data, compares the highest accumulation of edge strength data for the selected one of the plurality of prediction block sizes to a sum of the highest accumulation of edge strength data for each partitioned subblock and includes a partitioning of the selected one of the plurality of prediction block sizes in the first subset when the highest accumulation of edge strength data for the selected one of the plurality of prediction block sizes compares unfavorably to the sum of the highest accumulation of edge strength data for each partitioned subblock.
6. The intra-prediction module of claim 1 wherein the intra-prediction preprocessor selects the second subset based on an accumulation of edge strength data for each pixel having a common one of a plurality of possible edge angle range values.
7. The intra-prediction module of claim 6 wherein the intra-prediction preprocessor, for a selected one of a plurality of block sizes, includes in the second subset one of the plurality of intra-prediction modes corresponding to a particular edge angle range value when the accumulation of edge strength data for the particular edge angle range value compares favorably to a threshold.
8. The intra-prediction module of claim 6 wherein the intra-prediction preprocessor, for the selected one of the plurality of block sizes, excludes one of the plurality of intra-prediction modes corresponding to a particular edge angle range value from the second subset when the accumulation of edge strength data for the particular edge angle range value compares unfavorably to a highest accumulation of edge strength data.
9. The intra-prediction module of claim 1 wherein the intra-prediction preprocessor selectively enables and disables a most probable mode indicator.
10. The intra-prediction module of claim 1 wherein the edge strength data and the edge angle range data indicate a picture gradient for each of the plurality of pixels.
11. A method for use in a video encoder that encodes a video signal including picture data, the method comprising:
processing a plurality of pixels of blocks of the picture data to determine edge strength data and edge angle range data corresponding to the plurality of pixels of the blocks;
generating intra-prediction candidate data, wherein the intra-prediction candidate data indicates a first subset selected from a plurality of intra-prediction partitions and a second subset selected from a plurality of intra-prediction modes generated from the edge strength data and the edge angle range data; and
determining final intra-prediction data, based on rate distortion based costs of the intra-prediction candidate data.
12. The method of claim 11 wherein generating the intra-prediction candidate data includes selecting the first subset based on an accumulation of edge strength data for each pixel having a common one of a plurality of possible edge angle range values.
13. The method of claim 12 wherein generating the intra-prediction candidate data includes:
determining, for a selected one of a plurality of prediction block sizes, an edge angle range value of the plurality of possible edge angle range values having a second highest accumulation of edge strength data;
comparing the second highest accumulation of edge strength data to a threshold; and
excluding a partitioning of the selected one of the plurality of prediction block sizes from the first subset when the second highest accumulation of edge strength data compares favorably to the threshold.
14. The method of claim 12 wherein generating the intra-prediction candidate data includes:
determining, for a selected one of a plurality of prediction block sizes and for each partitioned subblock of the selected one of the plurality of prediction block sizes, an edge angle range value of the plurality of possible edge angle range values having a highest accumulation of edge strength data;
comparing the highest accumulation of edge strength data for the selected one of the plurality of prediction block sizes to a sum of the highest accumulation of edge strength data for each partitioned subblock; and
includes a partitioning of the selected one of the plurality of prediction block sizes in the first subset when the highest accumulation of edge strength data for the selected one of the plurality of prediction block sizes compares unfavorably to the sum of the highest accumulation of edge strength data for each partitioned subblock.
15. The method of claim 11 wherein generating the intra-prediction candidate data includes selecting the second subset based on an accumulation of edge strength data for each pixel having a common one of a plurality of possible edge angle range values.
16. The method of claim 15 wherein generating the intra-prediction candidate data includes:
including one of the plurality of intra-prediction modes corresponding to a particular edge angle range value in the second subset when the accumulation of edge strength data for the particular edge angle range value compares favorably to a threshold.
17. The method of claim 15 wherein generating the intra-prediction candidate data includes:
excluding one of the plurality of intra-prediction modes corresponding to a particular edge angle range value from the second subset when the accumulation of edge strength data for the particular edge angle range value compares unfavorably to a highest accumulation of edge strength data.