Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T5/00—Image enhancement or restoration
  • G06T5/77—Retouching; Inpainting; Scratch removal
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T11/00—2D [Two Dimensional] image generation
  • G06T11/003—Reconstruction from projections, e.g. tomography
  • G06T11/005—Specific pre-processing for tomographic reconstruction, e.g. calibration, source positioning, rebinning, scatter correction, retrospective gating
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01T—MEASUREMENT OF NUCLEAR OR X-RADIATION
  • G01T1/00—Measuring X-radiation, gamma radiation, corpuscular radiation, or cosmic radiation
  • G01T1/29—Measurement performed on radiation beams, e.g. position or section of the beam; Measurement of spatial distribution of radiation
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
  • A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
  • A61B6/03—Computed tomography [CT]
  • A61B6/032—Transmission computed tomography [CT]
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
  • A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
  • A61B6/5205—Devices using data or image processing specially adapted for radiation diagnosis involving processing of raw data to produce diagnostic data
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/10—Image acquisition modality
  • G06T2207/10072—Tomographic images
  • G06T2207/10081—Computed x-ray tomography [CT]
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/20—Special algorithmic details
  • G06T2207/20212—Image combination
  • G06T2207/20221—Image fusion; Image merging

Definitions

• the present invention relates to the field of X-Ray imaging and, in particular, relates to a method and an apparatus for data recovery of raw CT projection data as well as a CT imaging system.
• raw data collected from a detector is arranged into a two-dimensional matrix with a horizontal axis of detector channel and a vertical axis of scan field of view as raw CT projection data of the reconstructed image, which is also referred to as a sinogram and is essentially a winding superimposition of curves formed by the points on the image.
• CT projection data is reconstructed as an image with a size of 512 X 512
• any point on the image is a curve trace in the matrix of the raw CT projection data.
• the amplitude of the curve trace depends on a distance of the point from a center of rotation for virtual collection, in which the greater the distance, the greater the amplitude.
• the phase of the curve trace depends on a position of the point on a certain circle with the center of rotation as the center of the circle.
• One object of the present invention is to provide a method and an apparatus that can recover CT raw projection data more accurately, as well as a CT imaging system employing the apparatus.
• An exemplary embodiment of the present invention provides a method for data recovery of raw CT projection data, comprising: determining a region to be estimated and a trusted region adjacent to the region to be estimated in raw CT projection data; and performing at least one of a first processing and a second processing.
• the first processing comprises: performing data fitting for CT projection data of the trusted region to obtain a space curved surface equation; and re-estimating CT projection data of the region to be estimated according to the space curved surface equation.
• Said second processing comprises: acquiring multiple texture orientation information distributed in the trusted region according to the CT projection data of the trusted region; determining one or more matching lines in the region to be estimated according to each of the texture orientation information, each matching line passing through at least one data point to be estimated and being matched with at least one texture orientation information; performing interpolation operation along the matching lines, to re-estimate the CT projection data of the region to be estimated.
• An exemplary embodiment of the present invention further provides an apparatus for data recovery of raw CT projection data, comprising a region determination module and at least one of a first processing module and a second processing module.
• the region determination module is to determine a region to be estimated and a trusted region adjacent to the region to be estimated in the raw CT projection data.
• Said first processing module comprises a data fitting unit and a first estimation unit.
• the data fitting unit is to perform data fitting for the CT projection data of the trusted region to obtain a space curved surface equation.
• the first estimation unit is to re-estimate the CT projection data of the region to be estimated according to the space curved surface equation.
• the second processing module comprises a texture orientation information acquisition unit, a matching line determination unit and a second estimation unit.
• the texture orientation information acquisition unit is to acquire multiple texture orientation information distributed in the trusted region according to the CT projection data of the trusted region;
• the matching line determination unit is to determine one or more matching lines in the region to be estimated according to each of the texture orientation information, each matching line passing through at least one data point to be estimated and being matched with at least one texture orientation information;
• the second estimation unit is to perform interpolation operation along said one or more matching lines, to re-estimate the CT projection data of the region to be estimated.
• An exemplary embodiment of the present invention further provides a CT imaging system, comprising a bulb tube to emit X-rays to an object to be scanned, a detector to receive the X-rays that pass through the object to be scanned to generate said raw CT projection data and an apparatus for data recovery of CT raw projection data as stated above.
• Fig. 1 is a flow chart of a method for correcting CT image provided by one embodiment of the present invention
• Fig. 1 is a flow chart of a method for data recovery of raw CT projection data provided by a first embodiment of the present invention
• Fig. 2 is the raw CT projection data acquired in one exemplary embodiment of the present invention.
• FIG. 3 is a schematic diagram of determining a matching line in a region to be estimated according to texture orientation information of CT projection data of a trusted region, in one exemplary embodiment of the present invention
• FIG. 4 is a flow chart of a method for data recovery of raw CT projection data provided by a second embodiment of the present invention.
• FIG. 5 is a block diagram of an apparatus for data recovery of raw CT projection data provided by a third embodiment of the present invention.
• FIG. 6 is a block diagram of an apparatus for data recovery of raw CT projection data provided by a fourth embodiment of the present invention.
• FIG. 7 is a block diagram of a CT imaging system provided by a fifth embodiment of the present invention.
• Fig. 1 is a flow chart of a method for data recovery of raw CT projection data provided by a first embodiment of the present invention.
• the above mentioned raw CT projection data may be, for example, projection data collected from a detector channel of the CT imaging system, or projection data subjected to some pre-processing after being collected, in which the pre-processing may comprise, by way of example, removing dark current by offset correction, removing fluctuation of radial energy in respective fields of view by reference channel correction, removing non-uniformity of initial incoming energy of respective channels by aircal correction, removing inconsistency in absorption between high and low energy rays by beam hardening correction, converting data into summations in theory by -In mathematical transformation, and so on.
• Fig. 2 is the raw CT projection data acquired in one exemplary embodiment of the present invention.
• the horizontal axis in Fig.2 denotes the detector channel and the vertical axis in Fig.2 denotes the scan field of view.
• Each data point in the raw CT projection data is a superimposition of data collected in corresponding scan fields of view by corresponding detection channels.
• the method for data recovery of raw CT projection data comprises the following steps:
• a region determination step S103 determining a region to be estimated and a trusted region adjacent to the region to be estimated in the raw CT projection data.
• the raw CT projection data may have a region with Idata of low credibility, the CT projection data of each point in the region needs to be re-estimated so as to perform image reconstruction more precisely.
• the region with low credibility is just the region to be estimated.
• the region A in Fig.2 may be determined as a region to be estimated, through observation by naked-eye or data analysis by computer or the like.
• regions B1 and/or B2 with relatively high data credibility adjacent to the region A may be determined as trusted regions. It should be noted that the above determined trusted region and region to be estimated are not limited in their shape or size.
• data recovery may be performed for the region to be estimated with data characteristics of the trusted region, for example, at least one of a first processing and a second processing may be performed.
• the data characteristics of the trusted region may comprise, for example, CT projection values and coordinate values of the data points, texture orientation information of the trusted region and so on, in which the above mentioned texture orientation information may represent, e.g., texture orientation of the curve traces in the trusted regions B1 , B2 in Fig.2.
• the above mentioned first processing comprises a data fitting step S105 and a first estimation step S1 07.
• the data fitting step S105 performing data fitting for the CT projection data of the trusted region to obtain a space curved surface equation.
• the first estimation step S1 07 re-estimating the CT projection data of the region to be estimated according to the above mentioned space curved surface equation.
• data fitting may be performed for the CT projection data in the trusted regions B1 and B2 in Fig.2, and the above mentioned data fitting may comprise, e.g., least square data fitting.
• re-estimating the CT projection data of the region to be estimated according to the space curved surface equation comprises: taking coordinates and CT projection values of a data point to be estimated in the region to be estimated as input values for the above mentioned space curved surface equation, and calculating output values of the space curved surface equation as a low-frequency part of the new CT projection data of the data point.
• the low-frequency data in the CT projection data of the trusted region may be acquired prior to data fitting, for example, the low-frequency data in the raw CT projection data may be acquired by performing low-pass filtering for the raw CT projection data. Then in the data fitting step S1 05, data fitting may be performed for the low-frequency part of the CT projection data of the trusted region to obtain the space curved surface equation.
• the above mentioned second processing comprises a texture orientation information acquisition step S109, a matching line determination step S1 1 1 , a second estimation step S1 1 3.
• the texture orientation information acquisition step S109 acquiring multiple texture orientation information distributed in the trusted region according to the CT projection data in the trusted region.
• the matching line determination step S1 1 1 determining one or more matching lines in the region to be estimated according to each of the texture orientation information, each matching line passing through at least one data point to be estimated and being matched with at least one texture orientation information.
• the second estimation step S1 1 3 performing interpolation operation along the above mentioned one or more matching lines, to re-estimate the CT projection data of the region to be estimated.
• Fig.3 is a schematic diagram of determining a matching line in a region to be estimated according to texture orientation information of CT projection data of a trusted region, in one exemplary embodiment of the present invention. As shown in Fig.3, for the curve trace on the CT projection data, since its coordinates (or data points) vary continuously, its texture orientation information may comprise multiple specific directions along the curve trace.
• Each of the specific directions may be represented in a number of forms, for example, only in the form of stored data (such as angle, slope and the like of the curve trace at the corresponding coordinate position), or may also be represented by respective directional lines L1 shown in Fig.3, wherein each directional line L1 has a specific direction, i.e. has a specific angle or slope, representing that there is a curve trace in the direction pointed by the directional line L1 , or may be understood as: the directional line L is a part of a certain complete curve trace.
• the directional lines may be lines with a relatively short length.
• texture orientation information represented in other forms may also be acquired as long as it can represent the trend of the curve trace on the CT projection data.
• the texture orientation information acquisition step S109 may comprise: acquiring multiple texture orientation information distributed in the trusted region according to the high-frequency data in the CT projection data of the trusted region.
• the high-frequency data in the CT projection data of the trusted region may be acquired first prior to acquiring the texture orientation information, to acquire the corresponding texture orientation information according to the high-frequency data.
• the high-frequency data in the CT projection data of the trusted region may be acquired through the following two ways: one may be calculating the low-frequency data of the CT projection data of the trusted region according to the space curved space equation obtained in the data fitting step S105 (for example, taking the coordinates and the CT projection values of the data points to be estimated of the trusted region as input values to be introduced into the space curved surface equation and using the output values as the corrsponding low-frequency data), and subtracting the low-frequency data of the CT projection data of the trusted region calculated according to the space curved surface equation from the raw CT projection data of the trusted region, to obtain the corresponding high-frequency data; the other one may be performing filtering and strengthening for the raw CT projection data to obtain the raw high-frequency data, wherein the above high-frequency data in the CT projection data of the trusted region may be determined from the raw high-frequency data directly.
• the texture orientation information acquisition step S109 may comprise: filtering the CT projection data of the trusted region with a filter to acquire multiple texture orientation information distributed in the trusted region.
• filtering may be performed for only the raw CT projection data in the trusted region or for only the high-frequency part therein; filtering may also be performed for the raw CT projection data in the whole data region or the high-frequency part therein to obtain the texture orientation information on the whole data region, and the texture orientation information distributed in the trusted region may be determined directly from the texture orientation information of the whole data region.
• the above mentioned filter may comprise a Gabor filter, for example, Gabor filters of multiple directions may be generated. Said Gabor filters of multiple directions are used to filter the data to be filtered in the corresponding directions, and the directional result of each Gabor filter is just the highest amplitude of the Gabor filtering response at each data point, i.e., the above mentioned respective texture orientation information.
• the texture orientation information obtained with the Gabor filters may comprise multiple directional lines as shown in Fig.3.
• filters in other forms may also be used as long as the texture orientation information of the CT projection data can be obtained.
• the matching line determination step S1 1 1 may comprise a straight line determination step and a matching step:
• the straight line determination step determining, for each data point to be estimated, one first straight line passing through the data point.
• the matching step if the first straight line can rotate about the data point as a center of rotation to a matching angle, the first straight line at the matching angle is determined as one matching line. Specifically, at the matching angle, the angles formed by connecting the first straight line with at least two directional lines are less than or equal to a preset angle respectively. The above mentioned at least two directional lines are located at opposite sides of the data point to be estimated. The matching precision may be adjusted by adjusting the above preset angle; for example, if the preset angle is set to be 0, only one first straight line that is exactly in line with two directional lines at opposite sides of the data point P1 can be considered as the matching line.
• one straight line L1 may be at first determined with a horizontal angle or another angle as a starting point for the data point P1 in Fig.3, and the first straight line may be rotated about the data point P1 as a center of rotation within 360 degrees. If the straight line L1 is approximately in line with the directional lines D1 and D2 at opposite sides of the data point P1 after being rotated 30 degree, one matching line can be determined at the orientation of 30 degree; if the straight line L1 is approximately in line with the directional lines D3 and D4 at opposite sides of the data point P1 after being rotated 150 degree, one matching line can be determined at the orientation of 150 degree.
• the straight line L1 may also be determined as the matching line. That is, it is only required that each matching line is matched with at least one texture orientation information.
• the matching lines determined in the region to be estimated are not along the detecting channel direction or not all of them are along the detecting channel direction, i.e., are not horizontal lines or vertical lines or not all of them are horizontal lines or vertical lines, and they may be oblique lines.
• the matching line may be understood as representing the trend of the curve trace in the region to be estimated, which, for example, can smoothly be connected to one curve trace matched therewith in the trusted region.
• Fig.4 is a flow chart of a method for data recovery of raw CT projection data provided by a second embodiment of the present invention, which is similar to the first embodiment except that: in the second embodiment, the above mentioned second processing may further comprise a comparison step S1 15 and a matching line classification step S1 1 7.
• the comparison step S1 15 comparing a data intensity at a data point corresponding to the texture orientation information (e.g., directional lines D1 and/or D2) matched with each matching line with a preset data intensity;
• the texture orientation information e.g., directional lines D1 and/or D2
• the matching line classification step S1 17 if the data intensity at the data point corresponding to the texture orientation information matched with anyone of the matching line is greater than or equal to the preset data intensity, the corresponding matching line is determined as the first matching line; otherwise, if the data intensity at the data point corresponding to the texture orientation information (e.g., directional lines D1 and/or D2) matched with any matching line is less than the preset data intensity, the corresponding matching line is determined as the second matching line.
• the texture orientation information e.g., directional lines D1 and/or D2
• performing interpolation operation along the above mentioned one or more matching lines comprises: only choosing to perform interpolation operation along the first matching line. For the second matching line, it may be ignored.
• Performing interpolation operation only along the first matching line may be understood as: searching for a trace matched with the curve trace having a relatively high data intensity in the region to be estimated and performing interpolation operation along the trace.
• the above mentioned data intensity means an absolute value of the CT projection value at the corresponding data point.
• the method may only perform the first processing or only perform the second processing.
• data fitting may be performed only for the low-frequency data to re-estimate the low-frequency part of the CT projection data of the region to be estimated, or interpolation operation may be performed only for the high-frequency data along a matching line after the matching line is determined in the region to be estimated.
• the method for data recovery of raw CT projection data of the present invention may comprise the above mentioned first processing and second processing and may further comprise summation processing step S1 19: performing summation for the CT projection data estimated in the first estimation step and the CT projection data estimated in the second estimation step, to facilitate subsequent image reconstruction according to the CT projection data after summation.
• Fig. 5 is a block diagram of an apparatus for data recovery of raw CT projection data provided by a third embodiment of the present invention.
• the apparatus for data recovery of raw CT projection data comprises a region determination module 1 00 and at least one of the first processing module 200 and the second processing module 300. Specifically:
• the region determination module 100 may be used to determine a region to be estimated and a trusted region adjacent to the region to be estimated in the raw CT projection data.
• the first processing module 200 may comprise a data fitting unit 230 and a first estimation unit 250.
• the data fitting unit 230 may be used to perform data fitting for the CT projection data of the trusted region to obtain a space curved surface equation.
• the first estimation unit 250 may be used to re-estimate the CT projection data of the region to be estimated according to the space curved surface equation.
• the first estimation unit 250 may take coordinates and CT projection values of a data point to be estimated in the region to be estimated as input values for the above mentioned space curved surface equation, and calculate output values of the space curved surface equation as the low-frequency part of the new CT projection data of the data point.
• the above mentioned second processing module 300 may comprise a texture orientation information acquisition unit 31 0, a matching line determination unit 320 and a second estimation unit 330.
• the texture orientation information acquisition unit 31 0 may be used to acquire multiple texture orientation information distributed in the trusted region according to the CT projection data of the trusted region.
• the texture orientation information acquisition unit 310 may acquire multiple texture orientation information distributed in the trusted region according to the high-frequency data in the CT projection data of the trusted region.
• the method for obtaining the high-frequency data in the CT projection data of the trusted region has been introduced in the above mentioned first embodiment and would not be repeated in details here.
• the texture orientation information acquisition unit 310 may filter the CT projection data of the trusted region with a filter to acquire multiple texture orientation information distributed in the trusted region.
• the above mentioned filter may comprise a Gabor filter.
• the matching line determination unit 320 may be used to determine one or more matching lines in the region to be estimated according to each of the texture orientation information, each matching line passing through at least one data point to be estimated and being matched with at least one texture orientation information. [0067] Optionally, the matching line determination unit 320 may determine, for each data point to be estimated, one first straight line passing through the data point. If the first straight line can rotate about the data point as a center of rotation to a matching angle, the matching line determination unit 320 may determine the first straight line at the matching angle as one matching line.
• the second estimation unit 330 may be used to perform interpolation operation along the above mentioned one or more matching lines, to re-estimate the CT projection data of the region to be estimated.
• the apparatus may only include the first processing module 200 to perform data fitting for the trusted region adjacent to the region to be estimated to obtain the space curved surface equation, and to re-estimate the CT projection data of the region to be estimated according to the space curved surface equation; or the apparatus may only include the second processing module 300 to determine the matching line and to perform interpolation along the matching line, to re-estimate the CT projection data of the region to be estimated.
• Fig. 6 is a block diagram of an apparatus for data recovery of raw CT projection data provided by a fourth embodiment of the present invention.
• the fourth embodiment is similar to the third embodiment except that, in the fourth embodiment, the second processing module 300 may further comprise an intensity comparison unit 350 and a matching line classification unit 370.
• the intensity comparison unit 350 may be used to compare the data intensity at the data point corresponding to the texture orientation information matched with each matching line with a preset data intensity.
• the matching line classification unit 370 is used to determine a corresponding matching line as a first matching line or a second matching line based on the comparison result; for example, if the data intensity at the data point corresponding to the texture orientation information matched with any matching line is greater than or equal to the preset data intensity, the matching line classification unit 370 determines the corresponding matching line as the first matching line; if the data intensity at the data point corresponding to the texture orientation information matched with any matching line is less than the preset data intensity, the matching line classification unit 370 determines the corresponding matching line as the second matching line.
• the second estimation unit 330 may only choose to perform interpolation operation along the first matching line while ignoring the second matching line.
• the apparatus for data recovery of CT raw projection data of the third embodiment may only comprise the first processing module 200 while excluding the second processing module 300, and the first processing module 200 may be specifically used to re-estimate the low-frequency data of the region to be estimated; the apparatus for data recovery of CT raw projection data of the third embodiment may also only comprise the second processing module 300 while excluding the first processing module 200, and the second processing module 300 may be specifically used to re-estimate the high-frequency data of the region to be estimated.
• the difference of the fourth embodiment from the third embodiment may also be that the apparatus for data recovery of CT raw projection data may comprise both the first processing module 200 and the second processing module 300, where the apparatus for data recovery of CT raw projection data may further comprise a summation processing module 600 for performing summation operation for the CT projection data estimated by the first processing module 200 and the CT projection data estimated by the second processing module 300.
• Fig.7 is a block diagram of a CT imaging system provided by a fifth embodiment of the present invention.
• the CT imaging system comprises a bulb tube 710, a detector 720 and an apparatus 730 for data recovery of CT raw projection data.
• the bulb tube 710 may be used to emit X-rays to an object to be scanned
• the detector 720 may be used to receive the X-rays that pass through the above mentioned object to be scanned to generate raw CT projection data
• the apparatus 730 for data recovery of raw CT projection data may be used to perform data recovery for the raw CT projection data, to perform image reconstruction.
• the above mentioned apparatus 730 for data recovery of CT raw projection data may be an apparatus for data recovery of CT raw projection data in the embodiment shown in Fig.5 or Fig.6.
• the embodiments of the present invention by determining a trusted region adjacent to a region to be estimated in raw CT projection data and performing data fitting for the CT projection data in the trusted region to obtain a space curved surface equation for recovering the CT projection data in the region to be estimated, or by determining a matching line in the region to be estimated that is matched with the texture orientation information of the CT projection trace in the trusted region and performing interpolation operation along the matching line that may represent the actual trend of the CT projection trace, the embodiments of the present invention realize interpolation operation along the CT projection trace instead of interpolation within the channel row or interpolation between channels with the same opening angle between rows, by which the recovered data is more precise and quality of the acquired CT image is better.

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