This invention relates generally to methods and apparatus for computed tomographic imaging systems, and more particularly to methods and apparatus for slice-adaptive multislice helical weighting for image reconstruction.
In at least one known computed tomography (CT) imaging system configuration, an x-ray source projects a fan-shaped beam which is collimated to lie within an X-Y plane of a Cartesian coordinate system and generally referred to as the xe2x80x9cimaging planexe2x80x9d. The x-ray beam passes through the object being imaged, such as a patient. The beam, after being attenuated by the object, impinges upon an array of radiation detectors. The intensity of the attenuated beam radiation received at the detector array is dependent upon the attenuation of the x-ray beam by the object. Each detector element of the array produces a separate electrical signal that is a measurement of the beam attenuation at the detector location. The attenuation measurements from all the detectors are acquired separately to produce a transmission profile.
In known third generation CT systems, the x-ray source and the detector array are rotated with a gantry within the imaging plane and around the object to be imaged so that the angle at which the x-ray beam intersects the object constantly changes. A group of x-ray attenuation measurements, i.e., projection data, from the detector array at one gantry angle is referred to as a xe2x80x9cviewxe2x80x9d. A xe2x80x9cscanxe2x80x9d of the object comprises a set of views made at different gantry angles, or view angles, during one revolution of the x-ray source and detector. In an axial scan, the projection data is processed to construct an image that corresponds to a two dimensional slice taken through the object.
One method for reconstructing an image from a set of projection data is referred to in the art as the filtered back projection technique. This process converts the attenuation measurements from a scan into integers called xe2x80x9cCT numbersxe2x80x9d or xe2x80x9cHounsfield unitsxe2x80x9d, which are used to control the brightness of a corresponding pixel on a cathode ray tube display. Known single slice helical weighting algorithms typically rely on the conjugate measurements available for each ray for a 2xcfx80 data set. Some known algorithms utilize significantly more than one source rotation, but these algorithms lead to broadened slice sensitivity profiles (SSP). Accordingly, for a single slice system using 2xcfx80 worth of data, there exists little flexibility: two measurements are used to interpolate/extrapolate data onto a plane of reconstruction (POR). This algorithm does not vary with the helical pitch. At higher pitches, the measurements simply lie, on average, further away from the POR. Thus, SSP degrades monotonically with pitch.
In some CT imaging systems, the detector array is segmented so that a plurality of quasi-parallel slices of projection data is acquired and processed to construct a plurality of images corresponding to several slices though a volume. Such CT imaging systems are referred to as xe2x80x9cmultislicexe2x80x9d systems. Multislice systems provide availability of more data. Therefore, looking up the conjugate ray and weighting accordingly as in single slice systems either leads to undesirable SSP broadening (such as in a xe2x80x9cHigh Speedxe2x80x9d mode, defined below) or a breakdown when a larger number of slices is considered (as when changing from 4 to 8 slices in xe2x80x9cHigh Qualityxe2x80x9d mode). Another phenomenon associated with an increasing number of slices in multislice systems is that of helical weight cycles across a given projection fan. These helical weight cycles have a detrimental effect on image quality.
Increased flexibility in the choice of row or rows that provide data closest to the POR is possible with increasing amounts of data such as that made available by multislice CT imaging systems. This flexibility may exist at both ends of a given ray (i.e., for both conjugate measurements for the same ray through the patient). Where such flexibility is provided, a range of pitches exists for which measurements are available at least at two source locations. Measurements acquired at different source positions are known as xe2x80x9cconjugate measurements.xe2x80x9d Pitches for which conjugate measurements are available are known as xe2x80x9cHigh Qualityxe2x80x9d (HQ) pitches. When an HQ pitch is used, a CT imaging system is said to operate in xe2x80x9cHQ mode.xe2x80x9d For a range of pitches for which measurements are available at only one source position, the availability of multislice data allows interpolation/extrapolation from different rows at the same source position. These pitches are known as xe2x80x9cHigh Speedxe2x80x9d (HS) pitches. When an HS pitch is used, a CT imaging system is said to operate in xe2x80x9cHS mode.xe2x80x9d Higher HS pitches are made possible by allowing data extrapolation.
For a given helical pitch in HQ mode, a set of Radon points (or equivalently, a set of lines through a patient slice of interest) exists for which the conjugate measurements are offset by exactly half of the detected aperture xcex94z. (xe2x80x9cDetector aperturexe2x80x9d refers to an aperture on isocenter associated with one xe2x80x9cmacro detector row,xe2x80x9d or one measurement). For example, in one known four-slice scanner, a 3:1 pitch is selected for HQ mode because for all rays through isocenter xcex3=0), the conjugate measurements are exactly offset by xcex94z/2, half the aperture. In such a situation, a minimal z interpolation width is often used. However, this ideal sampling situation occurs only at a limited number of Radon points. Further away from isocenter, the offset gradually departs from optimum. The greater the number of rows (and correspondingly, the higher the pitch), the more rapid the departure from optimum. Further, a set of Radon points exists where the conjugate measurements exactly xe2x80x9cfacexe2x80x9d one another (i.e., the z-offset between the two is zero). Although this situation is optimal when the associated rays are exactly in a plane of reconstruction (as these rays in effect define measurements as good as would be acquired in axial (step-and-shoot) scanning), sampling is at its worst when both rays are offset from the POR by exactly xcex94z/2. In the latter case, the POR lies exactly between two detector rows for both conjugate source positions.
An interpolation width equal to xcex94z (base of triangle) leads to artifacts in the reconstructed image. In situations in which two conjugate measurements are exactly at the same z location, the only interpolation/extrapolation possible from these two samples (and these two samples only) with such a narrow interpolation width is a zeroth-order extrapolation of the measurements to the POR Indeed, if the two measurements are separated by xcex5 in z, attempts at linear interpolation/extrapolation might be unstable due to noise and other inconsistencies in the projection data. While zeroth-order extrapolation per se might be acceptable, it leads to sudden discontinuities in the data synthesized at the POR when both measurements become offset from the POR by xcex94z/2. In this case, and as the z-offset varies with source rotation, the rows that are used for interpolation are incremented by 1. The measurements suddenly jump from row i to row i+1 (or ixe2x88x921), introducing discontinuities that are amplified by the reconstruction filter and lead to streaks across the reconstructed image. Artifacts become particularly objectionable when the projection data vary rapidly from one row to the next, as when the patient anatomy varies rapidly in z.
Using 2xcex94z or any row-to-row interpolation, polynomial, etc., as a basis for a linear interpolation function leads to image quality trade-offs. Row-to-row discontinuities are reduced, as is the likelihood of streaking in a reconstructed image, but at a cost of significant slice broadening and z-resolution loss.
In one known helical weighting for a fan-beam, all rays corresponding to one row at a 6 o""clock source position are exactly in the POR, and accordingly should be weighted uniformly to 1.0. However, due to the helical scanning, conjugate rays across a conjugate arc will have variable distance in z from a corresponding conjugate detector row and the POR, and accordingly would see varying weights between 0 and 1.0. Upon normalizing the weights so that the sum of the weights is equal to 1.0, a weight function is obtained having a variation as a function of the fan-angle xcex3 across the Scan Field of View (SFOV). This variation can lead to artifacts in reconstructed images because the derivative of the weight is discontinuous and a high-pass reconstruction filter is applied along xcex3.
With minimum width linear interpolation, rays across a reconstruction plane exist such that for both conjugate source locations, both measurements lie exactly between two detector rows, leading to a vanishing total weight. In practice, minimum width interpolation leads to a zeroth-order extrapolation estimate from two conjugate rows. With a small displacement of the ray in the reconstruction plane, the two row indices from which the estimate is defined are changed by 1. Therefore, any abrupt change in the data from row-to-row will lead to a discontinuity in the estimate. This discontinuity, after filter and backprojection, in turn leads to a streak across the reconstructed image.
It would therefore be desirable to provide methods and apparatus that avoid such undesirable SSP broadening and/or breakdown. It would also be desirable to provide methods and apparatus that reduce the detrimental effect of helical weight cycles on image quality. In addition, it would also be desirable to provide methods and apparatus that provide row-to-row interpolation only when justified by an underlying sampling.
There is therefore provided, in one embodiment of the present invention, a method for reconstructing at least one image representative of an object. This embodiment includes steps of: helically scanning the object with a multislice computed tomographic imaging system to acquire data representative of a plurality of computed tomographic image slices of an object including measurement data representative of conjugate rays; performing a minimum width helical interpolation of the acquired data to determine conjugate ray weights; increasing interpolation width when a sum of the conjugate ray weights is less than a threshold value; and filtering and backprojecting data to produce at least one image representative of the object. The filtering and backprojecting step includes weighting interpolated measurement data representative of conjugate rays using the increased interpolation width.
Application of an adaptive, helical weighting algorithm leverages resolution inherently available from the available data, while reducing the likelihood of artifacts (such as streaks) arising from data inconsistencies across conjugate locations across rows.
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