Guided ringfix algorithm for image reconstruction

A present invention, in one form, is a method for removing artifacts from image data due to detector degradation. Particularly, data is obtained from a CT system including a detector and an x-ray source. The detector is formed from a plurality of detector cells. In accordance with one form of the invention, a detector degradation signature (S(i)) vector is generated prior to scanning a patient. Subsequent to scanning the patient, if the image data contains a ring error, a detector cell contributing to the error is identified using the detector degradation signature vector. The system then determines if the cell a degraded cell. If such cell is a degraded cell, ring error correction processing is then performed on the image data.

FIELD OF THE INVENTION 
This invention relates generally to computed tomography (CT) imaging and 
more particularly, to the reconstruction of images from projection data in 
a manner that corrects the data due to errors resulting from degraded 
detectors. 
BACKGROUND OF THE INVENTION 
In CT systems, an x-ray source projects a fan-shaped beam which is 
collimated to lie within an X-Y plane of a Cartesian coordinate system, 
termed the "imaging plane". The x-ray beam passes through the object being 
imaged, such as a patient, and impinges upon a linear array of radiation 
detectors. The intensity of the transmitted radiation is dependent upon 
the attenuation of the x-ray beam by the object. Each detector of the 
linear array produces a separate electrical signal that is a measurement 
of the beam attenuation. The attenuation measurements from all the 
detectors are acquired separately to produce a transmission profile. 
The x-ray source and the linear detector array in a CT system are rotated 
with a gantry within the imaging plane and around the object so that the 
angle at which the x-ray beam intersects the object constantly changes. A 
group of x-ray attenuation measurements from the detector array at one 
gantry angle is referred to as a "view". A "scan" of the object comprises 
a set of views made at different gantry angles during one revolution of 
the x-ray source and detector. In an axial scan, 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 
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 "CT numbers" or "Hounsfield units", which are used to 
control the brightness of a corresponding pixel on a cathode ray tube 
display. 
Detector arrays are constructed from a plurality of detectors cells. The 
cells may deteriorate to the extent that artifacts are introduced to the 
images. Visually, these artifacts may appear as rings or bands in an 
image. Particularly, the lack of uniformity across the detector array and 
the lack of similarity among the degraded cells causes artifacts in the 
shape of rings or bands when a sloped object is scanned. It is desirable, 
of course, to minimize artifacts in an image. 
Known algorithms exist for removing the rings from an image. Particularly, 
the rings may be detected and corrected in the projection space or in the 
image space. For example, in a known projection space correction 
algorithm, an "error candidate vector" is generated based on the 
filtration of pre-processed projections. The vector is then compared to 
the vectors generated from previous views to determine the amount of the 
correction required. Such an algorithm is described in U.S. Pat. No. 
5,301,108, which is assigned to the present assignee. 
With another known ringfix algorithm, a ring is identified by performing a 
high pass radial filtering. In the actual implementation, the radial 
filter is approximated by the weighted sum of horizontal and vertical one 
dimensional filters. Based on the filtering result and predefined 
criteria, errors in each previously defined segment in the image is 
azimuthally combined to determine the ring error for the entire segment. 
Ring artifacts are removed by subtracting the error from the original 
image on a segment by segment basis. 
With known algorithms, the filter length cannot be adapted as a function of 
pixel size. Particularly, the kernel size and shape of the high pass 
filter is kept constant. This is non-optimal since the frequency response 
of the high pass filter depends strongly on the image pixel size. To 
maintain a relatively constant frequency response of the filter over the 
entire display field of view (DFOV), the kernel size of the boxcar filter 
needs to vary as a function of the DFOV. 
Another shortcoming of known algorithms is that such algorithms cannot be 
adapted to the error patterns. The width of a ring generated by an 
isolated channel will be roughly half as wide as the one generated by two 
adjacent channels. Therefore, in order to ensure that the filter can 
detect and correct double cell rings as effectively as the single cell 
rings, the filter length has to be adjusted accordingly. Naturally, this 
requires a-priori knowledge of the detector characteristics. 
Yet another shortcoming of known algorithms is their inability to deal with 
rings of high magnitude. Whenever a deep ring is present in the image, the 
known ringfix algorithms will correct it only to the extent of reducing 
its magnitude. After the ringfix correction, the residual ring is still 
quite visible. For many rings associated with degraded detectors, the 
magnitude of the rings are fairly deep and therefore cannot be corrected 
by the existing ringfix algorithms. 
SUMMARY OF THE INVENTION 
The present algorithm, in one form, includes generating a detector 
degradation signature (S(i)) vector. Such vector is identified prior to 
scanning a patient. In reconstructing an image from data obtained during a 
scan, and if a detector cell or channel i under examination is a degraded 
channel based on the vector S(i). the magnitude of the ring error for 
channel i associated with each ring segment is examined. If the magnitude 
of such error for a channel i is larger than a predefined threshold, the 
error data for the neighboring rings in the segment (including the ring 
associated with channel i) are modified. The ring error is then eliminated 
by subtracting the error data, as modified, from the image data. The true 
image is then reconstructed from the corrected image data. 
The present ring correction algorithm is not only effective in removing 
ring artifacts related to the degraded detectors, but also has a minimal 
impact on the processing requirements of reconstructing an image. Further, 
the vector S(i) may be used to adapt the filter kernel used to detect 
rings in an image. The filter kernel may also be modified based on the 
reconstruction DFOV and the reconstruction kernel type as explained 
hereinafter in more detail.

DETAILED DESCRIPTION OF THE DRAWING 
With reference to FIGS. 1 and 2, a computed tomography (CT) imaging system 
10 includes a gantry 12 representative of a "third generation" CT scanner. 
Gantry 12 has an x-ray source 13 that projects a beam of x-rays 14 toward 
a detector array 16 on the opposite side of gantry 12. Detector array 16 
is formed by two rows of detector elements 18 which together sense the 
projected x-rays that pass through a medical patient 15. Each detector 
element 18 produces an electrical signal that represents the intensity of 
an impinging x-ray beam and hence the attenuation of the beam as it passes 
through patient 15. During a scan to acquire x-ray projection data, gantry 
12 and the components mounted thereon rotate about a center of rotation 
19. 
Rotation of gantry 12 and the operation of x-ray source 13 are governed by 
a control mechanism 20 of CT system 10. Control mechanism 20 includes an 
x-ray controller 22 that provides power and timing signals to x-ray source 
13 and a gantry motor controller 23 that controls the rotational speed and 
position of gantry 12. A data acquisition system (DAS) 24 in control 
mechanism 20 samples analog data from detector elements 18 and converts 
the data to digital signals for subsequent processing. An image 
reconstructor 25 receives sampled and digitized x-ray data from DAS 24 and 
performs high speed image reconstruction. The reconstructed image is 
applied as an input to a computer 26 which stores the image in a mass 
storage device 29. 
Computer 26 also receives commands and scanning parameters from an operator 
via console 30 that has a keyboard. An associated cathode ray tube display 
32 allows the operator to observe the reconstructed image and other data 
from computer 26. The operator supplied commands and parameters are used 
by computer 26 to provide control signals and information to DAS 24, x-ray 
controller 22 and gantry motor controller 23. In addition, computer 26 
operates a table motor controller 34 which controls a motorized table 36 
to position patient 15 in gantry 12. 
FIG. 3 illustrates a detector 100 coupled to a switch control assembly 102. 
Detector 100 is composed of a plurality of detector cells arranged in 
rows. As explained above, each detector cell produces an electrical signal 
that represents the intensity of an impinging x-ray beam and hence the 
attenuation of the beam as it passes through a patient. The output of each 
cell is supplied to a preamplifier 104 which supplies an amplified signal 
to an analog-to-digital converter 106. The digitized signal is then 
supplied to other components (not shown) for further processing and image 
reconstruction. 
Detector 100 is illustrated for explanation purposes only. The present 
algorithm can be used in connection with many other detector 
configurations. For example, it is contemplated that rather than a 
detector with a plurality of cells in the slice (horizontal) dimension, 
i.e., a two-dimensional detector, a detector with only one cell in such 
dimension, i.e., a one-dimensional, detector may be used. 
The detector cells may deteriorate to the extent that artifacts are 
introduced to the images. As explained above, these artifacts may appear 
as rings or bands in an image. The lack of uniformity across the detector 
array and the lack of similarity among the degraded cells, for example, 
cause artifacts in the shape of rings or bands when a sloped object is 
scanned. 
The present algorithm is directed to removing such artifacts from the image 
data. Unlike known ringfix algorithms based on the assumption that each 
detector cell or channel has an equal probability of producing the error, 
the present algorithm uses a-priori knowledge to determine whether a 
detector cell is, in fact, degraded. Rather than estimating ring error 
regardless of which detector channel produces the error, the present 
algorithm performs a separate ringfix correction only for data associated 
with a degraded cell. By knowing ahead of time that certain detector 
channels are degraded, the error detection and correction process can be 
optimized to tailor to the degraded channels. In other words the 
information related to the detector health is utilized to "guide" the 
ringfix process. 
In accordance with one form of the present invention, and prior to scanning 
a patient, a detector degradation signature (S(i)) vector is created. FIG. 
4 illustrates the process steps utilized to create the detector 
degradation signature (S(i)) vector. Particularly, to identify the 
defective channels in a detector, the z-axis profile data is normalized. 
The z-axis profile data may be acquired, for example, by masking the 
detector with a very narrow slit and moving the slit at a fine increment 
while exposing the detector to x-ray flux. In this manner, only a small 
area of the detector cell is exposed to the x-ray flux at a time. The 
readings taken at different locations along z-axis produce a sensitive map 
of the detector. A map of the detector, of course, may be generated using 
other techniques. For example, a narrow x-ray beam may be sweeped across 
the detector using a pre-patient collimator. 
To ensure the accuracy of the detector deterioration assessment, only the 
portion of the data that corresponds to the actual scan exposure is used. 
For example, if the image to be corrected is 10 mm in slice thickness, 
only the center 20 mm profile on the detector is used. For example, for a 
10 mm slice, the exposed area on a detector is roughly 17.5 mm. 
Considering the drift of the x-ray focal spot due to thermal and 
mechanical movement, a margin is added on each side of the cell to cover 
the entire area that could be exposed to the x-ray flux during the scan. 
For the same reason, the z-axis profile that is used for a 5 mm scan is 
roughly the center 11 mm. 
The normalized profile is obtained by dividing the active portion of the 
profile by its average. The average value is used as a normalization 
factor since it is more reliable than the maximum value. For an ideal 
detector, this should result in a profile with a constant magnitude of 
"1". 
Next, a degradation vector representative of the degradation status of each 
detector channel i, D(i), is created 152. Such vector may be created using 
the following formula: 
##EQU1## 
where N(z,i) is the normalized z-axis profile for channel i, and 
[-.alpha., .alpha.] denote the active region of the z-axis profile. The 
term inside the integration symbol is the absolute percent error of the 
detector profile. Vector D(i) represents the area under the absolute 
percent error curve over the entire active area. In general, a higher 
value in vector D(i) indicates a more degraded detector channel. 
If every detector cell is degraded in the same manner, no ring or band 
artifact will occur. Therefore, it is preferred to estimate the detector 
degradation based on the "channel to channel" errors rather than the 
absolute error. To accomplish this objective, the degradation vector D(i) 
is high-pass filtered 154 to obtain the true channel to channel variation, 
T(i): 
EQU T(i)=.vertline.D(i)-med[D(i)].vertline. (2) 
where med is a nine point median filter. A median filter is used to 
preserve the isolated multi-channel spikes in the degradation vector. 
Based on studies, it has been determined that almost all the degraded 
detector cells are isolated to the boundary channels. Therefore, to 
eliminate the unnecessary search process, the resulting vector in Equation 
(2) is masked 156 to preserve only the 4 boundaries cells for each module 
(2 cells at each end of a module). Since every detector cell is expected 
to experience some type of degradation during its life, it is very 
important to identify only those cells that have developed safety related 
degradation. This can be accomplished by a simple thresholding operation 
on the detector degradation vector T(i) to arrive at the final detector 
degradation signature S(i) 158: 
##EQU2## 
Any error whose magnitude is less than the threshold will be set to zero. 
In vector S(i) the non-zero elements correspond to the degraded cells. 
This vector is provided as an input to the ringfix algorithm to provide 
"guidance" to the correction as explained below. 
With respect to performing ring artifact correction, and after an image is 
generated from a set of projection data, the image data is scanned to 
identify rings and bands. Algorithms for performing such detection are 
generally well known, such as the algorithm described in U.S. Pat. No. 
4,670,840. The filter kernel size for performing detection is determined 
based on the vector S(i). Particularly, if there are two or more adjacent 
degraded channels, e.g., as established from vector S(i),, the rings will 
be wider. Therefore, the filter should be adjusted to detect wider rings. 
When the DFOV is reduced, the filter kernel length increases only slightly 
to improve the ring detection. For a standard convolution filter, a 
9-point boxcar filter is used in the ringfix correction for DFOV up to 24 
cm, and a 7-point for other DFOV. 
Once a ring is identified, the ring is mapped to the detector cell(s) that 
contributed to such ring. FIG. 5 illustrates the geometry utilized in 
performing such mapping. The detector cells contributing to the ring are 
then identified. Using the vector S(i), it is determined if the ring is 
associated with any of the degraded cells. If a detected ring is 
associated with one of the cells identified by vector S(i) as a degraded 
cell, then the magnitude of the ring error for channel i associated with 
each ring segment is examined. If the magnitude is larger than a 
predefined threshold, (e.g., 0.5) the errors for the neighboring rings in 
the segment (including channel i) are modified by applying the following 
function to the image data: 
EQU e(i+m,k)=e(i+m,k)+w(i+m).beta.(m)e(i,k) For --L.ltoreq.m&lt;N (4) 
where e(n,k) is the error associated with detector channel n and ring 
segment k, w(n) is a "bat wing" type of weighting factor to account for 
the characteristics of backprojection, and .beta.(m) is a weighting 
factor. Note that .beta.(m) changes with the tomographic reconstruction 
filter and the reconstruction DFOV. FIG. 6 shows an example of the 
weighting factor, .beta.(m), for a standard filter and a DFOV of 15 cm. 
Once the error data is so modified, such modified error data is subtracted 
from the image data. The true image is then reconstructed from the 
corrected image data. 
As explained above, the present algorithm utilizes a priori knowledge of 
the detector status to guide the ringfix correction. The advantage of this 
approach is not only its effectiveness in removing ring artifacts related 
to the degraded detectors, but also its minimal impact on the 
computational requirements. 
From the foregoing description of various embodiments of the present 
invention, it is evident that the objects of the invention are attained. 
Although the invention has been described and illustrated in detail, it is 
to be clearly understood that the same is intended by way of illustration 
and example only and is not to be taken by way of limitation. Accordingly, 
the spirit and scope of the invention are to be limited only by the terms 
of the appended claims.