The application of CT (Computed Tomography) in radiation therapy treatment planning has tremendously increased in recent years. Indeed, the CT information is essential in two aspects of treatment planning: a) delineation of target volume and the surrounding structures in relation to the external contour; and b) providing quantitative data, i.e. the attenuation coefficients converted into CT numbers in units of Hounsfield, for tissue heterogeneity corrections. For instance, in the treatment of prostate cancer, contouring the prostate and simulating the dose distribution are essential for planning. Meanwhile, the image artifacts produced by metal hip prostheses (see
Metal artifacts are a significant problem in x-ray computed tomography. Metal artifacts arise because the attenuation coefficient of a metal in the range of diagnostic X-rays is much higher than that of soft tissues and bone. The results of scanning a metal object are gaps in CT projections. The reconstruction of gapped projections using standard CT reconstruction algorithms, i.e. filtered backprojection (FBP), causes the effect of bright and dark streaks in CT images (
Many different techniques have been proposed to reduce metal artifacts in literature. Some techniques suggested to replace the metal implants with less attenuating materials or to use higher energy x-ray beams for preventing metal artifacts. Others used image windowing techniques to reduce the appearance of artifacts in the images. However, these case-by-case solutions are not ideal for most clinical applications. The most efficient methods work on the raw projection data, i.e. the matrix of ray attenuations related to different angles acquired by the CT scanner. In iterative reconstruction methods, the projection data associated with metal objects are disregarded and reconstruction is applied only for non-corrupted data. Briefly, in these methods, an initial guess of the reconstructed image is made and then the projections obtained of this initial image are compared to the raw projection data. By iteratively reconstructing projection ratios and applying an appropriate correction algorithm for initial image, an improved estimate of the image is obtained. Although these algorithms are reliable for incomplete/noisy projection data, they must deal with convergence problems and they are computationally expensive for clinical CT scanners (even with their fast implementation). In projection interpolation based methods, the projection data corresponding to rays through the metal objects are considered as missing data. A prior art technique identified manually the missing projections and replaced them by interpolation of non-missing neighbor projections. A prior art technique used a linear prediction method to replace the missing projections. In other work, a polynomial interpolation technique is used to bridge the missing projections. A wavelet multiresolution analysis of projection data is also proposed to detect the missing data and interpolate them. Although these methods do not increase significantly the computational cost, they have achieved varying degrees of success and appear to depend on the complexity of the structures examined and may still result in artifacts in the final reconstruction.
A prior art technique uses another strategy for computing the interpolation value by the sum of weighted nearest not-affected projection values within a window centered by the missing projection. The weights are modeled only based on the distance. Although they exploit the contribution of not-affected projections in all directions to determine the replacement values, they do not preserve the continuity of the structure of these projections. Furthermore, because there is no continuity between resulting replacement values, the risk of noise production is also high. In a prior art technique, we used an optimization scheme exploiting both the distance and the value of not affected projections to determine the interpolation values and by using still an interpolation scheme to preserve the continuity of replacement values. This new scheme computed more effectively the interpolation values based on the structure of nearest not affected projections and resulted an excellent performance in the case of hip prosthesis.
Although the interpolation-based methods do not increase significantly the computational cost and achieve a good degree of success in image quality for the case of hip prosthesis, their performance is severely degraded in the presence of multiple and closed metallic objects such as dental fillings. Indeed, these methods are so sensitive to the correct detection of the missing projections. When multiple and closed metallic implants are present in the field of view of scanner, it is so difficult to exactly distinguish the missing projections due to each metallic objects by the sinogram. Consider the case of dental fillings (
A prior art technique proposes an adaptive filtering approach for MAR. First a tissue class model is created from initial CT image. Then a model sinogram is generated using this class and compared with original sinogram to identify and to replace missing projection. The difference between original and model sinograms is downscaled and then filtered adaptively. The corrected sinogram is used to regenerated the CT image. Although they used a more sophisticated approach for the metal detection step, their replacement scheme cannot achieve a good estimation of original values for the case of dental implants and resulted many false labellings near the metallic implants A prior art technique studies the metal artifacts in the wavelet domain especially for the case of dental fillings. Their approach consists of using a scale-level dependent of linear interpolation of wavelet coefficients of sinogram to reveal the corrupted data and a linear-interpolation scheme to replace missing projections. Although the use of wavelet domain aids to more implicitly detection of metal traces due to multiple metallic objects in the sinogram, their replacement scheme has still disadvantages of interpolation based methods. Moreover, an extremely delicate optimal selection of weight parameters for wavelet interpolation is required in this algorithm.
Our observation is that a most efficient replacement scheme can afford a more sophisticated metal artifact reduction method especially for the complex case of dental fillings. We propose a new replacement scheme to modify the sinogram containing the missing projections by searching the relevant replacement values in the opposite direction of original values, contrary to interpolation based scheme in which replacement values are computed artificially using nearest non-affected projections. Although this new replacement scheme is also based first on detecting of metallic objects, it is much less sensitive to this step. This approach is especially applicable in Head and Neck cases with metal implants such as dental fillings and produces significantly better quality CT images than interpolation-based MAR algorithms.
In an embodiment, the present invention provides a method for reducing artifacts in an original computed tomography (CT) image of a subject, the original (CT) image being produced from original sinogram data. The method comprises detecting an artifact creating object in the original CT image; re-projecting the artifact creating object in the original sinogram data to produce modified sinogram data in which missing projection data is absent; interpolating replacement data for the missing projection data; replacing the missing projection data in the original sinogram data with the interpolated replacement data to produce final sinogram data; and reconstructing a final CT image using the final sinogram data to thereby obtain an artifact-reduced CT image.
In an embodiment a CT scanner device capable of reducing artifacts in an original computed tomography (CT) image of a subject, the original (CT) image being produced from original sinogram data. The CT scanner comprising:
An approach for metal artifact reduction is proposed that is practical for use in radiation therapy. It is based on interpolation of the projections associated with metal implants at helical CT (computed tomography) scanner. The present invention comprises an automatic algorithm for metal implant detection, a correction algorithm for helical projections, and a more efficient algorithm for projection interpolation. Moreover, this approach can be used clinically as complete modified raw projection data is transferred back to the CT scanner device where CT slices are regenerated using the built-in reconstruction operator. So, all detail information on scanner geometry and file format is preserved and no changes in routine practices are needed. The validations on a CT calibration phantom with various inserts of known densities prove the efficiency of the algorithm to improve the overall image quality and more importantly to preserve the form and the representative CT number of objects in the image. The results of application of the algorithm on prostate cancer patients with hip replacements demonstrate the significant improvement in image quality and allow a more precise treatment planning.
There are no automatic and robust algorithms for metal artifact reduction which can be practical for routine clinical applications. The goal of this work is to investigate a clinical approach to effectively improve the quality of the helical CT images in the presence of metal artifacts for treatment planning process. The approach is based on the projection interpolation because of its simplicity and speed. The results are presented for both phantom and patient images obtained with a Helical-CT scanner (Siemens, Somatom).
This approach has three main advantages; i) the algorithm can be used clinically as we currently use it as a pre-processing technique for prostate treatment planning; ii) the metal markers which are used for virtual simulation planning are also another source of artifacts with a much lower degree of importance and should not be eliminated from CT images. These markers can be easily distinguished from other metal objects and will be maintained for other processing; iii) virtual simulation is a tool for planning and designing radiation therapy treatment. Since the virtual simulation needs the parameters produced during the patient scanning, we transfer the modified projection data back to the scanner device and use its built-in reconstruction operators. Thus, the routine application will be the same and all detail information on scanner geometry and file format will be maintained.
This clinical approach for metal artifact reduction can be successfully applied for the therapy treatment planning. This technique brings three improvements to the conventional approaches for metal artifact reduction using projection interpolation scheme. These improvements are adapted to the clinical application. The proposed algorithm can be applied for helical and non-helical CT scanners. In both phantom experiment and patient studies, the algorithm resulted in significant artifact reduction with increases in the reliability of planning procedure for the case of metallic hip prostheses. This algorithm is currently used as a pre-processing for prostate planning treatment in presence of metal artifacts.
These and other features, aspects and advantages of the present invention will become better understood with regard to the following description and accompanying drawings wherein:
In a first example, the algorithm is based on the interpolation of missing projections in raw projection data. The modified projection data is used to generate slice images by scanner standard reconstruction algorithm. No further modification in the employed operators is required for this reconstruction. The resulting tomographies are still subject to minor artifact in the area near to the boundary of metal implants, but there are significant gains in image quality for regions of interest such as prostate.
Three extensions are introduced: the first step is to detect the projections affected by metal implants. Some authors proposed to isolate the correspondence of the metal implants directly from the projection, but have difficulties to fix the appropriate thresholds because of the complex structure of the projection data. Others are identifying the sinusoidal curves resulting from metal implant in the projection data. Although these approaches are interesting, they still need to fix some parameters and studies are limited to parallel projections. In this algorithm, the metal prostheses are identified quasi-automatically from reconstructed images. First, we reconstruct an initial image from the 360 degrees raw helical projection data using fan-beam FBP (see
In helical scanning, the patient is transported continuously as the tube and detector rotate around the patient. So, during one rotation (360 degrees) of tube, the patient may be translated from 1 mm to 10 mm for typical procedures. In this interval, the metallic prostheses may change orientation or undergo a deformation. To precisely detect the missing projections in helical raw projection data, we make a correction for reprojected metal implant regions adapted to these changes.
In conventional algorithms for replacing the missing projection, an interpolation scheme is generally applied using the projected edges for the same view angle. Although this strategy reduced the artifacts due to metal objects, the resulting tomographies are still subject to additional artifacts. Indeed, these additional artifacts are due to the destruction of boundary of other objects in the area of interpolated projections.
where x and y are the coordinates of a projected edge in the sinogram. Because the difference of only two projected edges is not reliable to determine that they belong to the same object, we select a group of adjacent projected edges around them to define the function of difference values V:
where I is the intensity value of a projected edge and N is the size of the group surrounding each projected edge (in this case N=2).
This goal is to find for each Pk the best Pj that optimizes simultaneously these functions. This type of problem is known as either a multiobjective, multicriteria, or a vector optimization problem. Many techniques have been proposed to solve this problem. We applied a min-max optimization method using Eq. (1) and Eq. (2) to determine the corresponding projected edges in both sides of the reprojected metal implant regions. Finally, we use a linear interpolation between these two corresponding projected edges to replace the projections in the metal implant regions. We continue this for all set of projected edges. Finally, we apply a median filter (size of 5×5 pixels) to remove the isolated high value projections which may not be interpolated in metal implant regions.
These steps are repeated for all raw projection data to remove and interpolate the projections affected by the implants. In a last step, the whole modified raw projection data is transferred back to reconstruction operator of CT scanner to regenerate slice images.
In a second example, the algorithm is based on replacing missing projections in sinogram by their unaffected correspondences in opposite direction. The modified sinogram is used to regenerate slice images by scanner standard reconstruction algorithm. No further modification in the employed operators is required for this reconstruction. The resulting tomographies by the proposed approach show significant improvements in image quality, especially for regions near the metallic implants, compared to those by interpolation-based approaches. In this work, we describe the algorithm for a helical scanner which is based on spiral projections. It is obvious that the extension of this work for a parallel projection will be trivial. The approach is composed of three steps.
First step is to detect the projections affected by metal implants. Some authors proposed to isolate the correspondence of the metal implants directly from the projection, but have difficulties to fix the appropriate thresholds because of the complex structure of the projection data. Others are identifying the sinusoidal curves resulting from metal implant in the projection data. Although these approaches are interesting, they still need to fix some parameters and studies are limited to parallel projections. In our algorithm, the metal objects are identified quasi-automatically from reconstructed images. First, we reconstruct an initial image from the 360 degrees raw helical projection data using fan-beam FBP (see
For the following discussion we focus our attention on the helical CT single-slice scanner. The results can be extended to multi-slice and cone-beam scanners.
In helical scanning the patient table is transported continuously as the tube and 1D detector array rotate around the patient. The geometry of this scanning is shown in
The idea behind the replacing scheme is due to the fact that the two projections along the same path but in the opposite sides would be the same in the absence of table motion. So, in the presence of table motion which is a real case for a CT exam, the opposite side projections are still very good approximations for the corresponding projections. The question is how we can compute the opposite side of a projection since in a fan-beam scanner the opposite sides are not exactly in 180 degrees apart.
The replacing scheme is followed by firstly projecting the metal components of the CT image, as identified in the step 1, onto the original sinogram, to detect missing projections and then by replacing each missing projection by its opposite side. When the replacement scheme is started for the first missing projections in the sinogram, they are replaced by their non-affected-by-metallic-object projections in opposite side. But, as we progress the replacing scheme for other missing projections, their opposite side projections may be the missing projections already replaced by their own opposite sides. Consequently, there is a risk that the errors in each step of replacing scheme are accumulated so that the synthesize date for replacing scheme become totally unreliable. Actually, this is the reason why we are limited to use the replacing scheme for the metallic objects with small size which appear in a limited number of CT slices. In order to make the replacing scheme more reliable, we propose to start it simultaneously from each side of missing projections area.
The whole modified raw projection data arising from Step 2 is transferred back to reconstruction operator of CT scanner to regenerate slice images. So, all detail information on scanner geometry and file format is preserved and no changes in routine practices are needed.
To quantitatively evaluate the performance of this algorithm for reducing metal artifacts, a phantom was used. This phantom is routinely employed for this CT scanner calibration. The phantom consists of several cylindrical inserts representing human organ densities (such as lung, muscle, liver, bone, etc.) embedded in a block of masonite in the form of human abdomen. We inserted two steel rods on each side of the phantom to represent the hip prostheses. The size of the rods was chosen to produce the same quantity of artifacts as in a real case. The phantom was scanned by a Siemens Somatom in helical mode with a pitch of 1.5 and 3-mm slice thickness with 130 kVp and 168 mA (which are the typical parameters for a pelvis scan) for two cases: without rods (case A) and with rods (case B). The raw projection data consisted of 1344 detectors and 1000 gantry positions in each tube rotation.
Distortion validation: We applied a Canny edge detector to automatically detect the boundary of different objects in the phantom. We used the same parameters for the detector in three cases.
CT number validation: We computed the statistical parameters of CT numbers, i.e. mean and standard deviation (std), for three regions representing the three objects in the middle of the phantom (see
From these validations, we conclude that the proposed approach improves the overall image quality and more importantly preserves the form and, in a large proportion, the representative CT number of objects in the image.
Many patients with hip prostheses are scanned each year at this institution. Recently, four patients with two hip prostheses were scanned and treated for prostate cancer in this institute. Here, we show the results for one of these patients. The same parameters as the above phantom experiment are used for the scanner.
Note that because of the interpolation step, information from the structures of metal implants is lost. We simply detect and contour the metal implants in the original images and then merge this information into the modified reconstructed images. Finally, we override the density inside the metal implant contours with a value closer to the real implant.
Following recommendations from the report of task group 63 of the AAPM Radiation Therapy Committee, we basically plan beam arrangements that avoid prostheses to shadow the target. This kind of planning on patients with two hip prostheses requires precise delineation of the target and sensitive structures. The improvement in image quality provided by the metal artifact reduction algorithm enables this approach without compromising target dosage and normal tissue complication probabilities. Without image quality enhancement, physician would have drawn bigger margins to be sure to include the target and at the same time, would have prescribed lower dose in order to keep the same level of normal tissue toxicity.
For a real patient with metallic teeth fillings, the topogram and a portion of the sinogram containing the affected projections by metallic objects are shown in
In order to evaluate the performance of the presented approach, we applied an interpolation-based algorithm on the same patient exam.
Our proposed replacement scheme is independent from the type of metallic object. However, in metal detection step, the threshold depends favorably on Z so that for high Z materials, the threshold will be augmented and vice versa. Consequently, the detection step is automatically adjusted for a different Z objects. The approach is entirely automatic and can be used easily by relatively little user interaction. Additionally, since the Head and Neck tumour treatment planning is often performed while the patient is waiting, the approach does not increase the time to the planning process and it can be clinically applicable.
Filing Document | Filing Date | Country | Kind | 371c Date |
---|---|---|---|---|
PCT/CA05/01582 | 10/12/2005 | WO | 00 | 10/15/2008 |
Number | Date | Country | |
---|---|---|---|
60617058 | Oct 2004 | US |