Photon Counting Detector CT (PCD-CT) is expected to provide new possibilities by its capability of energy decomposition. However, because of non-ideal phenomena like the finite widths of individual energy windows of the detector or the scattered radiation, PCD-CT can have artifacts of not only dark but bright bands in some images. Therefore, we developed a correction method to reduce both dark and bright bands and verified it with data acquired with a prototype machine. The correction is performed on the material decomposition images, and acrylic and iodine images were employed. The dark band observed in the iodine image was corrected with the method used in the Energy Integrated Detector CT system. To suppress the bright band seen in the acrylic image, several virtual monochromatic images were created from the material decomposition images, and the energy was sought at which the dark and bright bands were cancelled. The bright band correction was performed using this energy and the correction amount of the dark band in the iodine image. It was demonstrated with the prototype machine data that the dark and bright bands were reduced in the material decomposition image and the virtual monochromatic image, and it was shown that the artifacts of both dark and bright bands can be corrected by the proposed method.
Metals included inside an object (such as clips, bolts, or artificial joints) cause streaks and dark band artifacts in computed tomography. Although several metal artifact reduction (MAR) methods have been reported, they require a large amount of processing time such as for iterative forward projection from reconstructed image, but they do not provide sufficient correction depending on the metal and object and are sometimes accompanied by image degradation due to new artifacts. To overcome these problems, MAR methods using artificial intelligence (AI-MAR) with deep learning are reported. We have developed new AI-MAR to directly predict artifacts using deep learning and subtract them from the original filtered back-projection (FBP) image to maintain the structure of the object and achieve a high artifact reduction effect. The proposed AI-MAR was compared with FBP, linear interpolation method (LI), and conventional MAR. In a metal simulation experiment, the proposed AI-MAR successfully reduced the metal artifacts, and the structural similarity indices (SSIMs) evaluated with the FBP, LI, conventional MAR, and proposed AI-MAR were 0.941, 0.986, 0.969, and 0.988, respectively, and an improvement rate of SSIM of more than 80% was demonstrated. The proposed AI-MAR can improve device performance by providing high-speed and highly accurate images with reduced metal artifacts.
To suppress artifacts in X-ray digital tomosynthesis, a method which combines 3D and 2D region growing was proposed. However, it could not extract small metals such as pins, due to the discontinuity of the metal in the 3D projection data. A novel method is proposed to separate the metal from the projection data by combining pattern matching between neighboring projection angles and interpolating image pixels of enlarged projection data for each projection angle. The proposed method was compared with the conventional method based on 3D region growing. In a phantom experiment, the proposed method reduced the artifacts close to the metals. At a distance within 1 mm from the metal object, the mean absolute error evaluated without metal artifact correction (No MAC), with the conventional and proposed methods were 461.2, 461.2, and 100.2, respectively, and an improvement of more than 78% was demonstrated. When the distance from the object was shorter, the artifact became more significant in the No MAC and conventional methods, so the effectiveness of the proposed method was higher. Applying a suitable artifact correction on the basis of metal extraction makes it possible to effectively reduce artifacts in DT images.
We propose a new metal artifact correction method for the X-ray digital tomosynthesis by accurately detecting metal in the projection data. We combined 3D region growing for growing a few points in the metal to other projection angles and 2D region growing for growing the points further in order not to force the user to set the starting points at each projection angle. We compared the proposed method with the conventional FBP. In the phantom experiment with a mimicked artificial joint using the proposed method, the metal artifacts around the metal object were reduced. At the distance within 5 mm from the metal object, the root mean square errors evaluated with the conventional and proposed methods were 2700 and 200, respectively, and the root mean square errors improvement of more than 90% was demonstrated. When the distance from the metal object was shorter, the metal artifact became more significant in the conventional method, and the effectiveness of the proposed metal artifact correction was higher.
Iteratively reconstructing data only inside the region of interest (ROI) is widely used to acquire CT images in less
computation time while maintaining high spatial resolution. A method that subtracts projected data outside the ROI from
full-coverage measured data has been proposed. A serious problem with this method is that the accuracy of the measured
data confined inside the ROI decreases according to the truncation error outside the ROI. We propose a two-step iterative
method that reconstructs image inside the full-coverage in addition to a conventional iterative method inside the ROI to
reduce the truncation error inside full-coverage images. Statistical information (e.g., quantum-noise distributions)
acquired by detected X-ray photons is generally used in iterative methods as a photon weight to efficiently reduce image
noise. Our proposed method applies one of two kinds of weights (photon or constant weights) chosen adaptively by
taking into consideration the influence of truncation error. The effectiveness of the proposed method compared with that
of the conventional method was evaluated in terms of simulated CT values by using elliptical phantoms and an abdomen
phantom. The standard deviation of error and the average absolute error of the proposed method on the profile curve
were respectively reduced from 3.4 to 0.4 [HU] and from 2.8 to 0.8 [HU] compared with that of the conventional method.
As a result, applying a suitable weight on the basis of a target object made it possible to effectively reduce the errors in
CT images.
Two methods for preventing the deterioration of the accuracy of iterative region-of-interest (ROI) reconstruction are
proposed. Both methods apply filters; the first one applies them to the whole region the outside the region of interest
(outside ROI) without distinguishing objects (“method 1”, hereafter); and the second one applies them to only the air and
patient-table regions while masking other objects outside the ROI (“method 2”). The effectiveness of both methods was
evaluated in terms of simulated CT values by using two different phantoms. Method 1 reduced the artifact intensity level
by 86% (at most) compared with that obtained with the conventional method. In the case of an object with high
attenuation coefficient, method 2 decreases the level more than method 1. In other words, method 2 improves
reconstruction accuracy without causing deterioration by the filters. By selecting either method 1 or 2 in accordance with
the attenuation coefficient in regions of objects to be imaged, it is possible to reduce the error level compared with the
conventional method.
Dual energy computed tomography (DECT) has been widely used in clinical practice and has been particularly effective
for tissue diagnosis. In DECT the difference of two attenuation coefficients acquired by two kinds of X-ray energy
enables tissue segmentation. One problem in conventional DECT is that the segmentation deteriorates in some cases,
such as bone removal. This is due to two reasons. Firstly, the segmentation map is optimized without considering the Xray
condition (tube voltage and current). If we consider the tube voltage, it is possible to create an optimized map, but
unfortunately we cannot consider the tube current. Secondly, the X-ray condition is not optimized. The condition can be
set empirically, but this means that the optimized condition is not used correctly. To solve these problems, we have
developed methods for optimizing the map (Method-1) and the condition (Method-2). In Method-1, the map is optimized
to minimize segmentation errors. The distribution of the attenuation coefficient is modeled by considering the tube
current. In Method-2, the optimized condition is decided to minimize segmentation errors depending on tube voltagecurrent
combinations while keeping the total exposure constant. We evaluated the effectiveness of Method-1 by
performing a phantom experiment under the fixed condition and of Method-2 by performing a phantom experiment
under different combinations calculated from the total exposure constant. When Method-1 was followed with Method-2,
the segmentation error was reduced from 37.8 to 13.5 %. These results demonstrate that our developed methods can
achieve highly accurate segmentation while keeping the total exposure constant.
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