Motion blurring is still a challenge for cardiac CT imaging. A new motion estimation (ME) and motion compensation method is developed for cardiac CT. The proposed method estimates motion of entire heart, and then applies motion compensation. Therefore, the proposed method reduces motion artifacts not only in coronary artery region as most other methods did, but also reduces motion blurring in myocardium region. In motion compensated reconstruction, we use the Fourier transfer method proposed by Pack et al to obtain a series of partial images, and then warp and sum together to obtain final motion compensated images. The robustness and performance of the proposed method was verified with data from 10 patients and improvements in sharpness of both coronary arteries and myocardium were obtained.
A new motion estimation and compensation method for cardiac computed tomography (CT) was developed. By
combining two motion estimation (ME) approaches the proposed method estimates the local and global cardiac motion
and then preforms motion compensated reconstruction. The combined motion estimation method has two parts: one is
the local motion estimation, which estimates the coronary artery motion by using coronary artery tree tracking and
registration; the other one is the global motion estimation, which estimates the entire cardiac motion estimation by image
registration. The final cardiac motion is the linear combination of the coronary artery motion and entire cardiac motion
the. We use the backproject-then-warp method proposed by Pack et al. to perform motion compensation reconstruction
(MCR). The proposed method was evaluated with 5 patient data and improvements in sharpness of both coronary
arteries and heart chamber boundaries were obtained.
CT image quality is affected by various artifacts including noise. Among these artifacts of different causes, noisy data
due to photon starvation should be contained in early processing stage to better mitigate other artifacts as they can cause
severe streaks and noise in reconstructed CT image. For low dose imaging, it is critical to use effective processing
method to handle the photon starved data in order to obtain required image quality with desired resolution, texture, low
contrast detectability. In this paper, two promising projection domain noise reduction methods are proposed. They are
derived from (1) the noise model that connects the noise behaviors in count and attenuation; (2) predicted noise
reduction from a finite impulse response (FIR) filter; (3) two pre-determined noise reduction requirements (noise
equalization and electronic noise suppression). Both methods showed significant streaks and noise reduction in tested
cases while reasonably maintaining the resolution of the images.
In this work we apply the circle-and-line acquisition for the 256-detector row medical CT scanner. Reconstruction is based on the exact algorithm of the FBP type suggested recently by one of the co-authors. We derived equations for the cylindrical detector, common for medical CT scanners. To minimize hardware development efforts we use ramp-based reconstruction of the circle data. The line data provides an additional term that corrects the cone beam artifacts that are caused by the incompleteness of the circular trajectory. We illustrate feasibility of our approach using simulated data and real scanned data of the anthropomorphic phantom and evaluate stability of reconstruction to motion and misalignments during the scan. The additional patient dose from the line scan is relatively low compared to the circle scan. The proposed algorithm allows cone beam artifact-free reconstruction with large cone angle.
Proc. SPIE. 6316, Image Reconstruction from Incomplete Data IV
KEYWORDS: Fluctuations and noise, Sensors, Signal attenuation, Digital filtering, Medical research, Image filtering, Reconstruction algorithms, Binary data, Expectation maximization algorithms, Data analysis
Sinogram truncation is a common problem in tomographic reconstruction. Authors expand their previously published method of sinogram extension using decomposition into sinogram curves by using the adaptive convex filter. The main idea is to estimate the truncated parts of the projections of some object or patient using measured projections at different projection angles. This technique provides good estimation of the missing data near the edge of truncation. However, it is hard to estimate the outer edges of the truncated sinogram; in other words, the outer edge of the sinogram, and, consequently, reconstructed object, is invisible. To overcome this problem we introduce the adaptive convex filter that rounds off the outer portions of the extended sinogram, which tend to have a form of peak directed outwards. Here we assume that the truncated part of the reconstructed object has a round or elliptic shape, which holds with most clinical applications. The method automatically adjusts to the size of the truncated object, whether it is an arm or a part of torso.
Sinogram truncation is a common problem in tomographic reconstruction; it occurs when a scanned object or patient extends outside the scan field-of-view. The truncation artifact propagates from the edge of truncation towards the center, resulting in degraded image quality. Several methods have been proposed recently to reconstruct the image artifact-free within the scan FOV; however it is often necessary to recover image outside the scan FOV. We propose a novel truncation correction algorithm that accurately completes unmeasured data outside of the scan field-of-view, which allows us to extend the reconstruction field-of-view. Contrary to 1D extrapolation, we perform interpolation along the so-called sinogram curves. First, we propose an approach to parameterize the family of sinogram curves for efficient sinogram decomposition. Secondly, we propose two ways to estimate the truncated data outside the field-of-view. Both methods are combined for more accurate sinogram completion. Our evaluation shows the validity of our approach. Even objects completely outside the FOV can be accurately reconstructed using the proposed method. The proposed method can be used with any modality where sinogram truncation occurs, such as CT, C-arm, PET/CT, and SPECT.
Parametric eigenspace methods are well known appearance-based methods for object recognition, which involves object classification and pose estimation. However, ordinary parametric eigenspace methods consider only the expressive features, and they suffer from a problem arising from the fact that discriminative features are not considered. So, there have been developed some methods to construct such eigenspaces considering the discriminative features. However, the method might suffer from another problem, i.e., the so-called generalized eigenvalue problem: yet, we can manage to solve the problem. In this paper, two methods are referred to as representative methods considering discriminative features. Conducting an experiment of object recognition on two similar objects, performances of the methods are compared to one another, and a piece of important knowledge is also presented that the discriminative features are more effective than the expressive features.