Precise estimation of coronary arteries from computed tomography angiography (CTA) data is one of the challenging problems. This study focuses on automatic delineation of coronary arteries from 3D CTA data that may assess the clinicians in identifying the coronary pathologies. In this work, we present a technique that effectively segments the complete coronary arterial tree under the guidance of initial vesselness response without relying on heavily manual operations. The proposed method isolates the coronary arteries with accuracy by using localized statistical energy model in two directions provided with an automated seed which ensures an optimal segmentation of the coronaries. The detection of seed is carried out by analyzing the shape information of the coronary arteries in three successive cross-sections. To demonstrate the efficiency of the proposed algorithm, the obtained results are compared with the reference data provided by Rotterdam framework for lumen segmentation and the level-set active contour based method proposed by Lankton et al. Results reveal that the proposed method performs better in terms of leakages and accuracy in completeness of the coronary arterial tree.

In the acquisition of projection data of X-ray CT, logarithm operation is indispensable. But noise distribution is nonlinearly projected by the logarithm operation, and this deteriorates the precision of CT number. This influence becomes particularly remarkable when only a few photons are caught with a detector. It generates a strong streak artifact (SA) in a reconstructed image. Previously we have clarified the influence of the nonlinearity by statistical analysis and proposed a correction method for such nonlinearity. However, there is a problem that the compensation for clamp processing cannot be performed and that the suppression of SA is not enough in photon shortage state. In this paper, we propose a new technique for correcting the nonlinearity due to logarithm operation for noisy data by combining the previously presented method and an adaptive filtering method. The technique performs an adaptive filtering only when the number of captured photons is very few. Moreover we quantitatively evaluate the influence of noise on the reconstructed image in the proposed method by the experiment using numerical phantoms. The experimental results show that there is less influence on spatial resolution despite suppressing SA effectively and that CT number are hardly dependent on the number of the incident photons.

In medical diagnosis, cone beam CT increases the dose absorbed by a patient. However, the radiographic noise (such as quantum noise) in a CT image increases when radiation exposure is reduced. In this paper, we propose a method to improve the CT image degraded by the quantum mottle based on 2-D wavelet transform modulus sum (WTMS). The noise and regular parts of an image can be observed by tracing the evolution of its 2-D WTMS across scales. Our experimental results show that most of the quantum mottle in the 2-D projections is removed by the proposed method and the edges preserved well. We investigate the relation between the number of X-ray photons and the quality of the denoised images. The result shows the possibility that a patient's dose can be reduced about 10% with the same visual quality by our method.

To improve the CT image degraded by radiographic noise (such as quantum mottle), we propose a method based on the wavelet transform modulus sum (WTMS). The noise and regular parts of a signal can be observed by tracing the evolution of its WTMS across scales. Our results show that most of the quantum mottle in the projections of Shepp-Logan phantom has been removed by the proposed method with the supposed cranium well preserved. The denoised CT images show good signal to noise ratio in the region of interest. We also have investigated the relation between the number of X-ray photons and the quality of images reconstructed from denoised projections. From experimental results, this method shows the possibility to reduce a patient's dose about 1/10 with the same visual quality.

In Chirp-Pulse Microwave Computed Tomography (CP-MCT) the images are affected by the blur which is inherent to the measurement principle and is described by a space-variant Point Spread Function (PSF). In this paper we investigate the PSF of CP-MCT including the space dependence both experimentally and computationally. The experimental evaluation is performed by measuring the projections of a target consisting of a thin low-loss dielectric rod surrounded by a saline solution and placed at various positions in the measuring region. On the other hand, the theoretical evaluation is obtained by computing the projections of the same target via a numerical solution of Maxwell's equations. Since CP-MCT uses a chirp signal, the numerical evaluation is carried out by the use of a FD-TD method. The projections of the rod could be obtained by computing the field during the sweep time of the chirp signal for each position of the receiving antenna. Since this procedure is extremely time consuming, we compute the impulse response function of the system by exciting the transmitting antenna with a wide-band Gaussian pulse. Then the signal transmitted in CP-MCT is obtained by computing the convolution product in time domain of the input chirp pulse with the impulse response function of the system. We find a good agreement between measured and computed PSF. The rationality of the computed PSF is verified by three distinct ways and the usefulness of this function is shown by a remarkable effect in the restoration of CP-MCT images. Knowledge on the space-variant PSF will be utilized for more accurate image deblurring in CP-MCT.

An attenuation correction method was proposed for laser-produced plasma emission computed tomography (ECT), which is based on a relation of the attenuation coefficient and the emission coefficient in plasma. Simulation results show that the reconstructed images are dramatically improved in comparison to the reconstructions without attenuation correction.