Dong-goo Kang

Three-Dimensional Blood Vessel Quantification via Centerline Deformation

It is clinically important to quantify the geometric parameters of an abnormal vessel, as this information can aid radiologists in choosing appropriate treatments or apparatuses. Centerline and cross-sectional diameters are commonly used to characterize the morphology of vessel in various clinical applications. Due to the existence of stenosis or aneurysm, the associated vessel centerline is unable to truly portray the original, healthy vessel shape and may result in inaccurate quantitative measurement. To remedy such a problem, a novel method using an active tube model is proposed. In the method, a smoothened centerline is determined as the axis of a deformable tube model that is registered onto the vessel lumen. Three types of regions, normal, stenotic, and aneurysmal regions, are defined to classify the vessel segment under-analyzed by use of the algorithm of a cross-sectional-based distance field. The registration process used on the tube model is governed by different region-adaptive energy functionals associated with the classified vessel regions. The proposed algorithm is validated on the 3-D computer-generated phantoms and 3-D rotational digital subtraction angiography (DSA) datasets. Experimental results show that the deformed centerline provides better vessel quantification results compared with the original centerline. It is also shown that the registered model is useful for measuring the volume of aneurysmal regions.

Robust registration of 3-D ultrasound and CT images of the liver for image-guided intervention

The registration of multi-modal images of the same organ is beneficial in various clinical applications. However, conventional methods usually require a time-consuming and inconvenient manual process for pre-alignment. In this paper, we present an automatic and robust registration algorithm of 3-D B-mode US and CT images of the liver. The proposed algorithm first automatically segments vessels and liver surface from a 3-D B-mode US image by efficiently eliminating unwanted clutters and noise. It then predicts a reliable initial transform parameter set in a non-iterative manner by maximizing the geometric correlation between the skeletons of vessels segmented from both images. Finally, the algorithm refines the obtained parameters iteratively to find the optimal affine transform parameters by jointly using vessel and liver surface information. Experimental results for 20 clinical datasets show that the proposed algorithm successfully registers a 3-D B-mode US image to its corresponding 3-D CT image even with a large misalignment.

Multiple object decomposition based on independent component analysis of multi-energy x-ray projections

X-ray projection is not effective for representing complex overlapping objects. This paper presents a novel computational framework to decompose X-ray projections into multiple images with non-overlapping objects that are differentiated by their own material compositions. Based on energy-dependent X-ray attenuation characteristics for each material, multiple energy X-ray images are analyzed to obtain material-selective images, which correspond to projections of basis materials that constitute objects. We show that material-selective images can be considered as linear mixtures of independent components that are associated with object-selective images. As a result, multiple objects can be decomposed by independent component analysis (ICA) of material-selective images or ICA of multiple monochromatic energy X-ray images. To demonstrate the concept of the proposed method, we apply it to simulated images based on a 3-D human model.

A unified statistical framework for material decomposition using multienergy photon counting x-ray detectors

PURPOSE Material decomposition using multienergy photon counting x-ray detectors (PCXD) has been an active research area over the past few years. Even with some success, the problem of optimal energy selection and three material decomposition including malignant tissue is still on going research topic, and more systematic studies are required. This paper aims to address this in a unified statistical framework in a mammographic environment. METHODS A unified statistical framework for energy level optimization and decomposition of three materials is proposed. In particular, an energy level optimization algorithm is derived using the theory of the minimum variance unbiased estimator, and an iterative algorithm is proposed for material composition as well as system parameter estimation under the unified statistical estimation framework. To verify the performance of the proposed algorithm, the authors performed simulation studies as well as real experiments using physical breast phantom and ex vivo breast specimen. Quantitative comparisons using various performance measures were conducted, and qualitative performance evaluations for ex vivo breast specimen were also performed by comparing the ground-truth malignant tissue areas identified by radiologists. RESULTS Both simulation and real experiments confirmed that the optimized energy bins by the proposed method allow better material decomposition quality. Moreover, for the specimen thickness estimation errors up to 2 mm, the proposed method provides good reconstruction results in both simulation and real ex vivo breast phantom experiments compared to existing methods. CONCLUSIONS The proposed statistical framework of PCXD has been successfully applied for the energy optimization and decomposition of three material in a mammographic environment. Experimental results using the physical breast phantom and ex vivo specimen support the practicality of the proposed algorithm.

A novel material decomposition algorithm for multienergy X-ray radiography systems

A novel algorithm for multienergy X-ray radiography systems that simultaneously estimate multiple measurements with energy diversity is presented. In contrast to conventional dual source X-ray radiography which utilizes simple weighted subtractions, the proposed algorithm aims for an accurate material decomposition of more than two materials. The numerical simulation as well as the real experiment using an energy discriminating detector confirmed our finding.

Regularized polychromatic reconstruction for transmission tomography

A polychromatic reconstruction algorithm that accounts for the exact physical model of transmission tomography is presented. Based on the equivalence between the Poisson log-likelihood function and the I-divergence, we derived a fast convergencing algorithm with a pixel-wise updating scheme, which is an extended version of the AM-ICD algorithm. The objective function in each iteration consists of approximated I-divergence and the generalized Gaussian Markov random field (GGMRF) model based regularization term for preventing diverging due to additive noise and the approximation of I-divergence. In a simulation study, we observed that the beam hardening artifact was significantly reduced in the extended AM-ICD algorithm with the use of a reasonable number of iterations. In addition, the proposed algorithm also showed reliable reconstruction results even for low dose conditions.

A new calibration method and tissue cancellation in dual energy mammography

Mammography is stil the major tool for screening and diagnostic examinations of early breast cancer detection. It gives opportunities for early detection of breast cancer. For early detection of breast cancer, the mass visuality of mammography is very important. Direct mapping is suggested through which the breast radiography relates to the reference phantom radiography by finding the closest intensity values between those radiography images at both energy. With the proposed method, we could enhance the visuality of mass lesions in breast. And the results show that the proposed method has good performance, showing the possibility of cancer detection mammography.

Practical energy response estimation of photon counting detectors for spectral X-ray imaging

Spectral X-ray imaging is a promising technique to drastically improve the diagnostic quality of radiography and computed tomography (CT), since it enables material decomposition and/or identification based on the energy dependency of material-specific X-ray attenuation. Unlike the charge-integration based X-ray detectors, photon counting X-ray detectors (PCXDs) can discriminate the energies of incident X-ray photons and thereby multi-energy images can be obtained in single exposure. However, the measured data are not accurate since the spectra of incident X-rays are distorted according to the energy response function (ERF) of a PCXD. Thus ERF should be properly estimated in advance for accurate spectral imaging. This paper presents a simple method for ERF estimation based on a polychromatic X-ray source that is widely used for medical imaging. The method consists of three steps: source spectra measurement, detector spectra reconstruction, and ERF inverse estimation. Real spectra of an X-ray tube are first measured at all kVs by using an X-ray spectrometer. The corresponding detector spectra are obtained by threshold scans. The ERF is then estimated by solving the inverse problem. Simulations are conducted to demonstrate the concept of the proposed method.

Tissue Cancellation in Dual Energy Mammography Using a Calibration Phantom Customized for Direct Mapping

An easily implementable tissue cancellation method for dual energy mammography is proposed to reduce anatomical noise and enhance lesion visibility. For dual energy calibration, the images of an imaging object are directly mapped onto the images of a customized calibration phantom. Each pixel pair of the low and high energy images of the imaging object was compared to pixel pairs of the low and high energy images of the calibration phantom. The correspondence was measured by absolute difference between the pixel values of imaged object and those of the calibration phantom. Then the closest pixel pair of the calibration phantom images is marked and selected. After the calibration using direct mapping, the regions with lesion yielded different thickness from the background tissues. Taking advantage of the different thickness, the visibility of cancerous lesions was enhanced with increased contrast-to-noise ratio, depending on the size of lesion and breast thickness. However, some tissues near the edge of imaged object still remained after tissue cancellation. These remaining residuals seem to occur due to the heel effect, scattering, nonparallel X-ray beam geometry and Poisson distribution of photons. To improve its performance further, scattering and the heel effect should be compensated.

Quantitative breast imaging using photon counting detector

Possible limitations of current dual energy Contrast Enhanced Digital Marnmography(CEDM) are that over lapping normal breast tissue structures can obscure the visualization of iodine, and that the only two images acquired provide solution for two variable equations while three variables are required as the breast consists of three materials - adipose and glandular tissues and iodine. To solve this problem with dual energy CEDM, it requires knowledge of the breast thickness at each pixel. However, in many clinical mammography systems employing a spring-loaded paddle, the physical thickness of the breast may not be uniform due to deformation and tilt of the compression paddle. Therefore, we chose to use triple energy CEDM to overcome these limitations, which can provide a third image. However, the radiation dose can remain a major concern due to three exposures. Photon counting detector(PCD) can provide triple energy radiography without mentioned extra exposures. For triple energy CEDM, an iodine quantification method for breast imaging was suggested in this research. We acquired triple energy images of calibration phantom of different iodine concentrations first, using PCD. Then, intensity values at each energy of imaging object could be mapped pixel by pixel to different locations on the calibration phantom images of different iodine thicknesses(concentrations). Interpolation surface of iodine con centration was constructed from the mapped locations at each energy. Resultant triple surfaces were combined to find out the intersection of the three iodine thickness surfaces from the three energy images, which tells the estimated iodine thickness from the input intensity value. The result shows that the proposed method could quantify iodine inserts in breast phantom accurately, which simulate lesions in breast filled with different iodine concentrations.