다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Use of high-resolution micro-computed tomography (microCT) imaging to assess trabecular and cortical bone morphology has grown immensely. There are several commercially available microCT systems, each with different approaches to image acquisition, evaluation, and reporting of outcomes. This lack of consistency makes it difficult to interpret reported results and to compare findings across different studies. This article addresses this critical need for standardized terminology and consistent reporting of parameters related to image acquisition and analysis, and key outcome assessments, particularly with respect to ex vivo analysis of rodent specimens. Thus the guidelines herein provide recommendations regarding (1) standardized terminology and units, (2) information to be included in describing the methods for a given experiment, and (3) a minimal set of outcome variables that should be reported. Whereas the specific research objective will determine the experimental design, these guidelines are intended to ensure accurate and consistent reporting of microCT-derived bone morphometry and density measurements. In particular, the methods section for papers that present microCT-based outcomes must include details of the following scan aspects: (1) image acquisition, including the scanning medium, X-ray tube potential, and voxel size, as well as clear descriptions of the size and location of the volume of interest and the method used to delineate trabecular and cortical bone regions, and (2) image processing, including the algorithms used for image filtration and the approach used for image segmentation. Morphometric analyses should be based on 3D algorithms that do not rely on assumptions about the underlying structure whenever possible. When reporting microCT results, the minimal set of variables that should be used to describe trabecular bone morphometry includes bone volume fraction and trabecular number, thickness, and separation. The minimal set of variables that should be used to describe cortical bone morphometry includes total cross-sectional area, cortical bone area, cortical bone area fraction, and cortical thickness. Other variables also may be appropriate depending on the research question and technical quality of the scan. Standard nomenclature, outlined in this article, should be followed for reporting of results.

https://asbmr.onlinelibrary.wiley.com/doi/pdf/10.1002/jbmr.141

Guidelines for assessment of bone microstructure in rodents using micro–computed tomography

OBJECTIVE. In light of the rapidly increasing frequency of pediatric CT examinations, the purpose of our study was to assess the lifetime cancer mortality risks attributable to radiation from pediatric CT.MATERIALS AND METHODS. Organ doses as a function of age-at-diagnosis were estimated for common CT examinations, and estimated attributable lifetime cancer mortality risks (per unit dose) for different organ sites were applied. Standard models that assume a linear extrapolation of risks from intermediate to low doses were applied. On the basis of current standard practice, the same exposures (milliampere-seconds) were assumed, independent of age.RESULTS. The larger doses and increased lifetime radiation risks in children produce a sharp increase, relative to adults, in estimated risk from CT. Estimated lifetime cancer mortality risks attributable to the radiation exposure from a CT in a 1-year-old are 0.18% (abdominal) and 0.07% (head)—an order of magnitude higher than for adults—although those figures st...

https://www.ajronline.org/doi/pdf/10.2214/ajr.176.2.1760289

Estimated Risks of Radiation-Induced Fatal Cancer from Pediatric CT

In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. These methods produce excellent results, but can be challenging to deploy in practice due to factors including the high computational cost of the forward and adjoint operators and the difficulty of hyperparameter selection. The starting point of this paper is the observation that unrolled iterative methods have the form of a CNN (filtering followed by pointwise non-linearity) when the normal operator ( $H^{*}H$ , where $H^{*}$ is the adjoint of the forward imaging operator, $H$ ) of the forward model is a convolution. Based on this observation, we propose using direct inversion followed by a CNN to solve normal-convolutional inverse problems. The direct inversion encapsulates the physical model of the system, but leads to artifacts when the problem is ill posed; the CNN combines multiresolution decomposition and residual learning in order to learn to remove these artifacts while preserving image structure. We demonstrate the performance of the proposed network in sparse-view reconstruction (down to 50 views) on parallel beam X-ray computed tomography in synthetic phantoms as well as in real experimental sinograms. The proposed network outperforms total variation-regularized iterative reconstruction for the more realistic phantoms and requires less than a second to reconstruct a $512\times 512$ image on the GPU.

/pdf/deep-convolutional-neural-network-for-inverse-problems-in-20ejy5u0ob.pdf

Deep Convolutional Neural Network for Inverse Problems in Imaging

Artifacts can seriously degrade the quality of computed tomographic (CT) images, sometimes to the point of making them diagnostically unusable. To optimize image quality, it is necessary to understand why artifacts occur and how they can be prevented or suppressed. CT artifacts originate from a range of sources. Physics-based artifacts result from the physical processes involved in the acquisition of CT data. Patient-based artifacts are caused by such factors as patient movement or the presence of metallic materials in or on the patient. Scanner-based artifacts result from imperfections in scanner function. Helical and multisection technique artifacts are produced by the image reconstruction process. Design features incorporated into modern CT scanners minimize some types of artifacts, and some can be partially corrected by the scanner software. However, in many instances, careful patient positioning and optimum selection of scanning parameters are the most important factors in avoiding CT artifacts.

Artifacts in CT: Recognition and Avoidance

Introduction Preliminaries Image Reconstruction Image Presentation Key Performance Parameters of a CT Scanner Major Components of CT Scanner Image Artifacts: Appearances, Causes, and Corrections Computer Simulation and Analysis. Helical or Spiral CT Multislice CT X-ray Dose and Reduction Techniques Advanced CT Applications.

Computed Tomography: Principles, Design, Artifacts, and Recent Advances

Computed Tomography: Principles, Design, Artifacts, and Recent Advances, Fourth Edition

Multislice helical computed tomography scanning offers the advantages of faster acquisition and wide organ coverage for routine clinical diagnostic purposes. However, image reconstruction is faced with the challenges of three-dimensional cone-beam geometry, data completeness issues, and low dosage. Of all available reconstruction methods, statistical iterative reconstruction (IR) techniques appear particularly promising since they provide the flexibility of accurate physical noise modeling and geometric system description. In this paper, we present the application of Bayesian iterative algorithms to real 3D multislice helical data to demonstrate significant image quality improvement over conventional techniques. We also introduce a novel prior distribution designed to provide flexibility in its parameters to fine-tune image quality. Specifically, enhanced image resolution and lower noise have been achieved, concurrently with the reduction of helical cone-beam artifacts, as demonstrated by phantom studies. Clinical results also illustrate the capabilities of the algorithm on real patient data. Although computational load remains a significant challenge for practical development, superior image quality combined with advancements in computing technology make IR techniques a legitimate candidate for future clinical applications.

/pdf/a-three-dimensional-statistical-approach-to-improved-image-1jddsdidms.pdf

A three-dimensional statistical approach to improved image quality for multislice helical CT.

Although diagnostic medical imaging provides enormous benefits in the early detection and accuracy diagnosis of various diseases, there are growing concerns on the potential side effect of radiation induced genetic, cancerous and other diseases. How to reduce radiation dose while maintaining the diagnostic performance is a major challenge in the computed tomography (CT) field. Inspired by the compressive sensing theory, the sparse constraint in terms of total variation (TV) minimization has already led to promising results for low-dose CT reconstruction. Compared to the discrete gradient transform used in the TV method, dictionary learning is proven to be an effective way for sparse representation. On the other hand, it is important to consider the statistical property of projection data in the low-dose CT case. Recently, we have developed a dictionary learning based approach for low-dose X-ray CT. In this paper, we present this method in detail and evaluate it in experiments. In our method, the sparse constraint in terms of a redundant dictionary is incorporated into an objective function in a statistical iterative reconstruction framework. The dictionary can be either predetermined before an image reconstruction task or adaptively defined during the reconstruction process. An alternating minimization scheme is developed to minimize the objective function. Our approach is evaluated with low-dose X-ray projections collected in animal and human CT studies, and the improvement associated with dictionary learning is quantified relative to filtered backprojection and TV-based reconstructions. The results show that the proposed approach might produce better images with lower noise and more detailed structural features in our selected cases. However, there is no proof that this is true for all kinds of structures.

/pdf/low-dose-x-ray-ct-reconstruction-via-dictionary-learning-3jx69t6xok.pdf

Low-Dose X-ray CT Reconstruction via Dictionary Learning

Purpose: To retrospectively compare image quality, radiation dose, and blood vessel assessability for coronary artery computed tomographic (CT) angiograms obtained with a prospectively gated transverse (PGT) CT technique and a retrospectively gated helical (RGH) CT technique. Materials and Methods: This HIPAA-compliant study received a waiver for approval from the institutional review board, including one for informed consent. Coronary CT angiograms obtained with 64–detector row CT were retrospectively evaluated in 203 clinical patients. A routine RGH technique was evaluated in 82 consecutive patients (44 males, 38 females; mean age, 55.6 years). The PGT technique was then evaluated in 121 additional patients (71 males, 50 females; mean age, 56.7 years). All images were evaluated for image quality, estimated radiation dose, and coronary artery segment assessability. Differences in image quality score were evaluated by using a proportional odds logistic regression model, with main effects for three readers...

Prospectively Gated Transverse Coronary CT Angiography versus Retrospectively Gated Helical Technique : Improved Image Quality and Reduced Radiation Dose

Jiang Hsieh

Papers

Computed Tomography: Principles, Design, Artifacts, and Recent Advances

Computed Tomography: Principles, Design, Artifacts, and Recent Advances, Fourth Edition

A three-dimensional statistical approach to improved image quality for multislice helical CT.

Low-Dose X-ray CT Reconstruction via Dictionary Learning

Prospectively Gated Transverse Coronary CT Angiography versus Retrospectively Gated Helical Technique : Improved Image Quality and Reduced Radiation Dose