X-ray Dark-Field Chest Imaging: Qualitative and Quantitative Results in Healthy Humans.

Background X-ray dark-field radiography takes advantage of the wave properties of x-rays, with a relatively high signal in the lungs due to the many air-tissue interfaces in the alveoli. Purpose To describe the qualitative and quantitative characteristics of x-ray dark-field images in healthy human subjects. Materials and Methods Between October 2018 and January 2020, patients of legal age who underwent chest CT as part of their diagnostic work-up were screened for study participation.

Reproducibility of four-dimensional computed tomography-based lung ventilation imaging.

Rationale And Objectives: A novel ventilation imaging method based on four-dimensional (4D) computed tomography (CT) has been applied to the field of radiation oncology. Understanding its reproducibility is a prerequisite for clinical applications. The purpose of this study was to quantify the reproducibility of 4D CT ventilation imaging over different days and the same session.

Segmentation of the heart and great vessels in CT images using a model-based adaptation framework.

Recently, model-based methods for the automatic segmentation of the heart chambers have been proposed. An important application of these methods is the characterization of the heart function. Heart models are, however, increasingly used for interventional guidance making it necessary to also extract the attached great vessels.

Four-dimensional computed tomography pulmonary ventilation images vary with deformable image registration algorithms and metrics.

Purpose: A novel pulmonary ventilation imaging technique based on four-dimensional (4D) CT has advantages over existing techniques and could be used for functional avoidance in radiotherapy. There are various deformable image registration (DIR) algorithms and two classes of ventilation metric that can be used for 4D-CT ventilation imaging, each yielding different images. The purpose of this study was to quantify the variability of the 4D-CT ventilation to DIR algorithms and metrics.

Investigation of four-dimensional computed tomography-based pulmonary ventilation imaging in patients with emphysematous lung regions.

A pulmonary ventilation imaging technique based on four-dimensional (4D) computed tomography (CT) has advantages over existing techniques. However, physiologically accurate 4D-CT ventilation imaging has not been achieved in patients. The purpose of this study was to evaluate 4D-CT ventilation imaging by correlating ventilation with emphysema.

Impact of four-dimensional computed tomography pulmonary ventilation imaging-based functional avoidance for lung cancer radiotherapy.

Purpose: To quantify the dosimetric impact of four-dimensional computed tomography (4D-CT) pulmonary ventilation imaging-based functional treatment planning that avoids high-functional lung regions.

Methods And Materials: 4D-CT ventilation images were created from 15 non-small-cell lung cancer patients using deformable image registration and quantitative analysis of the resultant displacement vector field. For each patient, anatomic and functional plans were created for intensity-modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT).

3D reconstruction of the human rib cage from 2D projection images using a statistical shape model.

Purpose: This paper describes an approach for the three-dimensional (3D) shape and pose reconstruction of the human rib cage from few segmented two-dimensional (2D) projection images. Our work is aimed at supporting temporal subtraction techniques of subsequently acquired radiographs by establishing a method for the assessment of pose differences in sequences of chest radiographs of the same patient.

Methods: The reconstruction method is based on a 3D statistical shape model (SSM) of the rib cage, which is adapted to binary 2D projection images of an individual rib cage.

Automatic model-based segmentation of the heart in CT images.

Automatic image processing methods are a prerequisite to efficiently analyze the large amount of image data produced by computed tomography (CT) scanners during cardiac exams. This paper introduces a model-based approach for the fully automatic segmentation of the whole heart (four chambers, myocardium, and great vessels) from 3-D CT images. Model adaptation is done by progressively increasing the degrees-of-freedom of the allowed deformations.

Automated model-based rib cage segmentation and labeling in CT images.

We present a new model-based approach for an automated labeling and segmentation of the rib cage in chest CT scans. A mean rib cage model including a complete vertebral column is created out of 29 data sets. We developed a ray search based procedure for rib cage detection and initial model pose.

Generation of a cardiac shape model from CT data.

In this paper we describe the generation of a geometric cardiac shape model based on cardiac CTA data. The model includes the four cardiac chambers and the trunks of the connected vasculature, as well as the coronary arteries and a set of cardiac landmarks. A mean geometric model for the end-diastolic heart has been built based on 27 end-diastolic cardiac CTA datasets and a mean motion model based on 11 multiphase datasets.

A geometric model of the beating heart.

Objectives: A comprehensive model of the human heart that covers multiple surfaces, like those of the four chambers and the attached vessels, is presented. It also contains the coronary arteries and a set of 25 anatomical landmarks. The statistical model is intended to provide a priori information for automated diagnostic and interventional procedures.

A comprehensive shape model of the heart.

Domain knowledge about the geometrical properties of cardiac structures is an important ingredient for the segmentation of these structures in medical images or for the simulation of cardiac physiology. So far, a strong focus was put on the left ventricle due to its importance for the general pumping performance of the heart and related functional indices. However, other cardiac structures are of similar importance, e.

Automated segmentation of the left ventricle in cardiac MRI.

We present a fully automated deformable model technique for myocardium segmentation in 3D MRI. Loss of signal due to blood flow, partial volume effects and significant variation of surface grey value appearance make this a difficult problem. We integrate various sources of prior knowledge learned from annotated image data into a deformable model.

Texture segmentation performance related to cortical geometry.

There are two prevailing explanations for the foveal deficit in texture segmentation reported in previous works. One is based on the spatial and temporal properties of the stimuli, which means in terms of physiology a strong contribution of the Magno-channel. The other one is purely spatial and assigns filters of different bandwidths to each eccentricity in the visual field.