Vascular tree object segmentation by deskeletonization of valley courses

Patient-specific in silico 3D coronary model in cardiac catheterisation laboratories

Coronary artery disease is caused by the buildup of atherosclerotic plaque in the coronary arteries, affecting the blood supply to the heart, one of the leading causes of death around the world. X-ray coronary angiography is the most common procedure for diagnosing coronary artery disease, which uses contrast material and x-rays to observe vascular lesions. With this type of procedure, blood flow in coronary arteries is viewed in real-time, making it possible to detect stenoses precisely and control percutaneous coronary interventions and stent insertions. Angiograms of coronary arteries are used to plan the necessary revascularisation procedures based on the calculation of occlusions and the affected segments. However, their interpretation in cardiac catheterisation laboratories presently relies on sequentially evaluating multiple 2D image projections, which limits measuring lesion severity, identifying the true shape of vessels, and analysing quantitative data. In silico modelling, which involves computational simulations of patient-specific data, can revolutionise interventional cardiology by providing valuable insights and optimising treatment methods. This paper explores the challenges and future directions associated with applying patient-specific in silico models in catheterisation laboratories. We discuss the implications of the lack of patient-specific in silico models and how their absence hinders the ability to accurately predict and assess the behaviour of individual patients during interventional procedures. Then, we introduce the different components of a typical patient-specific in silico model and explore the potential future directions to bridge this gap and promote the development and utilisation of patient-specific in silico models in the catheterisation laboratories.

Characterization of single thyroid nodules by contrast-enhanced 3-D ultrasound

High-resolution ultrasonography (HRUS) has potentialities in differential diagnosis between malignant and benign thyroid lesions, but interpretative pitfalls remain and accuracy is still poor. We developed an image processing technique for characterizing the intra-nodular vascularization of thyroid lesions. Twenty nodules (10 malignant) were analyzed by three-dimensional (3-D) contrast-enhanced ultrasound imaging. The 3-D volumes were preprocessed and skeletonized. Seven vascular parameters were computed on the skeletons: number of vascular trees (NT); vascular density (VD); number of branching nodes (or branching points) (NB); mean vessel radius (MR); 2-D (DM) and 3-D (SOAM) tortuosity; and inflection count metric (ICM). Results showed that the malignant nodules had higher values of NT (83.1 vs. 18.1), VD (00.4 vs. 0.01), NB (1453 vs. 552), DM (51 vs. 18), ICM (19.9 vs. 8.7) and SOAM (26 vs. 11). Quantification of nodular vascularization based on 3-D contrast-enhanced ultrasound and skeletonization could help differential diagnosis of thyroid lesions.

Quantitative coronary angiography using image recovery techniques for background estimation in unsubtracted images

Densitometry measurements have been performed previously using subtracted images. However, digital subtraction angiography (DSA) in coronary angiography is highly susceptible to misregistration artifacts due to the temporal separation of background and target images. Misregistration artifacts due to respiration and patient motion occur frequently, and organ motion is unavoidable. Quantitative densitometric techniques would be more clinically feasible if they could be implemented using unsubtracted images. The goal of this study is to evaluate image recovery techniques for densitometry measurements using unsubtracted images. A humanoid phantom and eight swine (25-35 kg) were used to evaluate the accuracy and precision of the following image recovery techniques: Local averaging (LA), morphological filtering (MF), linear interpolation (LI), and curvature-driven diffusion image inpainting (CDD). Images of iodinated vessel phantoms placed over the heart of the humanoid phantom or swine were acquired. In addition, coronary angiograms were obtained after power injections of a nonionic iodinated contrast solution in an in vivo swine study. Background signals were estimated and removed with LA, MF, LI, and CDD. Iodine masses in the vessel phantoms were quantified and compared to known amounts. Moreover, the total iodine in left anterior descending arteries was measured and compared with DSA measurements. In the humanoid phantom study, the average root mean square errors associated with quantifying iodine mass using LA and MF were approximately 6% and 9%, respectively. The corresponding average root mean square errors associated with quantifying iodine mass using LI and CDD were both approximately 3%. In the in vivo swine study, the root mean square errors associated with quantifying iodine in the vessel phantoms with LA and MF were approximately 5% and 12%, respectively. The corresponding average root mean square errors using LI and CDD were both 3%. The standard deviations in the differences between measured iodine mass in left anterior descending arteries using DSA and LA, MF, LI, or CDD were calculated. The standard deviations in the DSA-LA and DSA-MF differences (both approximately 21 mg) were approximately a factor of 3 greater than that of the DSA-LI and DSA-CDD differences (both approximately 7 mg). Local averaging and morphological filtering were considered inadequate for use in quantitative densitometry. Linear interpolation and curvature-driven diffusion image inpainting were found to be effective techniques for use with densitometry in quantifying iodine mass in vitro and in vivo. They can be used with unsubtracted images to estimate background anatomical signals and obtain accurate densitometry results. The high level of accuracy and precision in quantification associated with using LI and CDD suggests the potential of these techniques in applications where background mask images are difficult to obtain, such as lumen volume and blood flow quantification using coronary arteriography.

Three-dimensional analysis of complex branching vessels in confocal microscopy images

The characteristic of confocal microscopy (CM) vascular data is that it contains many tiny vessels with branching and complex structure. In this work, an automated method for quantitative analysis and reconstruction of cerebral vessels from CM images is presented in which the extracted centerline of the vessels plays the key role. To assess the efficiency and accuracy of different centerline extraction methods, a comparison among three fully automated approaches is given. The centerline extraction methods studied in this work are a snake model, a path planning approach, and a distance transform-based method. To evaluate the accuracy of the quantitative parameters of vessels such as length and diameter, we apply the method to synthetic data. These results indicate that the snake model and the path planning method are more accurate in extracting the quantitative parameters. The efficiency of the approach in clinical applications is then confirmed by applying the method to real CM images. All three methods investigated in this work are accurate enough to correctly distinguish between normal and stroke brain data, while the snake model is the fastest for clinical applications. In addition, three-dimensional visualization, reconstruction, and characterization of CM vascular images of rat brains are presented.

3-D quantification and visualization of vascular structures from confocal microscopic images using skeletonization and voxel-coding

This paper presents an image processing approach for information extraction from three-dimensional (3-D) images of vasculature. It extracts quantitative information such as skeleton, length, diameter, and vessel-to-tissue ratio for different vessels as well as their branches. Furthermore, it generates 3-D visualization of vessels based on desired anatomical characteristics such as vessel diameter or 3-D connectivity. Steps of the proposed approach are: (1) pre-processing, (2) distance mappings, (3) branch labeling, (4) quantification, and (5) visualization. We have tested and evaluated the proposed algorithms using simulated images of multi-branch vessels and real confocal microscopic images of the vessels in rat brains. Experimental results illustrate performance of the methods and usefulness of the results for medical image analysis applications.

Regional volumetric coronary blood flow measurement by digital angiography: in vivo validation

RATIONALE AND OBJECTIVES

There are well-known limitations to the use of visual estimation to assess the severity of coronary artery disease and luminal stenosis. This is especially true in the case of an intermediate coronary lesion (30%-70% diameter stenosis), where coronary arteriography is very limited in distinguishing ischemia-producing intermediate coronary lesions from non-ischemia-producing ones. For this reason, a functional measure of stenosis severity is desirable. The goal of this study is to validate a video densitometry technique for quantitative assessment of regional volumetric coronary blood flow.

MATERIALS AND METHODS

Coronary arteriography was performed in eight swine (body weight, 25-50 kg) after power injection of contrast material into the left main coronary artery. Phase-matched subtracted images were used to quantify regional coronary blood flow using a video densitometry technique. The in vivo regional flow measurements were validated using a transit time ultrasound flow probe.

RESULTS

In 44 measurements, the ultrasound (Q(US)) and video densitometry (Q(VD)) regional flow measurements were related by Q(VD) = 0.98 Q(US) + 0.11 mL/min (r = 0.98). The results of mean regional coronary blood flow measurements for repeated coronary arteriograms of the first (Q(VD1)) and second (Q(VD2)) measured flows were related by Q(VD1) = 1.04 Q(VD2) + 0.05 mL/min (r = 0.97).

CONCLUSIONS

A video densitometry technique for quantification of regional coronary blood flow was validated using a swine animal model. The results demonstrated the feasibility and potential utility of the video densitometry technique for accurate measurement of regional coronary blood flow, in vivo. This study provides an angiographic method that can potentially be used to evaluate intermediate coronary lesions during routine coronary arteriography.