Virtual 3D IVUS vessel model for intravascular brachytherapy planning. I. 3D segmentation, reconstruction, and visualization of coronary artery architecture and orientation

Investigation of Cylindrical Piezoelectric and Specific Multi-Channel Circular MEMS-Transducer Array Resonator of Ultrasonic Ablation

BACKGROUND

A cylindrical piezoelectric element and a specific multi-channel circular microelectromechanical systems (MEMS)-transducer array of ultrasonic system were used for ultrasonic energy generation and ablation. A relatively long time is required for the heat to be conducted to the target position. Ultrasound thermal therapy has great potential for treating deep hyperplastic tissues and tumors, such as breast cancer and liver tumors.

METHODS

Ultrasound ablation technology produces thermal energy by heating the surface of a target, and the heat gradually penetrates to the target's interior. Beamforming was performed to observe energy distribution. A resonance method was used to generate ablation energy for verification. Energy was generated according to the coordinates of geometric graph positions to reach the ablation temperature.

RESULTS

The mean resonance frequency of Channels 1-8 was 2.5 MHz, and the cylindrical piezoelectric ultrasonic element of Channel A was 4.2546 Ω at 5.7946 MHz. High-intensity ultrasound has gradually been applied in clinical treatment. Widely adopted, ultrasonic hyperthermia involves the use of high-intensity ultrasound to heat tissues at 42-45 °C for 30-60 min.

CONCLUSION

In the ultrasonic energy method, when the target position reaches a temperature that significantly reduces the cell viability (46.9 °C), protein surface modification occurs on the surface of the target.

Lumen-intima and media-adventitia segmentation in IVUS images using supervised classifications of arterial layers and morphological structures

BACKGROUND

Intravascular ultrasound (IVUS) provides axial grey-scale images of blood vessels. The large number of images require automatic analysis, specifically to identify the lumen and outer vessel wall. However, the high amount of noise, the presence of artifacts and anatomical structures, such as bifurcations, calcifications and fibrotic plaques, usually hinder the proper automatic segmentation of the vessel wall.

METHODS

Lumen, media, adventitia and surrounding tissues are automatically detected using Support Vector Machines (SVMs). The classification performance of the SVMs vary according to the kind of structure present within each region of the image. Random Forest (RF) is used to detect different morphological structures and to modify the initial layer classification depending on the detected structure. The resulting classification maps are fed into a segmentation method based on deformable contours to detect lumen-intima (LI) and media-adventitia (MA) interfaces.

RESULTS

The modifications in the layer classifications according to the presence of structures proved to be effective improving LI and MA segmentations. The proposed method reaches a Jaccard Measure (JM) of 0.88 ± 0.08 for LI segmentation, compared with 0.88 ± 0.05 of a semiautomatic method. When looking at MA, our method reaches a JM of 0.84 ± 0.09, and outperforms previous automatic methods in terms of HD, with 0.51mm ± 0.30.

CONCLUSIONS

A simple modification to the arterial layer classification produces results that match and improve state-of-the-art fully-automatic segmentation methods for LI and MA in 20MHz IVUS images. For LI segmentation, the proposed automatic method performs accurately as semi-automatic methods. For MA segmentation, our method matched the quality of state-of-the-art automatic methods described in the literature. Furthermore, our implementation is modular and open-source, allowing for future extensions and improvements.

Optimization of intravascular brachytherapy treatment planning in peripheral arteries

This work deals with the treatment planning optimization for intravascular brachytherapy (IVB) in peripheral arteries. The objective is both to quantitatively study the validity of different hypotheses required for a reliable application of the treatment with current techniques, and to contribute to the definition and the specification of a new optimized procedure taking into account the actual patient's vessel geometry. The detection of vascular luminal surface was performed by an image analysis process, i.e., virtual active navigation, applied to standard CT data. Dose distribution was calculated according to the formalism proposed and recommended by the AAPM in TG43 and TG60. A method combining simulated annealing and BFGS algorithms was applied to optimize the parameters associated with the dwell points such as their number, positions, and dwell times. Dose-surface histogram (DSH) was used to evaluate the dose distribution results. Four levels of accuracy in target surface description were tested. The application of this optimization method to four different CT data sets including patient data, phantom and animal models showed that the treatment plan can be improved when the actual vessel geometry has been taken into account.

In-vivo validation of spatially correct three-dimensional reconstruction of human coronary arteries by integrating intravascular ultrasound and biplane angiography

OBJECTIVES

The in-vivo validation of geometrically correct three-dimensional reconstruction of human coronary arteries by integrating intravascular ultrasound and biplane coronary angiography has not been adequately investigated. The purpose of this study was to describe the reconstruction method and investigate its in-vivo feasibility and accuracy.

METHODS

In 17 coronary arteries (mean length, 85.7+/-17.1 mm) from nine patients, an intravascular ultrasound procedure along with a biplane coronary angiography was performed. From each angiographic projection, a single end-diastolic frame was selected in order to reconstruct the intravascular ultrasound catheter trajectory in space. In each end-diastolic intravascular ultrasound image, the lumen and media-adventitia contours were detected semi-automatically by an active contour algorithm. Each pair of contours was located on the catheter trajectory appropriately and interpolated with the adjacent pairs creating a three-dimensional volume of the arterial lumen and wall. The reconstructed lumen was back-projected onto both angiographic planes and the agreement between the back-projected and the angiographic luminal outlines was calculated.

RESULTS

The angiogram-derived catheter length showed very high correlation (y=0.97 x + 1.8, P<0.001) and agreement with the corresponding pullback-derived values. Accordingly, the semi-automated segmentation of intravascular ultrasound images was also in significant correlation (r> or =0.96, P<0.001) and agreement with the reference manual tracing. The back-projected luminal borders showed good overall association with the corresponding angiographic ones (r=0.78, P<0.001) as well as remarkable agreement.

CONCLUSIONS

Spatially correct three-dimensional reconstruction of human coronary arteries constitutes an imaging method with considerably high in-vivo feasibility and accuracy.

In-vivo accuracy of geometrically correct three-dimensional reconstruction of human coronary arteries: is it influenced by certain parameters?

OBJECTIVE

The geometrically correct three-dimensional reconstruction of human coronary arteries by integrating intravascular ultrasound (IVUS) and biplane angiography constitutes a promising imaging method for coronaries with broad clinical potential. The determinants of the accuracy of the method, however, have not been investigated before.

METHODS

In total, 17 arterial segments (right coronary artery, n=7; left anterior descending, n=4; left circumflex, n=6) derived from nine patients were three-dimensionally reconstructed by applying three-dimensional intravascular ultrasound. The degree of matching between the reconstructed lumen back-projected onto each angiographic plane and the actual lumen in each plane was used as a measure of method's accuracy. The investigated factors that could potentially affect the reliability of the method included the type of the artery (left anterior descending, left circumflex, right coronary artery) and several geometrical and morphological characteristics of the reconstructed arteries.

RESULTS

The correlation between the back-projected reconstructed lumens and the actual angiographic ones was found to be high (r=0.78, P<0.001). Neither the category of the reconstructed arteries nor their particular geometrical and morphological characteristics influenced the accuracy of the reconstruction method significantly. Nonetheless, the method exhibited slightly less accuracy in the reconstruction of right coronary arteries, an observation that could be attributed to the more intense pulsatile motion that this artery experiences during the cardiac cycle compared to the left anterior descending and left circumflex artery.

CONCLUSIONS

The in-vivo accuracy of three-dimensional intravascular ultrasound (3D IVUS) is significantly high regardless of the type of the coronary arteries or their particular geometrical and morphological characteristics. This finding further supports the applicability of the method for either diagnostic or investigational purposes.

Intravascular ultrasound image segmentation: a three-dimensional fast-marching method based on gray level distributions

Intravascular ultrasound (IVUS) is a catheter based medical imaging technique particularly useful for studying atherosclerotic disease. It produces cross-sectional images of blood vessels that provide quantitative assessment of the vascular wall, information about the nature of atherosclerotic lesions as well as plaque shape and size. Automatic processing of large IVUS data sets represents an important challenge due to ultrasound speckle, catheter artifacts or calcification shadows. A new three-dimensional (3-D) IVUS segmentation model, that is based on the fast-marching method and uses gray level probability density functions (PDFs) of the vessel wall structures, was developed. The gray level distribution of the whole IVUS pullback was modeled with a mixture of Rayleigh PDFs. With multiple interface fast-marching segmentation, the lumen, intima plus plaque structure, and media layers of the vessel wall were computed simultaneously. The PDF-based fast-marching was applied to 9 in vivo IVUS pullbacks of superficial femoral arteries and to a simulated IVUS pullback. Accurate results were obtained on simulated data with average point to point distances between detected vessel wall borders and ground truth <0.072 mm. On in vivo IVUS, a good overall performance was obtained with average distance between segmentation results and manually traced contours <0.16 mm. Moreover, the worst point to point variation between detected and manually traced contours stayed low with Hausdorff distances <0.40 mm, indicating a good performance in regions lacking information or containing artifacts. In conclusion, segmentation results demonstrated the potential of gray level PDF and fast-marching methods in 3-D IVUS image processing.

Automated image analysis methods for 3-D quantification of the neurovascular unit from multichannel confocal microscope images

BACKGROUND

There is a need for integrative and quantitative methods to investigate the structural and functional relations among elements of complex systems, such as the neurovascular unit (NVU), that involve multiple cell types, microvasculatures, and various genomic/proteomic/ionic functional entities.

METHODS

Vascular casting and selective labeling enabled simultaneous three-dimensional imaging of the microvasculature, cell nuclei, and cytoplasmic stains. Multidimensional segmentation was achieved by (i) bleed-through removal and attenuation correction; (ii) independent segmentation and morphometry for each corrected channel; and (iii) spatially associative feature computation across channels. The combined measurements enabled cell classification based on nuclear morphometry, cytoplasmic signals, and distance from vascular elements. Specific spatial relations among the NVU elements could be quantified.

RESULTS

A software system combining nuclear and vessel segmentation codes and associative features was constructed and validated. Biological variability contributed to misidentified nuclei (9.3%), undersegmentation of nuclei (3.7%), hypersegmentation of nuclei (14%), and missed nuclei (4.7%). Microvessel segmentation errors occurred rarely, mainly due to nonuniform lumen staining.

CONCLUSIONS

Associative features across fluorescence channels, in combination with standard features, enable integrative structural and functional analysis of the NVU. By labeling additional structural and functional entities, this method can be scaled up to larger-scale systems biology studies that integrate spatial and molecular information.

[Cardio-pulmonary vascular system. Three-dimensional quantitative evaluation by microcomputed tomography]

In recent years microcomputed tomography (microCT) has become more and more important in basic research. Now commercial microCT scanners are available. Thus, it is very likely that this new, accurate and promising method for three-dimensional and non-destructive quantitative evaluation of intact tissues including vessels will be applied more frequently. The review provides a survey of the basic technology of microCT and its current use for high resolution three-dimensional morphometric and functional analysis within the cardio-pulmonary vascular system.