Vein wall shrinkage induced by thermal coagulation with high-intensity-focused ultrasound: numerical modeling and in vivo experiments in sheep.

BACKGROUND: Varicose veins are a common disease that may significantly affect quality of life. Different approaches are currently used in clinical practice to treat this pathology. MATERIALS AND METHODS: In thermal therapy (radiofrequency or laser therapy), the vein is directly heated to a high temperature to induce vein wall coagulation, and the heat induces denaturation of the intramural collagen, which results macroscopically in vein shrinkage. Thermal vein shrinkage is a physical indicator of the efficiency of endovenous treatment. High-intensity focused ultrasound (HIFU) is a noninvasive technique that can thermally coagulate vein walls and induce vein shrinkage. In this study, we evaluated the vein shrinkage induced in vivo by extracorporeal HIFU ablation of sheep veins: six lateral saphenous veins (3.4mm mean diameter) were sonicated for 8 s with 3MHz continuous waves. Ultrasound imaging was performed before and immediately post-HIFU to quantify the HIFU-induced shrinkage. RESULTS: Luminal constriction was observed in 100% (6/6) of the treated veins. The immediate findings showed a mean diameter constriction of 53%. The experimental HIFU-induced shrinkage data were used to validate a numerical model developed to predict the thermally induced vein contraction during HIFU treatment. CONCLUSIONS: This model is based on the use of the k-wave library and published contraction rates of vessels immersed in hot water baths. The simulation results agreed well with those of in vivo experiments, showing a mean percent difference of 5%. The numerical model could thus be a valuable tool for optimizing ultrasound parameters as functions of the vein diameter, and future clinical trials are anticipated.

Efficacy and safety assessment of an ultrasound-based thermal treatment of varicose veins in a sheep model.

Purpose: Varicose veins are a common pathology that can be treated by endovenous thermal procedures like radiofrequency ablation (RFA). Such catheter-based techniques consist in raising the temperature of the vein wall to 70 to 120 °C to induce vein wall coagulation. Although effective, this treatment option is not suited for all types of veins and can be technically challenging.Materials and methods: In this study, we used High-Intensity Focused Ultrasound (HIFU) as a non-invasive thermal ablation procedure to treat varicose veins and we assessed the long-term efficacy and safety of the procedure in a sheep model. In vivo experiments were first conducted on two saphenous veins to measure the temperature rise induced at the vein wall during HIFU ablation and were compared with reported RFA-induced thermal rise. Thermocouples were inserted in situ to perform 20 measurements during 8-s ultrasound pulses at 3 MHz. Eighteen saphenous veins of nine anesthetized sheep (2-2.5 % Isoflurane) were then exposed to similar pulses (85 W acoustic, 8 s). After treatments, animals recovered from anesthesia and were followed up 30, 60 and 90 days post-treatment (n = 3 animals per group). At the end of the follow-up, vein segments and perivenous tissues were harvested and histologically examined.Results: Temperatures induced by HIFU pulses were found to be comparable to reported RFA treatments. Likewise, histological findings were similar to the ones reported after RFA and laser-based coagulation necrosis of the vein wall, thrombotic occlusions and vein wall fibrosis.Conclusion: These results support strongly the effectiveness and safety of HIFU for ablating non-invasively veins.

Automatic segmentation of antenatal 3-D ultrasound images.

The development of 3-D ultrasonic probes and 3-D ultrasound (3DUS) imaging offers new functionalities that call for specific image processing developments. In this paper, we propose an original method for the segmentation of the utero-fetal unit (UFU) from 3DUS volumes, acquired during the first trimester of gestation. UFU segmentation is required for a number of tasks, such as precise organ delineation, 3-D modeling, quantitative measurements, and evaluation of the clinical impact of 3-D imaging. The segmentation problem is formulated as the optimization of a partition of the image into two classes of tissues: the amniotic fluid and the fetal tissues. A Bayesian formulation of the partition problem integrates statistical models of the intensity distributions in each tissue class and regularity constraints on the contours. An energy functional is minimized using a level set implementation of a deformable model to identify the optimal partition. We propose to combine Rayleigh, Normal, Exponential, and Gamma distribution models to compute the region homogeneity constraints. We tested the segmentation method on a database of 19 antenatal 3DUS images. Promising results were obtained, showing the flexibility of the level set formulation and the interest of learning the most appropriate statistical models according to the idiosyncrasies of the data and the tissues. The segmentation method was shown to be robust to different types of initialization and to provide accurate results, with an average overlap measure of 0.89 when comparing with manual segmentations.

Hybrid 3D pregnant woman and fetus modeling from medical imaging for dosimetry studies.

PURPOSE: Numerical simulations studying the interactions between radiations and biological tissues require the use of three-dimensional models of the human anatomy at various ages and in various positions. Several detailed and flexible models exist for adults and children and have been extensively used for dosimetry. On the other hand, progress of simulation studies focusing on pregnant women and the fetus have been limited by the fact that only a small number of models exist with rather coarse anatomical details and a poor representation of the anatomical variability of the fetus shape and its position over the entire gestation. METHODS: In this paper, we propose a new computational framework to generate 3D hybrid models of pregnant women, composed of fetus shapes segmented from medical images and a generic maternal body envelope representing a synthetic woman scaled to the dimension of the uterus. The computational framework includes the following tasks: image segmentation, contour regularization, mesh-based surface reconstruction, and model integration. RESULTS: A series of models was created to represent pregnant women at different gestational stages and with the fetus in different positions, all including detailed tissues of the fetus and the utero-fetal unit, which play an important role in dosimetry. These models were anatomically validated by clinical obstetricians and radiologists who verified the accuracy and representativeness of the anatomical details, and the positioning of the fetus inside the maternal body. CONCLUSION: The computational framework enables the creation of detailed, realistic, and representative fetus models from medical images, directly exploitable for dosimetry simulations.

Whole-body pregnant woman modeling by digital geometry processing with detailed uterofetal unit based on medical images.

Anatomical models of pregnant women are used in several applications, such as numerical dosimetry, to assess the potential effects of electromagnetic fields on biological tissues, or medical simulation. Recent advances in medical imaging have enabled the generation of realistic and detailed models of human beings. The construction of pregnant woman models remains a complex task, since it is not possible to acquire whole-body images. Only few models have been developed up to now, and they all present some limitations regarding the representation of anatomical variability of the fetus shape and position over the entire gestation. This paper describes a complete methodology that intends to automate each step of the construction of pregnant women models. The proposed approach relies on the segmentation of 3-D ultrasonic and 3-D magnetic resonance imaging (MRI) data, and on dedicated computer graphics tools. The lack of complete anatomical information for the mother in image data is compensated, in an original way, by merging the available information with a synthetic woman model, deformed to match the image-based information. A set of models anatomically validated by clinical experts is presented. They include detailed information on uterofetal units and cover different gestational stages with various fetal positions.

Utero-fetal unit and pregnant woman modeling using a computer graphics approach for dosimetry studies.

Potential sanitary effects related to electromagnetic fields exposure raise public concerns, especially for fetuses during pregnancy. Human fetus exposure can only be assessed through simulated dosimetry studies, performed on anthropomorphic models of pregnant women. In this paper, we propose a new methodology to generate a set of detailed utero-fetal unit (UFU) 3D models during the first and third trimesters of pregnancy, based on segmented 3D ultrasound and MRI data. UFU models are built using recent geometry processing methods derived from mesh-based computer graphics techniques and embedded in a synthetic woman body. Nine pregnant woman models have been generated using this approach and validated by obstetricians, for anatomical accuracy and representativeness.