Hongyan Luo

Image-based 3D modeling and validation of radiofrequency interstitial tumor ablation using a tissue-mimicking breast phantom

PurposeMinimally invasive treatment of solid cancers, especially in the breast and liver, remains clinically challenging, despite a variety of treatment modalities, including radiofrequency ablation (RFA), microwave ablation or high-intensity focused ultrasound. Each treatment modality has advantages and disadvantages, but all are limited by placement of a probe or US beam in the target tissue for tumor ablation and monitoring. The placement is difficult when the tumor is surrounded by large blood vessels or organs. Patient-specific image-based 3D modeling for thermal ablation simulation was developed to optimize treatment protocols that improve treatment efficacy.MethodsA tissue-mimicking breast gel phantom was used to develop an image-based 3D computer-aided design (CAD) model for the evaluation of a planned RF ablation. First, the tissue-mimicking gel was cast in a breast mold to create a 3D breast phantom, which contained a simulated solid tumor. Second, the phantom was imaged in a medical MRI scanner using a standard breast imaging MR sequence. Third, the MR images were converted into a 3D CAD model using commercial software (ScanIP, Simpleware), which was input into another commercial package (COMSOL Multiphysics) for RFA simulation and treatment planning using a finite element method (FEM). For validation of the model, the breast phantom was experimentally ablated using a commercial (RITA) RFA electrode and a bipolar needle with an electrosurgical generator (DRE ASG-300). The RFA results obtained by pre-treatment simulation were compared with actual experimental ablation.ResultsA 3D CAD model, created from MR images of the complex breast phantom, was successfully integrated with an RFA electrode to perform FEM ablation simulation. The ablation volumes achieved both in the FEM simulation and the experimental test were equivalent, indicating that patient-specific models can be implemented for pre-treatment planning of solid tumor ablation.ConclusionA tissue-mimicking breast gel phantom and its MR images were used to perform FEM 3D modeling and validation by experimental thermal ablation of the tumor. Similar patient-specific models can be created from preoperative images and used to perform finite element analysis to plan radiofrequency ablation. Clinically, the method can be implemented for pre-treatment planning to predict the effect of an individual’s tissue environment on the ablation process, and this may improve the therapeutic efficacy.

A Micropower Miniature Piezoelectric Actuator for Implantable Middle Ear Hearing Device

This paper describes the design and development of a small actuator using a miniature piezoelectric stack and a flextensional mechanical amplification structure for an implantable middle ear hearing device (IMEHD). A finite-element method was used in the actuator design. Actuator vibration displacement was measured using a laser vibrometer. Preliminary evaluation of the actuator for an IMEHD was conducted using a temporal bone model. Initial results from one temporal bone study indicated that the actuator was small enough to be implanted within the middle ear cavity, and sufficient stapes displacement can be generated for patients with mild to moderate hearing losses, especially at higher frequency range, by the actuator suspended onto the stapes. There was an insignificant mass-loading effect on normal sound transmission (<;3 dB) when the actuator was attached to the stapes and switched off. Improved vibration performance is predicted by more firm attachment. The actuator power consumption and its generated equivalent sound pressure level are also discussed. In conclusion, the actuator has advantages of small size, lightweight, and micropower consumption for potential use as IMHEDs.

Bicomponent Conformal Electrode for Radiofrequency Sequential Ablation and Circumferential Separation of Large Tumors in Solid Organs: Development and In Vitro Evaluation

Objective: Complete destruction of large tumors by radiofrequency ablation (RFA) with surrounding tumor-free margin is difficult because of incomplete or nonuniform heating due to both heat-sink effect of circulating blood and limitations of existing RF electrode design. A new RF electrode is described to overcome this limitation. Methods: A bicomponent conformal (BCC) RFA probe providing sectorial sequential ablation followed by circumferential cutting is designed and evaluated. Three-dimensional finite-element analysis model was developed with temperature feedback-controlled simulation of RFA for electrode design and optimization. The prototype bipolar BCC probe with three embedded thermocouples was constructed and evaluated in tissue-mimicking phantoms. Results: Maximum tissue temperature was kept <100 ºC with power applied <15 W. A 10-min ablation time was used for each sequence and after four sequential RFA, a large ablation zone of 55 cm3 was achieved. Our experiment confirmed that lesions exceeding 3.7xa0cm could be ablated and separated from the surrounded tissue. Conclusion: The new BCC probe is, thus, capable of controlled ablation followed by circumferential separation of the lesions, when required. Significance: The results of these experiments provide the proof of concept validation that the BCC probe has the potential to ablate by sequential heating tumors in solid organs >3.5xa0cm then separate them by electrosurgical cutting from the surrounding normal parenchyma. The combined RF ablation and physical separation could completely destroy the cancer cells at the ablation site, thus, avoid any local recurrence of cancer. It requires further in vivo validation studies in large animals.

The impact of sEMG feature weight on the recognition of similar grasping gesture

This paper is aimed to study the impact of sEMG feature weight on the recognition of similar grasping gesture, of which the classification performance is hindered by their alike underlying muscle activation pattern. The sEMG were collected from six forearm hand muscles (EPB, EPI, FDS, PL, MB, ED) when subjects conducted a 4-second different grasping gestures. Then empirical mode decomposition (EMD) was employed to represent the sEMG as IMF components, of which the time-domain features (ZC and WAMP) were obtained. Then PCA was used to reduce the dimension of feature set. The optimal training set of similar grasping gestures was determined by adjusting the weight of sEMG feature in the feature space. SVM classifier and Genetic-Algorithm (GA) were used to classify the patterns of the gestures. Experimental results showed that the classification rate of a gesture was improved with the increasing proportion of its sEMG feature in training set, and different gesture exhibited different sensitivity for gesture recognition. The present work suggested that the proportion of candidate gesture should not be equal, and the contribution of different gesture should be considered to optimize the training set.