Capsule endoscopy is a promising technique for diagnosing diseases in the small intestines. Here we propose a miniature swimming mechanism that uses MRI’s magnetic fields for both propulsion and wireless energy delivery. Our method uses both the static and radio frequency (RF) magnetic field inherently available in MRI to generate propulsion force. The propulsion force is produced by a swimming tail containing waving beam consisting of three coils in a row. Alternating current in the coils acting on the static magnetic field of the MRI will generate waving movement to produce a propulsion force. RF magnetic field will provide power to generate the alternating currents in the coils. We developed a theoretical model to predict sinusoidal waves produced by the waving beam using the Euler-Bernoulli beam equation and multiple-input multiple-output system were solved using antenna design theory. This numerical model predicted that the maximal propulsion from a 10 mm long tail can produce a velocity of 7.9 mm/s force of 5.5 mN when placed in a 3T static magnetic field. A validation study with a single coil demonstrated that the theoretical and numerical model predicts well the proposed swimming mechanism and it is useful for the fabrication of swimming tails.

Simultaneous capturing of ultrasound (US) and magnetic resonance (MR) images allows fusion of information obtained from both modalities. We propose an MR-compatible US system where MR images are acquired in a known orientation with respect to the US imaging plane and concurrent real-time imaging can be achieved. Compatibility of the two imaging devices is a major issue in the physical setup. Tests were performed to quantify the radio frequency (RF) noise introduced in MR and US images, with the US system used in conjunction with MRI scanner of different field strengths (0.5 T and 3 T). Furthermore, simultaneous imaging was performed on a dual modality breast phantom in the 0.5 T open bore and 3 T close bore MRI systems to aid needle-guided breast biopsy. Fiducial based passive tracking and electromagnetic based active tracking were used in 3 T and 0.5 T, respectively, to establish the location and orientation of the US probe inside the magnet bore. Our results indicate that simultaneous US and MR imaging are feasible with properly-designed shielding, resulting in negligible broadband noise and minimal periodic RF noise in both modalities. US can be used for real time display of the needle trajectory, while MRI can be used to confirm needle placement.

We present a new algorithm to register 3-D preoperative magnetic resonance (MR) images to intraoperative MR images of the brain which have undergone brain shift. This algorithm relies on a robust estimation of the deformation from a sparse noisy set of measured displacements. We propose a new framework to compute the displacement field in an iterative process, allowing the solution to gradually move from an approximation formulation (minimizing the sum of a regularization term and a data error term) to an interpolation formulation (least square minimization of the data error term). An outlier rejection step is introduced in this gradual registration process using a weighted least trimmed squares approach, aiming at improving the robustness of the algorithm. We use a patient-specific model discretized with the finite element method in order to ensure a realistic mechanical behavior of the brain tissue. To meet the clinical time constraint, we parallelized the slowest step of the algorithm so that we can perform a full 3-D image registration in 35 s (including the image update time) on a heterogeneous cluster of 15 personal computers. The algorithm has been tested on six cases of brain tumor resection, presenting a brain shift of up to 14 mm. The results show a good ability to recover large displacements, and a limited decrease of accuracy near the tumor resection cavity.

MRI (magnetic resonance imaging) is a powerful medical diagnostic tool. Its value would be greatly increased if it were possible to physically manipulate objects within the MRI during imaging. However, the extraordinarily strong magnetic fields used by the MRI make conventional electromagnetic components, such as actuators and sensors, unusable. In this paper, it is shown that devices constructed using binary polymer based actuators, called electrostrictive polymer actuators (EPAM) are able to function effectively within the MRI without degrading its imaging performance. These actuators eliminate the need for conventional electromagnetic actuators and their associated electronics. The binary nature of the actuators eliminates the need for feedback sensors to control the devices motion. The basic concept called digital mechatronics is briefly summarized in this paper. Its application to a reconfigurable MRI surface-imaging coil (RMIC) is also presented. Experimental results are presented that show the EPAM RMIC is completely compatible in the MRI and can be used to enhance the diagnostic capabilities of MRI. The paper also suggests other applications of binary EPAM based actuators for use in MRI systems.

We present a new algorithm for the nonrigid registration of three-dimensional magnetic resonance (MR) intraoperative image sequences showing brain shift. The algorithm tracks key surfaces of objects (cortical surface and the lateral ventricles) in the image sequence using a deformable surface matching algorithm. The volumetric deformation field of the objects is then inferred from the displacements at the boundary surfaces using a linear elastic biomechanical finite-element model. Two experiments on synthetic image sequences are presented, as well as an initial experiment on intraoperative MR images showing brain shift. The results of the registration algorithm show a good correlation of the internal brain structures after deformation, and a good capability of measuring surface as well as subsurface shift. We measured distances between landmarks in the deformed initial image and the corresponding landmarks in the target scan. Cortical surface shifts of up to 10 mm and subsurface shifts of up to 6 mm were recovered with an accuracy of 1 mm or less and 3 mm or less respectively.

A magnetic resonance (MR) compatible surgical assistance robot system, designed to aid minimally invasive surgical techniques, such as positioning and directing a catheter or a laser pointer, has been built and is under preclinical evaluation. Two specific issues arise in the MR environment: (i) the reduced space in which to place the robot; and (ii) the MR compatibility. The main mechanical body is located above the head of the surgeon, with two rigid, hanging arms that reach into the workspace. This novel configuration contributes to a small occupancy in the workspace and to the MR compatibility. The robot is also carefully designed for safety and sterilization issues. Details of the kinematics and the design of the robot are given, and the MR compatibility is examined. This shows that the robot has no adverse effect on the imaging, even when it is in motion.

We aimed to study the performance of a parallel implementation of an intraoperative nonrigid registration algorithm that accurately simulates the biomechanical properties of the brain and its deformations during surgery. The algorithm was designed to allow for improved surgical navigation and quantitative monitoring of treatment progress in order to improve the surgical outcome and to reduce the time required in the operating room. We have applied the algorithm to two neurosurgery cases with promising results. High performance computing is a key enabling technology that allows the biomechanical simulation to be executed quickly enough for the algorithm to be practical. Our parallel implementation was evaluated on a symmetric multi-processor and two clusters and exhibited similar performance characteristics on each. The implementation was sufficiently fast to be used in the operating room during a neurosurgery procedure. It allowed a three-dimensional volumetric deformation to be simulated in less than ten seconds.

Image-guided surgery is an application for which high performance computing is increasingly becoming a critical technology. Advances in image-guided surgery techniques have made it possible to acquire images of a patient whilst the surgery is taking place, to align these images with high resolution 3D scans of the patient acquired preoperatively and to merge intraoperative images from multiple imaging modalities. The application of these technologies has now become a routine clinical procedure in some hospitals. However, as the type of procedures undertaken is expanded, it is becoming clear that the use of image fusion and linear registration technology alone has some limitations. We have developed a novel image segmentation algorithm that makes use of an individualized template of normal patient anatomy in order to compute the segmentation of intraoperative imaging data. Intraoperative image segmentation is highly data and compute intensive. In order to achieve accurate segmentation in a time frame compatible with surgical intervention, we have developed a parallel version of our segmentation algorithm, and implemented the algorithm on a symmetric multiprocessor architecture. We have studied the accuracy of the segmentation algorithm, and the scalability and bandwidth requirements of our parallel implementation.

Intensity-based classification of MR images has proven problematic, even when advanced techniques are used. Intrascan and interscan intensity inhomogeneities are a common source of difficulty. While reported methods have had some success in correcting intrascan inhomogeneities, such methods require supervision for the individual scan. This paper describes a new method called adaptive segmentation that uses knowledge of tissue intensity properties and intensity inhomogeneities to correct and segment MR images. Use of the expectation-maximization (EM) algorithm leads to a method that allows for more accurate segmentation of tissue types as well as better visualization of magnetic resonance imaging (MRI) data, that has proven to be effective in a study that includes more than 1000 brain scans. Implementation and results are described for segmenting the brain in the following types of images: axial (dual-echo spin-echo), coronal [three dimensional Fourier transform (3-DFT) gradient-echo T1-weighted] all using a conventional head coil, and a sagittal section acquired using a surface coil. The accuracy of adaptive segmentation was found to be comparable with manual segmentation, and closer to manual segmentation than supervised multivariant classification while segmenting gray and white matter.

We developed a three-dimensional (3D) digitized atlas of the human brain to visualize spatially complex structures. It was designed for use with magnetic resonance (MR) imaging data sets. Thus far, we have used this atlas for surgical planning, model-driven segmentation, and teaching. We used a combination of automated and supervised segmentation methods to define regions of interest based on neuroanatomical knowledge. We also used 3D surface rendering techniques to create a brain atlas that would allow us to visualize complex 3D brain structures. We further linked this Information to script files in order to preserve both spatial information and neuroanatomical knowledge. We present here the application of the atlas for visualization in surgical planning far model-driven segmentation and for the teaching of neuroanatomy. This digitized human brain has the potential to provide important reference information for the planning of surgical procedures. It can also serve as a powerful teaching tool, since spatial relationships among neuroanatomical structures can be more readily envisioned when the user is able to view and rotate the structures in 3D space. Moreover, each element of the brain atlas is associated with a name tag, displayed by a user controlled pointer. The atlas holds a major promise as a template for model-driven segmentation. Using this technique, many regions of interest can be characterized simultaneously on new brain images.

In contrast to acquisition-based noise reduction methods a postprocess based on anisotropic diffusion is proposed. Extensions of this technique support 3-D and multiecho magnetic resonance imaging (MRI), incorporating higher spatial and spectral dimensions. The procedure overcomes the major drawbacks of conventional filter methods, namely the blurring of object boundaries and the suppression of fine structural details. The simplicity of the filter algorithm permits an efficient implementation, even on small workstations. The efficient noise reduction and sharpening of object boundaries are demonstrated by applying this image processing technique to 2-D and 3-D spin echo and gradient echo MR data. The potential advantages for MRI, diagnosis, and computerized analysis are discussed in detail.

Hierarchical image segmentation techniques have been applied to spin-echo magnetic resonance images of the brain. From T1- and T2-weighted clinical images, areas representing gray matter, white matter and cerebrospinal fluid were segmented. In the segmentations à priori anatomic information was used and the procedures were performed in an ordered sequence of anatomy.