Evaluation of targeting errors in ultrasound-assisted radiotherapy.

A method for validating the start-to-end accuracy of a 3-D ultrasound (US)-based patient positioning system for radiotherapy is described. A radiosensitive polymer gel is used to record the actual dose delivered to a rigid phantom after being positioned using 3-D US guidance. Comparison of the delivered dose with the treatment plan allows accuracy of the entire radiotherapy treatment process, from simulation to 3-D US guidance, and finally delivery of radiation, to be evaluated.

Compensation of geometric distortion effects on intraoperative magnetic resonance imaging for enhanced visualization in image-guided neurosurgery.

Objective: Preoperative magnetic resonance imaging (MRI), functional MRI, diffusion tensor MRI, magnetic resonance spectroscopy, and positron-emission tomographic scans may be aligned to intraoperative MRI to enhance visualization and navigation during image-guided neurosurgery. However, several effects (both machine- and patient-induced distortions) lead to significant geometric distortion of intraoperative MRI. Therefore, a precise alignment of these image modalities requires correction of the geometric distortion.

A validation framework for brain tumor segmentation.

Rationale And Objectives: We introduce a validation framework for the segmentation of brain tumors from magnetic resonance (MR) images. A novel unsupervised semiautomatic brain tumor segmentation algorithm is also presented.

Materials And Methods: The proposed framework consists of 1) T1-weighted MR images of patients with brain tumors, 2) segmentation of brain tumors performed by four independent experts, 3) segmentation of brain tumors generated by a semiautomatic algorithm, and 4) a software tool that estimates the performance of segmentation algorithms.

Non-rigid alignment of pre-operative MRI, fMRI, and DT-MRI with intra-operative MRI for enhanced visualization and navigation in image-guided neurosurgery.

Objective: The usefulness of neurosurgical navigation with current visualizations is seriously compromised by brain shift, which inevitably occurs during the course of the operation, significantly degrading the precise alignment between the pre-operative MR data and the intra-operative shape of the brain. Our objectives were (i) to evaluate the feasibility of non-rigid registration that compensates for the brain deformations within the time constraints imposed by neurosurgery, and (ii) to create augmented reality visualizations of critical structural and functional brain regions during neurosurgery using pre-operatively acquired fMRI and DT-MRI.

Materials And Methods: Eleven consecutive patients with supratentorial gliomas were included in our study.

Volumetric anatomical modeling from medical images.

Many clinical applications, such as surgical planning, require volumetric models of anatomical structures represented as a set of tetrahedra. A method of constructing anatomical models from medical images is presented. The method starts with a set of contours segmented from the medical images by a clinician and produces a model that has high fidelity with the contours.

Anatomical structure modeling from medical images.

Some clinical applications, such as surgical planning, require volumetric models of anatomical structures represented as a set of tetrahedra. A practical method of constructing anatomical models from medical images is presented. The method starts with a set of contours segmented from the medical images by a clinician and produces a model that has high fidelity with the contours.

Spectral clustering algorithms for ultrasound image segmentation.

Image segmentation algorithms derived from spectral clustering analysis rely on the eigenvectors of the Laplacian of a weighted graph obtained from the image. The NCut criterion was previously used for image segmentation in supervised manner. We derive a new strategy for unsupervised image segmentation.

Ultrasound image segmentation using spectral clustering.

Segmentation of ultrasound images is necessary in a variety of clinical applications, but the development of automatic techniques is still an open problem. Spectral clustering techniques have recently become popular for data and image analysis. In particular, image segmentation has been proposed via the normalized cut (NCut) criterion.

Automatic detection of fiducial markers in fluoroscopy images for on-line calibration.

Calibrated C-arm fluoroscopy is used for a variety of medical procedures where objects and anatomical structures need to be located in space. Calibration is often based on imaging a grid of fiducial markers and using the C-arm image's geometrical measurements (radius and center) together with the positions of the markers. An on-line technique is developed to automatically locate the fiducial markers and validated on 97 images.

Automatic measurements of image geometry for online calibration of C-arm fluoroscopy.

Calibrated C-arm fluoroscopy is used for a variety of surgical procedures where surgical tools and anatomical structures need to be located in space. Calibration can be performed online by combining measurements of the geometry of the image (center and radius) with measurements of a grid of fiducial markers. This article focuses on the first aspect--image geometry--and describes a method to perform the geometry measurements automatically.

A knowledge-based approach to automatic detection of the spinal cord in CT images.

Accurate planning of radiation therapy entails the definition of treatment volumes and a clear delimitation of normal tissue of which unnecessary exposure should be prevented. The spinal cord is a radiosensitive organ, which should be precisely identified because an overexposure to radiation may lead to undesired complications for the patient such as neuronal disfunction or paralysis. In this paper, a knowledge-based approach to identifying the spinal cord in computed tomography images of the thorax is presented.

A computer aided diagnostic system for radiotherapy planning.

Planning for radiation therapy intervention implies the definition of treatment volumes as well as a clear delimitation of normal tissue. This paper presents a Computer Aided Diagnostic system for the automatic CT image analysis. Two important problems are solved: the spinal cord segmentation and the detection of lung metastases.