Biomedical subjects

B M Dawant

Publications and source records attributed to B M Dawant.

At least 19 recordsLinked to original sources

PubMed2002

Brain atlas deformation in the presence of small and large space-occupying tumors.

Brain atlases contain a wealth of information that could be used in radiation therapy or neurosurgical planning. Until now, however, when large space-occupying tumors and lesions drastically alter the shape of brain structures and substructures, atlas-based methods have been of limited use. In this work, we present a new technique that permits a brain atlas to be warped onto image volumes in which large lesions are present. First we show that a method previously used for atlas-based segmentation of normal brains can also be used for brains with small lesions. We then present an extension of this technique for brains with large lesions. This involves several steps: a global registration to bring the two volumes into approximate correspondence; a local registration to warp the atlas onto the patient volume; the seeding of the warped atlas with a tumor model derived from patient data; and the deformation of the seeded atlas. Global registration is performed using a mutual information criterion. The method we have used for atlas warping is derived from optical flow principles. Preliminary results obtained on real patient images are presented. These results indicate that the proposed method can be used to automatically segment structures of interest in brains with gross deformation. Potential areas of application for this method include automatic labeling of critical structures for radiation therapy and presurgical planning.

Anatomy, Artistic↗

PubMed2001

Automatic lumbar vertebral identification using surface-based registration.

This work proposes the use of surface-based registration to automatically select a particular vertebra of interest during surgery. Manual selection of the correct vertebra can be a challenging task, especially for closed-back, minimally invasive procedures. Our method uses shape variations that exist among lumbar vertebrae to automatically determine the portion of the spinal column surface that correctly matches a set of physical vertebral points. In our experiments, we register vertebral points representing posterior elements of a single vertebra in physical space to spinal column surfaces extracted from computed tomography images of multiple vertebrae. After registering the set of physical points to each vertebral surface that is a potential match, we then compute the standard deviation of the surface error for each registration trial. The registration that corresponds to the lowest standard deviation designates the correct match. We have performed our current experiments on two plastic spine phantoms and two patients.

Computational Biology↗

PubMed2001

Closing the loop in ICU decision support: physiologic event detection, alerts, and documentation.

Automated physiologic event detection and alerting is a challenging task in the ICU. Ideally care providers should be alerted only when events are clinically significant and there is opportunity for corrective action. However, the concepts of clinical significance and opportunity are difficult to define in automated systems, and effectiveness of alerting algorithms is difficult to measure. This paper describes recent efforts on the Simon project to capture information from ICU care providers about patient state and therapy in response to alerts, in order to assess the value of event definitions and progressively refine alerting algorithms. Event definitions for intracranial pressure and cerebral perfusion pressure were studied by implementing a reliable system to automatically deliver alerts to clinical users alphanumeric pagers, and to capture associated documentation about patient state and therapy when the alerts occurred. During a 6-month test period in the trauma ICU at Vanderbilt University Medical Center, 530 alerts were detected in 2280 hours of data spanning 14 patients. Clinical users electronically documented 81% of these alerts as they occurred. Retrospectively classifying documentation based on therapeutic actions taken, or reasons why actions were not taken, provided useful information about ways to potentially improve event definitions and enhance system utility.

Decision Support Systems, Clinical↗

PubMed2000

Surface registration for use in interactive, image-guided liver surgery.

OBJECTIVE: Liver surgery is difficult because of limited external landmarks, significant vascularity, and inexact definition of intra-hepatic anatomy. Intra-operative ultrasound (IOUS) has been widely used in an attempt to overcome these difficulties, but is limited by its two-dimensional nature, inter-user variability, and image obliteration with ablative or resectional techniques. Because the anatomy of the liver and intra-operative removal of hepatic ligaments make intrinsic or extrinsic point-based registration impractical, we have implemented a surface registration technique to map physical space into CT image space, and have tested the accuracy of this method on an anatomical liver phantom with embedded tumor targets. MATERIALS AND METHODS: Liver phantoms were created from anatomically correct molds with "tumors" embedded within the substance of the liver. Helical CT scans were performed with 3-mm slices. Using an optically active position sensor, the surface of the liver was digitized according to anatomical segments. A surface registration was performed and RMS errors of the locations of internal tumors are presented as verification. An initial point-based marker registration was performed and considered the "gold standard" for error measurement. RESULTS: Errors for surface registration were 2.9 mm for the entire surface and 2.8 mm for embedded targets. CONCLUSION: This is an initial study considering the use of surface registration for the purpose of physical-to-image registration in the area of liver surgery.

Computer Simulation↗

PubMed2000

Registration of physical space to laparoscopic image space for use in minimally invasive hepatic surgery.

While laparoscopes are used for numerous minimally invasive (MI) procedures, MI liver resection and ablative surgery is infrequently performed. The paucity of cases is due to the restriction of the field of view by the laparoscope and the difficulty in determining tumor location and margins under video guidance. By merging MI surgery with interactive, image-guided surgery (IIGS), we hope to overcome localization difficulties present in laparoscopic liver procedures. One key component of any IIGS system is the development of accurate registration techniques to map image space to physical or patient space. This manuscript focuses on the accuracy and analysis of the direct linear transformation (DLT) method to register physical space with laparoscopic image space on both distorted and distortion-corrected video images. Experiments were conducted on a liver-sized plastic phantom affixed with 20 markers at various depths. After localizing the points in both physical and laparoscopic image space, registration accuracy was assessed for different combinations and numbers of control points (n) to determine the quantity necessary to develop a robust registration matrix. For n = 11, average target registration error (TRE) was 0.70 +/- 0.20 mm. We also studied the effects of distortion correction on registration accuracy. For the particular distortion correction method and laparoscope used in our experiments, there was no statistical significance between physical to image registration error for distorted and corrected images. In cases where a minimum number of control points (n = 6) are acquired, the DLT is often not stable and the mathematical process can lead to high TRE values. Mathematical filters developed through the analysis of the DLT were used to prospectively eliminate outlier cases where the TRE was high. For n = 6, prefilter average TRE was 17.4 +/- 153 mm for all trials; when the filters were applied, average TRE decreased to 1.64 +/- 1.10 mm for the remaining trials.

Humans↗

PubMed1999

Model creation and deformation for the automatic segmentation of the brain in MR images.

In this paper a method for the automatic segmentation of the brain in magnetic resonance images is presented and validated. The proposed method involves two steps 1) the creation of an initial model and 2) the deformation of this model to fit the exact contours of the brain in the images. A new method to create the initial model has been developed and compared to a more traditional approach in which initial models are created by means of brain atlases. A comprehensive validation of the complete segmentation method has been conducted on a series of three-dimensional T1-weighted magnetization-prepared rapid gradient echo image volumes acquired both from control volunteers and patients suffering from Cushing's disease. This validation study compares results obtained with the method we propose and contours drawn manually. Averages differences between manual and automatic segmentation with the model creation method we propose are 1.7% and 2.7% for the control volunteers and the Cushing's patients, respectively. These numbers are 1.8% and 5.6% when the atlas-based method is used.

Algorithms↗

PubMed1999

Retrospective intermodality registration techniques for images of the head: surface-based versus volume-based.

The primary objective of this study is to perform a blinded evaluation of two groups of retrospective image registration techniques, using as a gold standard a prospective marker-based registration method, and to compare the performance of one group with the other. These techniques have already been evaluated individually [27]. In this paper, however, we find that by grouping the techniques as volume based or surface based, we can make some interesting conclusions which were not visible in the earlier study. In order to ensure blindness, all retrospective registrations were performed by participants who had no knowledge of the gold-standard results until after their results had been submitted. Image volumes of three modalities: X-ray computed tomography (CT), magnetic resonance (MR), and positron emission tomography (PET) were obtained from patients undergoing neurosurgery at Vanderbilt University Medical Center on whom bone-implanted fiducial markers were mounted. These volumes had all traces of the markers removed and were provided via the Internet to project collaborators outside Vanderbilt, who then performed retrospective registrations on the volumes, calculating transformations from CT to MR and/or from PET to MR. These investigators communicated their transformations, again via the Internet, to Vanderbilt, where the accuracy of each registration was evaluated. In this evaluation, the accuracy is measured at multiple volumes of interest (VOI's). Our results indicate that the volume-based techniques in this study tended to give substantially more accurate and reliable results than the surface-based ones for the CT-to-MR registration tasks, and slightly more accurate results for the PET-to-MR tasks. Analysis of these results revealed that the rotational component of error was more pronounced for the surface-based group. It was also apparent that all of the registration techniques we examined have the potential to produce satisfactory results much of the time, but that visual inspection is necessary to guard against large errors.

Algorithms↗

PubMed1999

Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformations: Part I, Methodology and validation on normal subjects.

The study presented in this paper tests the hypothesis that the combination of a global similarity transformation and local free-form deformations can be used for the accurate segmentation of internal structures in MR images of the brain. To quantitatively evaluate our approach, the entire brain, the cerebellum, and the head of the caudate have been segmented manually by two raters on one of the volumes (the reference volume) and mapped back onto all the other volumes, using the computed transformations. The contours so obtained have been compared to contours drawn manually around the structures of interest in each individual brain. Manual delineation was performed twice by the same two raters to test inter- and intrarater variability. For the brain and the cerebellum, results indicate that for each rater, contours obtained manually and contours obtained automatically by deforming his own atlas are virtually indistinguishable. Furthermore, contours obtained manually by one rater and contours obtained automatically by deforming this rater's own atlas are more similar than contours obtained manually by two raters. For the caudate, manual intra- and interrater similarity indexes remain slightly better than manual versus automatic indexes, mainly because of the spatial resolution of the images used in this study. Qualitative results also suggest that this method can be used for the segmentation of more complex structures, such as the hippocampus.

Algorithms↗

PubMed1999

Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformations: Part II, validation on severely atrophied brains.

Studies aimed at quantifying neuroanatomical differences between populations require the volume measurements of individual brain structures. If the study contains a large number of images, manual segmentation is not practical. This study tests the hypothesis that a fully automatic, atlas-based segmentation method can be used to quantify atrophy indexes derived from the brain and cerebellum volumes in normal subjects and chronic alcoholics. This is accomplished by registering an atlas volume with a subject volume, first using a global transformation, and then improving the registration using a local transformation. Segmented structures in the atlas volume are then mapped to the corresponding structures in the subject volume using the combined global and local transformations. This technique has been applied to seven normal and seven alcoholic subjects. Three magnetic resonance volumes were obtained for each subject and each volume was segmented automatically, using the atlas-based method. Accuracy was assessed by manually segmenting regions and measuring the similarity between corresponding regions obtained automatically. Repeatability was determined by comparing volume measurements of segmented structures from each acquisition of the same subject. Results demonstrate that the method is accurate, that the results are repeatable, and that it can provide a method for automatic quantification of brain atrophy, even when the degree of atrophy is large.

Alcohol-Induced Disorders, Nervous System↗

PubMed1998

Surface-based registration of CT images to physical space for image-guided surgery of the spine: a sensitivity study.

This paper presents a method designed to register preoperative computed tomography (CT) images to vertebral surface points acquired intraoperatively from ultrasound (US) images or via a tracked probe. It also presents a comparison of the registration accuracy achievable with surface points acquired from the entire posterior surface of the vertebra to the accuracy achievable with points acquired only from the spinous process and central laminar regions. Using a marker-based method as a reference, this work shows that submillimetric registration accuracy can be obtained even when a small portion of the posterior vertebral surface is used for registration. It also shows that when selected surface patches are used, CT slice thickness is not a critical parameter in the registration process. Furthermore, the paper includes qualitative results of registering vertebral surface points in US images to multiple CT slices. The method has been tested with US points and physical points on a plastic spine phantom and with simulated data on a patient CT scan.

Humans↗

PubMed1997

Web-based data integration and annotation in the intensive care unit.

Integrating patient data to facilitate review and annotation is a challenging task in the intensive care unit (ICU), partially due to the wide variety of proprietary systems and types of data involved. This paper describes SIMON-Web, a Java-based user interface to integrate data from an existing bedside monitoring system and a clinical information system (CIS). SIMON-Web displays graphical data from physiologic monitors and other bedside devices as well as information from the clinical laboratory and physician order-entry systems, and allows users to annotate the data using a point-and-click or free-text interface. By continuing to add functionality to SIMON-Web's extensible user interface, we hope to continue to augment and eventually replace manual care provider data charting in the ICU. As of March, 1997, SIMON-Web has been implemented on two bedside personal computer workstations in the cardiac care unit (CCU) at Vanderbilt University Medical Center.

Computer Communication Networks↗

PubMed1994

Adaptive signal analysis and interpretation for real-time intelligent patient monitoring.

On-line intelligent monitoring, diagnosis, and control of dynamic systems such as patients in intensive care units necessitates the context-dependent acquisition, processing, analysis, and interpretation of large amounts of possibly noisy and incomplete data. The dynamic nature of the process also requires a continuous evaluation and adaptation of the monitoring strategy to respond to changes both in the monitored patient and in the monitoring equipment. Moreover, real-time constraints may imply data losses, the importance of which has to be minimized. This paper presents a computer architecture designed to accomplish these tasks. Its main components are a model and a data abstraction module. The model provides the system with a monitoring context related to the patient status. The data abstraction module relies on that information to adapt the monitoring strategy and provide the model with the necessary information. This paper focuses on the data abstraction module and its interaction with the model.

Algorithms↗

PubMed1994

Brain segmentation and white matter lesion detection in MR images.

This paper presents a review of methods and techniques that have been proposed for the segmentation of magnetic resonance (MR) images of the brain, with a special emphasis on the segmentation of white matter lesions. First, artifacts affecting MR images (noise, partial volume effect, and shading artifact) are reviewed and methods that have been proposed to correct for these artifacts are discussed. Next, a taxonomy of generic segmentation algorithms is presented, categorized as region-based, edge-based, and classification algorithms. For each category, the applications proposed in the literature are subdivided into 2-D, 3-D, or multimodal approaches. In each case, tables listing authors, bibliographic references, and methods used have been compiled and are presented. This description of segmentation algorithms is followed by a section on techniques proposed specifically for the analysis of white matter lesions. Finally, a section is dedicated to a review and a comparison of validation methods proposed to assess the accuracy and the reliability of the results obtained with various segmentation algorithms.

Algorithms↗

PubMed1993

Model-based diagnosis in intensive care monitoring: the YAQ approach.

YAQ is an ontology for model-based reasoning in physiologic domains. YAQ is based on a hybrid algebra of qualitative and numerical values, and is designed to benefit from the rich and ever-changing nature of information available in a critical care monitoring environment. The focus of the project is on diagnosis of clinical conditions, prediction of the effects of therapy, and therapy management assistance. Two models of diagnosis are implemented in YAQ: diagnosis based on associations, and model-based diagnosis. The ontology is applied to the domain of ventilator management in infants with respiratory distress syndrome (RDS). The article describes the diagnostic capabilities of YAQ, illustrates these concepts on examples taken from actual patient records, and reports the results of an evaluation of the diagnostic performance on the RDS/assisted ventilation domain model.

Artificial Intelligence↗

PubMed1989

Knowledge-based approach to sleep EEG analysis--a feasibility study.

A knowledge-based approach to automated sleep EEG (electroencephalogram) analysis is described. In this system, an object-oriented approach is followed in which specific waveforms and sleep stages ("objects") are represented in terms of frames. The latter capture the morphological and spatio-temporal information for each object. An object detection module ("frame matcher"), operating on the frames, is employed to identify what features need to be extracted from the EEG and to trigger the appropriate "specialist"--specialized signal processing modules--to obtain values for these features. This leads to an opportunistic approach to EEG interpretation with quantitative information being extracted from the signal only when needed by the reasoning processes. The system has been tested on the detection of K complexes and sleep spindles. Its performance indicates that the approach followed is feasible and can become a powerful tool for automated EEG interpretation.

Electroencephalography↗

PubMedDate not supplied

Automatic detection of intracranial contours in MR images.

Segmentation of the intracranial cavity in medical images is valuable in several research areas such as the quantitative analysis of normal and abnormal brain tissues, the registration of different imaging modalities (MRI, PET, CT) based on surface models of the brain, and the rendering of volume data. Because the manual delineation of the brain contour in the images can be demanding and error prone, an automatic procedure to perform this task is desirable. We have developed and tested a robust method that permits the automatic detection of the intracranial contour in transverse MR images. The method is described and its performance evaluated.

Algorithms↗

PubMedDate not supplied

Image-guided MR spectroscopy volume of interest localization for longitudinal studies.

Longitudinal magnetic resonance spectroscopy (MRS) studies require accurate repositioning of the volume of interest (VOI) over which measurements are made. In this work we present and evaluate a method for the image-guided repositioning of brain volumes of interest. The point-based registration technique we developed allows the repositioning to be performed on-line (i.e. while the patient is in the scanner). MR image volumes were acquired from six subjects, three scans each over the course of a month. During the first scan, two spectroscopy VOIs are visually selected: one in the frontal white matter, the other in the superior cerebellar vermis. The coordinates of 13 internal brain landmarks are also identified. During both subsequent scans, the same 13 landmarks are identified, and the transformation that registers the first set of landmarks to the subsequent set is computed. This result is used to automatically map the position of the spectroscopy VOIs from the first volume to the current volume. For the six subjects evaluated to date, we show an average repositioning error of the spectroscopy VOIs in the order of 1 mm. This accuracy allows us to conclude that any variations in the MR spectra are unlikely to be due to repositioning error.

Algorithms↗

PubMedDate not supplied

Effect of geometrical distortion correction in MR on image registration accuracy.

In this article we investigate the effect of geometrical distortion correction in MR images on the accuracy of the registration of X-ray CT and MR head images for both a fiducial marker (extrinsic point) method and a surface-matching technique. We use CT and T2-weighted MR image volumes acquired from seven patients who underwent craniotomies in a stereotactic neurosurgical clinical trial. Each patient had four external markers attached to transcutaneous posts screwed into the outer table of the skull. The MR images are corrected for static field inhomogeneity by using an image rectification technique and corrected for scale distortion (gradient magnitude uncertainty) by using an attached stereotactic frame as an object of known shape and size. We define target registration error (TRE) as the distance between corresponding marker positions after registration and transformation. The accuracy of the fiducial marker method is determined by using each combination of three markers to estimate the transformation and the remaining marker to calculate registration error. Surface-based registration is accomplished by fitting MR contours corresponding to the CSF-dura interface to CT contours derived from the inner surface of the skull. The mean point-based TRE using three noncollinear fiducials improved 34%-from 1.15 to 0.76 mm-after correcting for both static field inhomogeneity and scale distortion. The mean surface-based TRE improved 46%-from 2.20 to 1.19 mm. Correction of geometrical distortion in MR images can significantly improve the accuracy of point-based and surface-based registration of CT and MR head images. Distortion correction can be important in clinical situations such as stereotactic and functional neurosurgery where 1 to 2 mm accuracy is required.

Head↗