Biomedical subjects

Benoit M Dawant

Publications and source records attributed to Benoit M Dawant.

14 recordsLinked to original sources

PubMed2005

Regulation of two insulin granule populations within the reserve pool by distinct calcium sources.

Insulin granule trafficking is a key step of glucose-stimulated insulin secretion from pancreatic beta cells. Using quantitative live cell imaging, we examined insulin granule movements within the reserve pool upon secretory stimulation in betaTC3 cells. For this study, we developed a custom image analysis program that permitted automatic tracking of the individual motions of over 20,000 granules. This analysis of a large sample size enabled us to study micro-populations of granules that were not quantifiable in previous studies. While over 90% of the granules depend on Ca2+ efflux from the endoplasmic reticulum for their mobilization, a small and fast-moving population of granules responds to extracellular Ca2+ influx after depolarization of the plasma membrane. We show that this differential regulation of the two granule populations is consistent with localized Ca2+ signals, and that the cytoskeletal network is involved in both types of granule movement. The fast-moving granules are correlated temporally and spatially to the replacement of the secreted insulin granules, which supports the hypothesis that these granules are responsible for replenishing the readily releasable pool. Our study provides a model by which glucose and other secretory stimuli can regulate the readily releasable pool through the same mechanisms that regulate insulin secretion.

Animals↗

PubMed2005

Automatic selection of DBS target points using multiple electrophysiological atlases.

In this paper we study and evaluate the influence of the choice of a particular reference volume as the electrophysiological atlas on the accuracy of the automatic predictions of optimal points for deep brain stimulator (DBS) implants. We refer to an electrophysiological atlas as a spatial map of electrophysiological information such as micro electrode recordings (MER), stimulation parameters, final implants positions, etc., which are acquired for each patient and then mapped onto a single reference volume using registration algorithms. An atlas-based prediction of the optimal point for a DBS surgery is made by registering a patient's image volume to that reference volume, that is, by computing a correct coordinate mapping between the two; and then by projecting the optimal point from the atlas to the patient using the transformation from the registration algorithm. Different atlases, as well as different parameterizations of the registration algorithm, lead to different and somewhat independent atlas-based predictions. We show how the use of multiple reference volumes can improve the accuracy of prediction by combining the predictions from the multiple reference volumes weighted by the accuracy of the non-rigid registration between each of the corresponding atlases and the patient volume.

Brain Mapping↗

PubMed2005

Three-dimensional visualization of protein expression in mouse brain structures using imaging mass spectrometry.

We have developed a method to visualize matrix-assisted laser desorption ionization imaging mass spectrometry (MALDI IMS) data aligned with optically determinable tissue structures in three dimensions. Details of the methodology are exemplified using the 3-D reconstruction of myelin basic protein (MBP) in the corpus callosum of a mouse brain. In this procedure, optical images obtained from serial coronal sections are first aligned to each other to reconstruct a surface of the corpus callosum from segmented contours of the aligned images. The MALDI IMS data are then coregistered to the optical images and superimposed into the surface to create the final 3-D visualization. Correlating proteomic data with anatomical structures provides a more comprehensive understanding of healthy and pathological brain functions, and holds promise to be utilized in more complex anatomical arrangements.

Animals↗

PubMed2005

Development of computer-generated phantoms for FMRI software evaluation.

Functional magnetic resonance imaging (FMRI) is a major tool for the evaluation of brain function and architecture. It is widely used by physicians, neuroscientists, psychologists and others. In order to process the data collected using FMRI, it is necessary to use post-acquisition processing software that employs motion correction and statistical modeling capabilities. These types of programs permit the user to extract the information about areas of brain activations that have occurred during a study. How well a particular motion-correction technique works and what effect it has on statistical processing are difficult to evaluate, since the level of activation present is not known a priori. This paper provides a description of the construction of a software phantom for use with FMRI post-acquisition processing tools with the properties that it is based on real subject data, has known locations and levels of activation, has known amounts of rigid body motion and noise added, and can be used to evaluate a processing system as if it were a real data set. Versions of the software phantom are available for downloading at the website: .

Humans↗

PubMed2005

Correction of motion artifact in cardiac optical mapping using image registration.

Cardiac motion is one of the main sources of artifacts in epifluorescence imaging experiments. It can cause significant error in electrophysiological measurements such as action potential duration. We present a novel approach that uses image registration based on maximization of mutual information to correct for in-plane cardiac motion in such experiments. The approach is relatively fast (a few seconds per frame) and is performed entirely post acquisition. The image registration approach is an alternative to traditional approaches such as mechanical restraint of the heart or addition of chemical uncouplers. Our results show that the image registration method significantly reduces motion-related artifacts in experimental data.

Algorithms↗

PubMed2005

A method to track cortical surface deformations using a laser range scanner.

This paper reports a novel method to track brain shift using a laser-range scanner (LRS) and nonrigid registration techniques. The LRS used in this paper is capable of generating textured point-clouds describing the surface geometry/intensity pattern of the brain as presented during cranial surgery. Using serial LRS acquisitions of the brain's surface and two-dimensional (2-D) nonrigid image registration, we developed a method to track surface motion during neurosurgical procedures. A series of experiments devised to evaluate the performance of the developed shift-tracking protocol are reported. In a controlled, quantitative phantom experiment, the results demonstrate that the surface shift-tracking protocol is capable of resolving shift to an accuracy of approximately 1.6 mm given initial shifts on the order of 15 mm. Furthermore, in a preliminary in vivo case using the tracked LRS and an independent optical measurement system, the automatic protocol was able to reconstruct 50% of the brain shift with an accuracy of 3.7 mm while the manual measurement was able to reconstruct 77% with an accuracy of 2.1 mm. The results suggest that a LRS is an effective tool for tracking brain surface shift during neurosurgery.

Algorithms↗

PubMed2005

Computer-aided placement of deep brain stimulators: from planning to intraoperative guidance.

In current practice, optimal placement of deep-brain stimulators (DBSs) used to treat movement disorders in patients with Parkinson's disease and essential tremor is an iterative procedure. A target is chosen preoperatively based on anatomical landmarks identified on magnetic resonance images. This point is used as an initial position that is refined intraoperatively using both microelectrode recordings and macrostimulation. In this paper, we report on our current progress toward developing a system for the computer-assisted preoperative selection of target points and for the intraoperative adjustment of these points. The system consists of a deformable atlas of optimal target points that can be used to select automatically the preoperative target, of an electrophysiological atlas, and of an intraoperative interface. Results we have obtained show that automatic prediction of target points is an achievable goal. Our results also indicate that electrophysiological information could be used to resolve structures not visible in anatomic images, thus improving both preoperative and intraoperative guidance. Our intraoperative system has reached the stage of a working prototype and we compare targeting accuracy as well as the number of paths needed to reach the targets with our system and with the method in current clinical use.

Algorithms↗

PubMed2004

Liver segmentation in living liver transplant donors: comparison of semiautomatic and manual methods.

PURPOSE: To compare the accuracy and repeatability of a semiautomatic segmentation algorithm with those of manual segmentation for determining liver volume in living liver transplant donors at magnetic resonance (MR) imaging. MATERIALS AND METHODS: The institutional review board approved this retrospective study and waived the requirement for informed consent. The semiautomatic segmentation algorithm is based on geometric deformable models and the level-set technique. It entails (a) placing initialization circle(s) on each image section, (b) running the algorithm, (c) inspecting and possibly manually modifying the contours obtained with the segmentation algorithm, and (d) placing lines to separate the liver segments. For 18 living donors (eight men and 10 women; mean age, 34 years; age range, 25-46 years), two observers each performed two semiautomatic and two manual segmentations on contrast material-enhanced T1-weighted MR images. Each measurement was timed. Actual graft weight was measured during surgery. The time needed for manual and that needed for semiautomatic segmentation were compared. Accuracy and repeatability were evaluated with the Bland-Altman method. RESULTS: Mean interaction time was reduced from 25 minutes with manual segmentation to 5 minutes with semiautomatic segmentation. The mean total time for the semiautomatic process was 7 minutes 20 seconds. Differences between the actual volume and the estimated volume ranged from -223 to +123 mL for manual segmentation and from -214 to +86 mL for semiautomatic segmentation. The 95% limits of agreement for the ratio of actual graft volume to estimated graft volume were 0.686 and 1.601 for semiautomatic segmentation and 0.651 and 1.957 for manual segmentation. Semiautomatic segmentation improved estimation in 15 of 18 cases. Inter- and intraobserver repeatability was higher with semiautomatic segmentation. CONCLUSION: Use of the semiautomatic segmentation algorithm substantially reduces the time needed for volumetric measurement of liver segments while improving both accuracy and repeatability.

Adult↗

PubMed2004

Registration of medical images using an interpolated closest point transform: method and validation.

Image registration is an important procedure for medical diagnosis. Since the large inter-site retrospective validation study led by Fitzpatrick at Vanderbilt University, voxel-based methods and more specifically mutual information-based registration methods (see for instance [IEEE Trans. Med. Imag. 22 (8) (2003) 986] for a review on these methods) have been regarded as the method of choice for rigid-body intra-subject registration problems. In this study we propose a method that is based on the Iterative Closest Point algorithm and a pre-computed closest point map obtained with a slight modification of the fast marching method proposed by Sethian. Pre-computing the closest point map speeds up the process because at each iteration point correspondence can be established by table lookup. We also show that because the closest point map is defined on a regular grid it introduces a registration error and we propose an interpolation scheme that addresses this issue. The method has been tested both on synthetic and real images, and registration results have been assessed quantitatively using the data set provided by the Retrospective Registration Evaluation Project. For these volumes, MR and CT head surfaces were extracted automatically using a level-set technique. Results show that on these data sets this registration method leads to accuracy numbers that are comparable to those obtained with voxel-based methods.

Algorithms↗

PubMed2004

Characterizing changes in MR images with color-coded Jacobians.

Image registration is the process of establishing spatial correspondence between two images or between two image volumes. Registration can be achieved by rigid, elastic, or a combination of rigid and elastic transforms that attempt to bring the two images into coincidence. A rigid transform accounts for differences in positioning and an elastic transform describes deformations due to differences in tissue properties, temporal changes due to growth or atrophy, or differences between individuals. Deformation-based morphometry uses the resulting deformation fields from these transforms to evaluate differences between the images being registered. Three methods of registration were evaluated: rigid (affine) transformation, elastic optical flow transformation, and elastic spline transformation. All three methods produce vector deformation fields that map each point in one image to a point in the other image. A 12-color map of the transformation Jacobian was used to represent local volume changes. Using the three registration methods, color-mapped Jacobians were determined using a simulated three-dimensional block with known translation, rotation, expansion, contraction, and intensity modulations. Color-coded Jacobians were also generated for experimentally measured magnetic resonance image volumes of water-filled balloons and 7-year-old twin boys. Color-coded Jacobians overlaid on anatomical images provide a convenient method to identify regional tissue expansion and contraction.

Algorithms↗

PubMed2004

Reduced heart rate volatility: an early predictor of death in trauma patients.

OBJECTIVE: To determine if using dense data capture to measure heart rate volatility (standard deviation) measured in 5-minute intervals predicts death. BACKGROUND: Fundamental approaches to assessing vital signs in the critically ill have changed little since the early 1900s. Our prior work in this area has demonstrated the utility of densely sampled data and, in particular, heart rate volatility over the entire patient stay, for predicting death and prolonged ventilation. METHODS: Approximately 120 million heart rate data points were prospectively collected and archived from 1316 trauma ICU patients over 30 months. Data were sampled every 1 to 4 seconds, stored in a relational database, linked to outcome data, and de-identified. HR standard deviation was continuously computed over 5-minute intervals (CVRD, cardiac volatility-related dysfunction). Logistic regression models incorporating age and injury severity score were developed on a test set of patients (N = 923), and prospectively analyzed in a distinct validation set (N = 393) for the first 24 hours of ICU data. RESULTS: Distribution of CVRD varied by survival in the test set. Prospective evaluation of the model in the validation set gave an area in the receiver operating curve of 0.81 with a sensitivity and specificity of 70.1 and 80.0, respectively. CVRD predict death as early as 24 hours in the validation set. CONCLUSIONS: CVRD identifies a subgroup of patients with a high probability of dying. Death is predicted within first 24 hours of stay. We hypothesize CVRD is a surrogate for autonomic nervous system dysfunction.

Adult↗

PubMed2003

The adaptive bases algorithm for intensity-based nonrigid image registration.

Nonrigid registration of medical images is important for a number of applications such as the creation of population averages, atlas-based segmentation, or geometric correction of functional magnetic resonance imaging (fMRI) images to name a few. In recent years, a number of methods have been proposed to solve this problem, one class of which involves maximizing a mutual information (MI)-based objective function over a regular grid of splines. This approach has produced good results but its computational complexity is proportional to the compliance of the transformation required to register the smallest structures in the image. Here, we propose a method that permits the spatial adaptation of the transformation's compliance. This spatial adaptation allows us to reduce the number of degrees of freedom in the overall transformation, thus speeding up the process and improving its convergence properties. To develop this method, we introduce several novelties: 1) we rely on radially symmetric basis functions rather than B-splines traditionally used to model the deformation field; 2) we propose a metric to identify regions that are poorly registered and over which the transformation needs to be improved; 3) we partition the global registration problem into several smaller ones; and 4) we introduce a new constraint scheme that allows us to produce transformations that are topologically correct. We compare the approach we propose to more traditional ones and show that our new algorithm compares favorably to those in current use.

Algorithms↗

PubMed2003

Incorporation of a laser range scanner into image-guided liver surgery: surface acquisition, registration, and tracking.

As image guided surgical procedures become increasingly diverse, there will be more scenarios where point-based fiducials cannot be accurately localized for registration and rigid body assumptions no longer hold. As a result, procedures will rely more frequently on anatomical surfaces for the basis of image alignment and will require intraoperative geometric data to measure and compensate for tissue deformation in the organ. In this paper we outline methods for which a laser range scanner may be used to accomplish these tasks intraoperatively. A laser range scanner based on the optical principle of triangulation acquires a dense set of three-dimensional point data in a very rapid, noncontact fashion. Phantom studies were performed to test the ability to link range scan data with traditional modes of image-guided surgery data through localization, registration, and tracking in physical space. The experiments demonstrate that the scanner is capable of localizing point-based fiducials to within 0.2 mm and capable of achieving point and surface based registrations with target registration error of less than 2.0 mm. Tracking points in physical space with the range scanning system yields an error of 1.4 +/- 0.8 mm. Surface deformation studies were performed with the range scanner in order to determine if this device was capable of acquiring enough information for compensation algorithms. In the surface deformation studies, the range scanner was able to detect changes in surface shape due to deformation comparable to those detected by tomographic image studies. Use of the range scanner has been approved for clinical trials, and an initial intraoperative range scan experiment is presented. In all of these studies, the primary source of error in range scan data is deterministically related to the position and orientation of the surface within the scanner's field of view. However, this systematic error can be corrected, allowing the range scanner to provide a rapid, robust method of acquiring anatomical surfaces intraoperatively.

Aged↗

PubMed2002

Longitudinal brain metabolic characterization of chronic alcoholics with proton magnetic resonance spectroscopy.

BACKGROUND: Proton magnetic resonance spectroscopy may elucidate the molecular underpinnings of alcoholism-associated brain shrinkage and the progression of alcohol dependence. METHODS: Using proton magnetic resonance spectroscopy, we determined absolute concentrations of -acetylaspartate (NAA), creatine/phosphocreatine (Cr), and choline (Cho)-containing compounds and -inositol (mI) in the anterior superior cerebellar vermis and frontal lobe white matter in 31 alcoholics and 12 normal controls. All patients were examined within 3 to 5 days of their last drink. Patients who did not relapse were again studied after 3 weeks and 3 months of abstinence by using an on-line repositioning technique that allows reliable localization of volumes of interest (VOIs). RESULTS: At 3 to 5 days after the last drink, frontal white matter metabolite concentrations were not significantly different from those of normal controls, whereas brain tissue in the VOI was reduced. Cerebellar [NAA] and [Cho] and brain and cerebellar volumes were decreased, but [Cr], [mI], and VOI brain tissue volume were not significantly different. Eight patients relapsed before 3 weeks (ER), 12 relapsed between 3 weeks and 3 months (LR), and 11 did not relapse (NR) during 3 months. Cerebellar [NAA] was reduced only in ER patients, despite the fact that ER patients drank for significantly fewer years and earlier in life than LR or NR patients. After 3 months, in the 11 continuously abstinent patients, cerebellar [NAA] and brain and cerebellar volumes increased; cerebellar [Cho], [Cr], and [mI] and VOI brain tissue did not change significantly. CONCLUSIONS: Decreased [NAA] and [Cho] in cerebellar vermis indicate a unique sensitivity to alcohol-induced brain injury. Cerebellar [NAA] increased with abstinence, but reduced [Cho] persisted beyond 3 months. Further studies are needed to determine whether low cerebellar [NAA] is a risk factor for, or consequence of, malignant, early-onset alcoholism.

Adult↗