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Ion-Florin Talos

Publications and source records attributed to Ion-Florin Talos.

13 recordsLinked to original sources
PubMed2006

Supratentorial low-grade glioma resectability: statistical predictive analysis based on anatomic MR features and tumor characteristics.

PURPOSE: To retrospectively assess the main variables that affect the complete magnetic resonance (MR) imaging-guided resection of supratentorial low-grade gliomas. MATERIALS AND METHODS: Institutional review board approval was obtained for this retrospective HIPAA-compliant study, with the requirement for informed consent waived. Data from 101 patients (61 men, 40 women; mean age, 39 years; age range, 18-72 years) who had nonenhancing supratentorial mass lesions that were histopathologically diagnosed as low-grade (World Health Organization grade II) gliomas and consecutively underwent surgery with intraoperative MR imaging guidance were analyzed. There were 21 low-grade astrocytomas, 64 oligodendrogliomas, and 16 mixed oligoastrocytomas. Initial and residual tumor volumes were measured on intraoperative T2-weighted MR images and three-dimensional spoiled gradient-echo MR images. The anatomic relationships between the tumor and eloquent cortical and/or subcortical regions and the influence of these relationships on the extent of resection were analyzed on the basis of preoperative MR imaging findings. Summary measures, univariate Fisher exact test and t test, and multivariate logistic regression analyses were performed. RESULTS: Tumor volume ranged from 2.7-231.0 mL. Univariate analyses revealed the following tumor characteristics to be significant predictive variables of incomplete tumor resection: diffuse tumor margin on T2-weighted MR images, oligodendroglioma or oligoastrocytoma histopathologic type, and large tumor volume (P < .05 for all). Tumor involvement of the following structures was associated with incomplete resection: corpus callosum, corticospinal tract, insular lobe, middle cerebral artery, motor cortex, optic radiation, visual cortex, and basal ganglia (P < .05 for all). Multivariate analyses revealed that incomplete tumor resection was due to tumor involvement of the corticospinal tract (P < .01), large tumor volume (P < .01), and oligodendroglioma histopathologic type (P = .02). CONCLUSION: The main variables associated with incomplete tumor resection in 101 patients were identified by using statistical predictive analyses.

Adolescent↗
PubMed2005

A global goodness-of-fit test for receiver operating characteristic curve analysis via the bootstrap method.

OBJECTIVE: Medical classification accuracy studies often yield continuous data based on predictive models for treatment outcomes. A popular method for evaluating the performance of diagnostic tests is the receiver operating characteristic (ROC) curve analysis. The main objective was to develop a global statistical hypothesis test for assessing the goodness-of-fit (GOF) for parametric ROC curves via the bootstrap. DESIGN: A simple log (or logit) and a more flexible Box-Cox normality transformations were applied to untransformed or transformed data from two clinical studies to predict complications following percutaneous coronary interventions (PCIs) and for image-guided neurosurgical resection results predicted by tumor volume, respectively. We compared a non-parametric with a parametric binormal estimate of the underlying ROC curve. To construct such a GOF test, we used the non-parametric and parametric areas under the curve (AUCs) as the metrics, with a resulting p value reported. RESULTS: In the interventional cardiology example, logit and Box-Cox transformations of the predictive probabilities led to satisfactory AUCs (AUC=0.888; p=0.78, and AUC=0.888; p=0.73, respectively), while in the brain tumor resection example, log and Box-Cox transformations of the tumor size also led to satisfactory AUCs (AUC=0.898; p=0.61, and AUC=0.899; p=0.42, respectively). In contrast, significant departures from GOF were observed without applying any transformation prior to assuming a binormal model (AUC=0.766; p=0.004, and AUC=0.831; p=0.03), respectively. CONCLUSIONS: In both studies the p values suggested that transformations were important to consider before applying any binormal model to estimate the AUC. Our analyses also demonstrated and confirmed the predictive values of different classifiers for determining the interventional complications following PCIs and resection outcomes in image-guided neurosurgery.

Adolescent↗
PubMed2005

Combining classifiers using their receiver operating characteristics and maximum likelihood estimation.

In any medical domain, it is common to have more than one test (classifier) to diagnose a disease. In image analysis, for example, there is often more than one reader or more than one algorithm applied to a certain data set. Combining of classifiers is often helpful, but determining the way in which classifiers should be combined is not trivial. Standard strategies are based on learning classifier combination functions from data. We describe a simple strategy to combine results from classifiers that have not been applied to a common data set, and therefore can not undergo this type of joint training. The strategy, which assumes conditional independence of classifiers, is based on the calculation of a combined Receiver Operating Characteristic (ROC) curve, using maximum likelihood analysis to determine a combination rule for each ROC operating point. We offer some insights into the use of ROC analysis in the field of medical imaging.

Artificial Intelligence↗
PubMed2005

Hybrid formulation of the model-based non-rigid registration problem to improve accuracy and robustness.

We present a new algorithm to register 3D pre-operative Magnetic Resonance (MR) images with intra-operative MR images of the brain. This algorithm relies on a robust estimation of the deformation from a sparse set of measured displacements. We propose a new framework to compute iteratively the displacement field starting from an approximation formulation (minimizing the sum of a regularization term and a data error term) and converging toward an interpolation formulation (least square minimization of the data error term). The robustness of the algorithm is achieved through the introduction of an outliers rejection step in this gradual registration process. We ensure the validity of the deformation by the use of a biomechanical model of the brain specific to the patient, discretized with the finite element method. The algorithm has been tested on six cases of brain tumor resection, presenting a brain shift up to 13 mm.

Algorithms↗
PubMed2005

Statistical validation of brain tumor shape approximation via spherical harmonics for image-guided neurosurgery.

RATIONALE AND OBJECTIVES: Surgical planning now routinely uses both two-dimensional (2D) and three-dimensional (3D) models that integrate data from multiple imaging modalities, each highlighting one or more aspects of morphology or function. We performed a preliminary evaluation of the use of spherical harmonics (SH) in approximating the 3D shape and estimating the volume of brain tumors of varying characteristics. MATERIALS AND METHODS: Magnetic resonance (MR) images from five patients with brain tumors were selected randomly from our MR-guided neurosurgical practice. Standardized mean square reconstruction errors (SMSRE) by tumor volume were measured. Validation metrics for comparing performances of the SH method against segmented contours (SC) were the dice similarity coefficient (DSC) and standardized Euclidean distance (SED) measure. RESULTS: Tumor volume range was 22,413-85,189 mm3, and range of number of vertices in triangulated models was 3674-6544. At SH approximations with degree of at least 30, SMSRE were within 1.66 x 10(-5) mm(-1). Summary measures yielded a DSC range of 0.89-0.99 (pooled median, 0.97 and significantly >0.7; P < .001) and an SED range of 0.0002-0.0028 (pooled median, 0.0005). CONCLUSION: 3D shapes of tumors may be approximated by using SH for neurosurgical applications.

Adult↗
PubMed2005

Robust nonrigid registration to capture brain shift from intraoperative MRI.

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.

Algorithms↗
PubMed2004

Capturing intraoperative deformations: research experience at Brigham and Women's Hospital.

During neurosurgical procedures the objective of the neurosurgeon is to achieve the resection of as much diseased tissue as possible while achieving the preservation of healthy brain tissue. The restricted capacity of the conventional operating room to enable the surgeon to visualize critical healthy brain structures and tumor margin has lead, over the past decade, to the development of sophisticated intraoperative imaging techniques to enhance visualization. However, both rigid motion due to patient placement and nonrigid deformations occurring as a consequence of the surgical intervention disrupt the correspondence between preoperative data used to plan surgery and the intraoperative configuration of the patient's brain. Similar challenges are faced in other interventional therapies, such as in cryoablation of the liver, or biopsy of the prostate. We have developed algorithms to model the motion of key anatomical structures and system implementations that enable us to estimate the deformation of the critical anatomy from sequences of volumetric images and to prepare updated fused visualizations of preoperative and intraoperative images at a rate compatible with surgical decision making. This paper reviews the experience at Brigham and Women's Hospital through the process of developing and applying novel algorithms for capturing intraoperative deformations in support of image guided therapy.

Algorithms↗
PubMed2004

Evaluating requirements for spatial resolution of fMRI for neurosurgical planning.

The unambiguous localization of eloquent functional areas is necessary to decrease the neurological morbidity of neurosurgical procedures. We explored the minimum spatial resolution requirements for functional magnetic resonance imaging (fMRI) data acquisition when brain mapping is used in neurosurgical planning and navigation. Using a 1.5 Tesla clinical MRI scanner, eight patients with brain tumors underwent fMRI scans using spatial resolution of approximately 4 x 4 x 4 mm(3) to map the eloquent motor and language areas during the performance of cognitive/sensorimotor tasks. The fMRI results were then used intra-operatively in an open MRI system to delineate eloquent areas. Retrospectively, activation patterns were visually inspected by a neurosurgeon to determine qualitatively whether ambiguity with respect to the activation boundaries, due to low spatial resolution, could be of potential significance for surgical guidance. A significant degree of ambiguity in both the extent and shape of activation was judged to be present in data from six of the eight patients. Analysis of fMRI data at multiple resolutions from a normal volunteer showed that at 3 mm isotropic resolution, eloquent areas were better localized within the gray matter although there was still some potential for ambiguity caused by activations appearing to cross a sulcus. The data acquired with 2-mm isotropic voxels significantly enhanced the spatial localization of activation to within the gray matter. Thus, isotropic spatial resolution on the order of 2 x 2 x 2 mm(3), which is much higher than the resolutions used in typical fMRI examinations, may be needed for the unambiguous identification of cortical activation with respect to tumors and important anatomical landmarks.

Adult↗
PubMed2004

The tale of Phineas Gage, digitally remastered.

The injury of Phineas Gage has fueled research on and fascination with the localization of cerebral functions in the past century and a half. Most physicians and anatomists believed that Gage sustained a largely bilateral injury to the frontal lobes. However, previous studies seem to have overlooked a few less obvious, but essential details. This has led us to reanalyze the injury using three-dimensional reconstruction and quantitative computer-aided techniques and to propose a new biomechanical model, in order to determine the location and extent of the injury and explain Gage's improbable survival. Unlike previous studies on this subject, our findings are based on computer-generated three-dimensional reconstructions of a thin-slice computed tomography scan (CAT) of Phineas Gage's skull. The results of our image analysis were corroborated with the clinical findings, thoroughly recorded by Dr. Harlow in 1848, as well as with a systematic examination of the original skull specimen. Our results show that the cerebral injury was limited to the left frontal lobe, did not extend to the contralateral side, did not affect the ventricular system, and did not involve vital intracranial vascular structures. Although modern neuroscience has perhaps outgrown the speculations prompted by this famous case, it is still a living part of the medical folklore and education. Setting the record straight based on clinical reasoning, observation of the physical evidence, and sound quantitative computational methods is more than mere minutia and of interest for the broad medical community.

Adult↗
PubMed2004

Method for combining information from white matter fiber tracking and gray matter parcellation.

We introduce a method for combining fiber tracking from diffusion-tensor (DT) imaging with cortical gray matter parcellation from structural high-spatial-resolution 3D spoiled gradient-recalled acquisition in the steady state images. We applied this method to a tumor case to determine the impact of the tumor on white matter architecture. We conclude that this new method for combining structural and DT imaging data is useful for understanding cortical connectivity and the localization of fiber tracts and their relationship with cortical anatomy and brain abnormalities.

Adult↗
PubMed2002

Intraoperative magnetic resonance imaging and magnetic resonance imaging-guided therapy for brain tumors.

Since their introduction into surgical practice in the mid 1990s, intraoperative MRI systems have evolved into essential, routinely used tools for the surgical treatment of brain tumors in many centers. Clear delineation of the lesion, "under-the-surface" vision, and the possibility of obtaining real-time feedback on the extent of resection and the position of residual tumor tissue (which may change during surgery due to "brain-shift") are the main strengths of this method. High-performance computing has further extended the capabilities of intraoperative MRI systems, opening the way for using multimodal information and 3D anatomical reconstructions, which can be updated in "near real time." MRI sensitivity to thermal changes has also opened the way for innovative, minimally invasive (LASER ablations) as well as noninvasive therapeutic approaches for brain tumors (focused ultrasound). Although we have not used intraoperative MRI in clinical applications sufficiently long to assess long-term outcomes, this method clearly enhances the ability of the neurosurgeon to navigate the surgical field with greater accuracy, to avoid critical anatomic structures with greater efficacy, and to reduce the overall invasiveness of the surgery itself.

Brain↗