Ron Kikinis

Multi-modal volume registration by maximization of mutual information

A new information-theoretic approach is presented for finding the registration of volumetric medical images of differing modalities. Registration is achieved by adjustment of the relative position and orientation until the mutual information between the images is maximized. In our derivation of the registration procedure, few assumptions are made about the nature of the imaging process. As a result the algorithms are quite general and can foreseeably be used with a wide variety of imaging devices. This approach works directly with image data; no pre-processing or segmentation is required. This technique is, however, more flexible and robust than other intensity-based techniques like correlation. Additionally, it has an efficient implementation that is based on stochastic approximation. Experiments are presented that demonstrate the approach registering magnetic resonance (MR) images with computed tomography (CT) images, and with positron-emission tomography (PET) images. Surgical applications of the registration method are described.

Adaptive segmentation of MRI data

Intensity-based classification of MR images has proven problematic, even when advanced techniques are used. Intra-scan and inter-scan intensity inhomogeneities are a common source of difficulty. While reported methods have had some success in correcting intra-scan 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 EM algorithm leads to a fully automatic method that allows for more accurate segmentation of tissue types as well as better visualization of MRI data, that has proven to be effective in a study that includes more than 1000 brain scans.

Abnormalities of the left temporal lobe and thought disorder in schizophrenia : a quantitative magnetic resonance imaging study

BACKGROUND Data from postmortem, CT, and magnetic resonance imaging (MRI) studies indicate that patients with schizophrenia may have anatomical abnormalities of the left temporal lobe, but it is unclear whether these abnormalities are related to the thought disorder characteristic of schizophrenia. METHODS We used new MRI neuroimaging techniques to derive (without knowledge of the diagnosis) volume measurements and three-dimensional reconstructions of temporal-lobe structures in vivo in 15 right-handed men with chronic schizophrenia and 15 matched controls. RESULTS As compared with the controls, the patients had significant reductions in the volume of gray matter in the left anterior hippocampus-amygdala (by 19 percent [95 percent confidence interval, 3 to 36 percent]), the left parahippocampal gyrus (by 13 percent [95 percent confidence interval, 3 to 23 percent], vs. 8 percent on the right), and the left superior temporal gyrus (by 15 percent [95 percent confidence interval, 5 to 25 percent]). The volume of the left posterior superior temporal gyrus correlated with the score on the thought-disorder index in the 13 patients evaluated (r = -0.81, P = 0.001). None of these regional volume decreases was accompanied by a decrease in the volume of the overall brain or temporal lobe. The volume of gray matter in a control region (the superior frontal gyrus) was essentially the same in the patients and controls. CONCLUSIONS Schizophrenia involves localized reductions in the gray matter of the left temporal lobe. The degree of thought disorder is related to the size of the reduction in volume of the left posterior superior temporal gyrus.

3D Slicer as an image computing platform for the Quantitative Imaging Network

Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open-source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions. In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future directions that can further facilitate development and validation of imaging biomarkers using 3D Slicer.

Nonlinear anisotropic filtering of MRI data

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.

Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images

This paper describes a method for the enhancement of curvilinear structures such as vessels and bronchi in three-dimensional (3-D) medical images. A 3-D line enhancement filter is developed with the aim of discriminating line structures from other structures and recovering line structures of various widths. The 3-D line filter is based on a combination of the eigenvalues of the 3-D Hessian matrix. Multi-scale integration is formulated by taking the maximum among single-scale filter responses, and its characteristics are examined to derive criteria for the selection of parameters in the formulation. The resultant multi-scale line-filtered images provide significantly improved segmentation and visualization of curvilinear structures. The usefulness of the method is demonstrated by the segmentation and visualization of brain vessels from magnetic resonance imaging (MRI) and magnetic resonance angiography (MRA), bronchi from a chest CT, and liver vessels (portal veins) from an abdominal CT.

Magnetic resonance imaging study of hippocampal volume in chronic, combat-related posttraumatic stress disorder

This study used quantitative volumetric magnetic resonance imaging techniques to explore the neuroanatomic correlates of chronic, combat-related posttraumatic stress disorder (PTSD) in seven Vietnam veterans with PTSD compared with seven nonPTSD combat veterans and eight normal nonveterans. Both left and right hippocampi were significantly smaller in the PTSD subjects compared to the Combat Control and Normal subjects, even after adjusting for age, whole brain volume, and lifetime alcohol consumption. There were no statistically significant group differences in intracranial cavity, whole brain, ventricles, ventricle:brain ratio, or amygdala. Subarachnoidal cerebrospinal fluid was increased in both veteran groups. Our finding of decreased hippocampal volume in PTSD subjects is consistent with results of other investigations which utilized only trauma-unexposed control groups. Hippocampal volume was directly correlated with combat exposure, which suggests that traumatic stress may damage the hippocampus. Alternatively, smaller hippocampi volume may be a pre-existing risk factor for combat exposure and/or the development of PTSD upon combat exposure.

Development and implementation of intraoperative magnetic resonance imaging and its neurosurgical applications.

OBJECTIVE We describe the development and implementation of a new open configuration magnetic resonance imaging (MRI) system, with which neurosurgical procedures can be performed using image guidance. Our initial neurosurgical experience consists of 140 cases, including 63 stereotactic biopsies, 16 cyst drainages, 55 craniotomies, 3 thermal ablations, and 3 laminectomies. The surgical advantages derived from this new modality are presented. METHODS The 0.5-T intraoperative MRI system (SIGNA SP, Boston, MA), developed by General Electric Medical Systems in collaboration with the Brigham and Womens Hospital, has a vertical gap within its magnet, providing the physical space for surgery. Images are viewed on monitors located within this gap and can also be acquired in conjunction with optical tracking of surgical instruments, establishing accurate intraoperative correlations between instrument position and anatomic structures. RESULTS A wide range of standard neurosurgical procedures can be performed using intraoperative MRI. The images obtained are clear and provide accurate and immediate information to use in the planning and assessment of the progress of the surgery. CONCLUSION Intraoperative MRI allows lesions to be precisely localized and targeted, and the progress of a procedure can be immediately evaluated. The constantly updated images help to eliminate errors that can arise during frame-based and frameless stereotactic surgery when anatomic structures alter their position because of shifting or displacement of brain parenchyma but are correlated with images obtained preoperatively. Intraoperative MRI is particularly helpful in determining tumor margins, optimizing surgical approaches, achieving complete resection of intracerebral lesions, and monitoring potential intraoperative complications.

Processing and visualization for diffusion tensor MRI

This paper presents processing and visualization techniques for Diffusion Tensor Magnetic Resonance Imaging (DT-MRI). In DT-MRI, each voxel is assigned a tensor that describes local water diffusion. The geometric nature of diffusion tensors enables us to quantitatively characterize the local structure in tissues such as bone, muscle, and white matter of the brain. This makes DT-MRI an interesting modality for image analysis. In this paper we present a novel analytical solution to the Stejskal-Tanner diffusion equation system whereby a dual tensor basis, derived from the diffusion sensitizing gradient configuration, eliminates the need to solve this equation for each voxel. We further describe decomposition of the diffusion tensor based on its symmetrical properties, which in turn describe the geometry of the diffusion ellipsoid. A simple anisotropy measure follows naturally from this analysis. We describe how the geometry or shape of the tensor can be visualized using a coloring scheme based on the derived shape measures. In addition, we demonstrate that human brain tensor data when filtered can effectively describe macrostructural diffusion, which is important in the assessment of fiber-tract organization. We also describe how white matter pathways can be monitored with the methods introduced in this paper. DT-MRI tractography is useful for demonstrating neural connectivity (in vivo) in healthy and diseased brain tissue.

Statistical validation of image segmentation quality based on a spatial overlap index.

RATIONALE AND OBJECTIVES To examine a statistical validation method based on the spatial overlap between two sets of segmentations of the same anatomy. MATERIALS AND METHODS The Dice similarity coefficient (DSC) was used as a statistical validation metric to evaluate the performance of both the reproducibility of manual segmentations and the spatial overlap accuracy of automated probabilistic fractional segmentation of MR images, illustrated on two clinical examples. Example 1: 10 consecutive cases of prostate brachytherapy patients underwent both preoperative 1.5T and intraoperative 0.5T MR imaging. For each case, 5 repeated manual segmentations of the prostate peripheral zone were performed separately on preoperative and on intraoperative images. Example 2: A semi-automated probabilistic fractional segmentation algorithm was applied to MR imaging of 9 cases with 3 types of brain tumors. DSC values were computed and logit-transformed values were compared in the mean with the analysis of variance (ANOVA). RESULTS Example 1: The mean DSCs of 0.883 (range, 0.876-0.893) with 1.5T preoperative MRI and 0.838 (range, 0.819-0.852) with 0.5T intraoperative MRI (P < .001) were within and at the margin of the range of good reproducibility, respectively. Example 2: Wide ranges of DSC were observed in brain tumor segmentations: Meningiomas (0.519-0.893), astrocytomas (0.487-0.972), and other mixed gliomas (0.490-0.899). CONCLUSION The DSC value is a simple and useful summary measure of spatial overlap, which can be applied to studies of reproducibility and accuracy in image segmentation. We observed generally satisfactory but variable validation results in two clinical applications. This metric may be adapted for similar validation tasks.