Aamer Aziz

High resolution CT anatomy of the pulmonary fissures.

Rationale and Objectives: Pulmonary interlobar fissures are important landmarks for proper identification of normal pulmonary anatomy and evaluation of disease. The purpose of this study was to define the radiologic anatomy of the pulmonary fissures using high resolution computed tomography (HRCT) in a large population. Methods: HRCT of the lungs from aortic arch to diaphragm was performed in 622 patients, with a slice thickness of 1 mm and slice interval of 10 mm. Major, minor, and accessory fissures were studied for their orientation and completeness. Results: Both major fissures were mostly facing laterally in their upper parts (100% and 89% right and left, respectively). The left major fissure faced medially (69%) while the right major fissure faced lateral (60%) in their lower parts. The right major fissure was more often incomplete (48% as compared with 43% on the left, P < 0.05). Minor fissures were convex superiorly with the apex in the anterolateral part of the base of the upper lobe, and were incomplete in 63% of cases. Azygos, inferior accessory, superior accessory, and left minor fissures were also seen in 1.2%, 8.6%, 4.6%, and 6.1% of the cases, respectively. Conclusion: The pulmonary fissures are highly variable and the right major fissure differs considerably from the left. The fissures are often incomplete.

Geometric modeling of the human normal cerebral arterial system

We propose an anatomy-based approach for an efficient construction of a three-dimensional human normal cerebral arterial model from segmented and skeletonized angiographic data. The centerline-based model is used for an accurate angiographic data representation. A vascular tree is represented by tubular segments and bifurcations whose construction takes into account vascular anatomy. A bifurcation is defined quantitatively and the algorithm calculating it is given. The centerline is smoothed by means of a sliding average filter. As the vessel radius is sensitive to quality of data as well as accuracy of segmentation and skeletonization, radius outlier removal and radius regression algorithms are formulated and applied. In this way, the approach compensates for some inaccuracies introduced during segmentation and skeletonization. To create the frame of vasculature, we use two different topologies: tubular and B-subdivision based. We also propose a technique to prevent vessel twisting. The analysis of the vascular model is done on a variety of data containing 258 vascular segments and 131 bifurcations. Our approach gives acceptable results from anatomical, topological and geometrical standpoints as well as provides fast visualization and manipulation of the model. The approach is applicable for building a reference cerebrovascular atlas, developing applications for simulation and planning of interventional radiology procedures and vascular surgery, and in education.

Extraction of the midsagittal plane from morphological neuroimages using the Kullback-Leibler's measure

A theoretically simple and computationally efficient method to extract the midsagittal plane (MSP) from volumetric neuroimages is presented. The method works in two stages (coarse and fine) and is based on calculation of the Kullback and Leiblers (KL) measure, which characterizes the difference between two distributions. Slices along the sagittal direction are analyzed with respect to a reference slice to determine the coarse MSP. To calculate the final MSP, a local search algorithm is applied. The proposed method does not need any preprocessing, like reformatting, skull stripping, etc. The algorithm was validated quantitatively on 75 MRI datasets of different pulse sequences (T1WI, T2WI, FLAIR and SPGR) and MRA. The angular and distance errors between the calculated MSP and the ground truth lines marked by the expert were calculated. The average distance and angular deviation were 1.25 pixels and 0.63 degrees , respectively. In addition, the algorithm was tested qualitatively on PD, FLAIR, MRA, and CT datasets. To analyze the robustness of the method against rotation, inhomogeneity and noise, the phantom data were used.

Fast Talairach Transformation for Magnetic Resonance Neuroimages

Abstract: We introduce and validate the Fast Talairach Transformation (FTT). FTT is a rapid version of the Talairach transformation (TT) with the modified Talairach landmarks. Landmark identification is fully automatic and done in 3 steps: calculation of midsagittal plane, computing of anterior commissure (AC) and posterior commissure (PC) landmarks, and calculation of cortical landmarks. To perform these steps, we use fast and anatomy-based algorithms employing simple operations. FTT was validated for 215 diversified T1-weighted and spoiled gradient recalled (SPGR) MRI data sets. It calculates the landmarks and warps the Talairach-Tournoux atlas fully automatically in about 5 sec on a standard computer. The average distance errors in landmark localization are (in mm): 1.16 (AC), 1.49 (PC), 0.08 (left), 0.13 (right), 0.48 (anterior), 0.16 (posterior), 0.35 (superior), and 0.52 (inferior). Extensions to FTT by introducing additional landmarks and applying nonlinear warping against the ventricular system are addressed. Application of FTT to other brain atlases of anatomy, function, tracts, cerebrovasculature, and blood supply territories is discussed. FTT may be useful in a clinical setting and research environment: (1) when the TT is used traditionally, (2) when a global brain structure positioning with quick searching and labeling is required, (3) in urgent cases for quick image interpretation (eg, acute stroke), (4) when the difference between nonlinear and piecewise linear warping is negligible, (5) when automatic processing of a large number of cases is required, (6) as an initial atlas-scan alignment before performing nonlinear warping, and (7) as an initial atlas-guided segmentation of brain structures before further local processing.

A knowledge-driven algorithm for a rapid and automatic extraction of the human cerebral ventricular system from MR neuroimages.

A knowledge-driven algorithm for a rapid, robust, accurate, and automatic extraction of the human cerebral ventricular system from MR neuroimages is proposed. Its novelty is in combination of neuroanatomy, radiological properties, and variability of the ventricular system with image processing techniques. The ventricular system is divided into six 3D regions: bodies and inferior horns of the lateral ventricles, third ventricle, and fourth ventricle. Within each ventricular region, a 2D region of interest (ROI) is defined based on anatomy and variability. Each ventricular region is further subdivided into subregions, and conditions detecting and preventing leakage into the extra-ventricular space are specified for each subregion. The algorithm extracts the ventricular system by (1) processing each ROI (to calculate its local statistics, determine local intensity ranges of cerebrospinal fluid and gray and white matters, set a seed point within the ROI, grow region directionally in 3D, check anti-leakage conditions, and correct growing if leakage occurred) and (2) connecting all unconnected regions grown by relaxing growing conditions. The algorithm was validated qualitatively on 68 and quantitatively on 38 MRI normal and pathological cases (30 clinical, 20 MGH Brain Repository, and 18 MNI BrainWeb data sets). It runs successfully for normal and pathological cases provided that the slice thickness is less than 3.0 mm in axial and less than 2.0 mm in coronal directions, and the data do not have a high inter-slice intensity variability. The algorithm also works satisfactorily in the presence of up to 9% noise and up to 40% RF inhomogeneity for the BrainWeb data. The running time is less than 5 s on a Pentium 4, 2.0 GHz PC. The best overlap metric between the results of a radiology expert and the algorithm is 0.9879 and the worst 0.9527; the mean and standard deviation of the overlap metric are 0.9723 and 0.01087, respectively.

Segmentation of brain from computed tomography head images

An algorithm to determine the human brain (gray matter (GM) and white matter (WM)) from computed tomography (CT) head volumes with large slice thickness is proposed based on thresholding and brain mask propagation. Firstly, a 2D reference image is chosen to represent the intensity characteristics of the original 3D data set. Secondly, the region of interest of the reference image is determined as the space enclosed by the skull. Fuzzy C-means clustering is employed to determine the threshold for head mask and the low threshold for brain segmentation. The high threshold is calculated as the weighted intensity average of the boundary pixels between bones and GM/WM. Based on the low and high thresholds, the CT volume is binarized, followed by finding the brain candidates through distance criterion. Finally the brain is identified through brain mask propagation using the spatial relationship of neighboring axial slices. The algorithm has been validated against one non-enhanced CT and one enhanced CT volume with pathology

Rapid and automatic detection of brain tumors in MR images

An algorithm to automatically detect brain tumors in MR images is presented. The key concern is speed in order to process efficiently large brain image databases and provide quick outcomes in clinical setting. The method is based on study of asymmetry of the brain. Tumors cause asymmetry of the brain, so we detect brain tumors in 3D MR images using symmetry analysis of image grey levels with respect to the midsagittal plane (MSP). The MSP, separating the brain into two hemispheres, is extracted using our previously developed algorithm. By removing the background pixels, the normalized grey level histograms are calculated for both hemispheres. The similarity between these two histograms manifests the symmetry of the brain, and it is quantified by using four symmetry measures: correlation coefficient, root mean square error, integral of absolute difference (IAD), and integral of normalized absolute difference (INAD). A quantitative analysis of brain normality based on 42 patients with tumors and 55 normals is presented. The sensitivity and specificity of IAD and INAD were 83.3% and 89.1%, and 85.7% and 83.6%, respectively. The running time for each symmetry measure for a 3D 8bit MR data was between 0.1 - 0.3 seconds on a 2.4GHz CPU PC.

Simplifying the Exploration of Volumetric Images: Development of a 3D User Interface for the Radiologist’s Workplace

Volumetric imaging (computed tomography and magnetic resonance imaging) provides increased diagnostic detail but is associated with the problem of navigation through large amounts of data. In an attempt to overcome this problem, a novel 3D navigation tool has been designed and developed that is based on an alternative input device. A 3D mouse allows for simultaneous definition of position and orientation of orthogonal or oblique multiplanar reformatted images or slabs, which are presented within a virtual 3D scene together with the volume-rendered data set and additionally as 2D images. Slabs are visualized with maximum intensity projection, average intensity projection, or standard volume rendering technique. A prototype has been implemented based on PC technology that has been tested by several radiologists. It has shown to be easily understandable and usable after a very short learning phase. Our solution may help to fully exploit the diagnostic potential of volumetric imaging by allowing for a more efficient reading process compared to currently deployed solutions based on conventional mouse and keyboard.

A Virtual Reality Simulator for Remote Interventional Radiology: Concept and Prototype Design

We present a virtual reality simulator to realize interventional radiology (IR) procedures remotely. The simulator contains two subsystems: one at the local site and the other at the remote site. At the local site, the interventional radiologist interacts with a three-dimensional (3-D) vascular model extracted from the patients data and inserts IR devices through the Motion Tracking Box (MTB), which converts physical motion (translation and rotation) of IR devices into digital signal. This signal is transferred to the Actuator Box (AB) at the remote site that drives the IR devices in the patient. The status of the IR devices is subsequently fed back to the local site and displayed on the vascular model. To prove the concept, the prototype developed employs a physical angiography phantom (mimicking the patient) and its corresponding 3-D digital model. A magnetic tracking system provides information about positioning of the IR devices in the phantom. The initial results are encouraging. The AB controlled remotely drives IR devices with resolution of 0.00288 mm/step in translation and 0.079 deg/step in rotation

Three dimensional digital atlas of the orbit constructed from multi-modal radiological images

The human orbit has numerous structures packed in a relatively small space, the study of which is essential and difficult due to complex three dimensional relationships. Available printed orbital atlases do not convey the three dimensional information and are not interactive. To overcome these limitations, we built a digital 3D orbital atlas presented in axial, coronal and sagittal planes, and as three dimensional geometric models of the muscles, bones, and eyeball. The bone models are from a CT scan, the muscle and optic nerve from a MR scan, and other components that cannot be distinguished radiologically are modeled as geometric primitives from anatomic literature. All multi-modal data including the models and images are registered into the same space to form a complete atlas. All structures in the atlas are labeled with their names. An atlas browser is developed for user-friendly manipulation and presentation of the atlas content. Each structure can be turned on or off, rotated, zoomed, or moved, either individually or in unison with other selected structures. Thus, the relationships between different structures can be studied in greater depth. The method developed to build the orbital atlas is general and can be used to create other atlases or to build patient specific geometric models. The orbital atlas may be used for studying the orbital anatomy, as a reference guide for practitioners, and as a base for simulation of orbital surgery.