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Purpose: Tissue radiotracer activity measured from positron emission tomography (PET) images is an important biomarker that is clinically utilized for diagnosis, staging, prognostication, and treatment response assessment in patients with cancer and other clinical disorders. Using PET image values to define a normal range of metabolic activity for quantification purposes is challenging due to variations in patient-related factors and technical factors. Although the formulation of standardized uptake value (SUV) has compensated for some of these variabilities, significant non-standardness still persists. We propose an image processing method to substantially mitigate these variabilities. Methods: The standardization method is similar for activity concentration (AC) PET and SUV PET images, with some differences, and consists of two steps. The calibration step is performed only once for both AC PET or SUV PET, employs a set of images of normal subjects, and requires a reference object, while the transformation step is executed for each patient image to be standardized. In the calibration step, a standardized scale is determined along with 3 key image intensity landmarks defined on it: the minimum percentile intensity smin, median intensity sm, and high percentile intensity smax. smin and sm are estimated based on image intensities within the body region in the normal calibration image set. The optimal value of the maximum percentile β corresponding to the intensity smax is estimated via an optimization process by using the reference object to optimally separate the highly variable high uptake values from the normal uptake intensities. In the transformation step, the first two landmarks--the minimum percentile intensity pa(I), and the median intensity pm(I)--are found for the given image I for the body region, and the high percentile intensity pβ(I) is determined corresponding to the optimally estimated high percentile value β. Subsequently, intensities of I are mapped to the standard scale piecewise linearly for different segments. We employ three strategies for evaluation and comparison with other standardization methods: (i) comparing coefficient of variation (CVO) of mean intensity within test objects O across different normal test subjects before and after standardization, (ii) comparing mean absolute difference (MDO) of mean intensity within test objects O across different subjects in repeat scans before and after standardization, and (iii) comparing CVO of mean intensity across different normal subjects before and after standardization where the scans came from different brands of scanners. Results: Our data set consisted of 84 FDG-PET/CT scans of the body torso including 38 normal subjects and two repeat-scans of 23 patients. We utilized one of two objects--liver and spleen--as a reference object and the other for testing. The proposed standardization method reduced CVO and MDO by a factor of 3-8 in comparison to other standardization methods and no standardization. Upon standardization by our method, the image intensities (both for AC and SUV) from two different brands of scanners become statistically indistinguishable, while without standardization, they differ significantly and by a factor of 3-9. Conclusions: The proposed method is automatic, outperforms current standardization methods, and effectively overcomes the residual variation left over in SUV and interscanner variations. [ABSTRACT FROM AUTHOR]

This paper presents a near-automatic process for separating vessels from background and other clutter as well as for separating arteries and veins in contrast-enhanced magnetic resonance angiographic (CE-MRA) image data, and an optimal method for three-dimensional visualization of vascular structures. The separation process utilizes fuzzy connected object delineation principles and algorithms. The first step of this separation process is the segmentation of the entire vessel structure from the background and other clutter via absolute fuzzy connectedness. The second step is to separate artery from vein within this entire vessel structure via iterative relative fuzzy connectedness. After seed voxels are specified inside artery and vein in the CE-MRA image, the small regions of the bigger aspects of artery and vein are separated in the initial iterations, and further detailed aspects of artery and vein are included in later iterations. At each iteration, the artery and vein compete among themselves to grab membership of each voxel in the vessel structure based on the relative strength of connectedness of the voxel in the artery and vein. This approach has been implemented in a software package for routine use in a clinical setting and tested on 133 CE-MRA studies of the pelvic region and two studies of the carotid system from six different hospitals, in all studies, unified parameter settings produced correct artery-vein separation. When compared with manual segmentation/separation, our algorithms were able to separate higher order branches, and therefore produced vastly more details in the segmented vascular structure. The total operator and computer time taken per study is on the average about 4.5 min. To date, this technique seems to be the only image processing approach that can be routinely applied for artery and vein separation. Index Terms--Artery-vein separation, contrast-enhanced MR angiography, fuzzy connectedness, image segmentation.

The purpose of this work is to characterize the three--dimensional (3-D) motion of the peritalar joint complex in vivo using magnetic resonance imaging (MRI). Each image data set utilized in this study is made of 60 longitudinal MR slices of the foot in each of eight positions from extreme pronation to extreme supination. We acquired and analyzed ten such data sets from normal subjects, seven data sets from pathological joints and two postoperative data sets. We segmented and formed the surfaces of the calcaneus, talus, cuboid and navicular from all data sets. About 30 geometrical parameters are computed for each joint in each position. The results present features of normal motion and show how normal and abnormal motion can be distinguished. They also show the consequences of surgery on the motion. This non- invasive method offers a unique tool to characterize and quantify the 3-D motion of the rearfoot in vivo from MR images. Index Terms--Classification, kinematics, MR imaging, principal axes, tarsal bones, 3-D imaging.

The purpose of this work is to study the architecture of the rearfoot using in vivo MR image data. Each data set used in this study is made of sixty sagittal slices of the foot acquired in a 1.5-T commercial GE MR system. We use the live-wire method to delineate boundaries and form the surfaces of the bones. In the first part of this work, we describe a new method to characterize the three-dimensional (3-D) relationships of four bones of the peritalar complex and apply this description technique to data sets from ten normal subjects and from seven pathological cases. In the second part, we propose a procedure to classify feet, based on the values of these new architectural parameters. We conclude that this noninvasive method offers a unique tool to characterize the 3-D architecture of the feet in live patients, based on a set of new architectural parameters. This can be integrated into a set of tools to improve diagnosis and treatment of foot malformations. Index Terms--Classification, MR imaging, mid-tarsal joint architecture, principal axes, tarsal bones, 3-D imaging.