Validation of new clinical quantitative analysis software applicable in spine orthopaedic studies.
(73/1404)
The objective of this study was to evaluate an X-ray films analysis software, i.e. to estimate the reliability and validity of clinical measurements by means of this software. The authors first performed tests of precision and reproducibility of measures. The precision for dynamic modules was estimated at +/-2 degrees for the lumbar analysis and +/-3 degrees for the cervical one. Mean reproducibility coefficients calculated for postural modules are about 4 degrees for the angular parameters and 3 mm for the linear ones. We also evaluated clinical applicability of the software through its validity. Reference values calculated on a population of healthy subjects showed agreement with the literature. Then, when analysing postural X-ray films of severe scoliotic patients, we found that inter-observer reproducibility coefficients show a lower reliability of measurements; the main cause seems to be the low visibility of anatomic landmarks due to the quality of X-ray films and to the degree of deformity. This study allowed to better estimate the reliability and the usefulness of this tool, allowing for multicentric studies and exchanges. (+info)
Dynamic concision for three-dimensional reconstruction of human organ built with virtual reality modelling language (VRML).
(74/1404)
This research studies the process of 3D reconstruction and dynamic concision based on 2D medical digital images using virtual reality modelling language (VRML) and JavaScript language, with a focus on how to realize the dynamic concision of 3D medical model with script node and sensor node in VRML. The 3D reconstruction and concision of body internal organs can be built with such high quality that they are better than those obtained from the traditional methods. With the function of dynamic concision, the VRML browser can offer better windows for man-computer interaction in real-time environment than ever before. 3D reconstruction and dynamic concision with VRML can be used to meet the requirement for the medical observation of 3D reconstruction and have a promising prospect in the fields of medical imaging. (+info)
Technical report on semiautomatic segmentation using the Adobe Photoshop.
(75/1404)
The purpose of this research is to enable users to semiautomatically segment the anatomical structures in magnetic resonance images (MRIs), computerized tomographs (CTs), and other medical images on a personal computer. The segmented images are used for making 3D images, which are helpful to medical education and research. To achieve this purpose, the following trials were performed. The entire body of a volunteer was scanned to make 557 MRIs. On Adobe Photoshop, contours of 19 anatomical structures in the MRIs were semiautomatically drawn using MAGNETIC LASSO TOOL and manually corrected using either LASSO TOOL or DIRECT SELECTION TOOL to make 557 segmented images. In a similar manner, 13 anatomical structures in 8,590 anatomical images were segmented. Proper segmentation was verified by making 3D images from the segmented images. Semiautomatic segmentation using Adobe Photoshop is expected to be widely used for segmentation of anatomical structures in various medical images. (+info)
Current clinical applications of stress wall motion analysis with cardiac magnetic resonance imaging.
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Over the last years the indications for cardiac magnetic resonance (CMR) imaging have rapidly broadened, in particular those dealing with the non-invasive detection of myocardial ischemia. This review describes the imaging technique, methodology and safety aspects of stress cine magnetic resonance imaging and summarizes the current knowledge with regard to its applicability in clinical routine. (+info)
Theoretical accuracy of model-based shape matching for measuring natural knee kinematics with single-plane fluoroscopy.
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Quantification of knee motion under dynamic, in vivo loaded conditions is necessary to understand how knee kinematics influence joint injury, disease, and rehabilitation. Though recent studies have measured three-dimensional knee kinematics by matching geometric bone models to single-plane fluoroscopic images, factors limiting the accuracy of this approach have not been thoroughly investigated. This study used a three-step computational approach to evaluate theoretical accuracy limitations due to the shape matching process alone. First, cortical bone models of the femur tibia/fibula, and patella were created from CT data. Next, synthetic (i.e., computer generated) fluoroscopic images were created by ray tracing the bone models in known poses. Finally, an automated matching algorithm utilizing edge detection methods was developed to align flat-shaded bone models to the synthetic images. Accuracy of the recovered pose parameters was assessed in terms of measurement bias and precision. Under these ideal conditions where other sources of error were eliminated, tibiofemoral poses were within 2 mm for sagittal plane translations and 1.5 deg for all rotations while patellofemoral poses were within 2 mm and 3 deg. However, statistically significant bias was found in most relative pose parameters. Bias disappeared and precision improved by a factor of two when the synthetic images were regenerated using flat shading (i.e., sharp bone edges) instead of ray tracing (i.e., attenuated bone edges). Analysis of absolute pose parameter errors revealed that the automated matching algorithm systematically pushed the flat-shaded bone models too far into the image plane to match the attenuated edges of the synthetic ray-traced images. These results suggest that biased edge detection is the primary factor limiting the theoretical accuracy of this single-plane shape matching procedure. (+info)
One-year increase of Coll 2-1, a new marker of type II collagen degradation, in urine is highly predictive of radiological OA progression.
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OBJECTIVE: To analyse the relationship between the levels of urinary biochemical markers of type II collagen degradation and the clinical and radiological severity and progression of knee osteoarthritis (OA). METHOD: Seventy-five patients with primary knee OA were included in this 3-year follow-up study. Mean joint space width (JSW) of the medial compartment of the femorotibial joint was measured with a computer assisted method on standardized radiographs taken at baseline and after a 3-year follow-up. Pain, stiffness, and physical function subscales of the Western Ontario and McMaster Universities (WOMAC) were assessed at the same time points. Type II collagen peptides Coll 2-1 and Coll 2-1 NO(2), as well as pyridinoline (Pyr) and deoxypyridinoline (D-Pyr) were measured in urines at baseline, after 1 year and 3 years, with specific immunoassays. RESULTS: At baseline, significant correlations were found between the urinary Coll 2-1 and Coll 2-1 NO(2) levels and the global WOMAC score (Coll 2-1: r=0.28, P=0.01; Coll 2-1 NO(2): r=0.27, P=0.02) and its subscales for pain (Coll 2-1: r=0.27, P=0.01; Coll 2-1 NO(2): r=0.30, P=0.01) and function (Coll 2-1: r=0.29, P=0.01; Coll 2-1 NO(2): r=0.27, P=0.02). Pyr and D-Pyr levels were not significantly correlated with the WOMAC scores. One-year change in Coll 2-1 and Coll 2-1 NO(2) urinary levels were negatively correlated with a 3-year change in JSW (Coll 2-1: r=-0.31, P=0.03; Coll 2-1 NO(2): r=-0.31, P=0.03), indicating that an increase of Coll 2-1 or Coll 2-1 NO(2) over 1 year is predictive of subsequent joint space narrowing. Neither Pyr nor D-Pyr was correlated with radiological OA progression. CONCLUSIONS: At baseline, Coll 2-1 and Coll 2-1 NO(2) urinary levels were indicative of the clinical activity of knee OA and the increase of these peptides over 1 year was predictive of the radiological progression of knee OA. (+info)
Radiology interpretation process modeling.
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Information and communication technology in healthcare promises optimized patient care while ensuring efficiency and cost-effectiveness. However, the promised results are not yet achieved; the healthcare process requires analysis and radical redesign to achieve improvements in care quality and productivity. Healthcare process reengineering is thus necessary and involves modeling its workflow. Even though the healthcare process is very large and not very well modeled yet, its sub-processes can be modeled individually, providing fundamental pieces of the whole model. In this paper, we are interested in modeling the radiology interpretation process that results in generating a diagnostic radiology report. This radiology report is an important clinical element of the patient healthcare record and assists in healthcare decisions. We present the radiology interpretation process by identifying its boundaries and by positioning it on the large healthcare process map. Moreover, we discuss an information data model and identify roles, tasks and several information flows. Furthermore, we describe standard frameworks to enable radiology interpretation workflow implementations between heterogeneous systems. (+info)
Computer-aided detection of lung nodules: false positive reduction using a 3D gradient field method and 3D ellipsoid fitting.
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We are developing a computer-aided detection system to assist radiologists in the detection of lung nodules on thoracic computed tomography (CT) images. The purpose of this study was to improve the false-positive (FP) reduction stage of our algorithm by developing features that extract three-dimensional (3D) shape information from volumes of interest identified in the prescreening stage. We formulated 3D gradient field descriptors, and derived 19 gradient field features from their statistics. Six ellipsoid features were obtained by computing the lengths and the length ratios of the principal axes of an ellipsoid fitted to a segmented object. Both the gradient field features and the ellipsoid features were designed to distinguish spherical objects such as lung nodules from elongated objects such as vessels. The FP reduction performance in this new 25-dimensional feature space was compared to the performance in a 19-dimensional space that consisted of features extracted using previously developed methods. The performance in the 44-dimensional combined feature space was also evaluated. Linear discriminant analysis with stepwise feature selection was used for classification. The parameters used for feature selection were optimized using the simplex algorithm. Training and testing were performed using a leave-one-patient-out scheme. The FP reduction performances in different feature spaces were evaluated by using the area Az under the receiver operating characteristic curve and the number of FPs per CT section at a given sensitivity as accuracy measures. Our data set consisted of 82 CT scans (3551 axial sections) from 56 patients with section thickness ranging from 1.0 to 2.5 mm. Our prescreening algorithm detected 111 of the 116 solid nodules (nodule size: 3.0-30.6 mm) marked by experienced thoracic radiologists. The test Az values were 0.95 +/- 0.01, 0.88 +/- 0.02, and 0.94 +/- 0.01 in the new, previous, and combined feature spaces, respectively. The number of FPs per section at 80% sensitivity in these three feature spaces were 0.37, 1.61, and 0.34, respectively. The improvement in the test Az with the 25 new features was statistically significant (p<0.0001) compared to that with the previous 19 features alone. (+info)