Search

Fractional flow reserve (FFR) guided percutaneous coronary intervention (PCI) is associated with favourable outcome compared with revascularization based on angiographic stenosis severity alone. The feasibility of the new image-based quantitative flow ratio (QFR) assessed from 3D quantitative coronary angiography (QCA) and thrombolysis in myocardial infarction (TIMI) frame count using three different flow models has been reported recently. The aim of the current study was to assess the accuracy, and in particular, the reproducibility of these three QFR techniques when compared with invasive FFR. QFR was derived (1) from adenosine induced hyperaemic coronary angiography images (adenosine-flow QFR [aQFR]), (2) from non-hyperemic images (contrast-flow QFR [cQFR]) and (3) using a fixed empiric hyperaemic flow [fixed-flow QFR (fQFR)]. The three QFR values were calculated in 17 patients who prospectively underwent invasive FFR measurement in 20 vessels. Two independent observers performed the QFR analyses. Mean difference, standard deviation and 95% limits of agreement (LOA) between invasive FFR and aQFR, cQFR and fQFR for observer 1 were: 0.01 ± 0.04 (95% LOA: -0.07; 0.10), 0.01 ± 0.05 (95% LOA: -0.08; 0.10), 0.01 ± 0.04 (95% LOA: -0.06; 0.08) and for observer 2: 0.00 ± 0.03 (95% LOA: -0.06; 0.07), -0.01 ± 0.03 (95% LOA: -0.07; 0.05), 0.00 ± 0.03 (95% LOA: -0.06; 0.05). Values between the 2 observers were (to assess reproducibility) for aQFR: 0.01 ± 0.04 (95% LOA: -0.07; 0.09), for cQFR: 0.02 ± 0.04 (95% LOA: -0.06; 0.09) and for fQFR: 0.01 ± 0.05 (95% LOA: -0.07; 0.10). In a small number of patients we showed good accuracy of three QFR techniques (aQFR, cQFR and fQFR) to predict invasive FFR. Furthermore, good inter-observer agreement of the QFR values was observed between two independent observers.

Purpose: Whole lung densitometry on chest CT images is an accepted method for measuring tissue destruction in patients with pulmonary emphysema in clinical trials. Progression measurement is required for evaluation of change in health condition and the effect of drug treatment. Information about the location of emphysema progression within the lung may be important for the correct interpretation of drug efficacy, or for determining a treatment plan. The purpose of this study is therefore to develop and validate methods that enable the local measurement of lung density changes, which requires proper modeling of the effect of respiration on density., Methods: Four methods, all based on registration of baseline and follow-up chest CT scans, are compared. The first naïve method subtracts registered images. The second employs the so-called dry sponge model, where volume correction is performed using the determinant of the Jacobian of the transformation. The third and the fourth introduce a novel adaptation of the dry sponge model that circumvents its constant-mass assumption, which is shown to be invalid. The latter two methods require a third CT scan at a different inspiration level to estimate the patient-specific density-volume slope, where one method employs a global and the other a local slope. The methods were validated on CT scans of a phantom mimicking the lung, where mass and volume could be controlled. In addition, validation was performed on data of 21 patients with pulmonary emphysema., Results: The image registration method was optimized leaving a registration error below half the slice increment (median 1.0 mm). The phantom study showed that the locally adapted slope model most accurately measured local progression. The systematic error in estimating progression, as measured on the phantom data, was below 2 gr/l for a 70 ml (6%) volume difference, and 5 gr/l for a 210 ml (19%) difference, if volume correction was applied. On the patient data an underlying linearity assumption relating lung volume change with density change was shown to hold (fitR(2) = 0.94), and globalized versions of the local models are consistent with global results (R(2) of 0.865 and 0.882 for the two adapted slope models, respectively)., Conclusions: In conclusion, image matching and subsequent analysis of differences according to the proposed lung models (i) has good local registration accuracy on patient data, (ii) effectively eliminates a dependency on inspiration level at acquisition time, (iii) accurately predicts progression in phantom data, and (iv) is reasonably consistent with global results in patient data. It is therefore a potential future tool for assessing local emphysema progression in drug evaluation trials and in clinical practice.

Evolution strategies (ESs) are powerful probabilistic search and optimization algorithms gleaned from biological evolution theory. They have been successfully applied to a wide range of real world applications. The modern ESs are mainly designed for solving continuous parameter optimization problems. Their ability to adapt the parameters of the multivariate normal distribution used for mutation during the optimization run makes them well suited for this domain. In this article we describe and study mixed integer evolution strategies (MIES), which are natural extensions of ES for mixed integer optimization problems. MIES can deal with parameter vectors consisting not only of continuous variables but also with nominal discrete and integer variables. Following the design principles of the canonical evolution strategies, they use specialized mutation operators tailored for the aforementioned mixed parameter classes. For each type of variable, the choice of mutation operators is governed by a natural metric for this variable type, maximal entropy, and symmetry considerations. All distributions used for mutation can be controlled in their shape by means of scaling parameters, allowing self-adaptation to be implemented. After introducing and motivating the conceptual design of the MIES, we study the optimality of the self-adaptation of step sizes and mutation rates on a generalized (weighted) sphere model. Moreover, we prove global convergence of the MIES on a very general class of problems. The remainder of the article is devoted to performance studies on artificial landscapes (barrier functions and mixed integer NK landscapes), and a case study in the optimization of medical image analysis systems. In addition, we show that with proper constraint handling techniques, MIES can also be applied to classical mixed integer nonlinear programming problems.

Multidetector computed tomography angiography (CTA) provides information on plaque extent and stenosis in the coronary wall. More accurate lesion assessment may be feasible with CTA as compared to invasive coronary angiography (ICA). Accordingly, lesion length assessment was compared between ICA and CTA in patients referred for CTA who underwent subsequent percutaneous coronary intervention (PCI). 89 patients clinically referred for CTA were subsequently referred for ICA and PCI. On CTA, lesion length was measured from the proximal to the distal shoulder of the plaque. Quantitative coronary angiography (QCA) was performed to analyze lesion length. Stent length was recorded for each lesion. In total, 119 lesions were retrospectively identified. Mean lesion length on CTA was 21.4 ± 8.4 mm and on QCA 12.6 ± 6.1 mm. Mean stent length deployed was 17.4 ± 5.3 mm. Lesion length on CTA was significantly longer than on QCA (difference 8.8 ± 6.7 mm, P < 0.001). Moreover, lesion length visualized on CTA was also significantly longer than mean stent length (CTA lesion length-stent length was 4.2 ± 8.7 mm, P < 0.001). Lesion length assessed by CTA is longer than that assessed by ICA. Possibly, CTA provides more accurate lesion length assessment than ICA and may facilitate improved guidance of percutaneous treatment of coronary lesions.

Background and Purpose: Automatically identifying carotid plaque composition using MR imaging remains a challenging task in vivo. The purpose of our study was to compare the detection and quantification of carotid artery atherosclerotic plaque components based on in vivo MR imaging data using manual and automated segmentation., Materials and Methods: Sixty patients from a multicenter study were split into a training group (20 patients) and a study group (40 patients). Each MR imaging study consisted of 4 high-resolution carotid wall sequences (T1, T2, PDw, TOF). Manual segmentation was performed by delineation of the vessel wall and different plaque components. Automated segmentation was performed in the study group by a supervised classifier trained on images from the training group of patients., Results: For the detection of plaque components, the agreement between the visual and automated analysis was moderate for calcifications (κ = 0.59, CI 95% [0.36-0.82]) and good for hemorrhage (0.65 [0.42-0.88]) and lipids (0.65 [0.03-1.27]). For quantification of plaque volumes, the intraclass correlation was high for hemorrhage (0.80 [0.54-0.92]) and fibrous tissue (0.80 [0.65-0.89]), good for lipids (0.65 [0.43-0.80]), and poor for calcifications., Conclusions: In 40 patients with carotid stenosis, our results indicated that it was possible to automatically detect carotid plaque components with substantial or good agreement with visual identification, and that the volumes obtained manually and automatically were reasonably consistent for hemorrhage and lipids but not for calcium.

Purpose: In clinical practice, both coronary anatomy and myocardial perfusion information are needed to assess coronary artery disease (CAD). The extent and severity of coronary stenoses can be determined using computed tomography coronary angiography (CTCA); the presence and amount of ischemia can be identified using myocardial perfusion imaging, such as perfusion magnetic resonance imaging (PMR). To determine which specific stenosis is associated with which ischemic region, experts use assumptions on coronary perfusion territories. Due to the high variability between patient's coronary artery anatomies, as well as the uncertain relation between perfusion territories and supplying coronary arteries, patient-specific systems are needed., Material and Methods: We present a patient-specific visualization system, called Synchronized Multimodal heART Visualization (SMARTVis), for relating coronary stenoses and perfusion deficits derived from CTCA and PMR, respectively. The system consists of the following comprehensive components: (1) two or three-dimensional fusion of anatomical and functional information, (2) automatic detection and ranking of coronary stenoses, (3) estimation of patient-specific coronary perfusion territories., Results: The potential benefits of the SMARTVis tool in assessing CAD were investigated through a case-study evaluation (conventional vs. SMARTVis tool): two experts analyzed four cases of patients with suspected multivessel coronary artery disease. When using the SMARTVis tool, a more reliable estimation of the relation between perfusion deficits and stenoses led to a more accurate diagnosis, as well as a better interobserver diagnosis agreement., Conclusion: The SMARTVis comprehensive visualization system can be effectively used to assess disease status in multivessel CAD patients, offering valuable new options for the diagnosis and management of these patients.

The purpose of this study was to evaluate the performance of 320-row computed tomography angiography (CTA) in the identification of significant coronary artery disease (CAD) in patients presenting with acute chest pain and to examine the relation to outcome during follow-up. A total of 106 patients with acute chest pain underwent CTA to evaluate presence of CAD. Each CTA was classified as: normal, non-significant CAD (<50% luminal narrowing) and significant CAD (≥50% luminal narrowing). CTA results were compared with quantitative coronary angiography. After discharge, the following cardiovascular events were recorded: cardiac death, non-fatal infarction, and unstable angina requiring revascularization. Among the 106 patients, 23 patients (22%) had a normal CTA, 19 patients (18%) had non-significant CAD on CTA, 59 patients (55%) had significant CAD on CTA, and 5 patients (5%) had non-diagnostic image quality. In total, 16 patients (15%) were immediately discharged after normal CTA and 90 patients (85%) underwent invasive coronary angiography. Sensitivity, specificity, and positive and negative predictive values to detect significant CAD on CTA were 100, 87, 93, and 100%, respectively. During mean follow-up of 13.7 months, no cardiovascular events occurred in patients with a normal CTA examination. In patients with non-significant CAD on CTA, no cardiac death or myocardial infarctions occurred and only 1 patient underwent revascularization due to unstable angina. In patients presenting with acute chest pain, an excellent clinical performance for the non-invasive assessment of significant CAD was demonstrated using CTA. Importantly, normal or non-significant CAD on CTA predicted a low rate of adverse cardiovascular events and favorable outcome during follow-up.

Accurate in vivo measurements methods of wear in total knee arthroplasty are required for a timely detection of excessive wear and to assess new implant designs. Component separation measurements based on model-based Roentgen stereophotogrammetric analysis (RSA), in which 3-dimensional reconstruction methods are used, have shown promising results, yet the robustness of these measurements is unknown. In this study, the accuracy and robustness of this measurement for clinical usage was assessed. The validation experiments were conducted in an RSA setup with a phantom setup of a knee in a vertical orientation. 72 RSA images were created using different variables for knee orientations, two prosthesis types (fixed-bearing Duracon knee and fixed-bearing Triathlon knee) and accuracies of the reconstruction models. The measurement error was determined for absolute and relative measurements and the effect of knee positioning and true seperation distance was determined. The measurement method overestimated the separation distance with 0.1mm on average. The precision of the method was 0.10mm (2*SD) for the Duracon prosthesis and 0.20mm for the Triathlon prosthesis. A slight difference in error was found between the measurements with 0° and 10° anterior tilt. (difference=0.08mm, p=0.04). The accuracy of 0.1mm and precision of 0.2mm can be achieved for linear wear measurements based on model-based RSA, which is more than adequate for clinical applications. The measurement is robust in clinical settings. Although anterior tilt seems to influence the measurement, the size of this influence is low and clinically irrelevant., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Purpose: Interactive visualization is required to inspect and monitor the automatic segmentation of vessels derived from contrast-enhanced magnetic resonance angiography (CE-MRA). A dual-view visualization scheme consisting of curved planar reformation (CPR) and direct volume rendering (DVR) was developed for this purpose and tested., Methods: A dual view visualization scheme was developed using the vessel pathline for both camera position and rotation in 3D, greatly reducing the degrees of freedom (DOF) required for navigation. Pathline-based navigation facilitates coupling of the CPR and DVR views, as local position and orientation can be matched precisely. The new technique was compared to traditional techniques in a user study. Layperson users were required to perform a visual search task that involves checking for (minor) errors in segmentations of MRA data from a software phantom. The task requires the user to examine both views., Results: Pathline-based navigation and coupling of CPR and DVR provide user speed performance improvements in a vessel inspection task. Interactive MRA visualization with this method, where rotational degrees of freedom were reduced, had no negative effect., Conclusions: The DOF reduction achieved by the new navigation technique is beneficial to user performance. The technique is promising and merits comprehensive evaluation in a realistic clinical setting.

Real-time three-dimensional (3D) ultrasound imaging has been proposed as an alternative for two-dimensional stress echocardiography for assessing myocardial dysfunction and underlying coronary artery disease. Analysis of 3D stress echocardiography is no simple task and requires considerable expertise. In this paper, we propose methods for automated analysis, which may provide a more objective and accurate diagnosis. Expert knowledge is incorporated via statistical modelling of patient data. Methods for identifying anatomical views, detecting endocardial borders, and classification of wall motion are described and shown to provide favourable results. We also present software developed especially for analysis of 3D stress echocardiography in clinical practice. Interobserver agreement in wall motion scoring is better using the dedicated software (96%) than commercially available software not dedicated for this purpose (79%). The developed tools may provide useful quantitative and objective parameters to assist the clinical expert in the diagnosis of left ventricular function.

Fluoroscopic analysis is an important tool for assessing in vivo kinematics of knee prostheses. Most commonly, a single-plane fluoroscopic setup is used to capture the motion of prostheses during a particular task. Unfortunately, single-plane fluoroscopic analysis is imprecise in the out-of-plane direction. This can result in reconstructing physically impossible poses, in which--for example--the femoral component intersects with the insert, as the normal pose estimation process does not take into account the relation between the components. In the proposed method, the poses of both components are estimated simultaneously, while preventing femur-insert collisions. In a phantom study, the accuracy and precision of the new method in estimating the relative pose of the femoral component were compared to those of the original method. With reverse engineered models, the errors in estimating the out-of-plane position decreased from 2.0+/-0.7 to 0.1+/-0.1 mm, without effects on the errors in rotations and the in-plane positions. With CAD models, the errors in estimating the out-of-plane position decreased from 5.3+/-0.7 mm (mean+/-SD) to 0.0+/-0.4 mm, at the expense of a decreased precision for the other position or orientation parameters. In conclusion, collision detection can prevent reconstructing impossible poses and it improves the position and motion estimation in the out-of-plane direction., (Copyright 2009 Elsevier Ltd. All rights reserved.)

The existing literature on the magnetic resonance imaging of murine models of Alzheimer's disease is reviewed. Particular attention is paid to the possibilities for the early detection of the disease. To this effect, not only are relaxometric and volumetric approaches discussed, but also mathematical models for plaque distribution and aggregation. Image analysis plays a prominent role in this line of research, as stochastic image models and texture analysis have shown some success in the classification of subjects affected by Alzheimer's disease. It is concluded that relaxometric approaches seem to be a promising candidate for the task at hand, especially when combined with sophisticated image analysis, and when data from more than one time-point is available. There have been few longitudinal studies of mice models so far, so this direction of research warrants future efforts.

Background: Imaging of coronary plaques has traditionally focused on evaluating degree of stenosis, as the risk for adverse cardiac events increases with stenosis severity. However, the relation between plaque composition and severity of stenosis remains largely unknown., Objective: To assess plaque composition (non-invasively by multislice computed tomography (MSCT) angiography and invasively by virtual histology intravascular ultrasound (VH IVUS)) in relation to degree of stenosis., Methods: 78 patients underwent MSCT (identifying three plaque types; non-calcified, calcified, mixed) followed by invasive coronary angiography and VH IVUS. VH IVUS evaluated plaque burden, minimal lumen area and plaque composition (fibrotic, fibro-fatty, necrotic core, dense calcium) and plaques were classified as fibrocalcific, fibroatheroma, thin-capped fibroatheroma (TCFA), pathological intimal thickening. For each plaque, percentage stenosis was evaluated by quantitative coronary angiography. Significant stenosis was defined >50% stenosis., Results: Overall, 43 plaques (19%) corresponded to significant stenosis. Of the 227 plaques analysed, 70 were non-calcified plaques (31%), 96 mixed (42%) and 61 calcified (27%) on MSCT. Plaque types on MSCT were equally distributed among significant and non-significant stenoses. VH IVUS identified that plaques with significant stenosis had higher plaque burden (67% (11%) vs 53% (12%), p<0.05) and smaller minimal lumen area (4.6 (3.8-6.8) mm(2) vs 7.3 (5.4-10.5) mm(2), p<0.05). Interestingly, no differences were observed in percentage fibrotic, fibro-fatty, necrotic core and dense calcium. Non-significant stenoses were more frequently classified as pathological intimal thickening (46 (25%) vs 3 (7%), p<0.05), although TCFA (more vulnerable plaque) was distributed equally (p = 0.18)., Conclusion: No evident association exists between the degree of stenosis and plaque composition or vulnerability, as evaluated non-invasively by MSCT and invasively by VH IVUS.

Objectives: To determine the accuracy of automated vessel-segmentation software for vessel-diameter measurements based on three-dimensional contrast-enhanced magnetic resonance angiography (3D-MRA)., Method: In 10 patients with high-grade carotid stenosis, automated measurements of both carotid arteries were obtained with 3D-MRA by two independent investigators and compared with manual measurements obtained by digital subtraction angiography (DSA) and 2D maximum-intensity projection (2D-MIP) based on MRA and duplex ultrasonography (US). In 42 patients undergoing carotid endarterectomy (CEA), intraoperative measurements (IOP) were compared with postoperative 3D-MRA and US., Results: Mean interoperator variability was 8% for measurements by DSA and 11% by 2D-MIP, but there was no interoperator variability with the automated 3D-MRA analysis. Good correlations were found between DSA (standard of reference), manual 2D-MIP (rP=0.6) and automated 3D-MRA (rP=0.8). Excellent correlations were found between IOP, 3D-MRA (rP=0.93) and US (rP=0.83)., Conclusion: Automated 3D-MRA-based vessel segmentation and quantification result in accurate measurements of extracerebral-vessel dimensions.

Migration measurements of hip prostheses using marker-based Roentgen stereophotogrammetric analysis (RSA) require the attachment of markers to the prostheses. The model-based approach, which does not require these markers, is, however, less precise. One of the reasons may be the fact that the spherical head has not been modelled. Therefore, we added a 3D surface model of the spherical head and estimated the position and orientation of the combined stem-head model. The new method using a combined stem-head model was compared in a phantom study on five prostheses (of different types) and in a clinical study using double examinations of implanted hip prostheses, with two existing methods: a standard model-based approach and one using elementary geometrical shapes. The combined model showed the highest precision for the rotation about the longitudinal axis in the phantom experiments. With a standard deviation of 0.69 degrees it showed a significant improvement (p=0.02) over the model-based approach (0.96 degrees ) on the phantom data, but no improvement on the clinical data. Overall, the use of elementary geometrical shapes was worse with respect to the model-based approach, with a standard deviation of 1.02 degrees on the phantom data and 0.79 degrees on the clinical data. This decrease in precision was significant (p<0.01) on the clinical data. With relatively small differences in the other migration directions, these results demonstrate that the new method with a combined stem-head model can be a useful alternative to the standard model-based approach.

Automated segmentation approaches for the left ventricle (LV) in 3-D echocardiography (3DE) often rely on manual initialization. So far, little effort has been put into automating the initialization procedure to get to a fully automatic segmentation approach. We propose a fully automatic method for the detection of the LV long axis (LAX) and the mitral valve plane (MVP) over the full cardiac cycle, for the initialization of segmentation algorithms in 3DE. Our method exploits the cyclic motion of the LV and therefore detects salient structures in a time-continuous way. Probabilities to candidate LV center points are assigned through a Hough transform for circles. The LV LAX is detected by combining dynamic programming detections on these probabilities in 3-D and 2D + time to obtain a time continuous solution. Subsequently, the mitral valve plane is detected in a projection of the data on a plane through the previously detected LAX. The method easily adjusts to different acquisition routines and combines robustness with good accuracy and low computational costs. Automatic detection was evaluated using patient data acquired with the fast rotating ultrasound (FRU) transducer (n=11 patients) and with the Philips Sonos 7500 ultrasound system (Philips Medical Systems, Andover, MA, USA), with the X4 matrix transducer (n=14 patients). For the FRU-transducer data, the LAX was estimated with a distance error of 2.85+/-1.70 mm (mean+/-SD) and an angle of 5.25+/-3.17 degrees; the mitral valve plane was estimated with a distance of -1.54+/-4.31 mm. For the matrix data, these distances were 1.96+/-1.30 mm with an angle error of 5.95+/-2.11 and -1.66+/-5.27 mm for the mitral valve plane. These results confirm that the method is very suitable for automatic detection of the LV LAX and MVP. It provides a basis for further automatic exploration of the LV and could therefore serve as a replacement of manual initialization of 3-D segmentation approaches.

Objective: To quantify osteoarthritis (OA) by automatic measurement of the joint space width (JSW) in the finger joints in hand radiographs, and validate this against semi-quantitative scoring., Methods: Forty subjects with primary OA at multiple sites were selected, 20 of which had no joint space narrowing (JSN) in the hand, and 20 had moderate to severe JSN. Conventional hand radiographs were acquired and the image set was split into a training and test set, for developing and evaluating the method, respectively. No methods to quantify the JSW automatically in OA exist; therefore a method was developed. First the semi-automatic image analysis program identifies the metacarpal and interphalangeal joints. Subsequently, the corresponding joint margins and measurement interval are determined. Finally the JSW is measured in millimetres as the average distance between the joint margins enclosed by the measurement interval. The success rates of different steps of the image analysis were evaluated and a comparison was made between the JSW and the semi-quantitative Osteoarthritis Research Society International score for JSN., Results: The success rates for the identification of the joint locations and margins were 97.5% and 64.2%, respectively. The JSW decreased with increasing semi-quantitative scores, but increased as the joint was severely damaged. The agreement between the JSW and the semi-quantitative score was confirmed by a significant correlation (R2=0.54 and P<0.01) while assessing hand pairs instead of individual joints., Conclusion: The JSW measurement closely reflects semi-quantitative scoring of JSN. Therefore, this method is expected to offer a reproducible and accurate measurement of the JSW in OA.

Image-based Roentgen stereophotogrammetric analysis (IBRSA) integrates 2D-3D image registration and conventional RSA. Instead of radiopaque RSA bone markers, IBRSA uses 3D CT data, from which digitally reconstructed radiographs (DRRs) are generated. Using 2D-3D image registration, the 3D pose of the CT is iteratively adjusted such that the generated DRRs resemble the 2D RSA images as closely as possible, according to an image matching metric. Effectively, by registering all 2D follow-up moments to the same 3D CT, the CT volume functions as common ground. In two experiments, using RSA and using a micromanipulator as gold standard, IBRSA has been validated on cadaveric and sawbone scapula radiographs, and good matching results have been achieved. The accuracy was: |mu |< 0.083 mm for translations and |mu| < 0.023 degrees for rotations. The precision sigma in x-, y-, and z-direction was 0.090, 0.077, and 0.220 mm for translations and 0.155 degrees , 0.243 degrees , and 0.074 degrees for rotations. Our results show that the accuracy and precision of in vitro IBRSA, performed under ideal laboratory conditions, are lower than in vitro standard RSA but higher than in vivo standard RSA. Because IBRSA does not require radiopaque markers, it adds functionality to the RSA method by opening new directions and possibilities for research, such as dynamic analyses using fluoroscopy on subjects without markers and computer navigation applications.

This paper presents a novel method for registration of cardiac perfusion MRI. The presented method successfully corrects for breathing motion without any manual interaction using Independent Component Analysis to extract physiologically relevant features together with their time-intensity behavior. A time-varying reference image mimicking intensity changes in the data of interest is computed based on the results of ICA, and used to compute the displacement caused by breathing for each frame. Qualitative and quantitative validation of the method is carried out using 46 clinical quality, short-axis, perfusion MR datasets comprising 100 images each. Validation experiments showed a reduction of the average LV motion from 1.26+/-0.87 to 0.64+/-0.46 pixels. Time-intensity curves are also improved after registration with an average error reduced from 2.65+/-7.89% to 0.87+/-3.88% between registered data and manual gold standard. We conclude that this fully automatic ICA-based method shows an excellent accuracy, robustness and computation speed, adequate for use in a clinical environment.

The role of quantitative image analysis in large clinical trials is continuously increasing. Several methods are available for performing white matter hyperintensity (WMH) volume quantification. They vary in the amount of the human interaction involved. In this paper, we describe a fully automatic segmentation that was used to quantify WMHs in a large clinical trial on elderly subjects. Our segmentation method combines information from 3 different MR images: proton density (PD), T2-weighted and fluid-attenuated inversion recovery (FLAIR) images; our method uses an established artificial intelligent technique (fuzzy inference system) and does not require extensive computations. The reproducibility of the segmentation was evaluated in 9 patients who underwent scan-rescan with repositioning; an inter-class correlation coefficient (ICC) of 0.91 was obtained. The effect of differences in image resolution was tested in 44 patients, scanned with 6- and 3-mm slice thickness FLAIR images; we obtained an ICC value of 0.99. The accuracy of the segmentation was evaluated on 100 patients for whom manual delineation of WMHs was available; the obtained ICC was 0.98 and the similarity index was 0.75. Besides the fact that the approach demonstrated very high volumetric and spatial agreement with expert delineation, the software did not require more than 2 min per patient (from loading the images to saving the results) on a Pentium-4 processor (512 MB RAM).

Roentgen stereophotogrammetric analysis (RSA) measures micromotion of an orthopaedic implant with respect to its surrounding bone. A problem in RSA is that the markers are sometimes overprojected by the implant itself. This study describes the so-called Marker Configuration Model-based RSA (MCM-based RSA) that is able to measure the pose of a rigid body in situations where less than three markers could be detected in both images of an RSA radiograph. MCM-based RSA is based on fitting a Marker Configuration model (MC-model) to the projection lines from the marker projection positions in the image to their corresponding Roentgen foci. An MC-model describes the positions of markers relative to each other and is obtained using conventional RSA. We used data from 15 double examinations of a clinical study of total knee prostheses and removed projections of the three tibial component markers, simulating occlusion of markers. The migration of the tibial component with respect to the bone, which should be zero, for the double examination is a measure of the accuracy of algorithm. With the new algorithm, it is possible to estimate the pose of a rigid body of which one or two markers are occluded in one of the images of the RSA radiograph with high accuracy as long as a proper MC-model of the markers in the rigid body is available. The new algorithm makes RSA more robust for occlusion of markers. This improves the results of clinical RSA studies because the number of lost RSA follow-up moments is reduced.

To qualitatively assess the accuracy of automated cardiovascular magnetic resonance planning procedures devised from scout acquisitions in free-breathing and breath-holding modes, to quantitatively evaluate the accuracy of the derived left ventricular volumes, mass and function and compare these parameters with the ones obtained from the manually planned acquisitions. Ten healthy volunteers underwent cardiovascular MR (CMR) acquisitions for ventricular function assessment. Short-axis data sets of the left ventricle (LV) were manually planned and generated twice in an automatic fashion. Automated planning parameters were derived from gated scout acquisitions in free-breathing and breath-holding modes. End-diastolic volume (EDV), end-systolic volume (ESV), ejection fraction (EF), and left ventricular mass (LVM) were measured. The agreement between the manual and automatic planning methods, as well as the variability of the aforementioned measurements were assessed. The differences between two automated planning methods were also compared. The mean differences between the manual and automated CMR planning derived from gated scouts in free-breathing mode were 8.05 ml (EDV), 1.84 ml (ESV), 0.69% (EF), and 4.72 g (LVM). The comparison between manual and automated CMR planning derived from gated scouts in breath-holding mode yielded the following differences: 4.22 ml (EDV), 0.34 ml (ESV), 0.3% (EF), and -0.72 mg (LVM). The variability coefficients were 3.72 and 3.66 (EDV), 5.6 and 8.19 (ESV), 3.46 and 4.31 (EF), 6.49 and 5.20 (LVM) for the automated CMR planning methods derived from scouts in free-breathing and breath-holding modes, respectively. Automated CMR planning methods can provide accurate measurements of LV dimensions in normal subjects, and therefore may be utilized in the clinical environment to provide a cost-effective solution for functional assessment of the human cardiovascular system.

Automatic segmentation of the left ventricular (LV) myocardial borders in cardiovascular MR (CMR) images allows a significant speed-up of the procedure of quantifying LV function, and improves its reproducibility. The automated boundary delineation is usually based on a set of parameters that define the algorithms. Since the automatic segmentation algorithms are usually sensitive to the image quality and frequently depend heavily on the acquisition protocol, optimizing the parameters of the algorithm for such different protocols may be necessary to obtain optimal results. In other words, using a default set of parameters may be far from optimal for different scanners or protocols. For the MASS-software, for example, this means that a total of 14 parameters need to be optimized. This optimization is a difficult and labor-intensive process. To be able to more consistently and rapidly tune the parameters, an automated optimization system would be extremely desirable. In this paper we propose such an approach, which is based on genetic algorithms (GAs). The GA is an unsupervised iterative tool that generates new sets of parameters and converges toward an optimal set. We implemented and compared two different types of the genetic algorithms: a simple GA (SGA) and a steady state GA (2SGA). The difference between these two algorithms lies in the characteristics of the generated populations: "nonoverlapping populations" and "overlapping populations," respectively "nonoverlapping" population means that the two populations are disjoint, and "overlapping" means that the best parameters found in the previous generation are included in the present population. The performance of both algorithms was evaluated on twenty routinely obtained short-axis examinations (eleven examinations acquired with a steady-state free precession pulse sequence, and nine examinations with a gradient echo pulse sequence). The optimal parameters obtained with the GAs were used for the LV myocardial border delineation. Finally, the automatically outlined contours were compared to the gold standard--manually drawn contours by experts. The result of the comparison was expressed as a degree of similarity after a processing time of less than 72 h to a 59.5% of degree of similarity for SGA and a 66.7% of degree of similarity for 2SGA. In conclusion, genetic algorithms are very suitable to automatically tune the parameters of a border detection algorithm. Based on our data, the 2SGA was more suitable than the SGA method. This approach can be generalized to other optimization problems in medical image processing.

An important assessment in patients with ischemic heart disease is whether myocardial contractility may improve after treatment. The prediction of myocardial contractility improvement is generally performed under physical or pharmalogical stress conditions. In this paper, we present a technique to build a statistical model of healthy myocardial contraction using independent component analysis. The model is used to detect regions with abnormal contraction in patients both during rest and stress.

Objective: To evaluate long term (six years) lumen changes after balloon angioplasty by using quantitative coronary angiography., Methods: Complete serial quantitative coronary angiography (before and after angioplasty and at six months, three years, and six years) was performed in 100 patients with successful angioplasty and without subsequent repeated revascularisation. In all, 198 dilated segments were compared with 395 non-dilated segments that were obtained from non-target arteries of study patients., Results: From six months to three years after angioplasty, minimum lumen diameter (MLD) increased significantly by 0.13 (0.28) (mean (SD)) mm in dilated segments and decreased significantly by 0.04 (0.27) mm in non-dilated segments. From three years to six years, MLD remained stable in dilated segments but decreased further (by 0.04 (0.28) mm) in non-dilated segments. Consequently, the DeltaMLD between six months and six years was larger in dilated segments than in non-dilated segments (0.12 (0.32) v -0.08 (0.34); p < 0.001). Further, DeltaMLD from six months to six years correlated positively with the percentage diameter stenosis (DS) at six months in each group (dilated segments r = 0.47, p < 0.0001; non-dilated segments r = 0.49, p < 0.0001). Multivariate analysis showed that the only independent predictor of DeltaMLD over six years for each group was the DS at six months., Conclusions: Lesion regression occurs within the first three years after angioplasty and reaches a plateau thereafter. Moreover, the stenosis severity at six months predicts the magnitude of late regression after angioplasty.