Marcel Quist

Perfusion-CT Assessment of Infarct Core and Penumbra: Receiver Operating Characteristic Curve Analysis in 130 Patients Suspected of Acute Hemispheric Stroke

Background and Purpose— Different definitions have been proposed to define the ischemic penumbra from perfusion-CT (PCT) data, based on parameters and thresholds tested only in small pilot studies. The purpose of this study was to perform a systematic evaluation of all PCT parameters (cerebral blood flow, volume [CBV], mean transit time [MTT], time-to-peak) in a large series of acute stroke patients, to determine which (combination of) parameters most accurately predicts infarct and penumbra. Methods— One hundred and thirty patients with symptoms suggesting hemispheric stroke ≤12 hours from onset were enrolled in a prospective multicenter trial. They all underwent admission PCT and follow-up diffusion-weighted imaging/fluid-attenuated inversion recovery (DWI/FLAIR); 25 patients also underwent admission DWI/FLAIR. PCT maps were assessed for absolute and relative reduced CBV, reduced cerebral blood flow, increased MTT, and increased time-to-peak. Receiver-operating characteristic curve analysis was performed to determine the most accurate PCT parameter, and the optimal threshold for each parameter, using DWI/FLAIR as the gold standard. Results— The PCT parameter that most accurately describes the tissue at risk of infarction in case of persistent arterial occlusion is the relative MTT (area under the curve=0.962), with an optimal threshold of 145%. The PCT parameter that most accurately describes the infarct core on admission is the absolute CBV (area under the curve=0.927), with an optimal threshold at 2.0 ml×100 g−1. Conclusion— In a large series of 130 patients, the optimal approach to define the infarct and the penumbra is a combined approach using 2 PCT parameters: relative MTT and absolute CBV, with dedicated thresholds.

A Generic Framework for Non-rigid Registration Based on Non-uniform Multi-level Free-Form Deformations

This work presents a framework for non-rigid registration which extends and generalizes a previously developed technique by Rueckert et al. [1]. We combine multi-resolution optimization with free-form deformations (FFDs) based on multi-level B-splines to simulate a non-uniform control point distribution. We have applied this to a number of different medical registration tasks to demonstrate its wide applicability, including interventional MRI brain tissue deformation compensation, breathing motion compensation in liver MRI, intra-modality inter-modality registration of pre-operative brain MRI to CT electrode implant data, and inter-subject registration of brain MRI. Our results demonstrate that the new algorithm can successfully register images with an improved performance, while achieving a significant reduction in run-time.

Automatic quantitative analysis of cardiac MR perfusion images

Magnetic Resonance Imaging (MRI) is a powerful technique for imaging cardiovascular diseases. The introduction of cardiovascular MRI into clinical practice is however hampered by the lack of efficient and accurate image analysis methods. This paper focuses on the evaluation of blood perfusion in the myocardium (the heart muscle) from MR images, using contrast-enhanced ECG-triggered MRI. We have developed an automatic quantitative analysis method, which works as follows. First, image registration is used to compensate for translation and rotation of the myocardium over time. Next, the boundaries of the myocardium are detected and for each position within the myocardium a time-intensity profile is constructed. The time interval during which the contrast agent passes for the first time through the left ventricle and the myocardium is detected and various parameters are measured from the time-intensity profiles in this interval. The measured parameters are visualized as color overlays on the original images. Analysis results are stored, so that they can later on be compared for different stress levels of the heart. The method is described in detail in this paper and preliminary validation results are presented.

Gray-Value Based Registration of CT and MR Images by Maximization of Local Correlation

For gray-value based multi-modality registration the similarity measure is essential. Excellent results have been obtained with mutual information for various modality combinations. In this contribution we consider local correlation as similarity measure for multi-modality registration. Using a software phantom it is analyzed why local correlation is suitable for this registration task whereas direct gray-value correlation itself is usually not. It is shown that registration with local correlation can be done using only a fraction of the image volume offering an opportunity to accelerate the algorithm. Within validation, registration of the phantom images, two simultaneously acquired dual contrast MR images, and a clinical CT-MR data set has been studied. For comparison, the data sets have also been registered with mutual information. The results show that not only mutual information, but also local correlation is suitable for gray-value based multi-modality registration.

Magnetic resonance image registration in multiple sclerosis: comparison with repositioning error and observer-based variability.

To study the use of image registration in the analysis of multiple sclerosis (MS) lesion volume and compare this with repositioning error and observer‐based variability.

Towards automatic quantitative analysis of cardiac MR perfusion images

Magnetic Resonance Imaging (MRI) is a powerful technique for imaging cardiovascular diseases. The introduction of cardiovascular MRI into clinical practice is however hampered by the lack of efficient and reliable automatic image analysis methods. This paper focuses on the automatic evaluation of the perfusion of blood in the myocardium (the heart muscle) from cardiac MR perfusion image series, acquired using contrast-enhanced ECG-triggered MRI. We have developed a semi-automatic quantitative analysis method with which the perfusion image series can be analysed in only a few minutes. The method is described in this paper and preliminary validation results are presented.

Improved interobserver agreement for visual detection of active T2 lesions on serial MR scans in multiple sclerosis using image registration

Abstract The aim of this study was to analyse the effect of image registration on interobserver agreement in the visual detection of active multiple sclerosis (MS) lesions from serial magnetic resonance (MR) scans. T2W spin-echo MR scans (3-mm slices) of 16 MS patients participating in a treatment trial were selected. For each patient, two pairs of scans were used: an original (i. e., non-registered) and a registered pair. For the original pair, baseline and month 6 were used, and for the registered pair month 3 and 9. For registration an automatic matching algorithm based on Mutual Information was used. Six observers identified active lesions on both original and registered scans. Kappa values were calculated to assess interobserver agreement. Reslicing caused a slight blurring of the images, but near perfect registration. The kappa value of 0.35 ± 0.07 for new lesions on original images improved to 0.62 (± 0.06) by registration (p = 0.004). For enlarging lesions on original images it was extremely poor (κ 0.11 ± 0.05), and did not benefit much by registration (κ 0.20 ± 0.11). Thus, image registration improved interobserver agreement for visual detection of new lesions. For enlarging lesions, registration improved agreement but still not to a satisfactory level.

Template selection and rejection for robust nonrigid 3D registration in the presence of large deformations

The purpose of the proposed template propagation method is to support the comparative analysis of image pairs even when large deformations (e.g. from movement) are present. Starting from a position where valid starting estimates are known, small sub-volumes (templates) are registered rigidly. Propagating registration results to neighboring templates, the algorithm proceeds layer by layer until corresponding points for the whole volume are available. Template classification is important for defining the templates to be registered, for propagating registration results and for selecting successfully registered templates which finally represent the motion vector field. This contribution discusses a template selection and classification strategy based on the analysis of the similarity measure in the vicinity of the optimum. For testing the template propagation and classification methods, deformation fields of four volume pairs exhibiting considerable deformations have been estimated and the results have been compared to corresponding points picked by an expert. In all four cases, the proposed classification scheme was successful. Based on homologous points resulting from template propagation, an elastic transformation was performed.

Robust 3D Deformation Field Estimation by Template Propagation

A new robust method to automatically determine a 3D motion vector field for medical images in the presence of large deformations is proposed. The central idea of this approach is template propagation. Starting from an image position where valid starting estimates are known, small sub-volumes (templates) are registered rigidly. Parameters of successfully registered templates serve as starting estimates for its neighbors. The registration proceeds layer by layer until the relevant image volume is covered. Based on this principle, a template-based registration algorithm has been implemented. Using the resulting set of corresponding points, the parameters of a non-rigid transformation scheme are determined. The complete procedure has been validated using four MR image pairs containing considerable deformations. In order to obtain an estimate for the accuracy, homologous points determined by template propagation are compared to corresponding landmarks defined by an expert. For landmarks with sufficient structure, the average deviation is well below the voxel size of the images. Because of the larger number of homologous points available, transformations incorporating the output of template propagation yielded a larger similarity between the reference image and the transformed image than an elastic transformation based on landmark pairs.