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Effiziente Verpunktung pulmonaler MR-Bilder zur Evaluierung von Registrierungsergebnissen
Abstract
Die aussagekräftige Validierung von nichtlinearen Registrierungsverfahren gehört nach wie vor zu den großen Herausforderungen der medizinischen Bildverarbeitung. Ein vielversprechender Ansatz besteht in der Generierung vieler Landmarken in den zu registrierenden Objekten, welche dann in einem zweiten Schritt zur Validierung des Deformationsfeldes genutzt werden können. Wir präsentieren ein flexibles Verfahren zur Erstellung solcher Referenzstandards für MR-Scans. In einem ersten Schritt werden in der Referenz (Baseline-Scan) automatisch Landmarken erzeugt. Die entsprechende Zuordnung im Template (Follow-up-Scan) erfolgt nach einer kurzen Benutzerinitialisierung automatisch durch Thin-Plate-Spline-Transformation und lokale, affin-lineare Nachregistrierung. Eine fehleranfällige und zeitaufwändige manuelle Verpunktung entfällt fast vollständig. Wir evaluieren unser Verfahren anhand einer Studie mit drei Experten auf pulmonalen MR-Scans. Die ersten Ergebnisse erwiesen sich als sehr vielversprechend. Es stellte sich heraus, dass die vom System bestimmten Punkte im Bereich der Variabilit ät der Experten untereinander liegen.
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EMPIRE10 (Evaluation of Methods for Pulmonary Image REgistration 2010) is a public platform for fair and meaningful comparison of registration algorithms which are applied to a database of intrapatient thoracic CT image pairs. Evaluation of nonrigid registration techniques is a nontrivial task. This is compounded by the fact that researchers typically test only on their own data, which varies widely. For this reason, reliable assessment and comparison of different registration algorithms has been virtually impossible in the past. In this work we present the results of the launch phase of EMPIRE10, which comprised the comprehensive evaluation and comparison of 20 individual algorithms from leading academic and industrial research groups. All algorithms are applied to the same set of 30 thoracic CT pairs. Algorithm settings and parameters are chosen by researchers expert in the configuration of their own method and the evaluation is independent, using the same criteria for all participants. All results are published on the EMPIRE10 website (http://empire10.isi.uu.nl). The challenge remains ongoing and open to new participants. Full results from 24 algorithms have been published at the time of writing. This paper details the organization of the challenge, the data and evaluation methods and the outcome of the initial launch with 20 algorithms. The gain in knowledge and future work are discussed.
For a long time, only chest X-ray and CT were used to image lung structure, while nuclear medicine was employed to assess lung function. During the past decade significant developments have been achieved in the field of magnetic resonance imaging (MRI), enabling MRI to enter the clinical arena of chest imaging. Standard protocols can now be implemented on up-to-date scanners, allowing MRI to be used as a first-line imaging modality for various lung diseases, including cystic fibrosis, pulmonary hypertension and even lung cancer. The diagnostic benefits stem from the ability of MRI to visualize changes in lung structure while simultaneously imaging different aspects of lung function, such as perfusion, respiratory motion, ventilation and gas exchange. On this basis, novel quantitative surrogates for lung function can be obtained.
This book provides a comprehensive overview of how to use MRI for imaging of lung disease. Special emphasis is placed on benign diseases requiring regular monitoring, given that it is patients with these diseases who derive the greatest benefit from the avoidance of ionizing radiation.
This book really shows how registration works: the flip-book appearing at the top right corners shows a registration of a human knee from bent to straight position (keeping bones rigid). Of course, the book also provides insight into concepts and practical tools. The presented framework exploits techniques from various fields such as image processing, numerical linear algebra, and optimization. Therefore, a brief overview of some preliminary literature in those fields is presented in the introduction (references [1–51]), and registration-specific literature is assembled at the end of the book (references [52–212]). Examples and results are based on the FAIR software, a package written in MATLAB. The FAIR software, as well as a PDF version of this entire book, can be freely downloaded from www.siam.org/books/fa06.
This book would not have been possible without the help of Bernd Fischer, Eldad Haber, Claudia Kremling, Jim Nagy, and the Safir Research Group from Lübeck: Sven Barendt, Björn Beuthin, Konstantin Ens, Stefan Heldmann, Sven Kabus, Janine Olesch, Nils Papenberg, Hanno Schumacher, and Stefan Wirtz. I'm also indebted to Sahar Alipour, Reza Heydarian, Raya Horesh, Ramin Mafi, and Bahram Marami Dizaji for improving the manuscript.
Pulmonary perfusion is the blood flow of an organ at the capillary level. It is closely related to the blood supply of the
lung and moreover to lung function. It is altered in various diseases of the lung such as pulmonary hypertension or cystic
fibrosis, etc. Therefore, perfusion is an important functional parameter in the diagnosis of pulmonary diseases and quantitative
values are urgently required to study physiology and pathophysiology of various lung diseases as well as monitor treatment
response and identify differences under therapy. Pulmonary perfusion MRI is based on three-dimensional time-resolved contrast-enhanced
T1-weighted sequences. The rapid acquisition of perfusion images facilitates the tracking of the first pass of a contrast
agent through the lung parenchyma. Based on this information, it is possible to quantify perfusion in the entire lung using
the indicator dilution theory. Quantification is challenging due to potential extravasation of the contrast agent during the
first pass as well as the non-linear relationship between the concentration of the contrast agent and signal intensity. Some
of these challenges can be addressed by a dual bolus technique.
Development and integration of image registration methods become increasingly important for clinical workstations. Due to the complexity of such methods, prototyping, evaluation and workflow integration require in-depth knowledge foremostly available to registration developers. Rapid development and deployment is therefore often difficult, particularly for comprehensive software frameworks. In this article, we introduce a novel rapid prototyping framework for voxel-based registration. It is specifically designed to allow evaluation and adaption by developers with less knowledge on registration internals. Based on a unique "one-iteration" paradigm, it enables accelerated algorithm development. Furthermore, methods are interchangeable at runtime using an intuitive graphical plugin interface, allowing a genuine comparison of methods. We discuss the concepts of this framework, compare it to other software and demonstrate its effectiveness for several demanding registration applications, highlighting its versatility and reliability.
Quantitative evaluation of image registration algorithms is a difficult and under-addressed issue due to the lack of a reference standard in most registration problems. In this work a method is presented whereby detailed reference standard data may be constructed in an efficient semi-automatic fashion. A well-distributed set of n landmarks is detected fully automatically in one scan of a pair to be registered. Using a custom-designed interface, observers define corresponding anatomic locations in the second scan for a specified subset of s of these landmarks. The remaining n-s landmarks are matched fully automatically by a thin-plate-spline based system using the s manual landmark correspondences to model the relationship between the scans. The method is applied to 47 pairs of temporal thoracic CT scans, three pairs of brain MR scans and five thoracic CT datasets with synthetic deformations. Interobserver differences are used to demonstrate the accuracy of the matched points. The utility of the reference standard data as a tool in evaluating registration is shown by the comparison of six sets of registration results on the 47 pairs of thoracic CT data.
Non-rigid registration accuracy assessment is typically performed by evaluating the target registration error at manually placed landmarks. For 4D-CT lung data, we compare two sets of landmark distributions: a smaller set primarily defined on vessel bifurcations as commonly described in the literature and a larger set being well-distributed throughout the lung volume. For six different registration schemes (three in-house schemes and three schemes frequently used by the community) the landmark error is evaluated and found to depend significantly on the distribution of the landmarks. In particular, lung regions near to the pleura show a target registration error three times larger than near-mediastinal regions. While the inter-method variability on the landmark positions is rather small, the methods show discriminating differences with respect to consistency and local volume change. In conclusion, both a well-distributed set of landmarks and a deformation vector field analysis are necessary for reliable non-rigid registration accuracy assessment.
MR Perfusion in the lung In: MRI of the Lung
- F Risse