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.

Modeling of respiratory motion has become increasingly important in various applications of medical imaging (e.g., radiation therapy of lung cancer). Current modeling approaches are usually confined to intra-patient registration of 3D image data representing the individual patient's anatomy at different breathing phases. We propose an approach to generate a mean motion model of the lung based on thoracic 4D computed tomography (CT) data of different patients to extend the motion modeling capabilities. Our modeling process consists of three steps: an intra-subject registration to generate subject-specific motion models, the generation of an average shape and intensity atlas of the lung as anatomical reference frame, and the registration of the subject-specific motion models to the atlas in order to build a statistical 4D mean motion model (4D-MMM). Furthermore, we present methods to adapt the 4D mean motion model to a patient-specific lung geometry. In all steps, a symmetric diffeomorphic nonlinear intensity-based registration method was employed. The Log-Euclidean framework was used to compute statistics on the diffeomorphic transformations. The presented methods are then used to build a mean motion model of respiratory lung motion using thoracic 4D CT data sets of 17 patients. We evaluate the model by applying it for estimating respiratory motion of ten lung cancer patients. The prediction is evaluated with respect to landmark and tumor motion, and the quantitative analysis results in a mean target registration error (TRE) of 3.3 ±1.6 mm if lung dynamics are not impaired by large lung tumors or other lung disorders (e.g., emphysema). With regard to lung tumor motion, we show that prediction accuracy is independent of tumor size and tumor motion amplitude in the considered data set. However, tumors adhering to non-lung structures degrade local lung dynamics significantly and the model-based prediction accuracy is lower in these cases. The statistical respiratory motion model is capable of providing valuable prior knowledge in many fields of applications. We present two examples of possible applications in radiation therapy and image guided diagnosis.

No abstract

No abstract

Accurate and robust estimation of motion fields in respiration-correlated CT (4D CT) images, usually performed by non-linear registration of the temporal CT frames, is a precondition for the analysis of patient-specific breathing dynamics and subsequent image-supported diagnostics and treatment planning. In this work, we present a comprehensive comparison and evaluation study of non-linear registration variants applied to the task of lung motion estimation in thoracic 4D CT data. In contrast to existing multi-institutional comparison studies (e.g. MIDRAS and EMPIRE10), we focus on the specific but common class of variational intensity-based non-parametric registration and analyze the impact of the different main building blocks of the underlying optimization problem: the distance measure to be minimized, the regularization approach and the transformation space considered during optimization. In total, 90 different combinations of building block instances are compared. Evaluated on proprietary and publicly accessible 4D CT images, landmark-based registration errors (TRE) between 1.14 and 1.20 mm for the most accurate registration variants demonstrate competitive performance of the applied general registration framework compared to other state-of-the-art approaches for lung CT registration. Although some specific trends can be observed, effects of interchanging individual instances of the building blocks on the TRE are in general rather small (no single outstanding registration variant existing); the same level of accuracy is, however, associated with significantly different degrees of motion field smoothness and computational demands. Consequently, the building block combination of choice will depend on application-specific requirements on motion field characteristics.

Abstract. The computation of accurate motion fields is a crucial aspect in 4D medical imaging. It is usually done using a non-linear registration without further modeling of physiological motion properties. However, a globally homogeneous smoothing (regularization) of the motion field during the registration process can contradict the characteristics of motion dynamics. This is particularly the case when two organs slip along each other which leads to discontinuities in the motion field. In this paper, we present a diffusion-based model for incorporating physiological knowledge in image registration. By decoupling normal-and tangentialdirected smoothing, we are able to estimate slipping motion at the organ borders while ensuring smooth motion fields in the inside and preventing gaps to arise in the field. We evaluate our model focusing on the estimation of respiratory lung motion. By accounting for the discontinuous motion of visceral and parietal pleurae, we are able to show a significant increase of registration accuracy with respect to the target registration error (TRE).

The assessment and prediction of a subject's current and future risk of developing neurodegenerative diseases like Alzheimer's disease is of great interest in both the design of clinical trials as well as in clinical decision making. Exploring the longitudinal trajectory of markers related to neurodegeneration is an important task when selecting subjects for treatment in trials and the clinic, in the evaluation of early disease indicators and the monitoring of disease progression. Given that there is substantial intersubject variability, models that attempt to describe marker trajectories for a whole population will likely lack specificity for the representation of individual patients. Therefore, we argue here that individualized models provide a more accurate alternative that can be used for tasks such as population stratification and a subject-specific prognosis. In the work presented here, mixed effects modeling is used to derive a global and individual marker trajectories for a training population. Test subject (new patient) specific models are then instantiated using a stratified "marker signature" that defines a subpopulation of similar * Corresponding author: Ricardo Guerrero Email address: reg09@imperial.ac.uk (R. Guerrero) 1 This project was partially funded by the Innovate UK (formerly Technology Strategy Board -TSB).2 Data used in the preparation of this article was obtained from the ADNI database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how to apply/ ADNI Acknowledgement List.pdf Preprint submitted to NeuroImage June 27, 2016 cases within the training database. From this subpopulation, personalized models of the expected trajectory of several markers are subsequently estimated for unseen patients. These patient specific models of markers are shown to provide better predictions of time-to-conversion to Alzheimer's disease than population based models.

No abstract