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Purpose: We aim to develop a robust 4D MRI method for large FOVs enabling the extraction of irregular respiratory motion that is readily usable with all MRI machines and thus applicable to support a wide range of interventional settings. Method: We propose a 4D MRI reconstruction method to capture an arbitrary number of breathing states. It uses template updates in navigator slices and search regions for fast and robust vessel cross-section tracking. It captures FOVs of 255 mm x 320 mm x 228 mm at a spatial resolution of 1.82 mm x 1.82 mm x 4mm and temporal resolution of 200ms. A total of 37 4D MRIs of 13 healthy subjects were reconstructed to validate the method. A quantitative evaluation of the reconstruction rate and speed of both the new and baseline method was performed. Additionally, a study with ten radiologists was conducted to assess the subjective reconstruction quality of both methods. Results: Our results indicate improved mean reconstruction rates compared to the baseline method (79.4% vs. 45.5%) and improved mean reconstruction times (24s vs. 73s) per subject. Interventional radiologists perceive the reconstruction quality of our method as higher compared to the baseline (262.5 points vs. 217.5 points, p = 0.02). Conclusions: Template updates are an effective and efficient way to increase 4D MRI reconstruction rates and to achieve better reconstruction quality. Search regions reduce reconstruction time. These improvements increase the applicability of 4D MRI as a base for seamless support of interventional image guidance in percutaneous interventions. [ABSTRACT FROM AUTHOR]

Purpose: In the last decades, the increasing medical interest in magnetic resonance imaging (MRI) of the spine gave rise to a growing number of publications on computerized methods for spine analysis, covering goals such as localization and segmentation of vertebrae and intervertebral discs as well as the extraction and segmentation of the spinal canal and cord. We provide a critical systematic review to work in the field, putting focus on approaches that can be applied to different imaging sequences and settings. Methods: Work is analysed on two levels. First, methods are reviewed in detail so that the reader understands justifications and constraints of particular work. Second, work is classified according to relevant attributes and tabulated to give an impression on recent trends. We discuss the general methodical and evaluational aspects of the work as well as challenges specific to MRI such as the lack of intensity standardization and partial volume effects. Results: Methods can be condensed to a small number of optimization frameworks, e.g., graphical models, cost-minimal paths and deformable models. Works sharing the same framework mainly differentiate by the types of information, i.e., pose, geometry and appearance, that are used and by the implementation thereof. MRI-specific challenges are rarely addressed explicitly, calling into question the applicability of most methods to changing imaging sequences or settings. Most often, little attention is paid to evaluation, meaning that results lack comparability and reproducibility although publicly available data sets exist. Conclusion: The diversity of MRI sequences and settings still poses challenges to computerized spine analysis. Further research is necessary to implement methods that are actually applicable in practice, e.g., in clinical routine or for study purposes. Certainly, manual guidance will be necessary at some point, for instance to deal with changing subject positions. Therefore, future work should put attention to the appropriate integration of manual interaction. [ABSTRACT FROM AUTHOR]

A rising number of epidemiological studies apply imaging technologies. Images are not features themselves but provide raw data from which features are extracted. Different to other applications of analysis of medical images the data is analyzed statistically across the cohort. It results in unique requirements regarding the development of methods to efficiently integrate varying domain knowledge into the process without compromising comparability of results across subjects or time. Examples from two different strategies are presented and discussed. [ABSTRACT FROM AUTHOR]

Background: Object detection in 3-D medical images is often necessary for constraining a segmentation or registration task. It may be a task in its own right as well, when instances of a structure, e.g. the lymph nodes, are searched. Problems from occlusion, illumination and projection do not arise, making the problem simpler than object detection in photographies. However, objects of interest are often not well contrasted against the background. Influence from noise and other artifacts is much stronger and shape and appearance may vary substantially within a class.Methods: Deformable models capture the characteristic shape of an anatomic object and use constrained deformation for hypothesing object boundaries in image regions of low or non-existing contrast. Learning these constraints requires a large sample data base. We show that training may be replaced by readily available user knowledge defining a prototypical deformable part model. If structures have a strong part-relationship, or if they may be found based on spatially related guiding structures, or if the deformation is rather restricted, the supporting data information suffices for solving the detection task. We use a finite element model to represent anatomic variation by elastic deformation. Complex shape variation may be represented by a hierarchical model with simpler part variation. The hierarchy may be represented explicitly as a hierarchy of sub-shapes, or implicitly by a single integrated model. Data support and model deformation of the complete model can be represented by an energy term, serving as quality-of-fit function for object detection.Results: The model was applied to detection and segmentation tasks in various medical applications in 2- and 3-D scenes. It has been shown that model fitting and object detection can be carried out efficiently by a combination of a local and global search strategy using models that are parameterized for the different tasks.Conclusions: A part-based elastic model represents complex within-class object variation without training. The hierarchy of parts may specify relationship to neighboring anatomical objects in object detection or a part-decomposition of a complex anatomic structure. The intuitive way to incorporate domain knowledge has a high potential to serve as easily adaptable method to a wide range of different detection tasks in medical image analysis. [ABSTRACT FROM AUTHOR]