In orthopedic surgeries, it is important to avoid intra-articular implant placements, which increase revision rates and the risk of arthritis. In order to support the intraoperative assessment and correction of surgical implants, we present an automatic detection approach using cone-beam computed tomography (CBCT).

Effective image-based artifact correction is an essential step in the application of higher order models in diffusion MRI. Most approaches rely on some kind of retrospective registration, which becomes increasingly challenging in the realm of high b-values and low signal-to-noise ratio (SNR), rendering standard correction schemes more and more ineffective. We propose a novel optimization scheme based on memetic search that allows for simultaneous exploitation of different signal intensity relationships between the images, leading to more robust registration results. We demonstrate the increased robustness and efficacy of our method on simulated as well as in-vivo datasets. The median TRE for an affine registration of b = 3000s/mm(2) acquisitions could be reduced from > 5 mm for a standard correction scheme to < 1 mm using our approach. In-vivo bootstrapping experiments revealed increased precision in all tensor-derived quantities.

The coaptation zone is the part of the two mitral valve leaflets that collide during the cardiac cycle. It is an important parameter for the valve’s function and closing capability, but difficult to assess. In this work, we present an automatic approach for leaflet segmentation from 4D ultrasound images, which incorporates steps for coaptation zone modelling and allows determining the coaptation zone from the resulting leaflet surface. The method segments the leaflets over the whole cardiac cycle given a previously segmented annulus model. To provide a meaningful analysis of the coaptation line assessment, the mean error between ground truth model and segmented model has been computed for each leaflet separately. For the anterior leaflet, we achieved a mean error of 1.16 ± 0.38 mm and 1.24 ± 0.37 mm for the posterior leaflet, respectively.

In clinical settings, application of the most recent modelling techniques is usually unfeasible due to the limited acquisition time. Localised acquisitions enclosing only the object of interest by reducing the field-of-view (FOV) counteract the time limitation but pose new challenges to the subsequent processing steps like motion correction. We use datasets from the Human Connectome Project (HCP) to simulate head motion distorted reduced FOV acquisitions and present an evaluation of head motion correction approaches: the commonly used affine registration onto an unweighted reference image guided by the mutual information (MI) metric and a model-based approach, which uses reference images computed from approximated tensor data to improve the performance of the MI metric. While the standard approach using the MI metric yields up to 15% outliers (error>5 mm) and a mean spatial error above 1.5 mm, the model-based approach reduces the number of outliers (1%) and the spatial error significantly (p<0.01). The behavior is also reflected by the visual analysis of the MI metric. The evaluation shows that the MI metric is of very limited use for reduced FOV data post-processing. The model-based approach has proven more suitable in this context.

Over 40,000 annuloplasty rings are implanted each year in the USA to treat mitral regurgitation. However, the used measuring techniques to select a suitable annuloplasty ring are imprecise and highly depending on the expert’s experience. This can cause a re-occurrence of the mitral regurgitation or an annuloplasty ring dehiscence, and thus the necessity of a re-operation. We propose a method to create a 4D model of the mitral annulus from ultrasound data to enable precise measurement and patient-specific implant planning.

Ultrasound (US) guided procedures are frequently performed for diagnosis and treatment of many diseases. However, there are safety and procedure duration limitations in US-guided interventions due to poor image quality and inadequate visibility of medical instruments in the field of view. To address this issue, we propose an interventional imaging system based on a mobile electromagnetic (EM) field generator (FG) attached to a US probe.

Apart from their robustness in anatomic surface segmentation, purely surface based 3D Active Shape Models lack the ability to automatically detect and annotate non-surface key points of interest. However, annotation of anatomic landmarks is desirable, as it yields additional anatomic and functional information. Moreover, landmark detection might help to further improve accuracy during ASM segmentation. We present an extension of surface-based 3D Active Shape Models incorporating isolated non-surface landmarks. Positions of isolated and surface landmarks are modeled conjoint within a point distribution model (PDM). Isolated landmark appearance is described by a set of haar-like features, supporting local landmark detection on the PDM estimates using a kNN-Classi er. Landmark detection was evaluated in a leave-one-out cross validation on a reference dataset comprising 45 CT volumes of the human liver after shape space projection. Depending on the anatomical landmark to be detected, our experiments have shown in about 1/4 up to more than 1/2 of all test cases a signi cant improvement in detection accuracy compared to the position estimates delivered by the PDM. Our results encourage further research with regard to the combination of shape priors and machine learning for landmark detection within the Active Shape Model Framework.

Early diagnosis of autism spectrum disorders (ASD) is difficult, as symptoms vary greatly and are difficult to quantify objectively. Recent work has focused on the assessment of non-invasive diffusion tensor imaging based biomarkers of the disease that reflect the microstructural characteristics of neuronal pathways in the brain. One of the most common symptoms is reduced language development. We quantify this reduction using a graph-based large-scale network analysis of the connectome with a focus on the language related areas of the brain. Using a group of 18 children suffering from ASD and 18 typically developed controls we show that the reduced capacity for comprehension of language in ASD is reflected in the significantly (p < 0.001) reduced network centrality of Wernicke's area while the motor cortex, that was used as a control region, did not show any significant alterations. These results suggest Wernicke's area is less well integrated within the brain connectome in children suffering from ASD.

2 and 3500 s/mm 2 and 180 directions each were acquired with two repetitions in order to increase SNR. The datasets were corrected for head motion and eddy-currents using FSL. Additionally, T1-datasets were acquired, from which the hippocampi were segmented using Freesurfer and registered with an affine transformation to the DWI datasets in order to serve as volume of interest (VOI) for further analysis. The standard DTI model, the two-compartment model of FWE, and the NODDI model are illustrated schematically in Fig. 1. The DTI model defines a single compartment. The FWE method adds a CSF compartment that is modeled as an isotropic free diffusion with fractional volume vfw. The remaining tissue compartment is modeled by a diffusion tensor, Dt. The NODDI model also assumes an isotropic CSF compartment (volume fraction viso). However, the tissue compartment is further divided into an intracellular (IC) compartment (volume fraction (1-viso)vic, also referred to as neurite density), which is modeled as a collection of impermeable sticks, and an extracellular (EC) compartment (volume fraction (1-viso) (1-vic), which is the space surrounding the neurites occupied by glial cells and cell bodies, modeled by a cylindrically symmetric diffusion tensor. NODDI also estimates the orientation dispersion (ODI) of the neurites. All NODDI parameters were extracted based on the two-shell acquisition. The FWE method and standard DTI were applied to the b=1000 s/mm 2 images. RESULTS As shown in Fig. 2, we found a high correlation between the FWE and NODDI estimates of the isotropic diffusion compartment in the hippocampus (r=0.91, p<10 -15 ). The FWE estimates of the isotropic volume fraction were consistently higher than the NODDI estimates (see Fig 3a+b). Statistical group comparison by means of t-tests yielded a similar p-value in both cases, viso (p=0.002) and vfw (p=0.002). viso and vfw both correlated highly with MD derived from the standard diffusion tensor (r=0.91, p<10 -15 and r=0.98, p<10 -15 ).

PURPOSE:Intra-procedural acquisition of the patient anatomy is a key technique in the context of computer-assisted interventions (CAI). Ultrasound (US) offers major advantages as an interventional imaging modality because it is real time and low cost and does not expose the patient or physician to harmful radiation. To advance US-related research, the purpose of this paper was to develop and evaluate an open-source framework for US-based CAI applications.MATERIALS AND METHODS:We developed the open-source software module MITK-US for acquiring and processing US data as part of the well-known medical imaging interaction toolkit (MITK). To demonstrate its utility, we applied the module to implement a new concept for US-guided needle insertion. Performance of the US module was assessed by determining frame rate and latency for both a simple sample application and a more complex needle guidance system.RESULTS:MITK-US has successfully been used to implement both sample applications. Modern laptops achieve frame rates above 24 frames per second. Latency is measured to be approximately 250 ms or less.CONCLUSION:MITK-US can be considered a viable rapid prototyping environment for US-based CAI applications.

There is an increasing interest in connectomics as means to characterize the brain both in healthy controls and in disease. Connectomics strongly relies on graph theory to derive quantitative network related parameters from data. So far only a limited range of possible parameters have been explored in the literature. In this work, we utilize a broad range of global statistic measures combined with supervised machine learning and apply it to a group of 16 children with autism spectrum disorders (ASD) and 16 typically developed (TD) children, which have been matched for age, gender and IQ. We demonstrate that 86.7 % accuracy is achieved in distinguishing between ASD patients and the TD control using highly discriminative graph features in a supervised machine learning setting.

SummaryBackground: Diffusion-MRI provides a unique window on brain anatomy and insights into aspects of tissue structure in living humans that could not be studied previously. There is a major effort in this rapidly evolving field of research to develop the algorithmic tools necessary to cope with the complexity of the datasets.Objectives: This work illustrates our strategy that encompasses the development of a modularized and open software tool for data processing, visualization and interactive exploration in diffusion imaging research and aims at reinforcing sustainable evaluation and progress in the field.Methods: In this paper, the usability and capabilities of a new application and toolkit component of the Medical Imaging and Interaction Toolkit (MITK, www.mitk.org), MITKDI, are demonstrated using in-vivo datasets.Results: MITK-DI provides a comprehensive software framework for high-performance data processing, analysis and interactive data exploration, which is designed in a modular, extensible fashion (using CTK) and in adherence to widely accepted coding standards (e.g. ITK, VTK). MITK-DI is available both as an open source software development toolkit and as a ready-to-use in stallable application.Conclusions: The open source release of the modular MITK-DI tools will increase verifiability and comparability within the research community and will also be an important step towards bringing many of the current techniques towards clinical application.

Mitralklappeninsuffizienz (MI) ist eine weit verbreitete Erkrankung. Für eine erfolgreiche und nachhaltige chirurgische Therapie ist die Ausmessung des Mitralannulus (MA) notwendig. Wir stellen eine Methode zur automatischen Bestimmung des MA Durchmessers auf Basis von Live-3D Ultraschall Daten vor, die zusätzlich für jeden Zeitschritt den Herzzyklus detektiert. Dies erreichen wir hauptsächlich durch die Verwendung von Graph Cut Segmentierung und morphologischen Operationen. Die Evaluation anhand von 13 Patienten zeigt, dass der Herzzyklus in 78

Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that provides information on the fiber architecture of the brain by measuring water diffusion. Prior work has shown that neuronal degeneration in Alzheimer's disease (AD) and mild cognitive impairment (MCI) alters this architecture. Since the conversion rate to AD is much higher for MCI patients than for normal healthy people, it is important to identify biomarkers with a predictive value on this conversion. In this study, we applied tract-based spatial statistics (TBSS) on datasets of 15 healthy controls, 15 AD patients, and 17 MCI patients. Of these MCI patients eight remained stable, whereas nine developed AD within the first 12–18 months of follow-up investigations. Analysis using TBSS combined with a maximum likelihood regression with random effects of the fornix, the corpus callosum, and the cingulum identified significant differences between these two types of MCI patients in fractional anisotropy (FA) and radial diffusivity (DR). Thus, DTI reveals Alzheimer-specific changes in those MCI subjects that later convert, although they were clinically identical to the other MCI-patients at the time the data were acquired. This finding could lead to early identification of AD and thereby aid early clinical intervention.

Navigationssysteme für minimal-invasive Nadelinsertionen basieren häufig auf externen oder internenMarkern zur Registrierung und Bewegungserfassung. Somit wird der bisherige klinische Workflow durch Verwendung zusätzlicher Hardware und speziell angefertigter Instrumente sowie teilweise durch erhöhte Invasivität drastisch verändert. Wir stellen das erste Navigationssystem für perkutane Nadelinsertionen vor, das, basierend auf der Time-of-Flight (ToF)-Kameratechnik, (1) ohne zusätzliche Marker auskommt und (2) sowohl Registrierung als auch Navigation mit einer einzigen Kamera ohne zusätzliche Hardware (z.B. Trackingsystem) ermöglicht. In einer ersten Phantomevaluation konnte eine Zielgenauigkeit im Bereich von 4 mm ermittelt werden.

Fragestellung: In einigen randomisierten Studien konnte gezeigt werden, dass dreidimensionale (3D) Darstellungen das Verständnis von komplexen räumlichen Strukturen erleichtern. Im klinischen Alltag werden jedoch nahezu ausschließlich zweidimensionale Bilder (2D) verwendet. Ziel dieser Studie war es zu untersuchen, ob Training an 3D Modellen im Vergleich zu Training an 2D Computertomografiebilder (CT) das Verständnis der chirurgischen Leberanatomie in CT-Bildern verbessert.