A density-adapted three-dimensional radial projection reconstruction pulse sequence is presented which provides a more efficient k-space sampling than conventional three-dimensional projection reconstruction sequences. The gradients of the density-adapted three-dimensional radial projection reconstruction pulse sequence are designed such that the averaged sampling density in each spherical shell of k-space is constant. Due to hardware restrictions, an inner sphere of k-space is sampled without density adaption. This approach benefits from both the straightforward handling of conventional three-dimensional projection reconstruction sequence trajectories and an enhanced signal-to-noise ratio (SNR) efficiency akin to the commonly used three-dimensional twisted projection imaging trajectories. Benefits for low SNR applications, when compared to conventional three-dimensional projection reconstruction sequences, are demonstrated with the example of sodium imaging. In simulations of the point-spread function, the SNR of small objects is increased by a factor 1.66 for the densityadapted three-dimensional radial projection reconstruction pulse sequence sequence. Using analytical and experimental phantoms, it is shown that the density-adapted three-dimensional radial projection reconstruction pulse sequence allows higher resolutions and is more robust in the presence of field inhomogeneities. High-quality in vivo images of the healthy human leg muscle and the healthy human brain are acquired. For equivalent scan times, the SNR is up to a factor of 1.8 higher and anatomic details are better resolved using density-adapted three-dimensional radial projection reconstruction pulse sequence. Key words: sodium magnetic resonance imaging; densityadapted sampling; radial imaging; projection reconstruction; sampling density; field inhomogeneities Sodium ( 23 Na) ions play an important role in cellular homeostasis and cell viability. In healthy tissue, the extracellular sodium concentration ([Na ϩ ] ex ϭ 145 mM) is about 10 times higher than the intracellular concentration ([Na ϩ ] in ϭ 10-15 mM) (1). Using sodium MRI, volume-and relaxation-weighted signal of these compartments can be measured. Thus, sodium MRI is a promising diagnostic tool since pathologic processes can alter this ion gradient.Many studies investigating the usefulness of sodium MRI in human pathologies have been performed recently. Brain neoplasia and sustained cell depolarization, a precursor of cell division, lead to an increase of the intracellular sodium concentration and to a rise in the average tissue sodium concentration (2). Furthermore, the application of sodium MRI has been shown to be valuable for muscular channelopathies (3,4), brain tumors (5), the human kidney (6), myocardial infarction (7), and cerebral ischemia (8,9) diagnostics.However, sodium MRI remains a challenging technique for several reasons. The sodium nucleus exhibits a fast biexponential transversal relaxation in the extreme narrowing limit, i.e., if the correlation time is much shorter...

International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now. We demonstrate the importance of challenges and show that the lack of quality control has critical consequences. First, reproducibility and interpretation of the results is often hampered as only a fraction of relevant information is typically provided. Second, the rank of an algorithm is generally not robust to a number of variables such as the test data used for validation, the ranking scheme applied and the observers that make the reference annotations. To overcome these problems, we recommend best practice guidelines and define open research questions to be addressed in the future.

Reaction-diffusion based tumor growth models have been widely used in the literature for modeling the growth of brain gliomas. Lately, recent models have started integrating medical images in their formulation. Including different tissue types, geometry of the brain and the directions of white matter fiber tracts improved the spatial accuracy of reaction-diffusion models. The adaptation of the general model to the specific patient cases on the other hand has not been studied thoroughly yet. In this work we address this adaptation. We propose a parameter estimation method for reaction-diffusion tumor growth models using time series of medical images. This method estimates the patient specific parameters of the model using the images of the patient taken at successive time instances. The proposed method formulates the evolution of the tumor delineation visible in the images based on the reaction-diffusion dynamics therefore it remains consistent with the information available. We perform thorough analysis of the method using synthetic tumors and show important couplings between parameters of the reaction-diffusion model. We show that several parameters can be uniquely identified in the case of fixing one parameter, namely the proliferation rate of tumor cells. Moreover, regardless of the value the proliferation rate is fixed to, the speed of growth of the tumor can be estimated in terms of the model parameters with accuracy. We also show that using the model-based speed we can simulate the evolution of the tumor for the specific patient case. Finally we apply our method to 2 real cases and show promising preliminary results.