We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell tracking algorithms. With twenty-one participating algorithms and a data repository consisting of thirteen datasets of various microscopy modalities, the challenge displays today’s state of the art in the field. We analyze the results using performance measures for segmentation and tracking that rank all participating methods. We also analyze the performance of all algorithms in terms of biological measures and their practical usability. Even though some methods score high in all technical aspects, not a single one obtains fully correct solutions. We show that methods that either take prior information into account using learning strategies or analyze cells in a global spatio-temporal video context perform better than other methods under the segmentation and tracking scenarios included in the challenge.

Magnetic particle imaging (MPI) is a novel imaging modality with important potential applications, such as angiography, stem cell tracking, and cancer imaging. Recently, there have been efforts to increase the functionality of MPI via multi-color imaging methods that can distinguish the responses of different nanoparticles, or nanoparticles in different environmental conditions. The proposed techniques typically rely on extensive calibrations that capture the differences in the harmonic responses of the nanoparticles. In this paper, we propose a method to directly estimate the relaxation time constant of the nanoparticles from the MPI signal, which is then used to generate a multi-color relaxation map. The technique is based on the underlying mirror symmetry of the adiabatic MPI signal when the same region is scanned back and forth. We validate the proposed method via simulations, and via experiments on our in-house magnetic particle spectrometer setup at 10.8 kHz and our in-house MPI scanner at 9.7 kHz. Our results show that nanoparticles can be successfully distinguished with the proposed technique, without any calibration or prior knowledge about the nanoparticles.

Magnetic particle imaging (MPI) is a fast emerging biomedical imaging modality that exploits the nonlinear response of superparamagnetic iron oxide (SPIO) nanoparticles to image their spatial distribution. Previously, various scanning trajectories were analyzed for the system function reconstruction (SFR) approach, providing important insight regarding their image quality performances. While Cartesian trajectories remain the most popular choice for x-spacebased reconstruction, recent work suggests that non-Cartesian trajectories such as the Lissajous trajectory may prove beneficial for improving image quality. In this work, we propose a generalized reconstruction scheme for x-space MPI that can be used in conjunction with any scanning trajectory. The proposed technique automatically tunes the reconstruction parameters from the scanning trajectory, and does not induce any additional blurring. To demonstrate the proposed technique, we utilize five different trajectories with varying density levels. Comparison to alternative reconstruction methods show significant improvement in image quality achieved by the proposed technique. Among the tested trajectories, the Lissajous and bidirectional Cartesian trajectories prove more favorable for x-space MPI, and the resolution of the images from these two trajectories can further be improved via deblurring. The proposed fully automated gridding reconstruction can be utilized with these trajectories to improve the image quality in x-space MPI.

A tensorial approach to computational continuum mechanics using object-oriented techniques Comput. Phys. 12, 620 (1998) Abstract. As high-performance computing (HPC) machines become increasingly complex, middleware-based programming paradigms have been particularly successful in reducing code development time and increasing simulation efficiency. The parallel particle-mesh (PPM) library is a state-of-the-art HPC middleware for parallel particle-mesh simulations. It is based on a concise set of six data and operation abstractions. The present paper describes the architecture of the new PPM library core. This new core architecture enables several simplifications in the library's user interface and supports for the first time the implementation of multi-resolution simulations using PPM. We further demonstrate the competitive performance of the new core architecture compared to the previous version of the PPM library.

To develop and evaluate a simultaneous multislice (SMS) reconstruction technique that provides noise reduction and leakage blocking for highly accelerated cardiac MRI. Methods: ReadOut Concatenated k-space SPIRiT (ROCK-SPIRiT) uses the concept of readout concatenation in image domain to represent SMS encoding, and performs coil self-consistency as in SPIRiT-type reconstruction in an extended k-space, while allowing regularization for further denoising. The proposed method is implemented with and without regularization, and validated on retrospectively SMS-accelerated cine imaging with threefold SMS and twofold in-plane acceleration. ROCK-SPIRiT is compared with two leakage-blocking SMS reconstruction methods: readout-SENSE-GRAPPA and split slice-GRAPPA. Further evaluation and comparisons are performed using prospectively SMS-accelerated cine imaging. Results: Results on retrospectively threefold SMS and twofold in-plane accelerated cine imaging show that ROCK-SPIRiT without regularization significantly improves on existing methods in terms of PSNR (readout-SENSE-GRAPPA:

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Abstract. We present the parallel particle filtering (PPF) software library, which enables hybrid shared-memory/distributedmemory parallelization of particle filtering (PF) algorithms combining the Message Passing Interface (MPI) with multithreading for multi-level parallelism. The library is implemented in Java and relies on OpenMPI's Java bindings for inter-process communication. It includes dynamic load balancing, multi-thread balancing, and several algorithmic improvements for PF, such as input-space domain decomposition. The PPF library hides the difficulties of efficient parallel programming of PF algorithms and provides application developers with a tool for parallel implementation of PF methods. We demonstrate the capabilities of the PPF library using two distributed PF algorithms in two scenarios with different numbers of particles. The PPF library runs a 38 million particle problem, corresponding to more than 1.86 GB of particle data, on 192 cores with 67% parallel efficiency.

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