Intracellular imaging of nanoparticles: is it an elemental mistake to believe what you see?

In order to understand how nanoparticles (NPs <100 nm) interact with cellular systems, potentially causing adverse effects, it is important to be able to detect and localize them within cells. Due to the small size of NPs, transmission electron microscopy (TEM) is an appropriate technique to use for visualizing NPs inside cells, since light microscopy fails to resolve them at a single particle level. However, the presence of other cellular and non-cellular nano-sized structures in TEM cell samples, which may resemble NPs in size, morphology and electron density, can obstruct the precise intracellular identification of NPs. Therefore, elemental analysis is recommended to confirm the presence of NPs inside the cell. The present study highlights the necessity to perform elemental analysis, specifically energy filtering TEM, to confirm intracellular NP localization using the example of quantum dots (QDs). Recently, QDs have gained increased attention due to their fluorescent characteristics, and possible applications for biomedical imaging have been suggested. Nevertheless, potential adverse effects cannot be excluded and some studies point to a correlation between intracellular particle localization and toxic effects. J774.A1 murine macrophage-like cells were exposed to NH2 polyethylene (PEG) QDs and elemental co-localization analysis of two elements present in the QDs (sulfur and cadmium) was performed on putative intracellular QDs with electron spectroscopic imaging (ESI). Both elements were shown on a single particle level and QDs were confirmed to be located inside intracellular vesicles. Nevertheless, ESI analysis showed that not all nano-sized structures, initially identified as QDs, were confirmed. This observation emphasizes the necessity to perform elemental analysis when investigating intracellular NP localization using TEM.

Multi-modal diffeomorphic demons registration based on point-wise mutual information

In this paper we propose a variational approach for multimodal image registration based on the diffeomorphic demons algorithm. Diffeomorphic demons has proven to be a robust and efficient way for intensity-based image registration. However, the main drawback is that it cannot deal with multiple modalities. We propose to replace the standard demons similarity metric (image intensity differences) by point-wise mutual information (PMI) in the energy function. By comparing the accuracy between our PMI based diffeomorphic demons and the B-Spline based free-form deformation approach (FFD) on simulated deformations, we show the proposed algorithm performs significantly better.

Segmentation of brain tumor images based on atlas-registration combined with a Markov-Random-Field lesion growth model

We present an automatic method to segment brain tissues from volumetric MRI brain tumor images. The method is based on non-rigid registration of an average atlas in combination with a biomechanically justified tumor growth model to simulate soft-tissue deformations caused by the tumor mass-effect. The tumor growth model, which is formulated as a mesh-free Markov Random Field energy minimization problem, ensures correspondence between the atlas and the patient image, prior to the registration step. The method is non-parametric, simple and fast compared to other approaches while maintaining similar accuracy. It has been evaluated qualitatively and quantitatively with promising results on eight datasets comprising simulated images and real patient data.

Kernel density correlation based non-rigid point set matching for statistical model-based 2D/3D reconstruction

This paper presents a kernel density correlation based nonrigid point set matching method and shows its application in statistical model based 2D/3D reconstruction of a scaled, patient-specific model from an un-calibrated x-ray radiograph. In this method, both the reference point set and the floating point set are first represented using kernel density estimates. A correlation measure between these two kernel density estimates is then optimized to find a displacement field such that the floating point set is moved to the reference point set. Regularizations based on the overall deformation energy and the motion smoothness energy are used to constraint the displacement field for a robust point set matching. Incorporating this non-rigid point set matching method into a statistical model based 2D/3D reconstruction framework, we can reconstruct a scaled, patient-specific model from noisy edge points that are extracted directly from the x-ray radiograph by an edge detector. Our experiment conducted on datasets of two patients and six cadavers demonstrates a mean reconstruction error of 1.9 mm

Determining the optical properties of a gelatin-TiO2 phantom at 780 nm

Tissue phantoms play a central role in validating biomedical imaging techniques. Here we employ a series of methods that aim to fully determine the optical properties, i.e., the refractive index n, absorption coefficient μa, transport mean free path ℓ∗, and scattering coefficient μs of a TiO2 in gelatin phantom intended for use in optoacoustic imaging. For the determination of the key parameters μa and ℓ∗, we employ a variant of time of flight measurements, where fiber optodes are immersed into the phantom to minimize the influence of boundaries. The robustness of the method was verified with Monte Carlo simulations, where the experimentally obtained values served as input parameters for the simulations. The excellent agreement between simulations and experiments confirmed the reliability of the results. The parameters determined at 780 nm are n=1.359(±0.002), μ′s=1/ℓ∗=0.22(±0.02) mm-1, μa= 0.0053(+0.0006-0.0003) mm-1, and μs=2.86(±0.04) mm-1. The asymmetry parameter g obtained from the parameters ℓ∗ and μ′s is 0.93, which indicates that the scattering entities are not bare TiO2 particles but large sparse clusters. The interaction between the scattering particles and the gelatin matrix should be taken into account when developing such phantoms.

Dynamic registration using ultrasound for anatomical referencing

This paper proposes methods to circumvent the need to attach physical markers to bones for anatomical referencing in computer-assisted orthopedic surgery. Using ultrasound, a bone could be non-invasively referenced, and so the problem is formulated as the need for dynamic registration. A method for correspondence establishment is presented, and the matching step is based on three least-squares algorithms: two that are typically used in registration methods such as ICP, and the third is a form of the Unscented Kalman filter that was adapted to work in this context. A simulation was developed in order to reliably evaluate and compare the dynamic registration methods

Statistical shape analysis via principal factor analysis

Statistical shape analysis techniques commonly employed in the medical imaging community, such as active shape models or active appearance models, rely on principal component analysis (PCA) to decompose shape variability into a reduced set of interpretable components. In this paper we propose principal factor analysis (PFA) as an alternative and complementary tool to PCA providing a decomposition into modes of variation that can be more easily interpretable, while still being a linear efficient technique that performs dimensionality reduction (as opposed to independent component analysis, ICA). The key difference between PFA and PCA is that PFA models covariance between variables, rather than the total variance in the data. The added value of PFA is illustrated on 2D landmark data of corpora callosa outlines. Then, a study of the 3D shape variability of the human left femur is performed. Finally, we report results on vector-valued 3D deformation fields resulting from non-rigid registration of ventricles in MRI of the brain.

Precise pose recovery of distal locking holes from single calibrated fluoroscopic image via a novel variable decomposition approach

This paper presents a novel variable decomposition approach for pose recovery of the distal locking holes using single calibrated fluoroscopic image. The problem is formulated as a model-based optimal fitting process, where the control variables are decomposed into two sets: (a) the angle between the nail axis and its projection on the imaging plane, and (b) the translation and rotation of the geometrical model of the distal locking hole around the nail axis. By using an iterative algorithm to find the optimal values of the latter set of variables for any given value of the former variable, we reduce the multiple-dimensional model-based optimal fitting problem to a one-dimensional search along a finite interval. We report the results of our in vitro experiments, which demonstrate that the accuracy of our approach is adequate for successful distal locking of intramedullary nails.

Characterization of microfluidic systems with Doppler optical coherence tomography

Doppler Optical Coherence Tomography (DOCT) is a biomedical imaging technique that allows simultaneous structural imaging and flow monitoring inside biological tissues and materials with spatial resolution in the micrometer scale. It has recently been applied to the characterization of microfluidic systems. Structural and flow imaging of novel microfluidics platforms for cytotoxicologic applications were obtained with a real-time, Near Infrared Spectral Domain DOCT system. Characteristics such as flow homogeneity in the chamber, which is one of the most important parameters for cell culture, are investigated. OCT and DOCT images were used to monitor flow inside a specific platform that is based on microchannel division for a better flow homogeneity. In particular, the evolution of flow profile at the transition between the microchannel structure and the chamber is studied.