Correlation Between Optical Fluorescence and Microwave Transmission During Single-cell Electroporation
A New Image Similarity Metric for Improving Deformation Consistency in Graph Based Groupwise Image Registration
Image similarity metric (ISM) is used to construct the graph of input images in graph based groupwise image registration (G-GIR). Most existing G-GIR methods adopt image intensity-based ISM. However, image intensity is not directly related to deformation directions in image registration, and inconsistent deformation along the shortest paths in the resulting graph is inevitable, making G-GIR inefficient and inaccurate. We propose a new ISM to build the graph with deformation consistency. Our ISM reduces inconsistent deformation and improves registration outcome.
Detection of Vesicoureteral Reflux using Electrical Impedance Tomography
This study describes a novel approach to detect vesicoureteral reflux (VUR), non-invasively using electrical impedance tomography (EIT). VUR is the backflow of urine from the bladder to the kidneys and predisposes children to urinary tract infections (UTIs) and kidney damage. EIT measurements were taken in a porcine model. Data from 116 experiments were collected, processed and time-difference images reconstructed. Unilateral infusions were successfully detected in 94.83% of all mean voltage signals and in over 98.28% of the reconstructed images. The results show the promise of EIT as a screening tool for VUR in children.
Self-scanned HIFU ablation of moving tissue using real-time hybrid US-MR imaging
High Intensity Focused Ultrasound (HIFU) treatment in the abdominal cavity is challenging due to the respiratory motion. In the self-scanned HIFU ablation method the focal spot is kept static and the heating pattern is obtained through natural tissue motion. A novel approach is described for compensating for the effect of tissue motion on thermal build-up, by modulating the HIFU power during self-scanning according to tissue speed, estimated by speckle tracking on ultrasound images. This method provides the first experimental validation of the self-scanning HIFU ablation paradigm via a real-time hybrid MRI/US imaging, opening the path towards self-scanning in vivo therapies.
Achieving Automated Organelle Biopsy on Small Single Cells Using a Cell Surgery Robotic System
A robotic surgery system to achieve automated organelle biopsy of single cells with dimensions of less than 20 µm in diameter is presented. A microfludic device is used to pattern cells in 1D array. A sliding mode nonlinear controller is developed to enable extraction of organelles, such as the mitochondria and the nucleus, from single cells with high precision. An image processing algorithm is also developed to automatically detect the position of the desired organelle. Experiments of automated extraction of mitochondria and nucleus from human acute promyelocytic leukemia cells and human fibroblast cells are performed. The results presented here have revealed that the proposed approach of automated organelle biopsy on single small cells is feasible.
Sparse EEG Source Localization Using LAPPS: Least Absolute l -P (0 < p < 1) Penalized Solution
Electroencephalographic (EEG) is commonly used to study the brain activity with high temporal resolution, but it is usually inevitably contaminated by strong outliers. Here, we propose a novel EEG source localization algorithm, LAPPS, which employs the l 1-loss for the residual error to alleviate the effect of outliers and another l p-penalty norm (p=0.5) to obtain sparse sources while suppressing Gaussian noise in EEG recordings. The simulation results in various dipoles configurations under various SNRs prove the superiority of LAPPS. In a real visual oddball experiment, LAPPS also obtained sparse activations consistent with previous findings revealed by EEG and fMRI.
Electrophysiological Brain Connectivity: Theory and Implementation
In this tutorial paper, we describe the theoretical basis, computational algorithms, and applications of dynamic functional brain connectivity analysis from electromagnetic measurements, including electroencephalography (EEG), magnetoencephalography (MEG), electrocorticography (ECoG), and stereoelectroencephalography (SEEG). Various connectivity estimators are discussed, and algorithms introduced. The merits, limitations, and needs for future development are also discussed. The tutorial will serve both new comers and practitioners working on brain connectivity analysis.