Simultaneous Echo Refocused Magnetization Transfer Imaging

Purpose: To increase the efficiency of densely encoded magnetization transfer imaging of the brain, we time-multiplex multiple slices within the same readout using simultaneous echo refocusing FLASH imaging with magnetization transfer (MT) preparation (MT-SER-FLASH). Materials and Methods: Inefficiency in total scan time results from the number of frequency samples needed for sufficient quality of quantitative parameter maps for a binary spin bath model. We present a highly efficient multiplexing method, simultaneous echo refocused magnetization transfer imaging (MT-SER-FLASH) for reducing the total scan time of MT imaging by one-third. The specific absorption rate (SAR) was also reduced by reducing the number of MT-pulses per volume. Results: 2D-MT-SER-FLASH is performed in 19 minutes rather than 1 hour, acceptable for routine clinical application. The SAR could be reduced to 69% instead of more than 100% with a standard 2D or 3D-FLASH with MT-preparation. Conclusion: The net reduction of scan time and SAR enables the use of quantitative model based magnetization transfer imaging within a clinical environment.

Enhancing the expansion of a plasma shockwave by crater-induced laser refocusing in femtosecond laser ablation of fused silica

The dynamics of plasma and shockwave expansion during two femtosecond laser pulse ablation of fused silica are studied using a time-resolved shadowgraph imaging technique. The experimental results reveal that during the second pulse irradiation on the crater induced by the first pulse, the expansion of the plasma and shockwave is enhanced in the longitudinal direction. The plasma model and Fresnel diffraction theory are combined to calculate the laser intensity distribution by considering the change in surface morphology and transient material properties. The theoretical results show that after the free electron density induced by the rising edge of the pulse reaches the critical density, the originally transparent surface is transformed into a transient high-reflectivity surface(metallic state). Thus, the crater with a concave-lens-like morphology can tremendously reflect and refocus the latter part of the laser pulse, leading to a strong laser field with an intensity even higher than the incident intensity. This strong refocused laser pulse results in a stronger laser-induced air breakdown and enhances the subsequent expansion of the plasma and shockwave. In addition, similar shadowgraphs are also recorded in the single-pulse ablation of a concave microlens, providing experimental evidence for the enhancement mechanism.

Camera array calibration for light field acquisition

Light field cameras are becoming popular in com- puter vision and graphics, with many research and commercial applications already having been proposed. Various types of cameras have been developed with the camera array being one of the ways of acquiring a 4D light field image using multiple cameras. Camera calibration is essential, since each application requires the correct projection and ray geometry of the fight field. The calibrated parameters are used in the fight field image rectified from the images captured by multiple cameras. Various camera calibration approaches have been proposed for a single camera, multiple cameras, and a moving camera. However, although these approaches can be applied to calibrating camera arrays, they are not effective in terms of accuracy and computational cost. Moreover, less attention has been paid to camera calibration of a light field camera. In this paper, we propose a calibration method for a camera array and a rectification method for generating a light field image from the captured images. We propose a two-step algorithm consisting of closed form initialization and nonlinear refinement, which extends Zhang＇s well-known method to the camera array. More importantly, we introduce a rigid camera constraint whereby the array of cameras is rigidly aligned in the camera array and utilize this constraint in our calibration. Using this constraint, we obtained much faster and more accurate calibration results in the experiments.

A deep-learning-based workflow to deal with the defocusing problem in high-throughput experiments

The increasing throughput of experiments in biomaterials research makes automatic techniques more and more necessary.Among all the characterization methods,microscopy makes fundamental contributions to biomaterials science where precisely focused images are the basis of related research.Although automatic focusing has been widely applied in all kinds of microscopes,defocused images can still be acquired now and then due to factors including background noises of materials and mechanical errors.Herein,we present a deep-learning-based method for the automatic sorting and reconstruction of defocused cell images.First,the defocusing problem is illustrated on a high-throughput cell microarray.Then,a comprehensive dataset of phase-contrast images captured from varied conditions containing multiple cell types,magnifications,and substrate materials is prepared to establish and test our method.We obtain high accuracy of over 0.993 on the dataset using a simple network architecture that requires less than half of the training time compared with the classical ResNetV2 architecture.Moreover,the subcellular-level reconstruction of heavily defocused cell images is achieved with another architecture.The applicability of the established workflow in practice is finally demonstrated on the high-throughput cell microarray.The intelligent workflow does not require a priori knowledge of focusing algorithms,possessing widespread application value in cell experiments concerning high-throughput or time-lapse imaging.