Monitoring the Evolution of Relative Product Populations at Early Times during a Photochemical Reaction.

Identifying multiple rival reaction products and transient species formed during ultrafast photochemical reactions and determining their time-evolving relative populations are key steps toward understanding and predicting photochemical outcomes. Yet, most contemporary ultrafast studies struggle with clearly identifying and quantifying competing molecular structures/species among the emerging reaction products. Here, we show that mega-electronvolt ultrafast electron diffraction in combination with ab initio molecular dynamics calculations offer a powerful route to determining time-resolved populations of the various isomeric products formed after UV (266 nm) excitation of the five-membered heterocyclic molecule 2(5H)-thiophenone. This strategy provides experimental validation of the predicted high (∼50%) yield of an episulfide isomer containing a strained three-membered ring within ∼1 ps of photoexcitation and highlights the rapidity of interconversion between the rival highly vibrationally excited photoproducts in their ground electronic state.

Ultrafast electron diffraction instrument for gas and condensed matter samples.

We report the modification of a gas phase ultrafast electron diffraction (UED) instrument that enables experiments with both gas and condensed matter targets, where a time-resolved experiment with sub-picosecond resolution is demonstrated with solid state samples. The instrument relies on a hybrid DC-RF acceleration structure to deliver femtosecond electron pulses on the target, which is synchronized with femtosecond laser pulses. The laser pulses and electron pulses are used to excite the sample and to probe the structural dynamics, respectively. The new system is added with capabilities to perform transmission UED on thin solid samples. It allows for cooling samples to cryogenic temperatures and to carry out time-resolved measurements. We tested the cooling capability by recording diffraction patterns of temperature dependent charge density waves in 1T-TaS2. The time-resolved capability is experimentally verified by capturing the dynamics in photoexcited single-crystal gold.

Ultrafast electron diffraction from transiently aligned asymmetric top molecules: Rotational dynamics and structure retrieval.

Ultrafast electron diffraction (UED) from aligned molecules in the gas phase has successfully retrieved structures of both linear and symmetric top molecules. Alignment of asymmetric tops has been recorded with UED but no structural information was retrieved. We present here the extraction of two-dimensional structural information from simple transformations of experimental diffraction patterns of aligned molecules as a proof-of-principle for the recovery of the full structure. We align 4-fluorobenzotrifluoride with a linearly polarized laser and show that we can distinguish between atomic pairs with equal distances that are parallel and perpendicular to the aligned axis. We additionally show with numerical simulations that by cooling the molecules to a rotational temperature of 1 K, more distances and angles can be resolved through direct transformations.

Strong-field induced fragmentation and isomerization of toluene probed by ultrafast femtosecond electron diffraction and mass spectrometry.

We investigate the fragmentation and isomerization of toluene molecules induced by strong-field ionization with a femtosecond near-infrared laser pulse. Momentum-resolved coincidence time-of-flight ion mass spectrometry is used to determine the relative yield of different ionic products and fragmentation channels as a function of laser intensity. Ultrafast electron diffraction is used to capture the structure of the ions formed on a picosecond time scale by comparing the diffraction signal with theoretical predictions. Through the combination of the two measurements and theory, we are able to determine the main fragmentation channels and to distinguish between ions with identical mass but different structures. In addition, our diffraction measurements show that the independent atom model, which is widely used to analyze electron diffraction patterns, is not a good approximation for diffraction from ions. We show that the diffraction data is in very good agreement with ab initio scattering calculations.

Automated Quality Assessment of Colour Fundus Images for Diabetic Retinopathy Screening in Telemedicine.

Fundus images obtained in a telemedicine program are acquired at different sites that are captured by people who have varying levels of experience. These result in a relatively high percentage of images which are later marked as unreadable by graders. Unreadable images require a recapture which is time and cost intensive. An automated method that determines the image quality during acquisition is an effective alternative. To determine the image quality during acquisition, we describe here an automated method for the assessment of image quality in the context of diabetic retinopathy. The method explicitly applies machine learning techniques to access the image and to determine 'accept' and 'reject' categories. 'Reject' category image requires a recapture. A deep convolution neural network is trained to grade the images automatically. A large representative set of 7000 colour fundus images was used for the experiment which was obtained from the EyePACS that were made available by the California Healthcare Foundation. Three retinal image analysis experts were employed to categorise these images into 'accept' and 'reject' classes based on the precise definition of image quality in the context of DR. The network was trained using 3428 images. The method shows an accuracy of 100% to successfully categorise 'accept' and 'reject' images, which is about 2% higher than the traditional machine learning method. On a clinical trial, the proposed method shows 97% agreement with human grader. The method can be easily incorporated with the fundus image capturing system in the acquisition centre and can guide the photographer whether a recapture is necessary or not.

A Novel Method for Correcting Non-uniform/Poor Illumination of Color Fundus Photographs.

Retinal fundus images are often corrupted by non-uniform and/or poor illumination that occur due to overall imperfections in the image acquisition process. This unwanted variation in brightness limits the pathological information that can be gained from the image. Studies have shown that poor illumination can impede human grading in about 10~15% of retinal images. For automated grading, the effect can be even higher. In this perspective, we propose a novel method for illumination correction in the context of retinal imaging. The method splits the color image into luminosity and chroma (i.e., color) components and performs illumination correction in the luminosity channel based on a novel background estimation technique. Extensive subjective and objective experiments were conducted on publicly available DIARETDB1 and EyePACS images to justify the performance of the proposed method. The subjective experiment has confirmed that the proposed method does not create false color/artifacts and at the same time performs better than the traditional method in 84 out of 89 cases. The objective experiment shows an accuracy improvement of 4% in automated disease grading when illumination correction is performed by the proposed method than the traditional method.

A Two-Step Approach for Longitudinal Registration of Retinal Images.

This paper presents a novel two step approach for longitudinal (over time) registration of retinal images. Longitudinal registration is an important preliminary step to analyse longitudinal changes on the retina including disease progression. While potential overlap and minimal geometric distortion are likely in longitudinal images, identification of reliable features over time is a potential challenge for longitudinal registration. Relying on the widely accepted phenomenon that retinal vessels are more reliable over time, the proposed method aims to accurately match bifurcation and cross-over points between different timestamp images. Binary robust independent elementary features (BRIEF) are computed around bifurcation points which are then matched based on Hamming distance. Prior to computing BRIEF descriptors, a preliminary registration is performed relying on SURF key-point matching. Experiments are conducted on different image datasets containing 109 longitudinal image pairs in total. The proposed method has been found to produce accurate registration (i.e. registration with zero alignment error) for 97 % cases, which is significantly higher than the other methods in comparison. The paper also reveals the finding that both the number and distributions of accurately matching key-points pairs are important for successful registration of image pairs.

Sparsity Prior Computed Tomography Reconstruction Using a Nonstandard Simultaneous X-ray Acquisition Model.

In this article we systematically evaluate the performance of several state-of-the-art, sparsity prior computed tomography (CT) reconstruction algorithms, using a nonstandard simultaneous x-ray acquisition method. Sparsity prior is an efficient strategy in CT reconstruction, relying on iterative algorithms such as the algebraic reconstruction technique to produce a crude reconstruction, based on which sparse approximation is performed. The simultaneous x-ray acquisition model ensures rapid capture of x-rays; however, it captures a significantly fewer number of attenuation measurements, and the projections are nonuniform. We propose a weighted average filter in the reconstruction framework to ensure better quality reconstruction by minimizing the effect of nonuniform projections. The performance of the state-of-the-art algorithms is analyzed with and without weighted averaging before sparse approximation, in simulated and real environments. Experiments in the simulated environment are conducted with and without the presence of noise. From the results, it is evident that sparsity prior algorithms are capable of producing cross-sectional reconstruction using the simultaneous x-ray acquisition model, and better reconstruction quality is achievable with the incorporation of weighted averaging in the reconstruction framework.