Imaging coherent electronic motion in atoms by ultrafast electron diffraction

Ultrafast electron diffraction from time-varying coherent electronic states of the H atom is analyzed theoretically. This theoretical analysis identifies the conditions necessary to obtain time-resolved measurements. Electron diffraction from coherent electronic states exhibiting breathing and wiggling modes of electronic motion are simulated numerically in order to demonstrate the capability of attosecond electron pulses to image electron dynamics. The scattering patterns and their temporal behaviors are shown to differentiate the two kinds of target electronic motion. Moreover, our simulations show that inelastic processes contribute significantly to the diffraction patterns. Thus, although the diffraction patterns clearly distinguish different modes of target electronic motion, they cannot be easily related to the time-dependent target charge density

Time-resolved electron (\u3ci\u3ee\u3c/i\u3e,2\u3ci\u3ee\u3c/i\u3e) momentum spectroscopy: Application to laser-driven electron population transfer in atoms

Owing to its ability to provide unique information on electron dynamics, time-resolved electron momentum spectroscopy (EMS) is used to study theoretically a laser-driven electronic motion in atoms. Specifically, a chirped laser pulse is used to adiabatically transfer the populations of lithium atoms from the ground state to the first excited state. During this process, impact ionization near the Bethe ridge by time-delayed ultrashort, high-energy electron pulses is used to image the instantaneous momentum density of this electronic population transfer. Simulations with 100 fs and 1 fs pulse durations demonstrate the capability of EMS to image the time-varying momentum density, including its change of symmetry as the population transfer progresses. Moreover, the spectra corresponding to different pulse durations reveal different kinds of electronic motion.We discuss how to properly interpret these time-resolved EMS spectra, which represent a generalization of time-independent EMS

Detecting Electron Motion in Atoms and Molecules

The detection of spatial and temporal electronic motion by scattering of subfemtosecond pulses of 10 keV electrons from coherent superpositions of electronic states of both H and T+2 is investigated. For the H atom, we predict changes in the diffraction images that reflect the time-dependent effective radius of the electronic charge density. For an aligned T+2 molecule, the diffraction image changes reflect the time-dependent localization or delocalization of the electronic charge density

Dynamic CBCT Imaging using Prior Model-Free Spatiotemporal Implicit Neural Representation (PMF-STINR)

Dynamic cone-beam computed tomography (CBCT) can capture high-spatial-resolution, time-varying images for motion monitoring, patient setup, and adaptive planning of radiotherapy. However, dynamic CBCT reconstruction is an extremely ill-posed spatiotemporal inverse problem, as each CBCT volume in the dynamic sequence is only captured by one or a few X-ray projections. We developed a machine learning-based technique, prior-model-free spatiotemporal implicit neural representation (PMF-STINR), to reconstruct dynamic CBCTs from sequentially acquired X-ray projections. PMF-STINR employs a joint image reconstruction and registration approach to address the under-sampling challenge. Specifically, PMF-STINR uses spatial implicit neural representation to reconstruct a reference CBCT volume, and it applies temporal INR to represent the intra-scan dynamic motion with respect to the reference CBCT to yield dynamic CBCTs. PMF-STINR couples the temporal INR with a learning-based B-spline motion model to capture time-varying deformable motion during the reconstruction. Compared with previous methods, the spatial INR, the temporal INR, and the B-spline model of PMF-STINR are all learned on the fly during reconstruction in a one-shot fashion, without using any patient-specific prior knowledge or motion sorting/binning. PMF-STINR was evaluated via digital phantom simulations, physical phantom measurements, and a multi-institutional patient dataset featuring various imaging protocols (half-fan/full-fan, full sampling/sparse sampling, different energy and mAs settings, etc.). The results showed that the one-shot learning-based PMF-STINR can accurately and robustly reconstruct dynamic CBCTs and capture highly irregular motion with high temporal (~0.1s) resolution and sub-millimeter accuracy. It can be a promising tool for motion management by offering richer motion information than traditional 4D-CBCTs

Imaging coherent electronic motion in atoms by ultrafast electron diffraction

Ultrafast electron diffraction from time-varying coherent electronic states of the H atom is analyzed theoretically. This theoretical analysis identifies the conditions necessary to obtain time-resolved measurements. Electron diffraction from coherent electronic states exhibiting breathing and wiggling modes of electronic motion are simulated numerically in order to demonstrate the capability of attosecond electron pulses to image electron dynamics. The scattering patterns and their temporal behaviors are shown to differentiate the two kinds of target electronic motion. Moreover, our simulations show that inelastic processes contribute significantly to the diffraction patterns. Thus, although the diffraction patterns clearly distinguish different modes of target electronic motion, they cannot be easily related to the time-dependent target charge density

Performance of plastic electron optics components fabricated using a 3D printer

We show images produced by an electron beam deflector, a quadrupole lens and a einzel lens fabricated from conducting and non-conducting plastic using a 3D printer. Despite the difficulties associated with the use of plastics in vacuum, such as outgassing, poor conductivity, and print defects, the devices were used successfully in vacuum to steer, stretch and focus electron beams to millimeter diameters. Simulations indicate that much smaller focus spot sizes might be possible for such 3D-printed plastic electron lenses taking into account some possible surface defects. This work was motivated by our need to place electron optical components in difficult-to-access geometries. Our proof-of-principle demonstration opens the door to consider 3D-printed electron microscopes, whose reduced cost would make such microscopes more widely available. Potentially, this may have a significant impact on electron beam science and technology in general and electron microscopy in particular

Performance of plastic electron optics components fabricated using a 3D printer

We show images produced by an electron beam deflector, a quadrupole lens and a einzel lens fabricated from conducting and non-conducting plastic using a 3D printer. Despite the difficulties associated with the use of plastics in vacuum, such as outgassing, poor conductivity, and print defects, the devices were used successfully in vacuum to steer, stretch and focus electron beams to millimeter diameters. Simulations indicate that much smaller focus spot sizes might be possible for such 3D-printed plastic electron lenses taking into account some possible surface defects. This work was motivated by our need to place electron optical components in difficult-to-access geometries. Our proof-of-principle demonstration opens the door to consider 3D-printed electron microscopes, whose reduced cost would make such microscopes more widely available. Potentially, this may have a significant impact on electron beam science and technology in general and electron microscopy in particular

Zero-shot Medical Image Translation via Frequency-Guided Diffusion Models

Recently, the diffusion model has emerged as a superior generative model that can produce high quality and realistic images. However, for medical image translation, the existing diffusion models are deficient in accurately retaining structural information since the structure details of source domain images are lost during the forward diffusion process and cannot be fully recovered through learned reverse diffusion, while the integrity of anatomical structures is extremely important in medical images. For instance, errors in image translation may distort, shift, or even remove structures and tumors, leading to incorrect diagnosis and inadequate treatments. Training and conditioning diffusion models using paired source and target images with matching anatomy can help. However, such paired data are very difficult and costly to obtain, and may also reduce the robustness of the developed model to out-of-distribution testing data. We propose a frequency-guided diffusion model (FGDM) that employs frequency-domain filters to guide the diffusion model for structure-preserving image translation. Based on its design, FGDM allows zero-shot learning, as it can be trained solely on the data from the target domain, and used directly for source-to-target domain translation without any exposure to the source-domain data during training. We evaluated it on three cone-beam CT (CBCT)-to-CT translation tasks for different anatomical sites, and a cross-institutional MR imaging translation task. FGDM outperformed the state-of-the-art methods (GAN-based, VAE-based, and diffusion-based) in metrics of Frechet Inception Distance (FID), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM), showing its significant advantages in zero-shot medical image translation