DDMM-Synth: A Denoising Diffusion Model for Cross-modal Medical Image Synthesis with Sparse-view Measurement Embedding

Reducing the radiation dose in computed tomography (CT) is important to mitigate radiation-induced risks. One option is to employ a well-trained model to compensate for incomplete information and map sparse-view measurements to the CT reconstruction. However, reconstruction from sparsely sampled measurements is insufficient to uniquely characterize an object in CT, and a learned prior model may be inadequate for unencountered cases. Medical modal translation from magnetic resonance imaging (MRI) to CT is an alternative but may introduce incorrect information into the synthesized CT images in addition to the fact that there exists no explicit transformation describing their relationship. To address these issues, we propose a novel framework called the denoising diffusion model for medical image synthesis (DDMM-Synth) to close the performance gaps described above. This framework combines an MRI-guided diffusion model with a new CT measurement embedding reverse sampling scheme. Specifically, the null-space content of the one-step denoising result is refined by the MRI-guided data distribution prior, and its range-space component derived from an explicit operator matrix and the sparse-view CT measurements is directly integrated into the inference stage. DDMM-Synth can adjust the projection number of CT a posteriori for a particular clinical application and its modified version can even improve the results significantly for noisy cases. Our results show that DDMM-Synth outperforms other state-of-the-art supervised-learning-based baselines under fair experimental conditions.Comment: llncs.cls v2.20,12 pages with 6 figure

Modernization of B-2 Data, Video, and Control Systems Infrastructure

The National Aeronautics and Space Administration (NASA) Glenn Research Center (GRC) Plum Brook Station (PBS) Spacecraft Propulsion Research Facility, commonly referred to as B-2, is NASA's third largest thermal-vacuum facility with propellant systems capability. B-2 has completed a modernization effort of its facility legacy data, video and control systems infrastructure to accommodate modern integrated testing and Information Technology (IT) Security requirements. Integrated systems tests have been conducted to demonstrate the new data, video and control systems functionality and capability. Discrete analog signal conditioners have been replaced by new programmable, signal processing hardware that is integrated with the data system. This integration supports automated calibration and verification of the analog subsystem. Modern measurement systems analysis (MSA) tools are being developed to help verify system health and measurement integrity. Legacy hard wired digital data systems have been replaced by distributed Fibre Channel (FC) network connected digitizers where high speed sampling rates have increased to 256,000 samples per second. Several analog video cameras have been replaced by digital image and storage systems. Hard-wired analog control systems have been replaced by Programmable Logic Controllers (PLC), fiber optic networks (FON) infrastructure and human machine interface (HMI) operator screens. New modern IT Security procedures and schemes have been employed to control data access and process control flows. Due to the nature of testing possible at B-2, flexibility and configurability of systems has been central to the architecture during modernization

Ionospheric Signatures of Tohoku-Oki Tsunami of March 11, 2011: Model Comparisons Near the Epicenter

We observe ionospheric perturbations caused by the Tohoku earthquake and tsunami of March 11, 2011. Perturbations near the epicenter were found in measurements of ionospheric total electron content (TEC) from 1198 GPS receivers in the Japanese GEONET network. For the first time for this event, we compare these observations with the estimated magnitude and speed of a tsunami-driven atmospheric gravity wave, using an atmosphere-ionosphere-coupling model and a tsunami model of sea-surface height, respectively. Traveling ionospheric disturbances (TIDs) were observed moving away from the epicenter at approximate speeds of 3400 m/s, 1000 m/s and 200–300 m/s, consistent with Rayleigh waves, acoustic waves, and gravity waves, respectively. We focus our analysis on gravity waves moving south and east of the epicenter, since tsunamis propagating in the deep ocean have been shown to produce gravity waves detectable in ionospheric TEC in the past. Observed southeastward gravity wave perturbations, seen 60 min after the earthquake, are mostly between 0.5 to 1.5 TECU, representing up to 5% of the background vertical TEC (VTEC). Comparisons of observed TID gravity waves with the modeled tsunami speed in the ocean and the predicted VTEC perturbation amplitudes from an atmosphere-ionosphere-coupling model show the measurements and models to be in close agreement. Due to the dense GPS network and high earthquake magnitude, these are the clearest observations to date of the effect of a major earthquake and tsunami on the ionosphere near the epicenter. Such observations from a future real-time GPS receiver network could be used to validate tsunami models, confirm the existence of a tsunami, or track its motion where in situ buoy data is not available

Multi-Instrument Observations of a Geomagnetic Storm and its Effects on the Arctic Ionosphere: A Case Study of the 19 February 2014 Storm:Observations of a Geomagnetic Storm

We present a multiinstrumented approach for the analysis of the Arctic ionosphere during the 19 February 2014 highly complex, multiphase geomagnetic storm, which had the largest impact on the disturbance storm-time index that year. The geomagnetic storm was the result of two powerful Earth-directed coronal mass ejections (CMEs). It produced a strong long lasting negative storm phase over Greenland with a dominant energy input in the polar cap. We employed global navigation satellite system (GNSS) networks, geomagnetic observatories, and a specific ionosonde station in Greenland. We complemented the approach with spaceborne measurements in order to map the state and variability of the Arctic ionosphere. In situ observations from the Canadian CASSIOPE (CAScade, Smallsat and IOnospheric Polar Explorer) satellite's ion mass spectrometer were used to derive ion flow data from the polar cap topside ionosphere during the event. Our research specifically found that (1) thermospheric O/N2 measurements demonstrated significantly lower values over the Greenland sector than prior to the storm time. (2) An increased ion flow in the topside ionosphere was observed during the negative storm phase. (3) Negative storm phase was a direct consequence of energy input into the polar cap. (4) Polar patch formation was significantly decreased during the negative storm phase. This paper addresses the physical processes that can be responsible for this ionospheric storm development in the northern high latitudes. We conclude that ionospheric heating due to the CME's energy input caused changes in the polar atmosphere resulting in Ne upwelling, which was the major factor in high-latitude ionosphere dynamics for this storm. This research was originally published in Radio Science. © 2017 Wile

A state-space approach to dynamic tomography

The statistical inference of a hidden Markov random process is a problem encountered in numerous signal processing applications including dynamic tomography. In dynamic tomography, the goal is to form images of an object that changes in time from its projection measurements. This work focuses on the case where the object's temporal evolution is significant and governed by a physical model. Solar tomography, the remote sensing problem concerned with the reconstruction of the dynamic solar atmosphere, has served as the motivating application throughout the development of the dissertation. The proposed state-space formulation provides a natural and general statistical framework for the systematic tomographic reconstruction of dynamic objects when faced with inevitable measurement and modeling uncertainties. In addition, the dissertation offers signal processing methods that scale to meet the computational demands of high-dimensional state estimation problems such as dynamic tomography. Major contributions include a rigorous characterization of the convergence of the ensemble Kalman filter, a new method for ensemble Kalman smoothing and theory regarding its convergence, the first four-dimensional reconstruction of electron density in the solar atmosphere, a new method for dynamic tomography called the Kalman-Wiener filter that has the same computational complexity as filtered back-projection, and a means for characterizing the spatial-temporal resolution of dynamic reconstructions posed under the state-space formulation

A Localized Ensemble Kalman Smoother

Numerous geophysical inverse problems prove difficult because the available measurements are indirectly related to the underlying unknown dynamic state and the physics governing the system may involve imperfect models or unobserved parameters. Data assimilation addresses these difficulties by combining the measurements and physical knowledge. The main challenge in such problems usually involves their high dimensionality and the standard statistical methods prove computationally intractable. This paper develops and addresses the theoretical convergence of a new high-dimensional Monte-Carlo approach called the localized ensemble Kalman smoother

Resolution Assessment in Dynamic Image Formation

Remote sensing and astronomical image formation is often complicated by deficiencies in measurement quality, density, or diversity. Penalized likelihood methods can incorporate additional first-principles physical prior knowledge and improve the image reconstructions, but a systematic bias is unavoidable as a consequence. This work derives theory to understand the bias and develops a computational tool to probe its effect on the reconstructed image and bound resolution limits. Though the focus is on image formation, the contributions of this paper apply to any inference problem that can be expressed under the linear state-space signal model