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Contemporary whole-body low-field MRI scanners (< 1 T) present new and exciting opportunities for improved body imaging. The fundamental reason is that the reduced off-resonance and reduced SAR provide substantially increased flexibility in the design of MRI pulse sequences. Promising body applications include lung parenchyma imaging, imaging adjacent to metallic implants, cardiac imaging, and dynamic imaging in general. The lower cost of such systems may make MRI favorable for screening high-risk populations and population health research, and the more open configurations allowed may prove favorable for obese subjects and for pregnant women. This article summarizes promising body applications for contemporary whole-body low-field MRI systems, with a focus on new platforms developed within the past 5 years. This is an active area of research, and one can expect many improvements as MRI physicists fully explore the landscape of pulse sequences that are feasible, and as clinicians apply these to patient populations.

 

The successes of reinforcement learning in recent years are underpinned by the characterization of suitable reward functions. However, in settings where such rewards are non-intuitive, difficult to define, or otherwise error-prone in their definition, it is useful to instead learn the reward signal from expert demonstrations. This is the crux of inverse reinforcement learning (IRL). While eliciting learning requirements in the form of scalar reward signals has been shown to be effective, such representations lack explainability and lead to opaque learning. We aim to mitigate this situation by presenting a novel IRL method for eliciting declarative learning requirements in the form of a popular formal logic---Linear Temporal Logic (LTL)---from a set of traces given by the expert policy. 

For short-wavelength infrared (SWIR) avalanche photodiodes, a separate absorption, charge, and multiplication design is widely used. AlInAsSb on an InP substrate is a potential multiplication layer with a lattice match to absorber candidates across the SWIR. Our new measurements demonstrate that AlInAsSb on InP is a promising multiplier candidate with a relatively low dark current density of 10−4 A/cm2 at a gain of 30; a high gain, measured up to 245 in this study; and a large differentiation of electron and hole ionization leading to a low excess noise, measured to be 2.5 at a gain of 30. These characteristics are all improvements over commercially available SWIR detectors incorporating InAlAs or InP as the multiplier. We measured and analyzed gain for multiple wavelengths to extract the ionization coefficients as a function of an electric field over the range 0.33–0.6 MV/cm.

 

This paper presents an optimization based framework to automate system repair against omega-regular properties. In the proposed formalization of optimal repair, the systems are represented as Kripke structures, the properties as omega-regular languages, and the repair space as repair machines—weighted omega-regular transducers equipped with Büchi conditions—that rewrite strings and associate a cost sequence to these rewritings. To translate the resulting cost-sequences to easily interpretable payoffs, we consider several aggregator functions to map cost sequences to numbers—including limit superior, supremum, discounted-sum, and average-sum—to define quantitative cost semantics. The problem of optimal repair, then, is to determine whether traces from a given system can be rewritten to satisfy an omega-regular property when the allowed cost is bounded by a given threshold. We also consider the dual challenge of impair verification that assumes that the rewritings are resolved adversarially under some given cost restriction, and asks to decide if all traces of the system satisfy the specification irrespective of the rewritings. With a negative result to the impair verification problem, we study the problem of designing a minimal mask of the Kripke structure such that the resulting traces satisfy the specifications despite the threshold-bounded impairment. We dub this problem as the mask synthesis problem. This paper presents automata-theoretic solutions to repair synthesis, impair verification, and mask synthesis problem for limit superior, supremum, discounted-sum, and average-sum cost semantics. 

The reproducibility of scientific reports is crucial to advancing human knowledge. This paper is a summary of our experience in replicating a balanced SSFP half‐radial dual‐echo imaging technique (bSTAR) using open‐source frameworks as a response to the 2023 ISMRM “repeat it with me” Challenge.

We replicated the bSTAR technique for thoracic imaging at 0.55T. The bSTAR pulse sequence is implemented in Pulseq, a vendor neutral open‐source rapid sequence prototyping environment. Image reconstruction is performed with the open‐source Berkeley Advanced Reconstruction Toolbox (BART). The replication of bSTAR, termed open‐source bSTAR, is tested by replicating several figures from the published literature. Original bSTAR, using the pulse sequence and image reconstruction developed by the original authors, and open‐source bSTAR, with pulse sequence and image reconstruction developed in this work, were performed in healthy volunteers.

Both echo images obtained from open‐source bSTAR contain no visible artifacts and show identical spatial resolution and image quality to those in the published literature. A direct head‐to‐head comparison between open‐source bSTAR and original bSTAR on a healthy volunteer indicates that open‐source bSTAR provides adequate SNR, spatial resolution, level of artifacts, and conspicuity of pulmonary vessels comparable to original bSTAR.

We have successfully replicated bSTAR lung imaging at 0.55T using two open‐source frameworks. Full replication of a research method solely relying on information on a research paper is unfortunately rare in research, but our success gives greater confidence that a research methodology can be indeed replicated as described.

 

We report a measurement of the quantum efficiency for a surface plasma wave (SPW)-coupled InAs/In_0.15Ga_0.85As/GaAs dots-in-a-well (Dwell) quantum dot infrared photodetector (QDIP) having a single-color response at ∼10 µm. A gold film perforated with a square array of complex, non-circular apertures is employed to manipulate the near-fields of the fundamental SPW. The quantum efficiency is quantitatively divided into absorption efficiency strongly enhanced by the SPW, and collection efficiency mostly independent of it. In the absorption efficiency, the evanescent near-fields of the fundamental SPW critically enhances QDIP performance but undergoes the attenuation by the absorption in the Dwell that ultimately limits the quantum efficiency. For the highest quantum efficiency available with plasmonic coupling, an optimal overlap between Dwell and SPW near-fields is required. Based on experiment and simulation, the upper limit of the plasmonic enhancement in quantum efficiency for the present device is addressed.

 

Real-time magnetic resonance imaging (RT-MRI) of human speech production is enabling significant advances in speech science, linguistics, bio-inspired speech technology development, and clinical applications. Easy access to RT-MRI is however limited, and comprehensive datasets with broad access are needed to catalyze research across numerous domains. The imaging of the rapidly moving articulators and dynamic airway shaping during speech demands high spatio-temporal resolution and robust reconstruction methods. Further, while reconstructed images have been published, to-date there is no open dataset providing raw multi-coil RT-MRI data from an optimized speech production experimental setup. Such datasets could enable new and improved methods for dynamic image reconstruction, artifact correction, feature extraction, and direct extraction of linguistically-relevant biomarkers. The present dataset offers a unique corpus of 2D sagittal-view RT-MRI videos along with synchronized audio for 75 participants performing linguistically motivated speech tasks, alongside the corresponding public domain raw RT-MRI data. The dataset also includes 3D volumetric vocal tract MRI during sustained speech sounds and high-resolution static anatomical T2-weighted upper airway MRI for each participant.