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Physical imaging is a foundational characterization method in areas from condensed matter physics and chemistry to astronomy and spans length scales from atomic to universe. Images encapsulate crucial data regarding atomic bonding, materials microstructures, and dynamic phenomena such as microstructural evolution and turbulence, among other phenomena. The challenge lies in effectively extracting and interpreting this information. Variational Autoencoders (VAEs) have emerged as powerful tools for identifying the underlying factors of variation in image data, providing a systematic approach to distilling meaningful patterns from complex data sets. However, a significant hurdle in their application is the definition and selection of appropriate descriptors reflecting local structures. Here, we introduce the scale-invariant VAE approach (SI-VAE) based on the progressive training of the VAE with the descriptors sampled at different length scales. The SI-VAE allows the discovery of the length scale-dependent factors of variation in the system. Here, we illustrate this approach using the ferroelectric domain images and generalize it to the movies of the electron-beam induced phenomena in graphene and topography evolution across combinatorial libraries. This approach can further be used to initialize the decision making in automated experiments including structure–property discovery and can be applied across a broad range of imaging methods. This approach is universal and can be applied to any spatially resolved data including both experimental imaging studies and simulations, and can be particularly useful for exploration of phenomena such as turbulence and scale-invariant transformation fronts. [ABSTRACT FROM AUTHOR]

Magnetic Particle Imaging (MPI) has advanced rapidly from its first conceptualization, showing promise as a viable clinical imaging modality. Despite its promise, several aspects of MPI, such as hardware design, tracer design, and image reconstruction method, still require further technical development to overcome their current limitations. This review provides the basic concept for MPI, introduces the limitations of upscaling MPI, and discusses several aspects of MPI development. Among them are the advantages and disadvantages of using different field-free regions (FFR), bore configurations, elaborating on the challenges in upscaling, describing the optimal characteristics of MPI tracers, discussing tracer synthesis methods and biocompatible coatings, tracer toxicity reports, and finally a basic explanation regarding the various image reconstruction methods. Additionally, this review provides several examples of state-of-the-art MPI devices and prototypes with varying bore designs, FFR designs, magnetic field sources, and intended use cases to demonstrate both the wide range of applications and the progress of recent research in MPI. With all this information compiled, this review serves to shed insight for researchers in the field of MPI or those intending to enter the world of MPI. It is hoped that this review will encourage the future development of MPI, accelerating its viability for clinical implementation. [ABSTRACT FROM AUTHOR]

We report a velocity-mapped ion imaging study of the photodissociation of O3 in the Huggins band. The O(3PJ) images show evidence for three electronic channels producing O2(X3Σg−), O2(a1∆g), and O2(b1Σg+) state fragments, with the latter two arising from the spin-forbidden photodissociation of O3. Forward convolution simulations of the derived total translational energy distributions permit extraction of the vibrational state distribution for each O2 electronic state. All these distributions peak near v = 0 and decrease monotonically with the vibrational state. The wavelength-dependent branching of the three electronic channels has been determined and is approximately constant over the wavelength region studied (322–328 nm). We have observed that the O2 electronic state branching ratios depend on the coincident O(3PJ) spin–orbit state, and the O2(b1Σg+) state is particularly sensitive. These results are qualitatively consistent with previous calculations on the coupling of the initially excited state to dissociative states by Rosenwaks and Grebenshchikov [J. Phys. Chem. A. 114, 9809–9819 (2010)]. The spatial anisotropy of the three dissociation channels has been determined through analysis of the O(3P0) angular distributions. The results are consistent with recent calculations but differ from previous experimental reports. The experimental results provide detailed information on the dissociation dynamics and should motivate new calculations. [ABSTRACT FROM AUTHOR]

Abdominal pain is one of the most common presenting chief complaints in the emergency department. Erector spinae plane block (ESPB) is an ultrasound-guided nerve block with proven effectiveness in treating visceral and somatic abdominal pain. Despite the increasing popularity of ESPB, its role in the management of nonsurgical abdominal pain has not yet been characterized. Our scoping review aims to synthesize current knowledge on the safety and efficacy of ESPB in the management of patients experiencing intractable, nonsurgical abdominal pain. We searched PubMed and Scopus to evaluate the existing literature on ESPB for nonsurgical abdominal pain. A total of 14 journal articles were included: 12 case-based studies, one systematic review, and one narrative review. All cases described the successful use of ESPB in treating abdominal pain refractory to oral or intravenous analgesic medications, and no complications were reported in any cases. This scoping review provides support for the use of ESPB to manage intractable, nonsurgical abdominal pain. ESPB has demonstrated efficacy in alleviating various conditions such as functional abdominal pain, renal colic, pancreatitis, herpetic pain, and cancer-related pain. Theoretical risks such as pneumothorax, bleeding, and infection are possible, although the studies reviewed did not report such complications.

BACKGROUND/OBJECTIVES: Since the initial approach towards the clinical presentation of soft-tissue masses is challenging for frontline physicians, some countries use clinical practice guidelines. Proper imaging work-up is crucial to differentiate between soft-tissue tumors. Recently, ultrasonography and magnetic resonance imaging (MRI) have been widely used. But there is some controversy about whether pre-referral evaluations are being conducted properly. Thus, this study aims to assess whether the ultrasonography investigation of soft-tissue masses prior to referral to the musculoskeletal tumor center is being performed adequately in terms of indications, diagnostic accuracy, and referral interval, and to evaluate whether the pre-referral MRI for soft-tissue masses is being conducted reasonably concerning indications, imaging protocol, reporting, diagnostic accuracy, and cost-effectiveness. METHODS: The study protocol was registered. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. We performed a database search of the MEDLINE, Embase, and Cochrane Library. Then, two authors reviewed the studies, and the third author resolved any disagreement between them. A total of nine studies were included. The Risk Of Bias In Non-Randomized Studies-of Interventions was applied. Relevant data were extracted by two authors. RESULTS: This review showed that the way that the imaging investigations are performed in non-specialized centers before referral was generally considered inappropriate. CONCLUSIONS: Frontline physicians should regard the alarm symptom as an indication for advanced imaging evaluation. Education and certification may be required for ultrasonography. MRI should be performed and interpreted in a specialized center or by a specialist with relevant expertise. Guidance may help reduce inappropriate imaging.

In recent years, the role of Artificial Intelligence (AI) in medical imaging has become increasingly prominent, with the majority of AI applications approved by the FDA being in imaging and radiology in 2023. The surge in AI model development to tackle clinical challenges underscores the necessity for preparing high-quality medical imaging data. Proper data preparation is crucial as it fosters the creation of standardized and reproducible AI models while minimizing biases. Data curation transforms raw data into a valuable, organized, and dependable resource and is a fundamental process to the success of machine learning and analytical projects. Considering the plethora of available tools for data curation in different stages, it is crucial to stay informed about the most relevant tools within specific research areas. In the current work, we propose a descriptive outline for different steps of data curation while we furnish compilations of tools collected from a survey applied among members of the Society of Imaging Informatics (SIIM) for each of these stages. This collection has the potential to enhance the decision-making process for researchers as they select the most appropriate tool for their specific tasks.