Artificial intelligence in medical imaging has seen unprecedented growth in the last years, due to rapid advances in deep learning and computing resources. Applications cover the full range of existing medical imaging modalities, with unique characteristics driven by the physics of each technique. Yet, artificial intelligence professionals entering the field, and even experienced developers, often lack a comprehensive understanding of the physical principles underlying medical image acquisition, which hinders their ability to fully leverage its potential. The integration of physics knowledge into artificial intelligence algorithms enhances their trustworthiness and robustness in medical imaging, especially in scenarios with limited data availability. In this work, we review the fundamentals of physics in medical images and their impact on the latest advances in artificial intelligence, particularly, in generative models and reconstruction algorithms. Finally, we explore the integration of physics knowledge into physics-inspired machine learning models, which leverage physics-based constraints to enhance the learning of medical imaging features.

Zapping the brain has allowed partially paralysed patients to walk again in a'major milestone' for wheelchair users. Deep brain stimulation has been found to improve walking and promote recovery in two people with a spinal cord injury. The surgical procedure involves implanting electrodes into the brain to produce electrical impulses. These can be easily switched'on' and'off'. Traditionally, it has been used to treat movement disorders like Parkinson's by targeting areas of the brain responsible for motor control.

The field of medical imaging is an essential aspect of the medical sciences, involving various forms of radiation to capture images of the internal tissues and organs of the body. These images provide vital information for clinical diagnosis, and in this chapter, we will explore the use of X-ray, MRI, and nuclear imaging in detecting severe illnesses. However, manual evaluation and storage of these images can be a challenging and time-consuming process. To address this issue, artificial intelligence (AI)-based techniques, particularly deep learning (DL), have become increasingly popular for systematic feature extraction and classification from imaging modalities, thereby aiding doctors in making rapid and accurate diagnoses. In this review study, we will focus on how AI-based approaches, particularly the use of Convolutional Neural Networks (CNN), can assist in disease detection through medical imaging technology. CNN is a commonly used approach for image analysis due to its ability to extract features from raw input images, and as such, will be the primary area of discussion in this study. Therefore, we have considered CNN as our discussion area in this study to diagnose ailments using medical imaging technology.

Modelling the impact of a material's mesostructure on device level performance typically requires access to 3D image data containing all the relevant information to define the geometry of the simulation domain. This image data must include sufficient contrast between phases to distinguish each material, be of high enough resolution to capture the key details, but also have a large enough field-of-view to be representative of the material in general. It is rarely possible to obtain data with all of these properties from a single imaging technique. In this paper, we present a method for combining information from pairs of distinct but complementary imaging techniques in order to accurately reconstruct the desired multi-phase, high resolution, representative, 3D images. Specifically, we use deep convolutional generative adversarial networks to implement super-resolution, style transfer and dimensionality expansion. To demonstrate the widespread applicability of this tool, two pairs of datasets are used to validate the quality of the volumes generated by fusing the information from paired imaging techniques. Three key mesostructural metrics are calculated in each case to show the accuracy of this method. Having confidence in the accuracy of our method, we then demonstrate its power by applying to a real data pair from a lithium ion battery electrode, where the required 3D high resolution image data is not available anywhere in the literature. We believe this approach is superior to previously reported statistical material reconstruction methods both in terms of its fidelity and ease of use. Furthermore, much of the data required to train this algorithm already exists in the literature, waiting to be combined. As such, our open-access code could precipitate a step change by generating the hard to obtain high quality image volumes necessary to simulate behaviour at the mesoscale.

How can a blood clot be removed from the brain without any major surgical intervention? How can a drug be delivered precisely into a diseased organ that is difficult to reach? Those are just two examples of the countless innovations envisioned by the researchers in the field of medical microrobotics. Tiny robots promise to fundamentally change future medical treatments: one day, they could move through patient's vasculature to eliminate malignancies, fight infections or provide precise diagnostic information entirely noninvasively. In principle, so the researchers argue, the circulatory system might serve as an ideal delivery route for the microrobots, since it reaches all organs and tissues in the body.

Imagine if researchers could use 8K premium resolution imaging techniques as seen on premium TVs to scan electron microscopy which is an essential equipment for material research. According to ScienceDaily, a new joint research time from both the Korea Institute of Materials Science, or KIMS, and POSTECH have applied deep learning in order to scan electron microscopy or SEM. This was in order to develop a super-resolution imaging technique which can help convert low-resolution electron backscattering diffraction, or EBSD, microstructure images that were obtained from other conventional analysis equipment into higher super-resolution images. The study findings were officially published in the npj Computational Materials. When it comes to modern-day materials research, SEM images actually play a huge role in developing new materials starting from microstructure visualization and characterization, as well as in the whole numerical material behavior analysis. AI has previously been used for a series of other health functions like AI being able to detect early stages of dementia.

The world's first high-resolution 3D image of a monkey brain has been revealed, in a breakthrough that could pave the way for treatments for human diseases including Parkinson's. A detailed map of a complete macaque monkey brain was created using fluorescent imaging techniques by a team from the Chinese Academy of Sciences in Beijing. The team used a new technique to show how nerve cells are organised and connected within the monkey brain at a'micron resolution'. The human brain comprises nearly a hundred billion nerve cells with delicate and complex connections, and while up to 17 times larger than that of a macaque, it is similar enough for comparisons to be made between the two, researchers claim. Until now, a mouse brain was the largest to be mapped, taking days to create a complete 3D image, but the new technique made it possible to move up to a macaque brain, which is about 200 times larger in volume than that of a mouse.

A representation of a neural network provides a backdrop to a fish larva's beating heart. The advent of deep learning, a powerful form of machine learning, has led to rapid advancements in areas such as speech recognition, visual object recognition, genomics and drug discovery. These methods are characterized by multiple processing layers that can tease out intricate patterns and structures in very large, complex data sets. Now, a team of European researchers has incorporated deep learning algorithms into a light-field microscope to enhance both its reconstruction speed and image quality (Nat. Methods, doi: 10.1038/s41592-021-01136-0). The results significantly extend the capabilities of light-field microscopy for whole-brain or whole-animal imaging of living specimens for biomedical research.

FREMONT, CA: Machine vision is one of the important additions to the manufacturing sector. It has provided automated inspection capabilities as part of QC procedures. Nevertheless, the world of automation is becoming more complex with time. With rapid developments in many different areas, such as imaging techniques, robot interfaces, CMOS sensors, machine and deep learning, embedded vision, data transmission standards, and image processing capabilities, vision technology can benefit the manufacturing industry at multiple different levels. New imaging techniques have brought new application opportunities.

Metal–air batteries, consisting of a metal anode and an air cathode, have been attracted significant interest by the research community as energy storage devices, because of their high energy density (in particular, compared to lithium ion batteries -LIBs-). A wide diversity of active metals can be used as anode material such as Li, Ca, Mg, Al, Fe, and Zn. However, they have so far found their use only in very particular markets requiring high energy density such as hearing aids. Indeed, despite very significant experimental research efforts, recharging them electrochemically constitute a significant challenge, that if unlocked, will pave the way to a wider diversity of ZAB applications such as Electric Vehicles. Reversing this process to recharge electrochemically a ZAB would imply a heterogeneous deposition of Zn in the anode and the formation of dendrites that can short-circuit the cell, similarly to what can happen in lithium metal batteries.