An update on computational anthropomorphic anatomical models

The prevalent availability of high-performance computing coupled with validated computerized simulation platforms as open-source packages have motivated progress in the development of realistic anthropomorphic computational models of the human anatomy. The main application of these advanced tools focused on imaging physics and computational internal/external radiation dosimetry research. This paper provides an updated review of state-of-the-art developments and recent advances in the design of sophisticated computational models of the human anatomy with a particular focus on their use in radiation dosimetry calculations. The consolidation of flexible and realistic computational models with biological data and accurate radiation transport modeling tools enables the capability to produce dosimetric data reflecting actual setup in clinical setting. These simulation methodologies and results are helpful resources for the medical physics and medical imaging communities and are expected to impact the fields of medical imaging and dosimetry calculations profoundly.</p

High-Brightness Solid-State Lasers for Compact Short-Wavelength Sources

Various types of compact short-wavelength sources are emerging in the region from EUV to hard X-ray and further to gamma ray. These high-energy photons are usually accessible in a large-scale facility such as SR or FEL, and the compactness of these new technologies provides new possibilities for broader applications in dedicated laboratories or factories. Laser-produced plasma is used for soft X-ray laser and high average power EUV sources for lithography. Laser Compton short-wavelength sources are now entering into practical applications in medical imaging. The performance of these sources critically depends on the laser driver performance. This chapter describes the recent progress of high-brightness, short-pulse solid-state laser technology in close relation to these new compact short-wavelength sources. Pulsed picosecond thin disc laser progress is reviewed with kW average power specifications. Cryogenic laser is reported for the advantage of higher beam quality in large-pulse energy operation

Innovations in thoracic imaging:CT, radiomics, AI and x-ray velocimetry

In recent years, pulmonary imaging has seen enormous progress, with the introduction, validation and implementation of new hardware and software. There is a general trend from mere visual evaluation of radiological images to quantification of abnormalities and biomarkers, and assessment of 'non visual' markers that contribute to establishing diagnosis or prognosis. Important catalysts to these developments in thoracic imaging include new indications (like computed tomography [CT] lung cancer screening) and the COVID-19 pandemic. This review focuses on developments in CT, radiomics, artificial intelligence (AI) and x-ray velocimetry for imaging of the lungs. Recent developments in CT include the potential for ultra-low-dose CT imaging for lung nodules, and the advent of a new generation of CT systems based on photon-counting detector technology. Radiomics has demonstrated potential towards predictive and prognostic tasks particularly in lung cancer, previously not achievable by visual inspection by radiologists, exploiting high dimensional patterns (mostly texture related) on medical imaging data. Deep learning technology has revolutionized the field of AI and as a result, performance of AI algorithms is approaching human performance for an increasing number of specific tasks. X-ray velocimetry integrates x-ray (fluoroscopic) imaging with unique image processing to produce quantitative four dimensional measurement of lung tissue motion, and accurate calculations of lung ventilation

DeTraC: Transfer Learning of Class Decomposed Medical Images in Convolutional Neural Networks

Due to the high availability of large-scale annotated image datasets, paramount progress has been made in deep convolutional neural networks (CNNs) for image classification tasks. CNNs enable learning highly representative and hierarchical local image features directly from data. However, the availability of annotated data, especially in the medical imaging domain, remains the biggest challenge in the field. Transfer learning can provide a promising and effective solution by transferring knowledge from generic image recognition tasks to the medical image classification. However, due to irregularities in the dataset distribution, transfer learning usually fails to provide a robust solution. Class decomposition facilitates easier to learn class boundaries of a dataset, and consequently can deal with any irregularities in the data distribution. Motivated by this challenging problem, the paper presents Decompose, Transfer, and Compose (DeTraC) approach, a novel CNN architecture based on class decomposition to improve the performance of medical image classification using transfer learning and class decomposition approach. DeTraC enables learning at the subclass level that can be more separable with a prospect to faster convergence.We validated our proposed approach with three different cohorts of chest X-ray images, histological images of human colorectal cancer, and digital mammograms. We compared DeTraC with the state-of-the-art CNN models to demonstrate its high performance in terms of accuracy, sensitivity, and specificity

Stride: a flexible software platform for high-performance ultrasound computed tomography

BACKGROUND AND OBJECTIVE: Advanced ultrasound computed tomography techniques like full-waveform inversion are mathematically complex and orders of magnitude more computationally expensive than conventional ultrasound imaging methods. This computational and algorithmic complexity, and a lack of open-source libraries in this field, represent a barrier preventing the generalised adoption of these techniques, slowing the pace of research, and hindering reproducibility. Consequently, we have developed Stride, an open-source Python library for the solution of large-scale ultrasound tomography problems. METHODS: On one hand, Stride provides high-level interfaces and tools for expressing the types of optimisation problems encountered in medical ultrasound tomography. On the other, these high-level abstractions seamlessly integrate with high-performance wave-equation solvers and with scalable parallelisation routines. The wave-equation solvers are generated automatically using Devito, a domain-specific language, and the parallelisation routines are provided through the custom actor-based library Mosaic. RESULTS: We demonstrate the modelling accuracy achieved by our wave-equation solvers through a comparison (1) with analytical solutions for a homogeneous medium, and (2) with state-of-the-art modelling software applied to a high-contrast, complex skull section. Additionally, we show through a series of examples how Stride can handle realistic numerical and experimental tomographic problems, in 2D and 3D, and how it can scale robustly from a local multi-processing environment to a multi-node high-performance cluster. CONCLUSIONS: Stride enables researchers to rapidly and intuitively develop new imaging algorithms and to explore novel physics without sacrificing performance and scalability. This will lead to faster scientific progress in this field and will significantly ease clinical translation

Echocardiography curriculum development for physician assistants using entrustable professional activities

BACKGROUND: With the projected increase of cardiovascular disease in the aging population, a higher demand for echocardiography use is predicted. However, there is a shortage in the supply of cardiologists, to the point that a 2009 American College of Cardiology survey report called it a "cardiology workforce crisis". The report also recommends a more aggressive use of PAs and NPs as one of the solutions to fill the shortage. Currently, echocardiography is not routinely included in the scope of practice for PAs in cardiology. While PAs attain strong basic science knowledge and clinical training experience in PA school, they typically do not receive additional formal postgraduate training. PAs have limited training opportunities to train in echocardiography and receive certification of recognition, but a formally standardized training program and certifying examination geared specifically for PAs are yet to be developed. This study seeks to develop a pilot curriculum in training echocardiography which can be standardized for utilization across various regions and medical subspecialties. The curriculum draws on the concept of Entrustable Professional Activities (EPA), which is being actively used in graduate medical education. HYPOTHESIS: After participating in the proposed pilot curriculum which involves online didactic learning and supervised hands-on clinical training, trained PAs will be able to reach proficiency in echocardiography operation and interpretation at level 4 supervision according to the EPA guidelines. METHODS: This study proposes a pilot curriculum with framework based on the EPA titled “performing and interpreting echocardiography” by PAs. The curriculum involves didactic and clinical training in echocardiography, with the goal to achieve mastery of level 4 supervision (minimal supervision). 2 subjects will be recruited from a teaching medical institution in the Greater Boston area with an IAC accredited echocardiography laboratory. After the 12-month training, participants will take ASCeXAM/ReASCE Online Practice Exam Simulation offered by the ASE. Upon 1) achievement of individualized EPAs as assessed by supervisor, and 2) simulation exam score of >80%, participants will earn a STAR in echocardiography. CONCLUSION: The study is the first step to establishing an effective training curriculum that will eventually be a basis for creating a certifying exam in echocardiography, designed specifically for PAs. As this study merely suggests a new curriculum, future studies should focus on identifying strengths and weaknesses of the curriculum after implementation and expansion to multiple sites, and gather data to use for continual improvement of the training curriculum