4,845 research outputs found
A focused ultrasound treatment system for moving targets (part I):generic system design and in-silico first-stage evaluation
Background
Focused ultrasound (FUS) is entering clinical routine as a treatment option. Currently, no clinically available FUS treatment system features automated respiratory motion compensation. The required quality standards make developing such a system challenging.
Methods
A novel FUS treatment system with motion compensation is described, developed with the goal of clinical use. The system comprises a clinically available MR device and FUS transducer system. The controller is very generic and could use any suitable MR or FUS device. MR image sequences (echo planar imaging) are acquired for both motion observation and thermometry. Based on anatomical feature tracking, motion predictions are estimated to compensate for processing delays. FUS control parameters are computed repeatedly and sent to the hardware to steer the focus to the (estimated) target position. All involved calculations produce individually known errors, yet their impact on therapy outcome is unclear. This is solved by defining an intuitive quality measure that compares the achieved temperature to the static scenario, resulting in an overall efficiency with respect to temperature rise. To allow for extensive testing of the system over wide ranges of parameters and algorithmic choices, we replace the actual MR and FUS devices by a virtual system. It emulates the hardware and, using numerical simulations of FUS during motion, predicts the local temperature rise in the tissue resulting from the controls it receives.
Results
With a clinically available monitoring image rate of 6.67 Hz and 20 FUS control updates per second, normal respiratory motion is estimated to be compensable with an estimated efficiency of 80%. This reduces to about 70% for motion scaled by 1.5. Extensive testing (6347 simulated sonications) over wide ranges of parameters shows that the main source of error is the temporal motion prediction. A history-based motion prediction method performs better than a simple linear extrapolator.
Conclusions
The estimated efficiency of the new treatment system is already suited for clinical applications. The simulation-based in-silico testing as a first-stage validation reduces the efforts of real-world testing. Due to the extensible modular design, the described approach might lead to faster translations from research to clinical practice
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Recent advancement in Disease Diagnostic using machine learning: Systematic survey of decades, comparisons, and challenges
Computer-aided diagnosis (CAD), a vibrant medical imaging research field, is
expanding quickly. Because errors in medical diagnostic systems might lead to
seriously misleading medical treatments, major efforts have been made in recent
years to improve computer-aided diagnostics applications. The use of machine
learning in computer-aided diagnosis is crucial. A simple equation may result
in a false indication of items like organs. Therefore, learning from examples
is a vital component of pattern recognition. Pattern recognition and machine
learning in the biomedical area promise to increase the precision of disease
detection and diagnosis. They also support the decision-making process's
objectivity. Machine learning provides a practical method for creating elegant
and autonomous algorithms to analyze high-dimensional and multimodal
bio-medical data. This review article examines machine-learning algorithms for
detecting diseases, including hepatitis, diabetes, liver disease, dengue fever,
and heart disease. It draws attention to the collection of machine learning
techniques and algorithms employed in studying conditions and the ensuing
decision-making process
Aerospace medicine and biology: A continuing bibliography with indexes, supplement 183
This bibliography lists 273 reports, articles, and other documents introduced into the NASA scientific and technical information system in July 1978
Recent Advances in Machine Learning Applied to Ultrasound Imaging
Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed. © 2022 by the authors. Licensee MDPI, Basel, Switzerland
Aerospace medicine and biology: A continuing bibliography with indexes, supplement 128, May 1974
This special bibliography lists 282 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1974
Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 127, April 1974
This special bibliography lists 279 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1974
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