Quantitative imaging of coronary blood flow
Bibliographic record
Abstract
Quantitative imaging of coronary blood flowAll authorsAdam M. Alessio, Erik Butterworth, James H. Caldwell & James B. Bassingthwaightehttps://doi.org/10.3402/nano.v1i0.5110Published online:02 April 2010Display full size Adam M. Alessio received his PhD in Electrical Engineering from the University of Notre Dame in 2003. During his graduate studies he developed tomographic reconstruction methods for correlated data and helped construct a high-resolution PET system. He is currently a Research Assistant Professor in Radiology at the University of Washington. His research interests focus on improved data processing and reconstruction algorithms for PET/CT systems with an emphasis on quantitative imaging. Quantitative imaging of coronary blood flowAll authorsAdam M. Alessio, Erik Butterworth, James H. Caldwell & James B. Bassingthwaightehttps://doi.org/10.3402/nano.v1i0.5110Published online:02 April 2010Display full size Erik Butterworth recieved the BA degree in Mathematics from the University of Chicago in 1977. Between 1977 and 1987 he worked as a computer programmer/analyst for several small commercial software firms. Since 1988, he has worked as a software engineer on various research projects at the University of Washington. Between 1988 and 1993 he developed a real-time data aquisition for the analysis of estuarine sediment transport in the department of Geophysics. Between 1988 and 2002 he developed I4, a system for the display and analysis of cardic PET images in the department of Cardiology. Since 1993 he has worked on physiological simulation systems (XSIM from 1993 to 1999, JSim since 1999) at the National Simulation Resource Facility in Cirulatory Mass Transport and Exchange, in the Department of Bioengineering. His research interests include simulation systems and medical imaging. Quantitative imaging of coronary blood flowAll authorsAdam M. Alessio, Erik Butterworth, James H. Caldwell & James B. Bassingthwaightehttps://doi.org/10.3402/nano.v1i0.5110Published online:02 April 2010Display full size James H. Caldwell, MD, University of Missouri-Columbia 1970, is Professor of Medicine (Cardiology) and Radiology and Adjunct Professor of Bioengineering at the University of Washington School of Medicine and Acting Head, Division of Cardiology and Director of Nuclear Cardiology for the University of Washington Hospitals, Seattle WA, USA. Quantitative imaging of coronary blood flowAll authorsAdam M. Alessio, Erik Butterworth, James H. Caldwell & James B. Bassingthwaightehttps://doi.org/10.3402/nano.v1i0.5110Published online:02 April 2010Display full size James B. Bassingthwaighte, MD, Toronto 1955, PhD Mayo Grad Sch Med 1964, was Professor of Physiology and of Medicine at Mayo Clinic until 1975 when he moved to the University of Washington to chair Bioengineering. He is Professor of Bioengineering and Radiology. In 1979, he established a National Simulation Resource Facility in Circulatory Mass Transport and Exchange and in 1997, he initiated the Human Physiome Projects. He is a member of the US National Academy of Engineering. His research is on quantitative integration of cellular and cardiovascular systems.Positron emission tomography (PET) is a nuclear medicine imaging modality based on the administration of a positron-emitting radiotracer, the imaging of the distribution and kinetics of the tracer, and the interpretation of the physiological events and their meaning with respect to health and disease. PET imaging was introduced in the 1970s and numerous advances in radiotracers and detection systems have enabled this modality to address a wide variety of clinical tasks, such as the detection of cancer, staging of Alzheimer's disease, and assessment of coronary artery disease (CAD). This review provides a description of the logic and the logistics of the processes required for PET imaging and a discussion of its use in guiding the treatment of CAD. Finally, we outline prospects and limitations of nanoparticles as agents for PET imaging.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".