Name
Title | ||
|---|---|---|
Adaptive sampling of CT data for myocardial blood flow estimation from dose-reduced dynamic CT |
Abstract | ||
|---|---|---|
Quantification of myocardial blood flow (MBF) can aid in the diagnosis and treatment of coronary artery disease (CAD). However, there are no widely accepted clinical methods for estimating MBF. Dynamic CT holds the promise of providing a quick and easy method to measure MBF quantitatively, however the need for repeated scans has raised concerns about the potential for high radiation dose. In our previous work, we explored techniques to reduce the patient dose by either uniformly reducing the tube current or by uniformly reducing the number of temporal frames in the dynamic CT sequence. These dose reduction techniques result in very noisy data, which can give rise to large errors in MBF estimation. In this work, we seek to investigate whether non-uniformly varying the tube current or sampling intervals can yield more accurate MBF estimates. Specifically, we try to minimize the dose and obtain the most accurate MBF estimate through addressing the following questions: when in the time attenuation curve (TAC) should the CT data be collected and at what tube current(s). We hypothesize that increasing the sampling rate and/or tube current during the time frames when the myocardial CT number is most sensitive to the flow rate, while reducing them elsewhere, can achieve better estimation accuracy for the same dose. We perform simulations of contrast agent kinetics and CT acquisitions to evaluate the relative MBF estimation performance of several clinically viable adaptive acquisition methods. We found that adaptive temporal and tube current sequences can be performed that impart an effective dose of about 5 mSv and allow for reductions in MBF estimation RMSE on the order of 11% compared to uniform acquisition sequences with comparable or higher radiation doses. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1117/12.2082258 | Proceedings of SPIE |
Keywords | Field | DocType |
blood flow,radiation | Biomedical engineering,Sampling (signal processing),Mean squared error,Artificial intelligence,Effective dose (radiation),CAD,Computer vision,Blood flow,Adaptive sampling,Dose Reduced,Medical physics,Sampling (statistics),Physics | Conference |
Volume | ISSN | Citations |
9413 | 0277-786X | 1 |
PageRank | References | Authors |
0.43 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| dimple modgil | 1 | 2 | 1.14 |
| michael d bindschadler | 2 | 2 | 1.14 |
| Adam M. Alessio | 3 | 33 | 7.95 |
| patrick j la riviere | 4 | 3 | 2.20 |