Name
Abstract | ||
|---|---|---|
Machine learning has found unique applications in nuclear medicine from photon detection to quantitative image reconstruction. Although there have been impressive strides in detector development for time-of-flight positron emission tomography (PET), most detectors still make use of simple signal processing methods to extract the time and position information from the detector signals. Now, with the availability of fast waveform digitizers, machine learning techniques have been applied to estimate the position and arrival time of high-energy photons. In quantitative image reconstruction, machine learning has been used to estimate various corrections factors, including scattered events and attenuation images, as well as to reduce statistical noise in reconstructed images. Here, machine learning either provides a faster alternative to an existing time-consuming computation, such as in the case of scatter estimation, or creates a data-driven approach to map an implicitly defined function, such as in the case of estimating the attenuation map for PET/MR scans. In this article, we will review the above-mentioned applications of machine learning in nuclear medicine. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/JPROC.2019.2936809 | Proceedings of the IEEE |
Keywords | DocType | Volume |
Photonics,Detectors,Machine learning,Image reconstruction,Attenuation,Positrons,Timing | Journal | 108 |
Issue | ISSN | Citations |
1 | 0018-9219 | 1 |
PageRank | References | Authors |
0.36 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kuang Gong | 1 | 23 | 5.10 |
| Eric Berg | 2 | 1 | 0.70 |
| Simon R. Cherry | 3 | 6 | 3.17 |
| Jinyi Qi | 4 | 284 | 35.82 |