Abstract:
:Positron emission tomography (PET) imaging is an effective tool used in determining disease stage and lesion malignancy; however, radiation exposure to patients and technicians during PET scans continues to draw concern. One way to minimize radiation exposure is to reduce the dose of radioactive tracer administered in order to obtain the scan. Yet, low-dose images are inherently noisy and have poor image quality making them difficult to read. This paper proposes the use of a deep learning model that takes specific image features into account in the loss function to denoise low-dose PET image slices and estimate their full-dose image quality equivalent. Testing on low-dose image slices indicates a significant improvement in image quality that is comparable to the ground truth full-dose image slices. Additionally, this approach can lower the cost of conducting a PET scan since less radioactive material is required per scan, which may promote the usage of PET scans for medical diagnosis.
journal_name
J Digit Imagingjournal_title
Journal of digital imagingauthors
Kaplan S,Zhu YMdoi
10.1007/s10278-018-0150-3subject
Has Abstractpub_date
2019-10-01 00:00:00pages
773-778issue
5eissn
0897-1889issn
1618-727Xpii
10.1007/s10278-018-0150-3journal_volume
32pub_type
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