Investigation of PET image quality with acquisition time/bed and enhancement of lesion quantification accuracy through deep progressive learning.
Hongxing YangShihao ChenMing QiWen ChenQing KongJianping ZhangShaoli SongPublished in: EJNMMI physics (2024)
The DPL algorithm dramatically enhanced the quality of PET images and enabled more accurate quantification of sub-centimeters lesions in patients and lesions in overweight or obese patients. This is particularly beneficial for overweight or obese patients who usually have lower image quality due to the increased attenuation.
Keyphrases
- image quality
- obese patients
- computed tomography
- weight loss
- bariatric surgery
- positron emission tomography
- roux en y gastric bypass
- dual energy
- ejection fraction
- physical activity
- deep learning
- end stage renal disease
- gastric bypass
- newly diagnosed
- multiple sclerosis
- pet ct
- weight gain
- machine learning
- adipose tissue
- type diabetes
- metabolic syndrome
- prognostic factors
- high resolution
- convolutional neural network
- pet imaging
- body mass index
- magnetic resonance imaging
- neural network