Machine Learning in Quantitative PET Imaging
This paper reviewed the machine learning-based studies for quantitative positron emission tomography (PET). Specifically, we summarized the recent developments of machine learning-based methods in PET attenuation correction and low-count PET reconstruction by listing and comparing the proposed methods, study designs and reported performances of the current published studies with brief discussion on representative studies. The contributions and challenges among the reviewed studies were summarized and highlighted in the discussion part followed by.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningSimilar Papers 제목 키워드 기반
Quantitative Phase Imaging and Artificial Intelligence: A Review
Recent advances in quantitative phase imaging (QPI) and artificial intelligence (AI) have opened up the possibility of an exciting frontier. The fast and label-free nature of QPI enables the rapid generation of large-sca…
BIG-bench Machine LearningContext encoding enables machine learning-based quantitative photoacoustics
Real-time monitoring of functional tissue parameters, such as local blood oxygenation, based on optical imaging could provide groundbreaking advances in the diagnosis and interventional therapy of various diseases. While…
BIG-bench Machine LearningAdvanced machine learning informatics modeling using clinical and radiological imaging metrics for characterizing breast tumor characteristics with the OncotypeDX gene array
Purpose-Optimal use of established and imaging methods, such as multiparametric magnetic resonance imaging(mpMRI) can simultaneously identify key functional parameters and provide unique imaging phenotypes of breast canc…
BIG-bench Machine LearningGeneral ClassificationSensitivitySpecificityHistomicsML2.0: Fast interactive machine learning for whole slide imaging data
Extracting quantitative phenotypic information from whole-slide images presents significant challenges for investigators who are not experienced in developing image analysis algorithms. We present new software that enabl…
BIG-bench Machine Learningwhole slide imagesDeriving a Quantitative Relationship Between Resolution and Human Classification Error
For machine learning perception problems, human-level classification performance is used as an estimate of top algorithm performance. Thus, it is important to understand as precisely as possible the factors that impact h…
AstronomyBIG-bench Machine LearningClassificationGeneral Classification