AIMC Topic: Positron-Emission Tomography
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Intraprostatic Tumor Segmentation on PSMA PET Images in Patients with Primary Prostate Cancer with a Convolutional Neural Network.
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Oct 30, 2020
Accurate delineation of the intraprostatic gross tumor volume (GTV) is a prerequisite for treatment approaches in patients with primary prostate cancer (PCa). Prostate-specific membrane antigen PET (PSMA PET) may outperform MRI in GTV detection. Howe...
Attenuation correction using deep Learning and integrated UTE/multi-echo Dixon sequence: evaluation in amyloid and tau PET imaging.
European journal of nuclear medicine and molecular imaging
Oct 27, 2020
PURPOSE: PET measures of amyloid and tau pathologies are powerful biomarkers for the diagnosis and monitoring of Alzheimer's disease (AD). Because cortical regions are close to bone, quantitation accuracy of amyloid and tau PET imaging can be signifi...
Visual interpretation of [F]Florbetaben PET supported by deep learning-based estimation of amyloid burden.
European journal of nuclear medicine and molecular imaging
Sep 29, 2020
PURPOSE: Amyloid PET which has been widely used for noninvasive assessment of cortical amyloid burden is visually interpreted in the clinical setting. As a fast and easy-to-use visual interpretation support system, we analyze whether the deep learnin...
Truncation compensation and metallic dental implant artefact reduction in PET/MRI attenuation correction using deep learning-based object completion.
Physics in medicine and biology
Sep 25, 2020
The susceptibility of MRI to metallic objects leads to void MR signal and missing information around metallic implants. In addition, body truncation occurs in MR imaging for large patients who exceed the transaxial field-of-view of the scanner. Body ...
Approximating anatomically-guided PET reconstruction in image space using a convolutional neural network.
NeuroImage
Sep 21, 2020
In the last two decades, it has been shown that anatomically-guided PET reconstruction can lead to improved bias-noise characteristics in brain PET imaging. However, despite promising results in simulations and first studies, anatomically-guided PET ...
Noise reduction with cross-tracer and cross-protocol deep transfer learning for low-dose PET.
Physics in medicine and biology
Sep 14, 2020
Previous studies have demonstrated the feasibility of reducing noise with deep learning-based methods for low-dose fluorodeoxyglucose (FDG) positron emission tomography (PET). This work aimed to investigate the feasibility of noise reduction for trac...
A machine learning framework with anatomical prior for online dose verification using positron emitters and PET in proton therapy.
Physics in medicine and biology
Sep 14, 2020
We developed a machine learning framework in order to establish the correlation between dose and activity distributions in proton therapy. A recurrent neural network was used to predict dose distribution in three dimensions based on the information o...
Artificial Intelligence and Machine Learning in Nuclear Medicine: Future Perspectives.
Seminars in nuclear medicine
Sep 12, 2020
Artificial intelligence and machine learning based approaches are increasingly finding their way into various areas of nuclear medicine imaging. With the technical development of new methods and the expansion to new fields of application, this trend ...
Improving depth-of-interaction resolution in pixellated PET detectors using neural networks.
Physics in medicine and biology
Aug 27, 2020
Parallax error is a common issue in high-resolution preclinical positron emission tomography (PET) scanners as well as in clinical scanners that have a long axial field of view (FOV), which increases estimation uncertainty of the annihilation positio...
A machine learning framework to trace tumor tissue-of-origin of 13 types of cancer based on DNA somatic mutation.
Biochimica et biophysica acta. Molecular basis of disease
Aug 7, 2020
Carcinoma of unknown primary (CUP), defined as metastatic cancers with unknown cancer origin, occurs in 3-5 per 100 cancer patients in the United States. Heterogeneity and metastasis of cancer brings great difficulties to the follow-up diagnosis and ...
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