AI Medical Compendium Journal:
European journal of nuclear medicine and molecular imaging

Showing 71 to 80 of 112 articles

A deep learning framework for F-FDG PET imaging diagnosis in pediatric patients with temporal lobe epilepsy.

European journal of nuclear medicine and molecular imaging
PURPOSE: Epilepsy is one of the most disabling neurological disorders, which affects all age groups and often results in severe consequences. Since misdiagnoses are common, many pediatric patients fail to receive the correct treatment. Recently, F-fl...

True ultra-low-dose amyloid PET/MRI enhanced with deep learning for clinical interpretation.

European journal of nuclear medicine and molecular imaging
PURPOSE: While sampled or short-frame realizations have shown the potential power of deep learning to reduce radiation dose for PET images, evidence in true injected ultra-low-dose cases is lacking. Therefore, we evaluated deep learning enhancement u...

Improved amyloid burden quantification with nonspecific estimates using deep learning.

European journal of nuclear medicine and molecular imaging
PURPOSE: Standardized uptake value ratio (SUVr) used to quantify amyloid-β burden from amyloid-PET scans can be biased by variations in the tracer's nonspecific (NS) binding caused by the presence of cerebrovascular disease (CeVD). In this work, we p...

Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment.

European journal of nuclear medicine and molecular imaging
PURPOSE: Advanced medical image analytics is increasingly used to predict clinical outcome in patients diagnosed with gastrointestinal tumors. This review provides an overview on the value of radiomics in predicting response to treatment in patients ...

Systematic imaging in medicine: a comprehensive review.

European journal of nuclear medicine and molecular imaging
Systematic imaging can be broadly defined as the systematic identification and characterization of biological processes at multiple scales and levels. In contrast to "classical" diagnostic imaging, systematic imaging emphasizes on detecting the overa...

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
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...

Decoding COVID-19 pneumonia: comparison of deep learning and radiomics CT image signatures.

European journal of nuclear medicine and molecular imaging
PURPOSE: High-dimensional image features that underlie COVID-19 pneumonia remain opaque. We aim to compare feature engineering and deep learning methods to gain insights into the image features that drive CT-based for COVID-19 pneumonia prediction, a...

Visual interpretation of [F]Florbetaben PET supported by deep learning-based estimation of amyloid burden.

European journal of nuclear medicine and molecular imaging
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...

Whole-body voxel-based internal dosimetry using deep learning.

European journal of nuclear medicine and molecular imaging
PURPOSE: In the era of precision medicine, patient-specific dose calculation using Monte Carlo (MC) simulations is deemed the gold standard technique for risk-benefit analysis of radiation hazards and correlation with patient outcome. Hence, we propo...

The deep learning model combining CT image and clinicopathological information for predicting ALK fusion status and response to ALK-TKI therapy in non-small cell lung cancer patients.

European journal of nuclear medicine and molecular imaging
PURPOSE: This study aimed to investigate the deep learning model (DLM) combining computed tomography (CT) images and clinicopathological information for predicting anaplastic lymphoma kinase (ALK) fusion status in non-small cell lung cancer (NSCLC) p...