Results 1 to 10 of about 43,566 (284)
Robust imaging habitat computation using voxel-wise radiomics features [PDF]
Scientific Reports, 2021 Tumor heterogeneity has been postulated as a hallmark of treatment resistance and a cure constraint in cancer patients. Conventional quantitative medical imaging (radiomics) can be extended to computing voxel-wise features and aggregating tumor ...
Kinga Bernatowicz +5 more
doaj +2 more sources
The transformational potential of molecular radiomics [PDF]
Journal of Medical Radiation Sciences, 2023 Conventional radiomics in nuclear medicine involve hand‐crafted and computer‐assisted regions of interest. Recent developments in artificial intelligence (AI) have seen the emergence of AI‐augmented segmentation and extraction of lower order traditional ...
Geoffrey Currie +2 more
doaj +2 more sources
British Journal of Radiology, 2020 Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes, As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information ...
Robert J. Gillies +2 more
+6 more sources
KYAMC Journal, 2019 Abstract not available KYAMC Journal Vol.
Farzad Khalvati +3 more
+6 more sources
Introduction to Radiomics [PDF]
Journal of Nuclear Medicine, 2020 Radiomics is a rapidly evolving field of research concerned with the extraction of quantitative metrics-the so-called radiomic features-within medical images. Radiomic features capture tissue and lesion characteristics such as heterogeneity and shape and may, alone or in combination with demographic, histologic, genomic, or proteomic data, be used for ...
Marius E, Mayerhoefer +6 more
openaire +2 more sources
, 2018 Recent advances in image-guided and adaptive radiotherapy have ushered new requirements for using single and/or multiple-imaging modalities in staging, treatment planning, and predicting response of different cancer types. Quantitative information analysis from multi-imaging modalities, known as ‘radiomics', have generated great promises to unravel ...
Julie Constanzo, Issam El Naqa
+4 more sources
emilywavery/Radiomics-data-sharing: Raw radiomics data release 6/14/22 V2 [PDF]
, 2022 In this release, we share the raw radiomics data corresponding to the 5 datasets described in Avery et al., 2022 (https://doi.org/10.1016/j.nicl.2022.103034).
emilywavery
core +1 more source
ERJ Open Research, 2022 Purpose In this study, we propose an artificial intelligence (AI) framework based on three-dimensional convolutional neural networks to classify computed tomography (CT) scans of patients with coronavirus disease 2019 (COVID-19), influenza/community ...
Akshayaa Vaidyanathan +14 more
doaj +1 more source
Multispectral Differential Reconstruction Strategy for Bioluminescence Tomography
Frontiers in Oncology, 2022 Bioluminescence tomography (BLT) is a promising in vivo molecular imaging tool that allows non-invasive monitoring of physiological and pathological processes at the cellular and molecular levels.
Yanqiu Liu +15 more
doaj +1 more source
Frontiers in Oncology, 2021 Cerenkov luminescence tomography (CLT) is a promising non-invasive optical imaging method with three-dimensional semiquantitative in vivo imaging capability.
Xiao Wei +12 more
doaj +1 more source