AI applications to medical images: From machine learning to deep learning

I Castiglioni, L Rundo, M Codari, G Di Leo, C Salvatore… - Physica medica, 2021 - Elsevier
Purpose Artificial intelligence (AI) models are playing an increasing role in biomedical
research and healthcare services. This review focuses on challenges points to be clarified …

Repeatability of multiparametric prostate MRI radiomics features

M Schwier, J Van Griethuysen, MG Vangel, S Pieper… - Scientific reports, 2019 - nature.com
In this study we assessed the repeatability of radiomics features on small prostate tumors
using test-retest Multiparametric Magnetic Resonance Imaging (mpMRI). The premise of …

Dynamic contrast-enhanced (DCE) MRI

X Li, W Huang, JH Holmes - Magnetic Resonance Imaging …, 2024 - mri.theclinics.com
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[HTML][HTML] Improving measurement of blood-brain barrier permeability with reduced scan time using deep-learning-derived capillary input function

J Bae, C Li, A Masurkar, Y Ge, SG Kim - NeuroImage, 2023 - Elsevier
Abstract Purpose In Dynamic contrast-enhanced MRI (DCE-MRI), Arterial Input Function
(AIF) has been shown to be a significant contributor to uncertainty in the estimation of kinetic …

Predictive value of quantitative 18F-FDG-PET radiomics analysis in patients with head and neck squamous cell carcinoma

RM Martens, T Koopman, DP Noij, E Pfaehler… - EJNMMI research, 2020 - Springer
Background Radiomics is aimed at image-based tumor phenotyping, enabling application
within clinical-decision-support-systems to improve diagnostic accuracy and allow for …

Imaging vascular and hemodynamic features of the brain using dynamic susceptibility contrast and dynamic contrast enhanced MRI

CC Quarles, LC Bell, AM Stokes - Neuroimage, 2019 - Elsevier
In the context of neurologic disorders, dynamic susceptibility contrast (DSC) and dynamic
contrast enhanced (DCE) MRI provide valuable insights into cerebral vascular function …

Effects of arterial input function selection on kinetic parameters in brain dynamic contrast-enhanced MRI

VC Keil, B Mädler, J Gieseke, R Fimmers… - Magnetic resonance …, 2017 - Elsevier
Purpose Kinetic parameters derived from dynamic contrast-enhanced MRI (DCE-MRI) were
suggested as a possible instrument for multi-parametric lesion characterization, but have not …

The use of quantitative imaging in radiation oncology: a quantitative imaging network (QIN) perspective

RH Press, HKG Shu, H Shim, JM Mountz… - International Journal of …, 2018 - Elsevier
Modern radiation therapy is delivered with great precision, in part by relying on high-
resolution multidimensional anatomic imaging to define targets in space and time. The …

[HTML][HTML] Multiparameter MRI predictors of long-term survival in glioblastoma multiforme

O Stringfield, JA Arrington, SK Johnston… - …, 2019 - pmc.ncbi.nlm.nih.gov
Standard-of-care multiparameter magnetic resonance imaging (MRI) scans of the brain were
used to objectively subdivide glioblastoma multiforme (GBM) tumors into regions that …

DCE-MRI perfusion and permeability parameters as predictors of tumor response to CCRT in patients with locally advanced NSCLC

X Tao, L Wang, Z Hui, L Liu, F Ye, Y Song, Y Tang… - Scientific reports, 2016 - nature.com
In this prospective study, 36 patients with stage III non-small cell lung cancers (NSCLC), who
underwent dynamic contrast-enhanced MRI (DCE-MRI) before concurrent chemo …