Publications by authors named "F L Lizzi"
Article Synopsis
- Researchers have developed a Physics Informed Neural Network (Myo-DINO) to improve Magnetic Resonance Imaging (mMRI) by efficiently mapping MR parameters like Fat Fraction and water-T in patients with Neuromuscular Disorders (NMDs).
- The study utilized a dataset of 2165 images from Multi-Echo Spin Echo (MESE) scans, where ground truth maps were derived using the MyoQMRI toolbox based on signal evolution theories.
- The Myo-DINO model incorporated unique physics-based loss functions to enhance accuracy, adjusting hyperparameters to balance the influence of physics and standard loss functions during training.
Background: Root canal irrigation endodontic solutions have effects on the chemistry of dentin. Infrared spectroscopy is a non-destructive chemical characterization method where the strength of absorption often correlates with mineral or organic composition.
Objectives: To survey effects of commonly used irrigation solutions on the composition of root dentin as detected by widely-available Fourier transform infrared spectroscopy (FTIR) methods.
. Radiomics is a promising valuable analysis tool consisting in extracting quantitative information from medical images. However, the extracted radiomics features are too sensitive to variations in used image acquisition and reconstruction parameters.
View Article and Find Full Text PDFBackground: The integration of the information encoded in multiparametric MRI images can enhance the performance of machine-learning classifiers. In this study, we investigate whether the combination of structural and functional MRI might improve the performances of a deep learning (DL) model trained to discriminate subjects with Autism Spectrum Disorders (ASD) with respect to typically developing controls (TD).
Material And Methods: We analyzed both structural and functional MRI brain scans publicly available within the ABIDE I and II data collections.