[HTML][HTML] Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization

F Hu, AA Chen, H Horng, V Bashyam, C Davatzikos… - NeuroImage, 2023 - Elsevier
Magnetic resonance imaging and computed tomography from multiple batches (eg sites,
scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to …

Artificial intelligence in emergency radiology: where are we going?

M Cellina, M Cè, G Irmici, V Ascenti, E Caloro… - Diagnostics, 2022 - mdpi.com
Emergency Radiology is a unique branch of imaging, as rapidity in the diagnosis and
management of different pathologies is essential to saving patients' lives. Artificial …

[HTML][HTML] Machine learning-based radiomics for amyotrophic lateral sclerosis diagnosis

B Tafuri, G Milella, M Filardi, A Giugno… - Expert Systems with …, 2024 - Elsevier
Timely diagnosis and accurate phenotyping of amyotrophic lateral sclerosis (ALS) is of
paramount importance for the clinical management of patients. Magnetic Resonance …

ComBat harmonization for MRI radiomics: impact on nonbinary tissue classification by machine learning

D Leithner, RB Nevin, P Gibbs, M Weber… - Investigative …, 2023 - journals.lww.com
Objectives The aims of this study were to determine whether ComBat harmonization
improves multiclass radiomics-based tissue classification in technically heterogeneous MRI …

Efficacy of MRI data harmonization in the age of machine learning: a multicenter study across 36 datasets

C Marzi, M Giannelli, A Barucci, C Tessa, M Mascalchi… - Scientific Data, 2024 - nature.com
Pooling publicly-available MRI data from multiple sites allows to assemble extensive groups
of subjects, increase statistical power, and promote data reuse with machine learning …

Automatic diagnosis of Parkinson's disease using artificial intelligence base on routine T1-weighted MRI

C Li, D Hui, F Wu, Y Xia, F Shi, M Yang, J Zhang… - Frontiers in …, 2024 - frontiersin.org
Background Parkinson's disease (PD) is the second most common neurodegenerative
disease. An objective diagnosis method is urgently needed in clinical practice. In this study …

Position dependence of recovery coefficients in 177Lu-SPECT/CT reconstructions – phantom simulations and measurements

J Leube, W Claeys, J Gustafsson, M Salas-Ramirez… - EJNMMI physics, 2024 - Springer
Background Although the importance of quantitative SPECT has increased tremendously
due to newly developed therapeutic radiopharmaceuticals, there are still no accreditation …

Explainable machine learning radiomics model for Primary Progressive Aphasia classification

B Tafuri, R De Blasi, S Nigro… - Frontiers in Systems …, 2024 - frontiersin.org
Introduction Primary Progressive Aphasia (PPA) is a neurodegenerative disease
characterized by linguistic impairment. The two main clinical subtypes are semantic (svPPA) …

A review of handcrafted and deep radiomics in neurological diseases: transitioning from oncology to clinical neuroimaging

E Lavrova, HC Woodruff, H Khan, E Salmon… - arXiv preprint arXiv …, 2024 - arxiv.org
Medical imaging technologies have undergone extensive development, enabling non-
invasive visualization of clinical information. The traditional review of medical images by …

[HTML][HTML] White matter biomarker for predicting de novo Parkinson's disease using tract-based spatial statistics: a machine learning-based model

Q Zhang, H Wang, Y Shi, W Li - Quantitative Imaging in Medicine …, 2024 - ncbi.nlm.nih.gov
Background Parkinson's disease (PD) is an irreversible, chronic degenerative disease of the
central nervous system, potentially associated with cerebral white matter (WM) lesions …