AIMC Topic: Magnetic Resonance Imaging

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Deep Learning-Driven Abbreviated Shoulder MRI Protocols: Diagnostic Accuracy in Clinical Practice.

Tomography (Ann Arbor, Mich.)
BACKGROUND: Deep learning (DL) reconstruction techniques have shown promise in reducing MRI acquisition times while maintaining image quality. However, the impact of different acceleration factors on diagnostic accuracy in shoulder MRI remains unexpl...

Assessment of AI-accelerated T2-weighted brain MRI, based on clinical ratings and image quality evaluation.

European journal of radiology
OBJECTIVE: To compare clinical ratings and signal-to-noise ratio (SNR) measures of a commercially available Deep Learning-based MRI reconstruction method (T2) against conventional T2- turbo spin echo brain MRI (T2).

Quantitative Ischemic Lesions of Portable Low-Field Strength MRI Using Deep Learning-Based Super-Resolution.

Stroke
BACKGROUND: Deep learning-based synthetic super-resolution magnetic resonance imaging (SynthMRI) may improve the quantitative lesion performance of portable low-field strength magnetic resonance imaging (LF-MRI). The aim of this study is to evaluate ...

Use of Artificial Intelligence on Imaging and Preoperatory Planning of the Knee Joint: A Scoping Review.

Medicina (Kaunas, Lithuania)
: This scoping review explores the current state of the art of AI-based applications in the field of orthopedics, focusing on its implementation in diagnostic imaging and preoperative planning of knee joint procedures. : The search was carried out us...

A multimodal MRI-based machine learning framework for classifying cognitive impairment in cerebral small vessel disease.

Scientific reports
The heterogeneity of cerebral small vessel disease (CSVD) with mild cognitive impairment (MCI) presents a challenge for diagnosis and classification. This study aims to propose a multimodal magnetic resonance imaging (MRI)-based machine learning fram...

Deep learning-based acceleration of muscle water T2 mapping in patients with neuromuscular diseases by more than 50% - translating quantitative MRI from research to clinical routine.

PloS one
BACKGROUND: Quantitative muscle water T2 (T2w) mapping is regarded as a biomarker for disease activity and response to treatment in neuromuscular diseases (NMD). However, the implementation in clinical settings is limited due to long scanning times a...

Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application.

BMC psychiatry
BACKGROUND: Early identification of Schizophrenia Spectrum Disorder (SSD) is crucial for effective intervention and prognosis improvement. Previous neuroimaging-based classifications have primarily focused on chronic, medicated SSD cohorts. However, ...

A radiomics approach to distinguish Progressive Supranuclear Palsy Richardson's syndrome from other phenotypes starting from MR images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Progressive Supranuclear Palsy (PSP) is an uncommon neurodegenerative disorder with different clinical onset, including Richardson's syndrome (PSP-RS) and other variant phenotypes (vPSP). Recognising the clinical progression...

Longitudinal brain age in first-episode mania youth treated with lithium or quetiapine.

European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology
It is unclear if lithium and quetiapine have neuroprotective effects, especially in early stages of bipolar and schizoaffective disorders. Here, an age-related multivariate brain structural measure (i.e., brain-PAD) at baseline and changes in respons...

Hybrid of DSR-GAN and CNN for Alzheimer disease detection based on MRI images.

Scientific reports
In this paper, we propose a deep super-resolution generative adversarial network (DSR-GAN) combined with a convolutional neural network (CNN) model designed to classify four stages of Alzheimer's disease (AD): Mild Dementia (MD), Moderate Dementia (M...