AIMC Topic: Magnetic Resonance Imaging

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Accelerated 3D qCEST of the Spine in a Porcine Model Using MR Multitasking at 3T.

NMR in biomedicine
To assess lower back pain using quantitative chemical exchange saturation transfer (qCEST) imaging in a porcine model by comparing exchange rate maps obtained from multitasking qCEST with conventional qCEST. Use a permuted random forest (PRF) model t...

Improving Clinically Significant Prostate Cancer Detection with a Multimodal Machine Learning Approach: A Large-Scale Multicenter Study.

Radiology. Imaging cancer
Purpose To develop and prospectively validate a clinical and radiologic model to predict clinically significant prostate cancer (csPCa) using biparametric MRI (bpMRI). Materials and Methods Retrospective data (acquired before March 31, 2022) from 12 ...

HSGDNet: Hybrid Synthetic-Data-Guided Deep Learning With NLS Refinement for Fast Multi-Component T1ρ Knee Mapping.

NMR in biomedicine
Multi-component T1ρ mapping of the knee joint using nonlinear least squares (NLS)-based methods is usually a computationally intensive task, limiting its application to only a few voxels in the knee joint. Deep learning (DL) is a computationally fast...

Accurate Paediatric Brain Tumour Classification Through Improved Quantitative Analysis of H MR Imaging and Spectroscopy.

NMR in biomedicine
Multimodality imaging is an emerging research topic in neuro-oncology for its potential of being able to demonstrate tumours in a more comprehensive manner. Diffusion-weighted magnetic resonance imaging (dMRI) and proton magnetic resonance spectrosco...

Simultaneous T and ADC Mapping of Acute-to-Chronic Ischemic Stroke With Multiple Overlapping-Echo Detachment Imaging.

NMR in biomedicine
Multiparametric quantitative MRI based on multiple overlapping-echo detachment imaging (MQMOLED) can simultaneously quantify T and ADC with whole brain coverage within 40 s. T and ADC play an important role in the assessment and management of ischemi...

Post-hoc eXplainable AI methods for analyzing medical images of gliomas (- A review for clinical applications).

Computers in biology and medicine
Deep learning (DL) has shown promise in glioma imaging tasks using magnetic resonance imaging (MRI) and histopathology images, yet their complexity demands greater transparency in artificial intelligence (AI) systems. This is noticeable when users mu...

Prediction of Early Neoadjuvant Chemotherapy Response of Breast Cancer through Deep Learning-based Pharmacokinetic Quantification of DCE MRI.

Radiology. Artificial intelligence
Purpose To improve the generalizability of pathologic complete response prediction following neoadjuvant chemotherapy using deep learning-based retrospective pharmacokinetic quantification of early treatment dynamic contrast-enhanced MRI. Materials a...

Dynamic abdominal MRI image generation using cGANs: A generalized model for various breathing patterns with extensive evaluation.

Computers in biology and medicine
Organ motion is a limiting factor during the treatment of abdominal tumors. During abdominal interventions, medical images are acquired to provide guidance, however, this increases operative time and radiation exposure. In this paper, conditional gen...

Artificial intelligence in cardiac sarcoidosis: ECG, Echo, CPET and MRI.

Current opinion in pulmonary medicine
PURPOSE OF REVIEW: Cardiac sarcoidosis is a form of inflammatory cardiomyopathy that varies in its clinical presentation. It is associated with significant clinical complications such as high-degree atrioventricular block, ventricular tachycardia, he...

Improving risk assessment of local failure in brain metastases patients using vision transformers - A multicentric development and validation study.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: This study investigates the use of Vision Transformers (ViTs) to predict Freedom from Local Failure (FFLF) in patients with brain metastases using pre-operative MRI scans. The goal is to develop a model that enhances risk stra...