Evaluating Brain Tumor Detection with Deep Learning Convolutional Neural Networks Across Multiple MRI Modalities.

Central Nervous System (CNS) tumors represent a significant public health concern due to their high morbidity and mortality rates. Magnetic Resonance Imaging (MRI) has emerged as a critical non-invasive modality for the detection, diagnosis, and management of brain tumors, offering high-resolution visualization of anatomical structures. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have shown potential in augmenting MRI-based diagnostic accuracy for brain tumor detection.

MR Imaging of Hemosiderin Deposition in the Ankle Joints of Patients with Haemophilia: The Contribution of a Multi-Echo Gradient-Echo Sequence-Correlation with Osteochondral Changes and the Number and Chronicity of Joint Bleeds.

We aim (a) to introduce an easy-to-perform multi-echo gradient-echo sequence (mGRE) for the detection of hemosiderin deposition in the ankle joints of boys with haemophilia (b) to explore the associations between the presence and severity of hemosiderin deposition and the other components of haemophilic arthropathy, the clinical score, and the number and chronicity of joint bleeds. An MRI of 41 ankle joints of 21 haemophilic boys was performed on a 3 T MRI system using an mGRE sequence in addition to the conventional protocol. Conventional MRI and mGRE were separately and independently assessed by three readers, namely, two musculoskeletal radiologists and a general radiologist for joint hemosiderin.

Left ventricular structural integrity on tetralogy of Fallot patients: approach using longitudinal relaxation time mapping.

Purpose: Tetralogy of Fallot (TOF) is a congenital heart disease, and patients undergo surgical repair early in their lives. The evaluation of TOF patients is continuous through their adulthood. The use of cardiac magnetic resonance imaging (CMR) is vital for the evaluation of TOF patients.