Synergistic Role of Quantitative Diffusion Magnetic Resonance Imaging and Structural Magnetic Resonance Imaging in Predicting Outcomes After Traumatic Brain Injury.

Objective: This study aimed to assess if quantitative diffusion magnetic resonance imaging analysis would improve prognostication of individual patients with severe traumatic brain injury.

Methods: We analyzed images of 30 healthy controls to extract normal fractional anisotropy ranges along 18 white-matter tracts. Then, we analyzed images of 33 patients, compared their fractional anisotropy values with normal ranges extracted from controls, and computed severity of injury to white-matter tracts.

3D MR Neurography.

High-resolution isotropic volumetric three-dimensional (3D) magnetic resonance neurography (MRN) techniques enable multiplanar depiction of peripheral nerves. In addition, 3D MRN provides anatomical and functional tissue characterization of different disease conditions affecting the peripheral nerves. In this review article, we summarize clinically relevant technical considerations of 3D MRN image acquisition and review clinical applications of 3D MRN to assess peripheral nerve diseases, such as entrapments, trauma, inflammatory or infectious neuropathies, and neoplasms.

Imaging Assessment of Thoracic Outlet Syndrome.

Imaging studies play a significant role in assessment of thoracic outlet syndrome. In this article, we discuss the etiology and definition of thoracic outlet syndrome and review the spectrum of imaging findings seen in patients with thoracic outlet syndrome. We then discuss an optimized technique for computed tomography and MRI of patients with thoracic outlet syndrome, based on the experience at our institution and present some representative examples.

Current Applications and Future Impact of Machine Learning in Radiology.

Recent advances and future perspectives of machine learning techniques offer promising applications in medical imaging. Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting. In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology.