The learning objectives for this course are to review conventional B-mode ultrasound image generation and the physics of sound-tissue interaction, summarize, compare, and contrast diverse ultrasound pulse sequences encountered in clinical practice, and learn about advanced ultrasound techniques including quantitative imaging and contrast enhanced ultrasound.

The learning objectives for this course are to learn why specular reflection occurs and how to recognize this artifact, understand when mirror image artifacts may occur, and recognize entities in which refractive edge shadows may be critical for diagnosis.

The learning objectives for this course are to discuss an approach to CT protocol development and management, review a strategy for tailoring CT protocols to the clinical indication with case examples, and discuss an approach to imaging pregnant patients with CT.

The learning objectives for this course are to review conventional energy integrating detector CT physics, review photon counting CT detector physics, and review clinical applications of multienergy and photon counting CT.

The learning objectives for this course are to review the technical principles and challenges unique to DECT technology, discuss the interpretive pitfalls with DECT and strategies to address them, and describe the artifacts related to DECT and possible solutions to mitigate them.

The learning objectives for this course are to list common MR artifacts and ways to mitigate them, identify important updates on ACR 2023 MR Safety Manual, and list precautions to increase safety in the MR environment.

The learning objectives for this course are to review available MR contrast agents on the market, discuss medical and patient safety concerns for MR contrast agents, and review current and updated guidelines for the screening and administration of MR contrast agents.

The learning objectives for this course are to review new and advanced MR sequences that can accelerate imaging time, discuss MR sequences that can improve image quality and reduce image artifacts, and introduce AI applications for current and future MR sequences.