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Multi-modal ct imaging learning center
The Multi-Modal CT Imaging Learning Center is a self-paced series of modules designed to strengthen skills in interpreting CT, CTA, and CT perfusion for stroke care. Each module guides clinicians through key imaging concepts and case examples, followed by a post-test to reinforce learning. This flexible, on-demand format allows learners to study at their own pace while building the expertise needed for timely, evidence-based treatment decisions.
Participants who register for the on-demand Multi-Modal CT Imaging Learning Center will receive three months of unlimited access. Learners are awarded 8 continuing education credits upon successful completion of the program.
| MODULE 1: NON-CONTRAST CT | MODULE 2: CTA & CTP |
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| PART A: Introduction to Computed Tomography (CT) Interpretation | PART A: ASPECTS Scoring |
| PART B: The Normal CT | PART B: Normal Vascular Anatomy on CT Angiography (CTA) |
| PART C: Ischemic Stroke | PART C: Abnormal Vascular Findings on CTA |
| PART D: Hemorrhagic Stroke | PART D: CT Perfusion (CTP) Imaging |
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- Multi-Modal CT Imaging Learning Center Member Price: $160.00
- Multi-Modal CT Imaging Learning Center Non-Member Price: $260.00
*Annual cost of ANVC Membership is $75.00 USD
ANVC's Refund/Cancellation policy is as follows:
Cancellation 30 days prior to event = Full Refund
Cancellation 15 days prior to event = 50% Refund
Cancellation less than 14 days prior to event = 0 Refund
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In support of improving patient care, Arizona State University is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC) to provide continuing education for the healthcare team. |
| This activity was planned by and for the healthcare team, and learners will receive a total of 1 credit hour per activity session of Interprofessional Continuing Education (IPCE) credits for learning and change. |
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The Multi-Modal CT Imaging Learning Center was made possible by an unrestricted educational grant from Viz.ai |
