Online Fellowship
9 CME Credits
·
Level III
Female Pelvis MRI Fellowship Part 1
This 3-day online fellowship (part 1) covers key topics related to MRI of the female pelvis and provides basic and advanced knowledge. We will discuss MRI findings of the normal a female pelvis as well as benign and malignant gynaecological conditions. This will be achieved through lectures and review on selected cases with subsequent group discussion.
5 - 7 October 2021
€1000
Bank transfer available – please email customer service for details at academy.radiology@unilabs.com
Description
Radiologists (to-be) who would like to become more confident on reading female pelvis MRI will find the fellowship useful.
The Online Female Pelvis MRI fellowship that TMC academy offers includes an overview of the main indications to perform MRI of the female pelvis: Diagnosis of benign and malignant conditions, staging (local and systemic) of uterine and ovarian malignancies, follow up and detection of tumour recurrence.
Each module starts with a lecture. After the lecture you will be challenged with practicing on real anonymised key cases from the TMC academy teaching file. After the individual case readings there will be an additional lecture followed by a ‘Questions and Answers’ session.
Learning Objectives
- Upgrade your skills in the interpretation of female pelvis MRI by immersion in a learning environment without distraction of daily practice.
- Become familiar and feel confident with basic and advanced MRI female pelvis as encountered in daily practice.
- Discriminate common variants from pathology to avoid pitfalls
- Learn to construct an adequate report through structured reading
Level
Level III - Subspecialisation training
Radiologists with little or with early advanced knowledge in female pelvis MRI will benefit most from the fellowship.
Technical Requirements
| Hardware | Tablets * | Minimum | Recommended |
|---|---|---|---|
| Memory (RAM): | 2 Gigabyte | 8 Gigabyte | 16 Gigabyte |
| Processor (CPU): | Dual core 1.85 Ghz | Dual core 2 Ghz | Quad core 2.5 Ghz |
| Internet connection | Minimum | Recommended | |
| Speed: | 10 Mbps | 25 Mbps | |
| Software | Tablets | Desktop | |
| Browser: | Safari * | Chrome ** | |
- * Tested with Safari on iPad 9.7 (2017), should also work on Android with Chrome. User interface not optimized for smaller screens. Large cases (more than 600 images) are not able to be opened on tablet or mobile devices due to memory consTableRowaints.
- ** Firefox, Edge and Safari also work but might not provide an equally smooth experience. Internet Explorer is not supported.
Lecturers
Susan Freeman M.D.
United Kingdom, Cambridge
Consultant Radiologist and Lead for Gynaecological Imaging at Cambridge University Hospitals, Cambridge, UK
Dr Susan Freeman has delivered numerous invited lectures in gynaecological imaging including at ESUR and has co-organised several national and international teaching courses and workshops. She is an outstanding educator and an excellent teacher having trained several radiology trainees and European School of Radiology Fellows.
Evis Sala M.D. Ph.D.
Italy, Rome
Evis Sala is Professor of Radiology and Director of the Radiology Training Program at Università Cattolica del Sacro Cuore, and Chair of Diagnostic Imaging and Radiotherapy at Fondazione Policlinico Universitario Agostino Gemelli IRCCS in Rome. She previously held senior roles at the University of Cambridge, Memorial Sloan Kettering Cancer Center, and Weill Cornell Medical College.
Her research focuses on integrating quantitative imaging with multi-omics and AI for cancer characterization. Dr. Sala has received multiple honors, including RSNA Honored Educator Awards, Fellowship of ISMRM, and honorary memberships from RSNA and the Japanese Society of Radiology.
Evis Sala is Professor of Radiology and Director of the Radiology Training Program at Università Cattolica del Sacro Cuore, and Chair of Diagnostic Imaging and Radiotherapy at Fondazione Policlinico Universitario Agostino Gemelli IRCCS in Rome. She previously held senior roles at the University of Cambridge, Memorial Sloan Kettering Cancer Center, and Weill Cornell Medical College. Her research focuses on integrating quantitative imaging with multi-omics and AI for cancer characterization. Dr. Sala has received multiple honors, including RSNA Honored Educator Awards, Fellowship of ISMRM, and honorary memberships from RSNA and the Japanese Society of Radiology.
