BNMS Spring Meeting 2024
Visit the Hermes Medical Solutions booth #28 at the BNMS to find out how the latest MDR-approved Hermia products for NM, SPECT/CT and PET/CT analysis, reconstruction and reporting can set your department free. You can also take a sneak peek at what is coming next within Hermia at the BNMS IDUG MRT Workshop on Monday 13th May and at the BNMS Cutting Edge Research session on Tuesday 14th May.
Monday 13th May, 11.15–13.00 and 14.00–15.30, in Room 3
Molecular Radiotherapy Dosimetry Training Sessions
This is the 7th annual IDUG MRT Workshop, hosted by the BNMS. This year’s workshop will focus on building foundation skills and knowledge base to complete some of the MRT competencies in the STP curriculum with a series of real-world scenarios to work through,
with both clinical and scientific context given.
11.15-11.20 Introduction Mr Matt Aldridge University College London Hospitals NHS Foundation Trust
11.20-11.50 MIRD methodology and development Mr Bruno Rojas-Fisher Royal Marsden Hospital London
11.50-13.00 Workshop 1: Wholebody I-131 dosimetry Mr Bruno Rojas-Fisher Royal Marsden Hospital London
14.00-15.30 Workshop 2: SPECT dosimetry Mr Matt Aldridge University College London Hospitals (now Kings College Hospital)
16.15-17.45 Workshop 3: Voxel Dosimetry (SIRT) Dr Allison Craig Royal Marsden Hospital
17.45-17.55 Final Remarks: Mr Matt Aldridge, University College London Hospitals (now Kings College Hospital)
Tuesday 14th May, 11.00 in Hall 2 – Research Power Pitch
Automatic Deep-Learning Organ Segmentation for Dosimetry using a Pre-Trained Convolutional Neural Network
Principal Investigator: Antti Sohlberg - Department of Clinical Physiology and Nuclear Medicine, Päijät-Häme Central Hospital, Lahti, Finland
Presenter: Helena McMeekin, Clinical Applications Scientist, Hermes Medical Solutions
Summary:
Hermia Voxel Dosimetry™ calculates a 3D map of absorbed dose from one or more SPECT/CT or PET/CT studies. This dose map can be further analyzed to provide organ doses and dose-volume-histograms. The research summarized here was to develop and implement an automatic organ segmentation based on deep-learning techniques using a pre-trained convolutional neural network.