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This is a three-year project funded by a Cancer Research UK Multidisciplinary Award and brings together a team from UCL Division of Medicine, Computer Science and University College London Hospital. The aim is to develop Magnetic Resonance (MR) sequences and mathematical algorithms to reduce the distortions in MR images, especially of the prostate.
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This is a three-year project funded by a Cancer Research UK Multidisciplinary Award and brings together a team from UCL Division of Medicine, Computer Science and University College London Hospital. The aim is to develop Magnetic Resonance (MR) sequences and mathematical algorithms to reduce the distortions in MR images, especially of the prostate. Current NICE guidelines include a type of MR imaging called Diffusion Weighted MRI for the detection of tumour within the prostate, and for active surveillance of low risk confirmed disease. However, approximately 40% of prostate diffusion images suffer from severe localised distortions and this is most marked in the peripheral zone of the prostate where 75% of prostate cancers occur. The source of these distortions is magnetic field imperfections due to the presence of rectal gas or metallic hip implants.
The research study will ask both healthy volunteers and patients to undergo research MR scans and use the acquired data for analysis. For patients, the scans may be either additional sequences acquired during an extended clinical session, or a separate additional session entirely for research.
The output from the research will be modified ways to run an MR scanner and compute the final images.
The work should lead to improved diagnostic accuracy and a reduced number of non-diagnostic studies. It will have broader impact through application to diffusion imaging of other body sites, including whole-body diffusion MRI and non-cancer applications. If successful, the results would provide evidence for a larger trial with the eventual outcome being manufacturers incorporating modified MR sequences and data processing into clinical systems worldwide.
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Data sourced from clinicaltrials.gov
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