Ultrafast dynamic contrast enhanced MRI using compressed sensing for improved prostate cancer diagnosis and staging

Key Information

Cancer type: 
Prostate
Research Institution: 
TCD & St James's
Grant Amount: 
€138,700
Start date: 
September 1, 2013
End date: 
August 31, 2017

Scientific Project Abstract

As with any disease, the early and accurate diagnosis of prostate cancer is essential for ensuring the patient has the best chance of recovery. However, it is extremely difficult to diagnose prostate cancer using existing techniques, and so patients must undergo a painful and uncomfortable biopsy procedure where a sample of tissue is taken and analysed to check for a tumour, with blood tests also carried out. These techniques can often give a false result (positive or negative) or miss the tumour altogether. We wish to develop a new imaging technique which will have a much higher success rate in detecting the tumour, and also will do so in a painless manner with no harm or discomfort to the patient. We will use a mathematical technique to increase the speed at which we will acquire the images, and analyse the image data to understand how aggressive the tumour is, which will help decide on the best course of treatment for the patient. We will compare the results from our new technique with those from the existing imaging techniques to prove that the new technique improves our ability to correctly diagnose the patient's disease state.

For the non-scientist

One-line description: 
Detection and diagnosis of prostate cancer using MRI
What this project involves: 

Prostate cancer is a difficult disease to adequately diagnose and patients often must undergo a painful and uncomfortable biopsy. This project aims to develop a new imaging technique which will be superior to currently used methods in detection of prostate cancer tumours. This imaging technique, which will be performed using MRI and will cause no pain or discomfort to the patient, may be capable of predicting how aggressive the prostate cancer is and may help clinicians to decide on the best course of treatment for the patient.