In line with its name ‘neural network’, the AI works like a simplified version of neurons in the brain and learns to reconstruct higher quality 4D MRI images via a series of training examples. The researchers used low quality 4D MRI images that are unfit for clinical use as the input data for Dracula.
With both information on the spatial dimensions and respiratory phases of a patient’s tumour and surrounding organs, the researchers could train Dracula to produce 4D MRI and midposition images that were considered acceptable for use in the clinic by both a radiologist and a radiation oncologist.
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Dracula’s performance was also verified against 4D MRI images reconstructed by another algorithm, MoCo-HDTV, for comparison, with image quality graded on a five-point Likert scale – zero denoting unreadable and five excellent.
Dracula-reconstructed images received scores between 1.8 and 3.4, with the experts reporting some minor blurring and streaking that reduced visibility.
Though not scoring as high as images produced by the time-intensive MoCo-HDTV, Dracula was still able to visualise the patient’s tumour and at-risk organs – in this case the heart and oesophagus – very well, so that it could be used in practice to plan and guide radiotherapy treatment.
Study leader Dr Andreas Wetscherek, Team Leader in Magnetic Resonance Imaging in Radiotherapy at the ICR, said:
“Using an AI to rapidly reconstruct 4D MRI images of a cancer patient’s anatomy lets us accurately determine the location of tumours and characterise their motion right before radiotherapy treatment, which will enable us to increase the radiation dose targeting just the tumour and not healthy organs.
“This study demonstrates the potential of neural networks to achieve comparable imaging results to traditional methods in only a few seconds. It would be especially useful for cancers where we need to avoid the healthy organs around the tumour, such as pancreatic cancer, which currently limits the delivery of high doses of radiation to the tumour.”Back to HCB News