Generating MRI images from ultrasounds scans

VANCOUVER – A study led by Vancouver Coastal Health Research Institute researcher, Dr. Ilker Hacihaliloglu (pictured), is the first to test the viability of a selection of image-to-image synthesizing technologies used to generate magnetic resonance imaging (MRI) images from ultrasound scans of prostate cancer.

Within the past two years, researchers have made incredible advances in image synthesizing technology. Similar to the popular OpenAI generative artificial intelligence (AI) algorithm that can create images and transform images into other images, synthetic image generation is a sophisticated AI computer algorithm.

Researchers feed image-synthesizing technology thousands of patient diagnostic scans to train it on identifying disease characteristics, such as images of variations of a certain type of cancer.

The more images input into the algorithm, the better it will be at accurately generating a synthetic image, explained Hacihaliloglu. For example, the software image synthesizing technology examined in their study can quickly translate a single ultrasound scan into another medical diagnostic image, such as an MRI image, with the click of a button.

“Our goal is for image-to-image technology to advance to the point at which it can be used in healthcare settings to reduce wait-times and travel time for high-quality clinical imaging, such as MRI, and reduce radiation exposure from computed tomography (CT), X-ray or mammography scans,” stated Hacihaliloglu.

Dr. Hacihaliloglu is an associate professor in the Department of Radiology and Department of Medicine at the University of British Columbia, where he heads the Ultrasound Technology and AI for Healthcare (ULTRAi) research laboratory. He is also a researcher with the Djavad Mowafaghian Centre for Brain Health, Centre for Heart Lung Innovation, Centre for Aging SMART and the Institute for Computing, Information and Cognitive Systems.

MRI technology uses magnetic field waves and computer-generated radio waves to take detailed images of internal tissue structures, including bones, blood vessels, organs and muscles. While non-invasive, the technology is generally only available as non-portable machines that may not be available outside of major city centres.

The same is the case for CT scanners, which are a radiating technology that takes a series of detailed X-ray images of internal soft tissues and bones.

Ultrasounds, on the other hand, are a non-radiating technology that uses sound waves to produce images of tissues, such as organs, inside the body. Available as handheld, portable machines, ultrasounds are a convenient and accessible means of generating real-time pictures of internal tissues. Available in remote areas that may be far from other scanning technology, such as MRI or CT scanners, ultrasounds are an ideal choice as a rapid medium from which to generate synthetic MRI or CT images.

For their study, Hacihaliloglu and his team evaluated the quality of synthetic MRI images generated from 794 ultrasound scans of prostate cancer using five widely recognized image-to-image translation networks in medical imaging: 2D-Pix2Pix, 2D-CycleGAN, 3D-CycleGAN, 3D-UNET and 3D-AutoEncoder. Five doctors with over five years of experience reading MRI scans were then given a random selection of 15 synthesized MRI images.

The team found that, while the synthetic MRI images were around 85 percent similar to those of the MRI scans, they lacked too many important clinical features to be useful in diagnostics at this stage.

“This technology has great potential to be used in clinical diagnostics, research and medication development,” says Hacihaliloglu. “As such, there is a pressing need for further refinement of image-to-image translation networks to enhance its performance.”