Accessing Super High Resolution Without Compromise

Dr Julien Savatovsky, a Neuroradiologist and the Deputy Head of Diagnostic Neuroradiology at Rothschild Foundation Hospital, Paris, France, uses advanced image reconstructions solutions, based on Artificial Intelligence (AI), in his daily routine. Collaborating closely with Canon Medical, he has contributed to the development of the latest reconstruction algorithms and evaluated them on the Hospital’s Vantage Orian 1.5T and Vantage Galan 3T MRI systems. This experience has convinced Dr. Savatovsky that AI can challenge the well-known ‘triangle of compromise’ in imaging between signal-to-noise ratio (SNR), spatial resolution, and acquisition time.

“Nowadays, Deep Learning technologies can be found in post-processing field, adding an extra layer to the reconstructed images, improving their quality", he said. "During years, we improved the SNR using filters, but it was at the cost of the image sharpness, due to the introduction of important blurring effects. And at the opposite, if we wanted a crisper image, we used high filter which also increase the noise. Now, thanks to Deep Learning recontructions such as AiCE, we have the possibility to improve SNR without compromising the sharpness of the images.”

“In the below image, there is a small Multiple Sclerosis (MS) lesion of the posterior fossa that is very important to detect because it's usually enough to cause very significant symptoms,” explained Dr. Savatovsky. “The lesion is much more visible with AiCE technology thanks to contrast-to-noise ratio improvement.”

“This cervical spinal cord lesion was not really visible previously. But with this one millimeter isotropic FLAIR reconstructed with AiCE, it was clearly visible without any doubts. By applying such reconstruction algorithm on 3D sequences, we get the benefits of the AI-based denoising in every planes," said Dr. Savatovsky.

"The same patient had an optic neuritis. This is usually very challenging to observe it on a FLAIR sequence because of the low SNR that we get in this difficult region. Once again, AiCE allows the inflammation visualization despite the very high resolution, emphasizing the power of the algorithm," remarked Dr. Savatovsky.

“This is an image created in three minutes with 300 Micron voxels on a different patient with a right optic neuritis. We want the voxel to be very small, but we end up with a very noisy image if we use common filters. When we apply Deep Learning reconstructions, we can see the optic neuritis really well and we recover some SNR and some CNR,” he added.

Dr Savatovsky and his team have performed a study to explore the image improvements allowed by DLR.

“We've proven on MS patients that both the SNR and the CNR were better with DLR. In addition, the stronger the DLR algorithm, the better the results,” he said. “Now, the ‘triangle of compromise’ is totally reconsidered. Using Deep Learning algorithms, we can improve the SNR, without compromise on spatial resolution and keeping the same scan time.”

Dr. Julien Savatovsky
Dr. Savatovsky is Deputy Head of the Imaging Department (Diagnostic Neuroradiology) at the Rothschild Foundation Hospital, Paris, France.

Dr. Savatovsky continued his undergraduate training in medicine at the University of Paris-VI Pierre and Marie Curie and his internship in Paris. He completed his training with a two-year clinic in the Neuroradiology Department of the Pitié- Salpêtrière hospital group. He continued his hospital career as a hospital practitioner in neuroradiology at the Rothschild Foundation Hospital.

He specializes in nervous system imaging and neck diseases. In order to give access to high quality imaging to patients, Dr. Savatovsky uses specifically optimized 3T MRI equipment and has advanced training in MRI techniques (including sequence development and tuning) and in advanced post-processing.