A brain tumor is an unfortunate life-changing diagnosis for a patient and everyone involved in the care of the patient. The journey with a brain tumor starts with an imaging study, more often an MRI study of the brain. At every stage in the treatment of the condition, an MRI study is the pivot for any decision. The recent stupendous progress of deep learning-based AI technologies is helping radiologists worldwide to analyze the images of patients with brain tumors more accurately and also measure several imaging markers of the tumor more precisely.

For any cancer, an ideal imaging study should help in the early detection of cancer, precise quantification of the tumor spread, the extent of tumor removal on a post-operative scan, differentiation of the effects of radiation therapy from a tumor recurrence, and early detection of tumor recurrence in follow up studies. Brain tumors are no different.

Several technological advancements in the past have helped in each stage of this journey.

Newer risk-free MR contrast agents, advanced MR imaging techniques like multidimensional diffusion (by Random Walk Imaging) sequences, DSC (dynamic susceptibility contrast), DCE (dynamic contrast-enhanced), and ASL (arterial spin labeling) perfusion techniques, and Compilation sequences for faster acquisitions (MAGIC by SyntheticMR) and several iterations of MR Spectroscopy have been helpful in either helping radiologists scan patients faster, see tumors earlier, or measure and monitor them better.

The new-age AI solutions are helping radiologists to do all of them simultaneously - scan, see, measure, and monitor – earlier, faster and better.

• Super-resolution algorithms that help scan faster (AIkenist QUICKSCAN, SubtleMR by subtle medical) can be executed to acquire high-quality scans in a much lesser time.

On these images, there are different types of AI solutions that can be executed.

• Classification algorithms - that automatically detect cancerous lesions (VinDr BrainMR)

• Texture analysis solutions - that quantify features related to the heterogeneity of the tissues and obtain texture parameters (Quibim Texture Analysis)

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