In our last issue, we reported on a new study underscoring the positive return on investment when deploying radiology AI at the hospital level. This week, we’re bringing you additional research that confirms AI’s economic value, this time when used to speed up MRI data reconstruction.
While AI for medical image analysis has garnered the lion’s share of attention, AI algorithms are also being developed for behind-the-scenes applications like facilitating staff workflow or reconstructing image data.
- For example, software developers have created solutions that enable scans to be acquired faster and with less input data (such as radiation dose) and then upscaled to resemble full-resolution images.
In the new study in European Journal of Radiology, researchers from Finland focused on whether accelerated data reconstruction could help their hospital avoid the need to buy a new MRI scanner.
- Six MRI scanners currently serve their hospital, but the radiology department will be losing access to one of them by the end of the year, leaving them with five.
They calculated that a 20% increase in capacity per remaining scanner could help them achieve the same MRI throughput at a lower cost; to test that hypothesis they evaluated Siemens Healthineers’ Deep Resolve Boost algorithm.
- Deep Resolve Boost uses raw-data-to-image deep learning reconstruction to denoise images and enable rapid acceleration of scan times; a total knee MRI exam can be performed in just two minutes.
Deep Resolve Boost was applied to 3T MRI scans of 78 patients acquired in fall of 2023, with the researchers finding that deep learning reconstruction…
- Reduced annual exam costs by 399k euros compared to acquiring a new scanner
- Enabled an overall increase in scanner capacity of 20-32%
- Had an acquisition cost 10% of the price of a new MRI scanner, leading to a cost reduction of 19 euros per scan
- Was a lower-cost option than operating five scanners and adding a Saturday shift
The Takeaway
As with last week’s study, the new research demonstrates that AI’s real value comes from helping radiologists work more efficiently and do more with less, rather than from direct reimbursement for AI use. It’s the same argument that was made to promote the adoption of PACS some 30 years ago – and we all know how that turned out.
