Tag: data reconstruction

AI Recon Cuts CT Radiation Dose

Posted on by Brian Casey

Artificial intelligence got its start in radiology as a tool to help medical image interpretation, but much of AI’s recent progress is in data reconstruction: improving images before radiologists even get to see them. Two new studies underscore the potential of AI-based reconstruction to reduce CT radiation dose while preserving image quality. 

Radiology vendors and clinicians have been remarkably successful in reducing CT radiation dose over the past two decades, but there’s always room for improvement. 

  • In addition to adjusting CT scanning protocols like tube voltage and current, data reconstruction protocols have been introduced to take images acquired at lower radiation levels and “boost” them to look like full-dose images. 

The arrival of AI and other deep learning-based technologies has turbocharged these efforts. 

They compared DLIR operating at high strength to GE’s older ASiR-V protocol in CCTA scans with lower tube voltage (80 kVp), finding that deep learning reconstruction led to …

  • 42% reduction in radiation dose (2.36 mSv vs. 4.07)
  • 13% reduction in contrast dose (50 mL vs. 58 mL).
  • Better signal- and contrast-to-noise ratios.
  • Higher image quality ratings.

In the second study, researchers from China including two employees of United Imaging Healthcare used a deep learning reconstruction algorithm to test ultralow-dose CT scans for coronary artery calcium scoring. 

  • They wanted to see if CAC scoring could be performed with lower tube voltage and current (80 kVp/20 mAs) and how the protocol compared to existing low-dose scans.

In tests with 156 patients, they found the ultralow-dose protocol produced …

  • Lower radiation dose (0.09 vs. 0.49 mSv).
  • No difference in CAC scoring or risk categorization. 
  • Higher contrast-to-noise ratio.

The Takeaway

AI-based data reconstruction gives radiologists the best of both worlds: lower radiation dose with better-quality images. These two new studies illustrate AI’s potential for lowering CT dose to previously unheard-of levels, with major benefits for patients.

AI Speeds Up MRI Scans

Posted on by Brian Casey

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.

Get every issue of The Imaging Wire, delivered right to your inbox.

You might also like..

Select All

You're signed up!

It's great to have you as a reader. Check your inbox for a welcome email.

-- The Imaging Wire team

You're all set!