Tag: deep learning

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.

Can AI Direct Breast MRI?

Posted on by Brian Casey

A deep learning algorithm trained to analyze mammography images did a better job than traditional risk models in predicting breast cancer risk. The study shows the AI model could direct the use of supplemental screening breast MRI for women who need it most. 

Breast MRI has emerged (along with ultrasound) as one of the most effective imaging modalities to supplement conventional X-ray-based mammography. Breast MRI performs well regardless of breast tissue density, and can even be used for screening younger high-risk women for whom radiation is a concern. 

But there are also disadvantages to breast MRI. It’s expensive and time-consuming, and clinicians aren’t always sure which women should get it. As a result, breast MRI is used too often in women at average risk and not often enough in those at high risk. 

In the current study in Radiology, researchers from MGH compared the Mirai deep learning algorithm to conventional risk-prediction models. Mirai was developed at MIT to predict five-year breast cancer risk, and the first papers on the model emerged in 2019; previous studies have already demonstrated the algorithm’s prowess for risk prediction. 

Mirai was used to analyze mammograms and develop risk scores for 2.2k women who also received 4.2k screening breast MRI exams from 2017-2020 at four facilities. Researchers then compared the performance of the algorithm to traditional risk tools like Tyrer-Cuzick and NCI’s Breast Cancer Risk Assessment (BCRAT), finding that … 

  • In women Mirai identified as high risk, the cancer detection rate per 1k on breast MRI was far higher compared to those classified as high risk by Tyrer-Cuzick and BCRAT (20.6 vs. 6.0 & 6.8)
  • Mirai had a higher PPV for predicting abnormal findings on breast MRI screening (14.6% vs. 5.0% & 5.5%)
  • Mirai scored higher in PPV of biopsies recommended (32.4% vs. 12.7% & 11.1%) and PPV for biopsies performed (36.4% vs. 13.5% & 12.5%)

The Takeaway
Breast imaging has become one of the AI use cases with the most potential, based on recent studies like PERFORMS and MASAI, and the new study shows Mirai could be useful in directing women to breast MRI screening. Like the previous studies, the current research is pointing to a near-term future in which AI and deep learning can make breast screening more accurate and cost-effective than it’s ever been before. 

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