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Chapter 3

AI to Advance Medical Imaging

Medical imaging is probably the most promising clinical application of AI. Whether it is diagnosing a cancer, detecting a fracture, or identifying neurological or thoracic conditions, AI can help quickly diagnose and assist doctors with required treatment options.

Two side-by-side lung images with colored bull’s-eye-shaped data overlays with red at the center, yellow at the edges. Both indicate probable COVID.

Visualization of Class Activation Mapping (CAM) results. AI-based model assessments of different COVID-19 cases provide doctors with insights into the algorithm’s decision.

It is estimated that there are about 40 million radiologist errors per annum due to either overworked radiologists or poor quality of imaging techniques [4]. AI algorithms help radiologists make diagnoses by recognizing subtle anatomical structures and deducing clinical meanings. AI also helps by processing and providing analyses of large volumes of images in a much shorter time.

The use of AI in diagnostic medical imaging is undergoing extensive evaluation. As of July 2022, 75% (391) of the devices approved by the FDA in the market were in radiology imaging alone [5].

Using Deep Learning to Reduce Radiation Exposure Risk in CT Imaging

Challenge

Exposure to radiation from computed tomography (CT) imaging is approximately 350 times that of a single X-ray dose and is associated with several risks, such as cancer. Medical researchers want to limit radiation exposure by using ultra-low-dose CT scans. However, this approach results in low-resolution images with high levels of noise, which make scans difficult for physicians to interpret.

A diagram showing the layers of the convolutional neural network as it is trained on supplied ultra-low-dose CT images.

CNNs trained on ultra-low-dose CT. (Image credit: Ritsumeikan University)

Solutions

A researcher, Ryohei Nakayama from Ritsumeikan University in Kyoto, Japan, used MATLAB to create a deep learning convolutional neural network (CNN) that reconstructs high-resolution images captured using ultra-low-dose CT scans.

  • At first, the researcher used MATLAB to divide CT images into small local regions and pair low-dose and normal-dose regions to create an image dictionary. As the dictionary grew, the search time became untenable, so Nakayama explored use of a convolutional neural network (CNN), which produces results much faster despite the training time.
  • Nakayama used MATLAB to evaluate about 128 different CNN variants, trying different input sizes and filters as well as various numbers of convolutional layers.
  • To accelerate the training process, he trained in parallel on multiple NVIDIA® GeForce series GPUs using Parallel Computing Toolbox™.
  • To monitor training progress, Nakayama plotted accuracy and loss using the monitoring visualization option in Deep Learning Toolbox™.

Results

The CNN-based system provides physicians with a comparable level of diagnostic information while reducing patient radiation exposure by as much as 95%.

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References

[4] Bruno, Michael A., Eric A. Walker, and Hani H. Abujudeh. “Understanding and Confronting Our Mistakes: The Epidemiology of Error in Radiology and Strategies for Error Reduction.” Radiographics 35, no. 6 (2015): 1668–1676. https://doi.org/10.1148/rg.2015150023.

[5] Center for Devices and Radiological Health. “Artificial Intelligence and Machine Learning (AI/ML) Enabled Medical Devices.” U.S. Food and Drug Administration. FDA. Updated October 5, 2022. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices