Abstract
The computed tomography angiography (CTA) postprocessing manually recognized by technologists is extremely labor intensive and error prone. We propose an artificial intelligence reconstruction system supported by an optimized physiological anatomical-based 3D convolutional neural network that can automatically achieve CTA reconstruction in healthcare services. This system is trained and tested with 18,766 head and neck CTA scans from 5 tertiary hospitals in China collected between June 2017 and November 2018. The overall reconstruction accuracy of the independent testing dataset is 0.931. It is clinically applicable due to its consistency with manually processed images, which achieves a qualification rate of 92.1%. This system reduces the time consumed from 14.22 ± 3.64 min to 4.94 ± 0.36 min, the number of clicks from 115.87 ± 25.9 to 4 and the labor force from 3 to 1 technologist after five months application. Thus, the system facilitates clinical workflows and provides an opportunity for clinical technologists to improve humanistic patient care.
Highlights
The computed tomography angiography (CTA) postprocessing manually recognized by technologists is extremely labor intensive and error prone
Considering that vessel imaging reconstruction is required in clinical settings, an automatic reconstruction system can be integrated into the clinical workflow if processed segmentations are available
We argue that artificial intelligence (AI) technology can be integrated into the radiology workflow to improve workflow efficiency and reduce medical costs
Summary
The computed tomography angiography (CTA) postprocessing manually recognized by technologists is extremely labor intensive and error prone. We propose an artificial intelligence reconstruction system supported by an optimized physiological anatomical-based 3D convolutional neural network that can automatically achieve CTA reconstruction in healthcare services. This system is trained and tested with 18,766 head and neck CTA scans from 5 tertiary hospitals in China collected between June 2017 and November 2018. A 3D-CNN model that conforms to the physiological, anatomical, and morphological features of the objective images is designed for segmentation tasks and has shown high segmentation performance for biomedical images[12] In this present study, we sought to develop an automatic imaging reconstruction system (CerebralDoc) based on an optimized anatomy prior-knowledge based 3D-CNN to reconstruct original head and neck CTA images, assist technologists in their daily work and establish a time-saving work process. We argue that artificial intelligence (AI) technology can be integrated into the radiology workflow to improve workflow efficiency and reduce medical costs
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