Artificial intelligence development in pediatric body magnetic resonance imaging: best ideas to adapt from adults

Abstract

Emerging manifestations of artificial intelligence (AI) have featured prominently in virtually all industries and facets of our lives. Within the radiology literature, AI has shown great promise in improving and augmenting radiologist workflow. In pediatric imaging, while greatest AI inroads have been made in musculoskeletal radiographs, there are certainly opportunities within thoracoabdominal MRI for AI to add significant value. In this paper, we briefly review non-interpretive and interpretive data science, with emphasis on potential avenues for advancement in pediatric body MRI based on similar work in adults. The discussion focuses on MRI image optimization, abdominal organ segmentation, and osseous lesion detection encountered during body MRI in children.

Keywords: Artificial intelligence; Children; Convolutional neural network; Magnetic resonance imaging; Optimization; Segmentation.

Fig. 1

Non-interpretive data from an academic medical center data science team demonstrate radiology exam volumes (per day, per accession number) that occurred during the coronavirus disease 2019 (COVID-19) pandemic. The data source was radiologists’ dictated reports, including both children and adults. CT computed tomography, DEXA dual energy X-ray absorptiometry, MR magnetic resonance, NM nuclear medicine, PET positron emission tomography, US ultrasound, XR radiography

Fig. 2

Magnetic resonance image optimization. Artistic rendering demonstrates the opportunity for convolutional neural networks (CNNs) to decrease motion and increase image sharpness on abdominopelvic MRI in children. The first (left) portion shows motion artifact resulting in blurriness on a coronal T2-weighted image. The CNN schematic (middle) shows white spheres representing nodes and blue lines representing connections between the nodal layers. The final (right) portion shows an image with decreased motion artifact. Underlying images are conventionally acquired coronal half-Fourier acquisition single-shot turbo spin-echo images in a 17-year-old girl experiencing claustrophobia at the time of acquisition. Image created by Devon Stuart, MA, CMI, in conjunction with Michael Moore, MD

Fig. 3

Magnetic resonance imaging liver segmentation. Artistic rendering demonstrates the opportunity for convolutional neural networks (CNNs) to efficiently segment abdominal organs in children. The first (left) portion shows an axial T2-weighted half-Fourier acquisition single-shot turbo spin-echo image. The CNN schematic (middle) shows white spheres representing nodes and blue lines representing connections between multiple nodal layers. The final (right) portion shows liver segmentation shaded orange. Underlying images are from a 15-year-old girl with segmentation performed manually. Bottom, the neurons represent a biological neural network that is often likened to a CNN. Image created by Devon Stuart, MA, CMI, in conjunction with Michael Moore, MD

Fig. 4

Magnetic resonance imaging osseous lesion detection. Artistic rendering demonstrates the opportunity for convolutional neural networks (CNNs) to detect osseous lesions on abdominopelvic MRI beyond the primary focus of abdominal organs and bowel. Proximal right femoral metastasis is highlighted with the marrow component shaded green and periosteal component shaded orange. Bottom, the neurons represent a biological neural network, which is often likened to a CNN. Underlying axial T2-W turbo spin-echo image is from a 14-year-old boy undergoing abdominopelvic MRI for metastatic disease evaluation. Image created by Devon Stuart, MA, CMI, in conjunction with Michael Moore, MD