Kris T. Huang, MD, PhD, CTO

Last time, we took a brief stroll through the timeline of radiology to take a look at the relationship between the evolution of imaging technologies and corresponding development in medical image registration. Digital radiology took root with the clinical introduction of CT in 1971, and despite the introduction of clinical MRI in 1980, it wasn’t until PET came of age in the early 1990s that the utility of image registration became plainly evident. Today we’ll dig into a couple of the early algorithms.

Continue reading “A Brief History of Image Registration: Part 2”

Today we’re lifting the curtain on the machine perception technology behind Autofuse, and showing how it works step by step in a complicated head and neck case and a pediatric spine growth case using scans taken 31 months apart! We also provide a glimpse of the future of patient-specific QA for deformable registration, and how Autofuse technology can be used to monitor how radiation treatment to the spine affects vertebral body growth in children.

Continue reading “Video Look Behind the Curtain at Autofuse”

Kris T. Huang, MD, PhD, CTO


Artificial intelligence, deep learning, machine learning – it’s popping up everywhere. When we hear those words, we think massive computers, convolutional neural nets, algorithms, and lifetimes of training data. And yet, my daughter, at 4 years old with zero radiology training, was able to register a pair of 3-dimensional CT scans in seconds, when specialized, purpose-built medical-grade software had difficulty. Machine learning systems are vaguely based on the brain, but clearly there is something missing.

Continue reading “Machine Perception vs. AI”