The comparison of serial MRI and CT imaging studies is a common task in clinical radiology. Such clinical judgments are, however, not very reproducible. There are a variety of reasons for this, including the confounding of acquisition related changes with disease related changes, and issues related to information presentation.
Change Detector is a machine-learning system that compares serial imaging studies, presenting changes between time points as a color-coded overlay indicating what is changing, where, and by what amount. Change Detector reduces information overload and enhances radiologist productivity, increasing value.
Change Detector analyzes the head MRI and CT image types that radiologists use, including T1 (pre- and post-contrast), T2, FLAIR, PD, MTS, ADC, CBV, CBF, MTT, and TTP. Change Detector automatically adapts to new image types, even ones that haven’t been invented yet.
Change Detector demonstrates how practical A.I. systems can free radiologists from the routine task of searching images for changes, making characterization easier, and allowing radiologists to focus on reaching critical judgments and directing therapy.
Change Detector is a software system that compares two or more serial imaging studies, spatially registering prior volumes against the corresponding current volumes of the same type, and presenting changes in the form of a color-coded change map, superimposed on the anatomical images.
Using Change Detector, it may be possible to identify changes months earlier than is possible using manual inspection alone.
Change Detector detects changes in the image types that radiologists use: T1-pre, T1-post, T2, T1-FLAIR, T2-FLAIR, proton density (PD), magnetization transfer suppression (MTS), apparent diffusion coefficient (ADC), cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT), and time to peak (TTP). Change Detector is not limited to currently used MRI pulse sequences; it will dynamically adapt to novel image types.
Change Detector is an example of a layered artificial intelligence (AI) system. It demonstrates how practical AI systems can free expert radiologists from routine tasks such as searching images for changing regions, to allow them to focus on reaching critical clinical judgments. The system demonstrates how AI can simply turn information overload into information affluence.
Change Detector post-processing can be performed by a desktop application running on a Windows PC or Mac. A modern laptop or desktop computer with at least 16 GB of RAM is capable of running Change Detector analyses with good performance. Alternatively, Change Detector analyses can be run on a cloud server, providing easy scalability with full security.