This technology is an iterative reconstruction algorithm that improves resolution and contrast in positron emission tomography (PET) image data.
Quantification of nuclear imaging and tomographic brain images, such as PET or single-photon emission computed tomography (SPECT), acquired with the use of injected contrast agents or tracers requires the measurement of tracer blood concentration over the course of the scan. Typically, sampling is done via an arterial catheter, which is costly, risky to patients, time consuming, and involves error prone blood chemistry analysis. Iterative reconstruction with point spread function (PSF) modeling is a non-invasive method for improving contrast recovery in PET images, but also introduces ringing artifacts and over enhancement that is contrast and object size dependent. Currently, there are no available methods to enhance PET images that are both non-invasive and address the artifacts introduced with PSF modeling.
This technology is a regularized reconstruction method that incorporates locally-weighted total variation denoising to suppress image artifacts induced by PSF modeling. This method is capable of suppressing ringing artifacts, while effectively maintaining contrast recovery. Additionally, this non-invasive approach is also capable of quantifying radioligand binding in patients without the need to collect arterial blood. For this, the imaging data and electronic health records can be used to predict one or more anchors to generate an arterial input function (AIF) for the radioligand. As a result, this technology provides an effective, non-invasive method for improving image data.
This reconstruction method has been validated on a simulated cylindrical phantom, where it demonstrated that ringing effects were effectively suppressed while maintaining contrast recovery.
Patent Pending (US 20170039706)
IR CU15180, CU14251
Licensing Contact: Jerry Kokoshka