Positron emission tomography (PET) enables 3D visualization of vital physiological information, e.g., metabolism, blood flow, and neuroreceptor concentration by using targeted radioisotope-labeled tracers. Quantitative interpretation of PET images is crucial both in diagnostic and therapeutic contexts in many areas of medicine, including oncology, neurology, and cardiology. My postdoctoral research at Massachusetts General Hospital (MGH) involved developing a range of techniques for denoising, motion compensation, deblurring of PET images with the goal of enhancing the quantitative capabilities of PET. Several of these methods achieve this by incorporating information from an anatomical imaging modality such as magnetic resonance imaging (MRI). Some of these projects are described below.

Image Denoising

The high levels of statistical noise in PET images pose a challenge to accurate quantitation. This issue is particularly well-pronounced at the early time frames of dynamic PET images, which are usually short to capture rapid changes in tracer uptake patterns. We developed a non-local means denoising filter for dynamic PET images which uses spatiotemporal patches for robust similarity computation. Realistic simulations of a dynamic digital mouse phantom showed improved bias-variance performance characterics relative to several well-known denoising approaches. Experiments in mice and humans showed clear improvement in contrast-to-noise ratio in Patlak parametric images. To further improve denoising performance along sharp edges, we used anatomical guidance to limit the spatial window for non-local similarity computation. The method was tested on the BrainWeb digital phantom and on human datasets and demonstrated robustness particularly at high noise levels and led to recovery of sharp edges (e.g. tissue and organ boundaries).

Link: PLOS One 2013 Paper

comparison

A transverse slice from a dynamic human Flortaucipir scan of a human subject with mild cognitive impairment. The columns represent the segmented MRI, noisy, NLM denoised, and ANLM denoised images respectively. The rows represent three time points (2.6 min, 19 min, and 37.5 min) reflecting the evolution of activity over time. The original image frames corresponding to the top and middle rows are noisier than the right column.

Motion-Compensated Image Reconstruction

Simultaneous PET/MRI is a new technology that combines the strengths of two complementary imaging modalities and is emerging as an increasingly potent tool for integrated imaging. While PET (positron emission tomography) reveals only functional or physiological information, MRI (magnetic resonance imaging) is able to generate structural or anatomical information, generally with higher resolution. In the context of lung imaging, where PET scans are severely compromised by respiratory motion, we have developed a maximum a posteriori estimation framework that incorporates deformation fields derived from simultaneously acquired MRI data. This technique enables the generation of PET images free of motion artifacts, which leads to improved image quantitation, thereby facilitating lung cancer staging and treatment optimization.
Links: MIC 2012 Paper, Med Phys 2015 Paper 

medical physics journal cover

Cover of the July 2015 issue of the Medical Physics journal

Image Deblurring

The quantitative accuracy of PET is degraded by partial volume effects caused by the limited spatial resolution capabilities of PET scanners. We have developed an image deblurring technique for PET that exploits the higher resolution imaging capabilities of MRI by means of an information-theoretic anatomical prior. Application of this method to a pool of clinical subjects revealed a marked improvement in the correlation of PET measures with well-recognized clinical metrics of cognitive performance.

Links: ISBI 2015 Paper, Sci Rep 2017 Paper 

comparison

Comparison of brain scans of a cognitively normal subject and and Alzheimer's disease patient. The subfigures show A. MPRAGE MRI images, B. Flortaucipir PET images of tau tangles reconstructed by the scanner, C. Flortaucipir PET images deblurred with a measured spatially varying point spread function, and D. Flortaucipir PET images deblurred with a measured spatially varying point spread function with the help of a MRI-based joint entropy prior.