Dynamic PET Denoising with HYPR Processing

Abstract

HighlY constrained backPRojection (HYPR) is a promising image-processing strategy with widespread application in time-resolved MRI that is also well suited for PET applications requiring time series data. The HYPR technique involves the creation of a composite image from the entire time series. The individual time frames then provide the basis for weighting matrices of the composite. The signal-to-noise ratio (SNR) of the individual time frames can be dramatically improved using the high SNR of the composite image. In this study, we introduced the modified HYPR algorithm (the HYPR method constraining the backprojections to local regions of interest [HYPR-LR]) for the processing of dynamic PET studies. We demonstrated the performance of HYPR-LR in phantom, small-animal, and human studies using qualitative, semiquantitative, and quantitative comparisons. The results demonstrate that significant improvements in SNR can be realized in the PET time series, particularly for voxel-based analysis, without sacrificing spatial resolution. HYPR-LR processing holds great potential in nuclear medicine imaging for all applications with low SNR in dynamic scans, including for the generation of voxel-based parametric images and visualization of rapid radiotracer uptake and distribution.

Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking

“…Highly Constrained Backprojection Reconstruction (HYPR) 8,24 is a popular method of smoothing of fMRI data that has recently been applied to PET. This spatial smoothing technique reduces noise without obliterating the temporal characteristics of the data we are interested in.…”

“…Apply a variant of the spatial filtering method, Highly Constrained Backprojection (HYPR-LR) to all PET images in a frame-by-frame manner following the work of Christian et al 8,24 . The appeal of HYPR-LR is that it reduces temporal noise without degrading the temporal information at every voxel that we will use to create our dopamine movies.…”

“…Movies are representations of the time course of dopamine levels at every voxel in an image. Voxel-by-voxel methods in PET generally suffer from low signal to noise ratio, so to minimize the noise inherent in voxel-based time-activity curves (TACs) we apply an innovative spatial filter, ‘HYPR’, 8 as a pre-processing step. This step preserves key temporal characteristics of the responding voxels while reducing noise.…”

“…The analysis technique is comprised of 5 main steps: pre-processing, modeling, statistical comparison, masking and visualization. Preprocessing is applied to the PET data with a unique ‘HYPR’ spatial filter 8 that reduces spatial noise but preserves critical temporal information. Modeling identifies the time-varying function that best describes the dopamine effect on 11 C-raclopride uptake.…”

A Novel Kernel-Based Regularization Technique for PET Image Reconstruction

“…Yet, because of the Poisson-distributed noise that is characteristic of projection data, FBP-reconstructed PET images are often of low quality. Alongside regular reconstruction methods, noise smoothing can be performed in the pre-reconstruction phase of the sinogram domain [11][12][13][14][15], in the post-reconstruction phase of the image [16][17][18], or during the iterative process of statistically-based reconstruction [19]. An alternative method that incorporates prior knowledge into de-noising images is non-local means (NLM) [20,21].…”