We describe a MR guided de-noising method for PET based on Iterative HighlY constrained back-PRojection Local Region (IHYPR-LR) post processing. IHYPR-LR is a modified version of HYPR-LR with the composite image updated iteratively, and HYPR-LR is a de-nosing method originally developed for time-resolved MRI. In this work, a co-registered T1-weighted MR image with high resolution and low noise was used as the initialization of the composite image in the IHYPRLR frame work for PET de-noising. A [11C]DASB Parkinsonian patient study conducted on the High Resolution Research Tomograph (HRRT) was used for the evaluations of the proposed method. The study was divided into high and low count frames with similar tracer distribution. The high count data were used to extract the optimal number of IHYPR iterations which minimizes the bias introduced by the MR composite without excessively degrading the level of noise reduction. The optimal number of iterations was then applied to low count PET data. The de-noised images were generated using the original HYPR-LR and IHYPRLR and compared with the OSEM images with a standard 2mm FWHM Gaussian post filter for the HRRT. As expected, since MR images do not always share the same contrast and structures with PET images, bias in contrast was observed from the denoised PET image using the original HYPR-LR. On the other hand, 3 iterations of IHYPR-LR successfully reduced the bias and outperformed the post filtered PET image in terms of noise reduction and structure boundary definitions. In summary, the proposed MR guided de-noising method achieves noise reduction and enhances structure boundary definitions without degrading the PET contrast.
|Name||2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings|
|Conference||2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017|
|Period||21/10/2017 → 28/10/2017|