HYPR4D kernel method on TOF PET data with validations including image-derived input function

Abstract

Background Positron emission tomography (PET) images are typically noisy especially in dynamic imaging where the PET data are divided into a number of short temporal frames often with a low number of counts. As a result, image features such as contrast and time–activity curves are highly variable. Noise reduction in PET is thus essential. Typical noise reduction methods tend to not preserve image features/patterns (e.g. contrast and size dependent) accurately. In this work, we report the first application of our HYPR4D kernel method on time-of-flight (TOF) PET data (i.e. PSF-HYPR4D-K-TOFOSEM). The proposed HYPR4D kernel method makes use of the mean 4D high frequency features and inconsistent noise patterns over OSEM subsets as well as the low noise property of the early reconstruction updates to achieve prior-free de-noising. The method was implemented and tested on the GE SIGNA PET/MR and was compared to the TOF reconstructions with PSF resolution modeling available on the system, namely PSF-TOFOSEM with and without standard post filter and PSF-TOFBSREM (TOF Q.Clear) with various beta values (regularization strengths). Results Results from experimental contrast phantom and human subject data with various PET tracers showed that the proposed method provides more robust and accurate image features compared to other regularization methods. The preservation of contrast for the PSF-HYPR4D-K-TOFOSEM was observed to be better and less dependent on the contrast and size of the target structures as compared to TOF Q.Clear and PSF-TOFOSEM with filter. At the same contrast level, PSF-HYPR4D-K-TOFOSEM achieved better 4D noise suppression than other methods (e.g. >2 times lower noise than TOF Q.Clear at the highest contrast). We also present a novel voxel search method to obtain an image-derived input function (IDIF) and demonstrate that the obtained IDIF is the most quantitative w.r.t. the measured blood samples when the acquired data are reconstructed with PSF-HYPR4D-K-TOFOSEM. Conclusions The overall results support superior performance of the PSF-HYPR4D-K-TOFOSEM for TOF PET data and demonstrate that the proposed method is likely suitable for all imaging tasks including the generation of IDIF without requiring any prior information as well as further improving the effective sensitivity of the imaging system.

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“…HYPR4D kernelized reconstruction has been shown to outperform the standard and current state-of-the-art clinical reconstructions in terms of 4 D noise reduction while preserving spatiotemporal patterns within data. IHYPR4D has been shown to provide improvements over HYPR3D in joint noise and bias reduction, and inherently bypasses limitations imposed by HYPR3D; for specific details see literature [30][31][32] .…”

“…Image processing. Raw PET data were reconstructed using PSF-HYPR4D-K-TOFOSEM, 31 with attenuation, scatter, randoms, and normalization corrections, after which PET images underwent frame realignment (i.e. inter-frame motion correction).…”

“…The dynamic images were then spatially filtered with a 1.8 Â 1.8 Â 3.6 mm FWHM Gaussian to simulate the SIGNA point spread function (PSF). 40 We generated 100 noisy realizations (NRs) using scaled Gaussian noise, 14 before post-processing with IHYPR4D (7.2 mm, two frames spatiotemporal kernel, one iteration) 30 ; the scaling coefficient was set so that the noise level after applying IHYPR4D matched human RAC scan noise levels reconstructed with PSF-HYPR4D-K-TOFOSEM (5.6 mm, ten frames spatiotemporal kernel, 10 iterations) 31 and post-processed with IHYPR4D. Implementation of RSD.…”

“…Due to typically high levels of noise in voxel-level TACs it is necessary to apply denoising to the data before applying subsequent analyses on voxel residuals. To best characterize the voxel residuals we rely on a combination of novel denoising methods, namely HYPR4D kernelized reconstruction, 30 , 31 along with denoising using IHYPR4D, 32 both of which extend the HYPR framework to the temporal domain ( Figure 1 ). HYPR4D kernelized reconstruction has been shown to outperform the standard and current state-of-the-art clinical reconstructions in terms of 4 D noise reduction while preserving spatiotemporal patterns within data.…”